src/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.cpp - ml/ComputeLibrary - Gitiles

 /*
  * 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/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.h"

 #include "arm_compute/core/AccessWindowStatic.h"
 #include "arm_compute/core/AccessWindowTranspose.h"
 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/GLES_COMPUTE/GCHelpers.h"
 #include "arm_compute/core/GLES_COMPUTE/GCKernelLibrary.h"
 #include "arm_compute/core/GLES_COMPUTE/IGCTensor.h"
 #include "arm_compute/core/GLES_COMPUTE/OpenGLES.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 <string>

 using namespace arm_compute;

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

 void GCGEMMMatrixMultiplyKernel::configure(const IGCTensor *input0, const IGCTensor *input1, IGCTensor *output, float alpha, bool is_interleaved_transposed)
 {
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F32, DataType::F16);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1, output);

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

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

     std::set<std::string> build_opts;
     Window                win;

     build_opts.emplace("#define LOCAL_SIZE_X " + support::cpp11::to_string(1));
     build_opts.emplace("#define LOCAL_SIZE_Y " + support::cpp11::to_string(1));
     build_opts.emplace("#define LOCAL_SIZE_Z " + support::cpp11::to_string(1));
     build_opts.emplace("#define COLS_A " + support::cpp11::to_string(input0->info()->dimension(0)));
     build_opts.emplace("#define COLS_B " + support::cpp11::to_string(input1->info()->dimension(0)));
     build_opts.emplace("#define ALPHA " + float_to_string_with_full_precision(alpha));

     // Check if the output tensor is a vector. If so,the kernel runs the vector-matrix multiplication
     if(is_interleaved_transposed)
     {
         switch(input0->info()->data_type())
         {
             case DataType::F16:
                 build_opts.emplace("#define DATA_TYPE_FP16");
                 break;

             case DataType::F32:
                 build_opts.emplace("#define DATA_TYPE_FP32");
                 break;

             default:
                 ARM_COMPUTE_ERROR("Current data type is not supported");
                 break;
         }

         build_opts.emplace("#define GEMM_MM_INTERLEAVED_TRANSPOSED");

         // Create kernel
         _kernel = GCKernelLibrary::get().create_kernel(("gemm_mm_interleaved_transposed"), build_opts);

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

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

         // Special case for 1xN, 2xN, 3xN and 4xN input0 tensor
         unsigned int num_elems_processed_per_iteration_x;
         unsigned int num_elems_processed_per_iteration_y;

         switch(input0->info()->data_type())
         {
             case DataType::F16:
                 num_elems_processed_per_iteration_x = 4;
                 num_elems_processed_per_iteration_y = 1;
                 build_opts.emplace("#define DATA_TYPE_FP16");
                 break;

             case DataType::F32:
                 num_elems_processed_per_iteration_x = max_gc_vector_width / data_size_from_type(input0->info()->data_type());
                 num_elems_processed_per_iteration_y = std::min(static_cast<int>(output->info()->dimension(1)), 4);
                 build_opts.emplace("#define DATA_TYPE_FP32");
                 break;

             default:
                 ARM_COMPUTE_ERROR("Current data type is not supported");
                 break;
         }

         build_opts.emplace("#define GEMM_MM_FLOATING_POINT");
         build_opts.emplace("#define NUM_ELEMS_PROCESSED_PER_THREAD_X " + support::cpp11::to_string(num_elems_processed_per_iteration_x));
         build_opts.emplace("#define NUM_ELEMS_PROCESSED_PER_THREAD_Y " + support::cpp11::to_string(num_elems_processed_per_iteration_y));

         // Create kernel
         _kernel = GCKernelLibrary::get().create_kernel("gemm_mm_floating_point", build_opts);

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

         AccessWindowStatic input0_access(input0->info(), 0, 0, ceil_to_multiple(input0->info()->dimension(0), num_elems_processed_per_iteration_x), ceil_to_multiple(input0->info()->dimension(1),
                                          num_elems_processed_per_iteration_y));
         AccessWindowStatic    input1_access(input1->info(), 0, 0, ceil_to_multiple(input1->info()->dimension(0), num_elems_processed_per_iteration_x), input1->info()->dimension(1));
         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);

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

     IGCKernel::configure(win);
 }

 void GCGEMMMatrixMultiplyKernel::run(const Window &window)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IGCKernel::window(), window);

     _kernel.use();

     Window slice          = window.first_slice_window_2D();
     Window slice_matrix_b = slice;

     slice_matrix_b.set(Window::DimX, Window::Dimension(0, 1, 1));
     slice_matrix_b.set(Window::DimY, 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;
         switch(_input0->info()->data_type())
         {
             case DataType::F16:
                 add_2D_tensor_argument(idx, _input0, BufferParam(1, 2), slice);
                 add_2D_tensor_argument(idx, _input1, BufferParam(2, 3), slice_b);
                 add_2D_tensor_argument(idx, _output, BufferParam(3, 3), slice);
                 break;

             case DataType::F32:
                 add_2D_tensor_argument(idx, _input0, BufferParam(1, 2), slice);
                 add_2D_tensor_argument(idx, _input1, BufferParam(2, 2), slice_b);
                 add_2D_tensor_argument(idx, _output, BufferParam(3, 2), slice);
                 break;

             default:
                 ARM_COMPUTE_ERROR("Current data type is not supported");
                 break;
         }

         _kernel.update_shader_params();
         enqueue(*this, slice);
     }
     while(window.slide_window_slice_2D(slice));
 }
	/*
	* 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/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.h"

	#include "arm_compute/core/AccessWindowStatic.h"
	#include "arm_compute/core/AccessWindowTranspose.h"
	#include "arm_compute/core/Error.h"
	#include "arm_compute/core/GLES_COMPUTE/GCHelpers.h"
	#include "arm_compute/core/GLES_COMPUTE/GCKernelLibrary.h"
	#include "arm_compute/core/GLES_COMPUTE/IGCTensor.h"
	#include "arm_compute/core/GLES_COMPUTE/OpenGLES.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 <string>

	using namespace arm_compute;

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

	void GCGEMMMatrixMultiplyKernel::configure(const IGCTensor input0, const IGCTensor input1, IGCTensor *output, float alpha, bool is_interleaved_transposed)
	{
	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F32, DataType::F16);
	ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1, output);

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

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

	std::set<std::string> build_opts;
	Window win;

	build_opts.emplace("#define LOCAL_SIZE_X " + support::cpp11::to_string(1));
	build_opts.emplace("#define LOCAL_SIZE_Y " + support::cpp11::to_string(1));
	build_opts.emplace("#define LOCAL_SIZE_Z " + support::cpp11::to_string(1));
	build_opts.emplace("#define COLS_A " + support::cpp11::to_string(input0->info()->dimension(0)));
	build_opts.emplace("#define COLS_B " + support::cpp11::to_string(input1->info()->dimension(0)));
	build_opts.emplace("#define ALPHA " + float_to_string_with_full_precision(alpha));

	// Check if the output tensor is a vector. If so,the kernel runs the vector-matrix multiplication
	if(is_interleaved_transposed)
	{
	switch(input0->info()->data_type())
	{
	case DataType::F16:
	build_opts.emplace("#define DATA_TYPE_FP16");
	break;

	case DataType::F32:
	build_opts.emplace("#define DATA_TYPE_FP32");
	break;

	default:
	ARM_COMPUTE_ERROR("Current data type is not supported");
	break;
	}

	build_opts.emplace("#define GEMM_MM_INTERLEAVED_TRANSPOSED");

	// Create kernel
	_kernel = GCKernelLibrary::get().create_kernel(("gemm_mm_interleaved_transposed"), build_opts);

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

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

	// Special case for 1xN, 2xN, 3xN and 4xN input0 tensor
	unsigned int num_elems_processed_per_iteration_x;
	unsigned int num_elems_processed_per_iteration_y;

	switch(input0->info()->data_type())
	{
	case DataType::F16:
	num_elems_processed_per_iteration_x = 4;
	num_elems_processed_per_iteration_y = 1;
	build_opts.emplace("#define DATA_TYPE_FP16");
	break;

	case DataType::F32:
	num_elems_processed_per_iteration_x = max_gc_vector_width / data_size_from_type(input0->info()->data_type());
	num_elems_processed_per_iteration_y = std::min(static_cast<int>(output->info()->dimension(1)), 4);
	build_opts.emplace("#define DATA_TYPE_FP32");
	break;

	default:
	ARM_COMPUTE_ERROR("Current data type is not supported");
	break;
	}

	build_opts.emplace("#define GEMM_MM_FLOATING_POINT");
	build_opts.emplace("#define NUM_ELEMS_PROCESSED_PER_THREAD_X " + support::cpp11::to_string(num_elems_processed_per_iteration_x));
	build_opts.emplace("#define NUM_ELEMS_PROCESSED_PER_THREAD_Y " + support::cpp11::to_string(num_elems_processed_per_iteration_y));

	// Create kernel
	_kernel = GCKernelLibrary::get().create_kernel("gemm_mm_floating_point", build_opts);

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

	AccessWindowStatic input0_access(input0->info(), 0, 0, ceil_to_multiple(input0->info()->dimension(0), num_elems_processed_per_iteration_x), ceil_to_multiple(input0->info()->dimension(1),
	num_elems_processed_per_iteration_y));
	AccessWindowStatic input1_access(input1->info(), 0, 0, ceil_to_multiple(input1->info()->dimension(0), num_elems_processed_per_iteration_x), input1->info()->dimension(1));
	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);

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

	IGCKernel::configure(win);
	}

	void GCGEMMMatrixMultiplyKernel::run(const Window &window)
	{
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IGCKernel::window(), window);

	_kernel.use();

	Window slice = window.first_slice_window_2D();
	Window slice_matrix_b = slice;

	slice_matrix_b.set(Window::DimX, Window::Dimension(0, 1, 1));
	slice_matrix_b.set(Window::DimY, 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;
	switch(_input0->info()->data_type())
	{
	case DataType::F16:
	add_2D_tensor_argument(idx, _input0, BufferParam(1, 2), slice);
	add_2D_tensor_argument(idx, _input1, BufferParam(2, 3), slice_b);
	add_2D_tensor_argument(idx, _output, BufferParam(3, 3), slice);
	break;

	case DataType::F32:
	add_2D_tensor_argument(idx, _input0, BufferParam(1, 2), slice);
	add_2D_tensor_argument(idx, _input1, BufferParam(2, 2), slice_b);
	add_2D_tensor_argument(idx, _output, BufferParam(3, 2), slice);
	break;

	default:
	ARM_COMPUTE_ERROR("Current data type is not supported");
	break;
	}

	_kernel.update_shader_params();
	enqueue(*this, slice);
	}
	while(window.slide_window_slice_2D(slice));
	}