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

 /*
  * Copyright (c) 2017-2018 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/TensorInfo.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 "arm_compute/core/utils/misc/ShapeCalculator.h"

 #include <set>
 #include <string>

 using namespace arm_compute;
 using namespace arm_compute::misc::shape_calculator;

 namespace
 {
 using ElementsProcessed = Steps;

 inline Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info)
 {
     ARM_COMPUTE_UNUSED(reshape_info);
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 3, "The number of dimensions for the matrix B must be <= 3");

     if(!is_interleaved_transposed)
     {
         ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != input1->dimension(1));

         if(output->total_size() != 0)
         {
             ARM_COMPUTE_RETURN_ERROR_ON(input1->dimension(0) != output->dimension(0));
             ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) != output->dimension(1));
             ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output);
         }
     }
     else
     {
         const int m                         = reshape_info.m();
         const int n                         = reshape_info.n();
         const int k                         = reshape_info.k();
         const int mult_transpose1xW_width   = reshape_info.mult_transpose1xW_width();
         const int mult_interleave4x4_height = reshape_info.mult_interleave4x4_height();

         TensorShape tensor_shape0{ input0->tensor_shape() };
         tensor_shape0.set(0, k);
         tensor_shape0.set(1, m);

         TensorShape tensor_shape1{ input1->tensor_shape() };
         tensor_shape1.set(0, n);
         tensor_shape1.set(1, k);

         const TensorInfo tensor_info0 = input0->clone()->set_tensor_shape(tensor_shape0);
         const TensorInfo tensor_info1 = input1->clone()->set_tensor_shape(tensor_shape1);

         const TensorInfo tensor_info_reshaped0 = input0->clone()->set_tensor_shape(compute_interleaved_shape(tensor_info0, mult_interleave4x4_height));
         const TensorInfo tensor_info_reshaped1 = input1->clone()->set_tensor_shape(compute_transpose1xW_with_element_size_shape(tensor_info1, mult_transpose1xW_width));

         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input0, &tensor_info_reshaped0);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input1, &tensor_info_reshaped1);

         if(output->total_size() != 0)
         {
             ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(0) != static_cast<size_t>(n));
             ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(1) != static_cast<size_t>(m));
             ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output);
             ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input0, output);
         }
     }

     return Status{};
 }

 inline std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output,
                                                                bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info,
                                                                GPUTarget gpu_target, ElementsProcessed &num_elements_processed)
 {
     ARM_COMPUTE_UNUSED(gpu_target);

     // Output tensor auto inizialitation if not yet initialized
     TensorShape tensor_shape{ input0->tensor_shape() };
     tensor_shape.set(0, is_interleaved_transposed ? reshape_info.n() : input1->dimension(0));
     tensor_shape.set(1, is_interleaved_transposed ? reshape_info.m() : input0->dimension(1));

     auto_init_if_empty(*output, input0->clone()->set_tensor_shape(tensor_shape));

     bool   window_changed = false;
     Window win{};

     const DataType data_type                           = input0->data_type();
     unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0];
     unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1];

     if(is_interleaved_transposed)
     {
         // Configure window kernel
         num_elems_processed_per_iteration_x = max_gc_vector_width / data_size_from_type(data_type);
         num_elems_processed_per_iteration_y = 4;

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

         AccessWindowRectangle input0_access(input0, 0, 0, num_elems_processed_per_iteration_y, 1, 1.f, 0.25f);
         AccessWindowTranspose input1_access(input1, 0, 0, num_elems_processed_per_iteration_x, 1, 0.f, 0.25f);
         AccessWindowRectangle output_access(output, 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(), output->tensor_shape()));
     }
     else // The input tensors have not been reshaped
     {
         // Special case for 1xN, 2xN, 3xN and 4xN input0 tensor.
         num_elems_processed_per_iteration_y = std::min(static_cast<int>(output->dimension(1)), 4);

         switch(data_type)
         {
             case DataType::F16:
                 num_elems_processed_per_iteration_x = 4;
                 break;

             case DataType::F32:
                 num_elems_processed_per_iteration_x = max_gc_vector_width / data_size_from_type(data_type);
                 break;

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

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

         AccessWindowStatic    input0_access(input0, 0, 0, ceil_to_multiple(input0->dimension(0), 8), ceil_to_multiple(input0->dimension(1), num_elems_processed_per_iteration_y));
         AccessWindowStatic    input1_access(input1, 0, 0, ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x), input1->dimension(1));
         AccessWindowRectangle output_access(output, 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->num_dimensions());
         output_access.set_valid_region(win, ValidRegion(coord, output->tensor_shape()));
     }

     Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
     return std::make_pair(err, win);
 }
 } // namespace

 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, const GEMMReshapeInfo &reshape_info)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output);

     // Perform validate step
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info(), is_interleaved_transposed, reshape_info));

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

     // Get target architecture
     GPUTarget gpu_target = get_target();

     ElementsProcessed num_elements_processed{};

     // Configure kernel window
     auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info(), is_interleaved_transposed, reshape_info, gpu_target, num_elements_processed);
     ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
     IGCKernel::configure(win_config.second);

     // Create build options
     std::set<std::string> build_opts;
     std::string           kernel_name;

     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)
     {
         const int mult_transpose1xW_width   = reshape_info.mult_transpose1xW_width();
         const int mult_interleave4x4_height = reshape_info.mult_interleave4x4_height();

         build_opts.emplace("#define MULT_TRANSPOSE1XW_WIDTH " + support::cpp11::to_string(mult_transpose1xW_width));
         build_opts.emplace("#define MULT_INTERLEAVE4X4_HEIGHT " + support::cpp11::to_string(mult_interleave4x4_height));

         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");

         kernel_name = "gemm_mm_interleaved_transposed";
     }
     else
     {
         // Special case for 1xN, 2xN, 3xN and 4xN input0 tensor

         GPUTarget arch_target = get_arch_from_target(gpu_target);
         switch(input0->info()->data_type())
         {
             case DataType::F16:
                 build_opts.emplace("#define DATA_TYPE_FP16");
                 build_opts.emplace("#define MM_PROCESS_4X_OPTIMIZED");
                 build_opts.emplace("#define GEMM_MM_FLOATING_POINT");
                 break;

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

                 if(arch_target == GPUTarget::BIFROST && input0->info()->num_dimensions() != 1)
                 {
                     build_opts.emplace("#define GEMM_MM_FLOATING_POINT_BIFROST");
                 }
                 else
                 {
                     build_opts.emplace("#define GEMM_MM_FLOATING_POINT");
                 }
                 break;

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

         build_opts.emplace("#define NUM_ELEMS_PROCESSED_PER_THREAD_X " + support::cpp11::to_string(num_elements_processed.x()));
         build_opts.emplace("#define NUM_ELEMS_PROCESSED_PER_THREAD_Y " + support::cpp11::to_string(num_elements_processed.y()));

         kernel_name = "gemm_mm_floating_point";
     }

     // Create kernel
     _kernel = GCKernelLibrary::get().create_kernel(kernel_name, build_opts);
 }

 Status GCGEMMMatrixMultiplyKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, float alpha, bool is_interleaved_transposed,
                                             const GEMMReshapeInfo &reshape_info, GPUTarget gpu_target)
 {
     ARM_COMPUTE_UNUSED(alpha);
     ElementsProcessed num_elements_processed{};
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output, is_interleaved_transposed, reshape_info));
     ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(),
                                                               input1->clone().get(),
                                                               output->clone().get(),
                                                               is_interleaved_transposed,
                                                               reshape_info,
                                                               gpu_target,
                                                               num_elements_processed)
                                 .first);
     return Status{};
 }

 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;

         add_2D_tensor_argument(idx, _input0, 1, slice);
         add_2D_tensor_argument(idx, _input1, 2, slice_b);
         add_2D_tensor_argument(idx, _output, 3, slice);
         _kernel.update_shader_params();
         enqueue(*this, slice);
     }
     while(window.slide_window_slice_2D(slice));
 }
	/*
	* Copyright (c) 2017-2018 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/TensorInfo.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 "arm_compute/core/utils/misc/ShapeCalculator.h"

	#include <set>
	#include <string>

	using namespace arm_compute;
	using namespace arm_compute::misc::shape_calculator;

	namespace
	{
	using ElementsProcessed = Steps;

	inline Status validate_arguments(const ITensorInfo input0, const ITensorInfo input1, const ITensorInfo *output, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info)
	{
	ARM_COMPUTE_UNUSED(reshape_info);
	ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F16, DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1);
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 3, "The number of dimensions for the matrix B must be <= 3");

	if(!is_interleaved_transposed)
	{
	ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != input1->dimension(1));

	if(output->total_size() != 0)
	{
	ARM_COMPUTE_RETURN_ERROR_ON(input1->dimension(0) != output->dimension(0));
	ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) != output->dimension(1));
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output);
	}
	}
	else
	{
	const int m = reshape_info.m();
	const int n = reshape_info.n();
	const int k = reshape_info.k();
	const int mult_transpose1xW_width = reshape_info.mult_transpose1xW_width();
	const int mult_interleave4x4_height = reshape_info.mult_interleave4x4_height();

	TensorShape tensor_shape0{ input0->tensor_shape() };
	tensor_shape0.set(0, k);
	tensor_shape0.set(1, m);

	TensorShape tensor_shape1{ input1->tensor_shape() };
	tensor_shape1.set(0, n);
	tensor_shape1.set(1, k);

	const TensorInfo tensor_info0 = input0->clone()->set_tensor_shape(tensor_shape0);
	const TensorInfo tensor_info1 = input1->clone()->set_tensor_shape(tensor_shape1);

	const TensorInfo tensor_info_reshaped0 = input0->clone()->set_tensor_shape(compute_interleaved_shape(tensor_info0, mult_interleave4x4_height));
	const TensorInfo tensor_info_reshaped1 = input1->clone()->set_tensor_shape(compute_transpose1xW_with_element_size_shape(tensor_info1, mult_transpose1xW_width));

	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input0, &tensor_info_reshaped0);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input1, &tensor_info_reshaped1);

	if(output->total_size() != 0)
	{
	ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(0) != static_cast<size_t>(n));
	ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(1) != static_cast<size_t>(m));
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input0, output);
	}
	}

	return Status{};
	}

	inline std::pair<Status, Window> validate_and_configure_window(ITensorInfo input0, ITensorInfo input1, ITensorInfo *output,
	bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info,
	GPUTarget gpu_target, ElementsProcessed &num_elements_processed)
	{
	ARM_COMPUTE_UNUSED(gpu_target);

	// Output tensor auto inizialitation if not yet initialized
	TensorShape tensor_shape{ input0->tensor_shape() };
	tensor_shape.set(0, is_interleaved_transposed ? reshape_info.n() : input1->dimension(0));
	tensor_shape.set(1, is_interleaved_transposed ? reshape_info.m() : input0->dimension(1));

	auto_init_if_empty(*output, input0->clone()->set_tensor_shape(tensor_shape));

	bool window_changed = false;
	Window win{};

	const DataType data_type = input0->data_type();
	unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0];
	unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1];

	if(is_interleaved_transposed)
	{
	// Configure window kernel
	num_elems_processed_per_iteration_x = max_gc_vector_width / data_size_from_type(data_type);
	num_elems_processed_per_iteration_y = 4;

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

	AccessWindowRectangle input0_access(input0, 0, 0, num_elems_processed_per_iteration_y, 1, 1.f, 0.25f);
	AccessWindowTranspose input1_access(input1, 0, 0, num_elems_processed_per_iteration_x, 1, 0.f, 0.25f);
	AccessWindowRectangle output_access(output, 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(), output->tensor_shape()));
	}
	else // The input tensors have not been reshaped
	{
	// Special case for 1xN, 2xN, 3xN and 4xN input0 tensor.
	num_elems_processed_per_iteration_y = std::min(static_cast<int>(output->dimension(1)), 4);

	switch(data_type)
	{
	case DataType::F16:
	num_elems_processed_per_iteration_x = 4;
	break;

	case DataType::F32:
	num_elems_processed_per_iteration_x = max_gc_vector_width / data_size_from_type(data_type);
	break;

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

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

	AccessWindowStatic input0_access(input0, 0, 0, ceil_to_multiple(input0->dimension(0), 8), ceil_to_multiple(input0->dimension(1), num_elems_processed_per_iteration_y));
	AccessWindowStatic input1_access(input1, 0, 0, ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x), input1->dimension(1));
	AccessWindowRectangle output_access(output, 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->num_dimensions());
	output_access.set_valid_region(win, ValidRegion(coord, output->tensor_shape()));
	}

	Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
	return std::make_pair(err, win);
	}
	} // namespace

	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, const GEMMReshapeInfo &reshape_info)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output);

	// Perform validate step
	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info(), is_interleaved_transposed, reshape_info));

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

	// Get target architecture
	GPUTarget gpu_target = get_target();

	ElementsProcessed num_elements_processed{};

	// Configure kernel window
	auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info(), is_interleaved_transposed, reshape_info, gpu_target, num_elements_processed);
	ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
	IGCKernel::configure(win_config.second);

	// Create build options
	std::set<std::string> build_opts;
	std::string kernel_name;

	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)
	{
	const int mult_transpose1xW_width = reshape_info.mult_transpose1xW_width();
	const int mult_interleave4x4_height = reshape_info.mult_interleave4x4_height();

	build_opts.emplace("#define MULT_TRANSPOSE1XW_WIDTH " + support::cpp11::to_string(mult_transpose1xW_width));
	build_opts.emplace("#define MULT_INTERLEAVE4X4_HEIGHT " + support::cpp11::to_string(mult_interleave4x4_height));

	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");

	kernel_name = "gemm_mm_interleaved_transposed";
	}
	else
	{
	// Special case for 1xN, 2xN, 3xN and 4xN input0 tensor

	GPUTarget arch_target = get_arch_from_target(gpu_target);
	switch(input0->info()->data_type())
	{
	case DataType::F16:
	build_opts.emplace("#define DATA_TYPE_FP16");
	build_opts.emplace("#define MM_PROCESS_4X_OPTIMIZED");
	build_opts.emplace("#define GEMM_MM_FLOATING_POINT");
	break;

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

	if(arch_target == GPUTarget::BIFROST && input0->info()->num_dimensions() != 1)
	{
	build_opts.emplace("#define GEMM_MM_FLOATING_POINT_BIFROST");
	}
	else
	{
	build_opts.emplace("#define GEMM_MM_FLOATING_POINT");
	}
	break;

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

	build_opts.emplace("#define NUM_ELEMS_PROCESSED_PER_THREAD_X " + support::cpp11::to_string(num_elements_processed.x()));
	build_opts.emplace("#define NUM_ELEMS_PROCESSED_PER_THREAD_Y " + support::cpp11::to_string(num_elements_processed.y()));

	kernel_name = "gemm_mm_floating_point";
	}

	// Create kernel
	_kernel = GCKernelLibrary::get().create_kernel(kernel_name, build_opts);
	}

	Status GCGEMMMatrixMultiplyKernel::validate(const ITensorInfo input0, const ITensorInfo input1, const ITensorInfo *output, float alpha, bool is_interleaved_transposed,
	const GEMMReshapeInfo &reshape_info, GPUTarget gpu_target)
	{
	ARM_COMPUTE_UNUSED(alpha);
	ElementsProcessed num_elements_processed{};
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output, is_interleaved_transposed, reshape_info));
	ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(),
	input1->clone().get(),
	output->clone().get(),
	is_interleaved_transposed,
	reshape_info,
	gpu_target,
	num_elements_processed)
	.first);
	return Status{};
	}

	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;

	add_2D_tensor_argument(idx, _input0, 1, slice);
	add_2D_tensor_argument(idx, _input1, 2, slice_b);
	add_2D_tensor_argument(idx, _output, 3, slice);
	_kernel.update_shader_params();
	enqueue(*this, slice);
	}
	while(window.slide_window_slice_2D(slice));
	}