src/gpu/cl/kernels/ClMatMulNativeKernel.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2023 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 "src/gpu/cl/kernels/ClMatMulNativeKernel.h"

 #include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/ICLTensor.h"
 #include "arm_compute/core/ITensorPack.h"
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/utils/ActivationFunctionUtils.h"
 #include "arm_compute/core/utils/StringUtils.h"
 #include "arm_compute/core/utils/helpers/AdjustVecSize.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"

 #include "src/common/utils/Log.h"
 #include "src/core/CL/CLUtils.h"
 #include "src/core/helpers/AutoConfiguration.h"
 #include "src/core/helpers/WindowHelpers.h"
 #include "src/gpu/cl/kernels/gemm/ClGemmHelpers.h"
 #include "src/gpu/cl/kernels/helpers/MatMulKernelHelpers.h"

 #include "support/Cast.h"
 #include "support/StringSupport.h"

 namespace arm_compute
 {
 namespace opencl
 {
 namespace kernels
 {
 namespace
 {
 Status validate_matmul_kernel_info(const MatMulKernelInfo &matmul_kernel_info)
 {
     const bool adj_lhs = matmul_kernel_info.adj_lhs;
     const bool adj_rhs = matmul_kernel_info.adj_rhs;
     const int  m0      = matmul_kernel_info.m0;
     const int  n0      = matmul_kernel_info.n0;
     const int  k0      = matmul_kernel_info.k0;

     // Validate M0
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(m0 < 1, "Only positive integers are supported for M0");

     if(adj_lhs)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MSG(((m0 & (m0 - 1)) && (m0 != 3)) || (m0 > 16), "Only 1,2,3,4,8,16 are supported for M0 for Lhs transposed");
     }

     // Validate N0
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(n0 < 1, "Only positive integers are supported for N0");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(((n0 & (n0 - 1)) && (n0 != 3)) || (n0 > 16), "Only 1,2,3,4,8,16 are supported for N0");

     // Validate K0
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(k0 < 1, "Only positive integers are supported for K0");
     if(!adj_lhs || adj_rhs)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MSG(((k0 & (k0 - 1)) && (k0 != 3)) || (k0 > 16), "Only 1,2,3,4,8,16 are supported for K0");
     }

     return Status{};
 }

 Status validate_export_to_cl_image(const ITensorInfo *rhs, const MatMulKernelInfo &matmul_kernel_info)
 {
     ARM_COMPUTE_RETURN_ERROR_ON(matmul_kernel_info.export_rhs_to_cl_image && rhs->lock_paddings());
     if(matmul_kernel_info.export_rhs_to_cl_image)
     {
         if(matmul_kernel_info.adj_rhs)
         {
             const int k0 = matmul_kernel_info.k0;
             ARM_COMPUTE_RETURN_ERROR_ON_MSG(k0 != 4 && k0 != 8 && k0 != 16, "K0 can only be: 4, 8, and 16 for Rhs transposed");
         }
         else
         {
             const int n0 = matmul_kernel_info.n0;
             ARM_COMPUTE_RETURN_ERROR_ON_MSG(n0 != 4 && n0 != 8 && n0 != 16, "N0 can only be: 4, 8, and 16 for Rhs non-transposed");
         }
         ARM_COMPUTE_RETURN_ERROR_ON_MSG(!export_to_cl_image(rhs), "Export to CLImage is not supported for this device/configuration");
     }

     return Status{};
 }
 }
 ClMatMulNativeKernel::ClMatMulNativeKernel()
 {
     _type = CLKernelType::GEMM;
 }

 Status ClMatMulNativeKernel::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *bias, const ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info,
                                       const ActivationLayerInfo &act_info)
 {
     ARM_COMPUTE_UNUSED(act_info);
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lhs, rhs, dst);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lhs, 1, DataType::F32, DataType::F16);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, rhs);
     ARM_COMPUTE_RETURN_ON_ERROR(validate_matmul_kernel_info(matmul_kernel_info));
     ARM_COMPUTE_RETURN_ON_ERROR(validate_matmul_input_shapes(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info));
     ARM_COMPUTE_RETURN_ON_ERROR(validate_export_to_cl_image(rhs, matmul_kernel_info));

     const TensorShape expected_output_shape = misc::shape_calculator::compute_matmul_shape(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info);

     if(dst->total_size() != 0)
     {
         const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(expected_output_shape);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_dst);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, dst);
     }

     if(bias != nullptr)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(bias, lhs);
         ARM_COMPUTE_RETURN_ERROR_ON_MSG((bias->num_dimensions() > 1), "Multi dimensional bias is unsupported.");
         ARM_COMPUTE_RETURN_ERROR_ON_MSG(bias->dimension(0) != expected_output_shape[0], "First dimension of bias and output tensors must match.");
     }

     return Status{};
 }
 void ClMatMulNativeKernel::configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *bias, ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info,
                                      const ActivationLayerInfo &act_info)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst, &compile_context, &matmul_kernel_info);
     ARM_COMPUTE_LOG_PARAMS(lhs, rhs, bias, dst, matmul_kernel_info);
     ARM_COMPUTE_ERROR_THROW_ON(validate(lhs, rhs, bias, dst, matmul_kernel_info));

     // dst tensor auto initialization if not yet initialized
     auto_init_if_empty(*dst, lhs->clone()->set_tensor_shape(misc::shape_calculator::compute_matmul_shape(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info)));

     const int  m       = dst->dimension(1);
     const int  n       = dst->dimension(0);
     const int  k       = matmul_kernel_info.adj_lhs ? lhs->tensor_shape().y() : lhs->tensor_shape().x();
     const bool adj_lhs = matmul_kernel_info.adj_lhs;

     int m0 = adj_lhs ? adjust_vec_size(matmul_kernel_info.m0, m) : std::min(matmul_kernel_info.m0, m);
     int n0 = adjust_vec_size(matmul_kernel_info.n0, n);

     _export_rhs_to_cl_image = matmul_kernel_info.export_rhs_to_cl_image && !rhs->lock_paddings();

     // Configure kernel window
     Window win = calculate_max_window(*dst, Steps(n0, m0));
     win        = win.collapse(win, Window::DimZ);
     IClKernel::configure_internal(win);

     // Calculate partial (store instead of load) M0 and partial N0 for the partial blocks at the end of a row/column if any. This is to avoid padding.
     const unsigned int partial_store_m0 = m % m0;
     const unsigned int partial_store_n0 = n % n0;

     CLBuildOptions build_opts;
     build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(lhs->data_type()));
     build_opts.add_option("-DM0=" + support::cpp11::to_string(m0));
     build_opts.add_option("-DN0=" + support::cpp11::to_string(n0));
     build_opts.add_option("-DK0=" + support::cpp11::to_string(matmul_kernel_info.k0));
     build_opts.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string(partial_store_m0));
     build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(partial_store_n0));
     build_opts.add_option("-DK=" + support::cpp11::to_string(k));
     build_opts.add_option_if(bias != nullptr, "-DBIAS");
     build_opts.add_option_if_else(_export_rhs_to_cl_image, "-DRHS_TENSOR_TYPE=IMAGE", "-DRHS_TENSOR_TYPE=BUFFER");

     // Define values for activation function
     build_opts.add_option(("-DA_VAL=" + float_to_string_with_full_precision(act_info.a())));
     build_opts.add_option(("-DB_VAL=" + float_to_string_with_full_precision(act_info.b())));
     build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(act_info.activation())));

     std::string kernel_name("mat_mul_native");
     kernel_name += matmul_kernel_info.adj_lhs ? "_t" : "_nt";
     kernel_name += matmul_kernel_info.adj_rhs ? "_t" : "_nt";

     // A macro guard to compile ONLY the kernel of interest
     build_opts.add_option("-D" + upper_string(kernel_name));

     if(_export_rhs_to_cl_image)
     {
         gemm::update_padding_for_cl_image(rhs);
     }

     // Create kernel
     _kernel = create_kernel(compile_context, kernel_name, build_opts.options());

     // Set config_id for enabling LWS tuning
     _config_id = kernel_name;
     _config_id += "_";
     _config_id += lower_string(string_from_data_type(lhs->data_type()));
     _config_id += "_";
     _config_id += support::cpp11::to_string(m);
     _config_id += "_";
     _config_id += support::cpp11::to_string(n);
     _config_id += "_";
     _config_id += support::cpp11::to_string(k);
     _config_id += "_";
     _config_id += support::cpp11::to_string(dst->dimension(2));
     _config_id += "_";
     _config_id += support::cpp11::to_string(_export_rhs_to_cl_image);
     _config_id += "_";
     _config_id += support::cpp11::to_string(m0);
     _config_id += "_";
     _config_id += support::cpp11::to_string(n0);
     _config_id += "_";
     _config_id += support::cpp11::to_string(matmul_kernel_info.k0);
 }

 void ClMatMulNativeKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);

     const ICLTensor *lhs  = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
     const ICLTensor *rhs  = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
     const ICLTensor *bias = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_2)); // nullptr if bias is not present
     ICLTensor       *dst  = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
     ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst);
     ARM_COMPUTE_LOG_PARAMS(lhs, rhs, bias, dst);

     unsigned int idx              = 0;
     Window       window_collapsed = window.collapse(ICLKernel::window(), Window::DimZ);

     add_3d_tensor_nhw_argument(idx, lhs);

     cl::Image2D rhs_cl_image;
     if(_export_rhs_to_cl_image)
     {
         const size_t      image_w = rhs->info()->dimension(0) / 4;
         const size_t      image_h = rhs->info()->tensor_shape().total_size() / rhs->info()->dimension(0);
         const TensorShape shape2d(image_w, image_h);
         const size_t      image_row_pitch = rhs->info()->strides_in_bytes()[1];

         // Export cl_buffer to cl_image
         rhs_cl_image = create_image2d_from_buffer(CLKernelLibrary::get().context(), rhs->cl_buffer(), shape2d, rhs->info()->data_type(), image_row_pitch, CLImage2DType::ReadOnly);
         _kernel.setArg(idx++, rhs_cl_image);
     }

     add_3d_tensor_nhw_argument(idx, rhs);
     if(bias != nullptr)
     {
         add_3d_tensor_nhw_argument(idx, bias);
     }
     add_3d_tensor_nhw_argument(idx, dst);

     enqueue(queue, *this, window_collapsed, lws_hint());
 }

 } // namespace kernels
 } // namespace opencl
 } // namespace arm_compute
	/*
	* Copyright (c) 2023 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 "src/gpu/cl/kernels/ClMatMulNativeKernel.h"

	#include "arm_compute/core/CL/CLHelpers.h"
	#include "arm_compute/core/CL/ICLTensor.h"
	#include "arm_compute/core/ITensorPack.h"
	#include "arm_compute/core/TensorInfo.h"
	#include "arm_compute/core/utils/ActivationFunctionUtils.h"
	#include "arm_compute/core/utils/StringUtils.h"
	#include "arm_compute/core/utils/helpers/AdjustVecSize.h"
	#include "arm_compute/core/utils/misc/ShapeCalculator.h"

	#include "src/common/utils/Log.h"
	#include "src/core/CL/CLUtils.h"
	#include "src/core/helpers/AutoConfiguration.h"
	#include "src/core/helpers/WindowHelpers.h"
	#include "src/gpu/cl/kernels/gemm/ClGemmHelpers.h"
	#include "src/gpu/cl/kernels/helpers/MatMulKernelHelpers.h"

	#include "support/Cast.h"
	#include "support/StringSupport.h"

	namespace arm_compute
	{
	namespace opencl
	{
	namespace kernels
	{
	namespace
	{
	Status validate_matmul_kernel_info(const MatMulKernelInfo &matmul_kernel_info)
	{
	const bool adj_lhs = matmul_kernel_info.adj_lhs;
	const bool adj_rhs = matmul_kernel_info.adj_rhs;
	const int m0 = matmul_kernel_info.m0;
	const int n0 = matmul_kernel_info.n0;
	const int k0 = matmul_kernel_info.k0;

	// Validate M0
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(m0 < 1, "Only positive integers are supported for M0");

	if(adj_lhs)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(((m0 & (m0 - 1)) && (m0 != 3)) \|\| (m0 > 16), "Only 1,2,3,4,8,16 are supported for M0 for Lhs transposed");
	}

	// Validate N0
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(n0 < 1, "Only positive integers are supported for N0");
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(((n0 & (n0 - 1)) && (n0 != 3)) \|\| (n0 > 16), "Only 1,2,3,4,8,16 are supported for N0");

	// Validate K0
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(k0 < 1, "Only positive integers are supported for K0");
	if(!adj_lhs \|\| adj_rhs)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(((k0 & (k0 - 1)) && (k0 != 3)) \|\| (k0 > 16), "Only 1,2,3,4,8,16 are supported for K0");
	}

	return Status{};
	}

	Status validate_export_to_cl_image(const ITensorInfo *rhs, const MatMulKernelInfo &matmul_kernel_info)
	{
	ARM_COMPUTE_RETURN_ERROR_ON(matmul_kernel_info.export_rhs_to_cl_image && rhs->lock_paddings());
	if(matmul_kernel_info.export_rhs_to_cl_image)
	{
	if(matmul_kernel_info.adj_rhs)
	{
	const int k0 = matmul_kernel_info.k0;
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(k0 != 4 && k0 != 8 && k0 != 16, "K0 can only be: 4, 8, and 16 for Rhs transposed");
	}
	else
	{
	const int n0 = matmul_kernel_info.n0;
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(n0 != 4 && n0 != 8 && n0 != 16, "N0 can only be: 4, 8, and 16 for Rhs non-transposed");
	}
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(!export_to_cl_image(rhs), "Export to CLImage is not supported for this device/configuration");
	}

	return Status{};
	}
	}
	ClMatMulNativeKernel::ClMatMulNativeKernel()
	{
	_type = CLKernelType::GEMM;
	}

	Status ClMatMulNativeKernel::validate(const ITensorInfo lhs, const ITensorInfo rhs, const ITensorInfo bias, const ITensorInfo dst, const MatMulKernelInfo &matmul_kernel_info,
	const ActivationLayerInfo &act_info)
	{
	ARM_COMPUTE_UNUSED(act_info);
	ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lhs, rhs, dst);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lhs, 1, DataType::F32, DataType::F16);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, rhs);
	ARM_COMPUTE_RETURN_ON_ERROR(validate_matmul_kernel_info(matmul_kernel_info));
	ARM_COMPUTE_RETURN_ON_ERROR(validate_matmul_input_shapes(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info));
	ARM_COMPUTE_RETURN_ON_ERROR(validate_export_to_cl_image(rhs, matmul_kernel_info));

	const TensorShape expected_output_shape = misc::shape_calculator::compute_matmul_shape(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info);

	if(dst->total_size() != 0)
	{
	const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(expected_output_shape);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_dst);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, dst);
	}

	if(bias != nullptr)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(bias, lhs);
	ARM_COMPUTE_RETURN_ERROR_ON_MSG((bias->num_dimensions() > 1), "Multi dimensional bias is unsupported.");
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(bias->dimension(0) != expected_output_shape[0], "First dimension of bias and output tensors must match.");
	}

	return Status{};
	}
	void ClMatMulNativeKernel::configure(const ClCompileContext &compile_context, ITensorInfo lhs, ITensorInfo rhs, ITensorInfo bias, ITensorInfo dst, const MatMulKernelInfo &matmul_kernel_info,
	const ActivationLayerInfo &act_info)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst, &compile_context, &matmul_kernel_info);
	ARM_COMPUTE_LOG_PARAMS(lhs, rhs, bias, dst, matmul_kernel_info);
	ARM_COMPUTE_ERROR_THROW_ON(validate(lhs, rhs, bias, dst, matmul_kernel_info));

	// dst tensor auto initialization if not yet initialized
	auto_init_if_empty(*dst, lhs->clone()->set_tensor_shape(misc::shape_calculator::compute_matmul_shape(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info)));

	const int m = dst->dimension(1);
	const int n = dst->dimension(0);
	const int k = matmul_kernel_info.adj_lhs ? lhs->tensor_shape().y() : lhs->tensor_shape().x();
	const bool adj_lhs = matmul_kernel_info.adj_lhs;

	int m0 = adj_lhs ? adjust_vec_size(matmul_kernel_info.m0, m) : std::min(matmul_kernel_info.m0, m);
	int n0 = adjust_vec_size(matmul_kernel_info.n0, n);

	_export_rhs_to_cl_image = matmul_kernel_info.export_rhs_to_cl_image && !rhs->lock_paddings();

	// Configure kernel window
	Window win = calculate_max_window(*dst, Steps(n0, m0));
	win = win.collapse(win, Window::DimZ);
	IClKernel::configure_internal(win);

	// Calculate partial (store instead of load) M0 and partial N0 for the partial blocks at the end of a row/column if any. This is to avoid padding.
	const unsigned int partial_store_m0 = m % m0;
	const unsigned int partial_store_n0 = n % n0;

	CLBuildOptions build_opts;
	build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(lhs->data_type()));
	build_opts.add_option("-DM0=" + support::cpp11::to_string(m0));
	build_opts.add_option("-DN0=" + support::cpp11::to_string(n0));
	build_opts.add_option("-DK0=" + support::cpp11::to_string(matmul_kernel_info.k0));
	build_opts.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string(partial_store_m0));
	build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(partial_store_n0));
	build_opts.add_option("-DK=" + support::cpp11::to_string(k));
	build_opts.add_option_if(bias != nullptr, "-DBIAS");
	build_opts.add_option_if_else(_export_rhs_to_cl_image, "-DRHS_TENSOR_TYPE=IMAGE", "-DRHS_TENSOR_TYPE=BUFFER");

	// Define values for activation function
	build_opts.add_option(("-DA_VAL=" + float_to_string_with_full_precision(act_info.a())));
	build_opts.add_option(("-DB_VAL=" + float_to_string_with_full_precision(act_info.b())));
	build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(act_info.activation())));

	std::string kernel_name("mat_mul_native");
	kernel_name += matmul_kernel_info.adj_lhs ? "_t" : "_nt";
	kernel_name += matmul_kernel_info.adj_rhs ? "_t" : "_nt";

	// A macro guard to compile ONLY the kernel of interest
	build_opts.add_option("-D" + upper_string(kernel_name));

	if(_export_rhs_to_cl_image)
	{
	gemm::update_padding_for_cl_image(rhs);
	}

	// Create kernel
	_kernel = create_kernel(compile_context, kernel_name, build_opts.options());

	// Set config_id for enabling LWS tuning
	_config_id = kernel_name;
	_config_id += "_";
	_config_id += lower_string(string_from_data_type(lhs->data_type()));
	_config_id += "_";
	_config_id += support::cpp11::to_string(m);
	_config_id += "_";
	_config_id += support::cpp11::to_string(n);
	_config_id += "_";
	_config_id += support::cpp11::to_string(k);
	_config_id += "_";
	_config_id += support::cpp11::to_string(dst->dimension(2));
	_config_id += "_";
	_config_id += support::cpp11::to_string(_export_rhs_to_cl_image);
	_config_id += "_";
	_config_id += support::cpp11::to_string(m0);
	_config_id += "_";
	_config_id += support::cpp11::to_string(n0);
	_config_id += "_";
	_config_id += support::cpp11::to_string(matmul_kernel_info.k0);
	}

	void ClMatMulNativeKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
	{
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);

	const ICLTensor lhs = utils::cast::polymorphic_downcast<const ICLTensor >(tensors.get_const_tensor(TensorType::ACL_SRC_0));
	const ICLTensor rhs = utils::cast::polymorphic_downcast<const ICLTensor >(tensors.get_const_tensor(TensorType::ACL_SRC_1));
	const ICLTensor bias = utils::cast::polymorphic_downcast<const ICLTensor >(tensors.get_const_tensor(TensorType::ACL_SRC_2)); // nullptr if bias is not present
	ICLTensor dst = utils::cast::polymorphic_downcast<ICLTensor >(tensors.get_tensor(TensorType::ACL_DST));
	ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst);
	ARM_COMPUTE_LOG_PARAMS(lhs, rhs, bias, dst);

	unsigned int idx = 0;
	Window window_collapsed = window.collapse(ICLKernel::window(), Window::DimZ);

	add_3d_tensor_nhw_argument(idx, lhs);

	cl::Image2D rhs_cl_image;
	if(_export_rhs_to_cl_image)
	{
	const size_t image_w = rhs->info()->dimension(0) / 4;
	const size_t image_h = rhs->info()->tensor_shape().total_size() / rhs->info()->dimension(0);
	const TensorShape shape2d(image_w, image_h);
	const size_t image_row_pitch = rhs->info()->strides_in_bytes()[1];

	// Export cl_buffer to cl_image
	rhs_cl_image = create_image2d_from_buffer(CLKernelLibrary::get().context(), rhs->cl_buffer(), shape2d, rhs->info()->data_type(), image_row_pitch, CLImage2DType::ReadOnly);
	_kernel.setArg(idx++, rhs_cl_image);
	}

	add_3d_tensor_nhw_argument(idx, rhs);
	if(bias != nullptr)
	{
	add_3d_tensor_nhw_argument(idx, bias);
	}
	add_3d_tensor_nhw_argument(idx, dst);

	enqueue(queue, *this, window_collapsed, lws_hint());
	}

	} // namespace kernels
	} // namespace opencl
	} // namespace arm_compute