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

 /*
  * Copyright (c) 2017-2023 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/ClGemmLowpReductionKernel.h"

 #include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/ICLTensor.h"
 #include "arm_compute/core/KernelDescriptors.h"
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/utils/helpers/AdjustVecSize.h"
 #include "arm_compute/core/utils/StringUtils.h"

 #include "src/core/helpers/AutoConfiguration.h"
 #include "src/core/helpers/WindowHelpers.h"
 #include "support/Cast.h"
 #include "support/StringSupport.h"

 namespace arm_compute
 {
 namespace opencl
 {
 namespace kernels
 {
 namespace
 {
 Status validate_arguments_matrix_a_reduction(const ITensorInfo *src, const ITensorInfo *dst)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED,
                                                          DataType::QSYMM8);

     if (dst->total_size() > 0)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 1, DataType::S32);
         ARM_COMPUTE_RETURN_ERROR_ON_MSG(
             dst->dimension(0) != src->dimension(1),
             "Output vector must have length equal to the number of rows of the input matrix");
     }
     return Status{};
 }

 Status validate_arguments_matrix_b_reduction(const ITensorInfo *src, const ITensorInfo *dst)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED,
                                                          DataType::QSYMM8, DataType::QSYMM8_PER_CHANNEL);

     if (dst->total_size() > 0)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 1, DataType::S32);
         ARM_COMPUTE_RETURN_ERROR_ON_MSG(
             dst->dimension(0) != src->dimension(0),
             "Output vector must have length equal to the number of columns of the input matrix");
     }
     return Status{};
 }
 } // namespace

 IClGemmLowpReductionKernel::IClGemmLowpReductionKernel()
 {
     _type = CLKernelType::ELEMENTWISE;
 }

 void ClGemmLowpMatrixAReductionKernel::configure(const CLCompileContext            &compile_context,
                                                  const ITensorInfo                 *mtx_a,
                                                  ITensorInfo                       *vector_sum_row,
                                                  const GEMMLowpReductionKernelInfo &info)
 {
     // Perform validate step
     ARM_COMPUTE_ERROR_ON_NULLPTR(mtx_a, vector_sum_row);
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_matrix_a_reduction(mtx_a, vector_sum_row));

     // Output auto initialization if not yet initialized
     auto_init_if_empty(*vector_sum_row, TensorShape(mtx_a->dimension(1)), 1, DataType::S32);

     auto padding_info = get_padding_info({mtx_a, vector_sum_row});

     // Set the arguments to pass at compile time
     CLBuildOptions build_opts;
     build_opts.add_option("-DCOLS_A=" + support::cpp11::to_string(mtx_a->dimension(0)));
     build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(mtx_a->data_type()));
     build_opts.add_option("-DACC_DATA_TYPE=" + get_cl_dot8_acc_type_from_data_type(mtx_a->data_type()));
     build_opts.add_option_if(info.mul_by_scalar, "-DSCALAR=" + support::cpp11::to_string(info.scalar));

     const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device());

     std::string kernel_name = "gemmlowp_matrix_a_reduction" + std::string(is_dot8_supported ? "_dot8" : "");

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

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

     // Configure kernel window
     // This kernel does not need padding
     Window win = calculate_max_window(*vector_sum_row, Steps());
     ICLKernel::configure_internal(win);

     _config_id = kernel_name;
     _config_id += "_";
     _config_id += support::cpp11::to_string(mtx_a->dimension(0));
     _config_id += "_";
     _config_id += support::cpp11::to_string(mtx_a->dimension(1));
     _config_id += "_";
     _config_id += support::cpp11::to_string(mtx_a->dimension(2));

     ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
 }

 Status ClGemmLowpMatrixAReductionKernel::validate(const ITensorInfo                 *mtx_a,
                                                   const ITensorInfo                 *vector_sum_row,
                                                   const GEMMLowpReductionKernelInfo &info)
 {
     ARM_COMPUTE_UNUSED(info);
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_matrix_a_reduction(mtx_a, vector_sum_row));

     return Status{};
 }

 void ClGemmLowpMatrixAReductionKernel::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 auto src =
         utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC));
     auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));

     Window collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimY);
     Window slice_in  = collapsed.first_slice_window_2D();
     Window slice_out = collapsed.first_slice_window_2D();

     // Setup input slice. Its dimensions are increased in the cl kernel.
     slice_in.set(Window::DimX, Window::Dimension(0, 0, 0));
     slice_in.set(Window::DimY, Window::Dimension(0, 0, 0));
     slice_in.set(Window::DimZ, Window::Dimension(0, 0, 0));

     do
     {
         unsigned int idx = 0;
         add_3D_tensor_argument(idx, src, slice_in);
         add_2D_tensor_argument(idx, dst, slice_out);
         enqueue(queue, *this, slice_out, lws_hint());
     } while (collapsed.slide_window_slice_2D(slice_out));
 }

 void ClGemmLowpMatrixBReductionKernel::configure(const CLCompileContext            &compile_context,
                                                  const ITensorInfo                 *mtx_b,
                                                  ITensorInfo                       *vector_sum_col,
                                                  const GEMMLowpReductionKernelInfo &info)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(mtx_b, vector_sum_col);
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_matrix_b_reduction(mtx_b, vector_sum_col));

     // Output auto initialization if not yet initialized
     auto_init_if_empty(*vector_sum_col, TensorShape(mtx_b->dimension(0)), 1, DataType::S32);

     auto padding_info = get_padding_info({mtx_b, vector_sum_col});

     const unsigned int num_elems_processed_per_iteration = adjust_vec_size(16, mtx_b->dimension(0));

     // Set the arguments to pass at compile time
     CLBuildOptions build_opts;
     build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration));
     build_opts.add_option("-DVEC_SIZE_LEFTOVER=" +
                           support::cpp11::to_string(mtx_b->dimension(0) % num_elems_processed_per_iteration));
     build_opts.add_option("-DCOLS_B=" + support::cpp11::to_string(mtx_b->dimension(0)));
     build_opts.add_option("-DROWS_B=" + support::cpp11::to_string(mtx_b->dimension(1)));
     build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(mtx_b->data_type()));
     build_opts.add_option("-DACC_DATA_TYPE=" + get_cl_dot8_acc_type_from_data_type(mtx_b->data_type()));
     build_opts.add_option_if(info.mul_by_scalar, "-DSCALAR=" + support::cpp11::to_string(info.scalar));

     const std::string kernel_name = "gemmlowp_matrix_b_reduction";

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

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

     // Configure kernel window
     Window win = calculate_max_window(*vector_sum_col, Steps(num_elems_processed_per_iteration));
     IClKernel::configure_internal(win);

     ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
 }

 Status ClGemmLowpMatrixBReductionKernel::validate(const ITensorInfo                 *mtx_b,
                                                   const ITensorInfo                 *vector_sum_col,
                                                   const GEMMLowpReductionKernelInfo &info)
 {
     ARM_COMPUTE_UNUSED(info);
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_matrix_b_reduction(mtx_b, vector_sum_col));

     return Status{};
 }

 void ClGemmLowpMatrixBReductionKernel::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 auto src =
         utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC));
     auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));

     Window collapsed = window.collapse_if_possible(IKernel::window(), Window::DimY);

     Window slice_out = collapsed.first_slice_window_2D();
     Window slice_in  = slice_out;

     slice_in.set(Window::DimY, Window::Dimension(0, 0, 0));
     slice_in.set(Window::DimZ, Window::Dimension(0, 0, 0));

     do
     {
         unsigned int idx = 0;
         add_3D_tensor_argument(idx, src, slice_in);
         add_2D_tensor_argument(idx, dst, slice_out);
         enqueue(queue, *this, slice_out, lws_hint());
     } while (collapsed.slide_window_slice_2D(slice_out));
 }
 } // namespace kernels
 } // namespace opencl
 } // namespace arm_compute
	/*
	* Copyright (c) 2017-2023 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/ClGemmLowpReductionKernel.h"

	#include "arm_compute/core/CL/CLHelpers.h"
	#include "arm_compute/core/CL/ICLTensor.h"
	#include "arm_compute/core/KernelDescriptors.h"
	#include "arm_compute/core/Utils.h"
	#include "arm_compute/core/utils/helpers/AdjustVecSize.h"
	#include "arm_compute/core/utils/StringUtils.h"

	#include "src/core/helpers/AutoConfiguration.h"
	#include "src/core/helpers/WindowHelpers.h"
	#include "support/Cast.h"
	#include "support/StringSupport.h"

	namespace arm_compute
	{
	namespace opencl
	{
	namespace kernels
	{
	namespace
	{
	Status validate_arguments_matrix_a_reduction(const ITensorInfo src, const ITensorInfo dst)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED,
	DataType::QSYMM8);

	if (dst->total_size() > 0)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 1, DataType::S32);
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(
	dst->dimension(0) != src->dimension(1),
	"Output vector must have length equal to the number of rows of the input matrix");
	}
	return Status{};
	}

	Status validate_arguments_matrix_b_reduction(const ITensorInfo src, const ITensorInfo dst)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED,
	DataType::QSYMM8, DataType::QSYMM8_PER_CHANNEL);

	if (dst->total_size() > 0)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 1, DataType::S32);
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(
	dst->dimension(0) != src->dimension(0),
	"Output vector must have length equal to the number of columns of the input matrix");
	}
	return Status{};
	}
	} // namespace

	IClGemmLowpReductionKernel::IClGemmLowpReductionKernel()
	{
	_type = CLKernelType::ELEMENTWISE;
	}

	void ClGemmLowpMatrixAReductionKernel::configure(const CLCompileContext &compile_context,
	const ITensorInfo *mtx_a,
	ITensorInfo *vector_sum_row,
	const GEMMLowpReductionKernelInfo &info)
	{
	// Perform validate step
	ARM_COMPUTE_ERROR_ON_NULLPTR(mtx_a, vector_sum_row);
	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_matrix_a_reduction(mtx_a, vector_sum_row));

	// Output auto initialization if not yet initialized
	auto_init_if_empty(*vector_sum_row, TensorShape(mtx_a->dimension(1)), 1, DataType::S32);

	auto padding_info = get_padding_info({mtx_a, vector_sum_row});

	// Set the arguments to pass at compile time
	CLBuildOptions build_opts;
	build_opts.add_option("-DCOLS_A=" + support::cpp11::to_string(mtx_a->dimension(0)));
	build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(mtx_a->data_type()));
	build_opts.add_option("-DACC_DATA_TYPE=" + get_cl_dot8_acc_type_from_data_type(mtx_a->data_type()));
	build_opts.add_option_if(info.mul_by_scalar, "-DSCALAR=" + support::cpp11::to_string(info.scalar));

	const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device());

	std::string kernel_name = "gemmlowp_matrix_a_reduction" + std::string(is_dot8_supported ? "_dot8" : "");

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

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

	// Configure kernel window
	// This kernel does not need padding
	Window win = calculate_max_window(*vector_sum_row, Steps());
	ICLKernel::configure_internal(win);

	_config_id = kernel_name;
	_config_id += "_";
	_config_id += support::cpp11::to_string(mtx_a->dimension(0));
	_config_id += "_";
	_config_id += support::cpp11::to_string(mtx_a->dimension(1));
	_config_id += "_";
	_config_id += support::cpp11::to_string(mtx_a->dimension(2));

	ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
	}

	Status ClGemmLowpMatrixAReductionKernel::validate(const ITensorInfo *mtx_a,
	const ITensorInfo *vector_sum_row,
	const GEMMLowpReductionKernelInfo &info)
	{
	ARM_COMPUTE_UNUSED(info);
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_matrix_a_reduction(mtx_a, vector_sum_row));

	return Status{};
	}

	void ClGemmLowpMatrixAReductionKernel::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 auto src =
	utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC));
	auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));

	Window collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimY);
	Window slice_in = collapsed.first_slice_window_2D();
	Window slice_out = collapsed.first_slice_window_2D();

	// Setup input slice. Its dimensions are increased in the cl kernel.
	slice_in.set(Window::DimX, Window::Dimension(0, 0, 0));
	slice_in.set(Window::DimY, Window::Dimension(0, 0, 0));
	slice_in.set(Window::DimZ, Window::Dimension(0, 0, 0));

	do
	{
	unsigned int idx = 0;
	add_3D_tensor_argument(idx, src, slice_in);
	add_2D_tensor_argument(idx, dst, slice_out);
	enqueue(queue, *this, slice_out, lws_hint());
	} while (collapsed.slide_window_slice_2D(slice_out));
	}

	void ClGemmLowpMatrixBReductionKernel::configure(const CLCompileContext &compile_context,
	const ITensorInfo *mtx_b,
	ITensorInfo *vector_sum_col,
	const GEMMLowpReductionKernelInfo &info)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(mtx_b, vector_sum_col);
	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_matrix_b_reduction(mtx_b, vector_sum_col));

	// Output auto initialization if not yet initialized
	auto_init_if_empty(*vector_sum_col, TensorShape(mtx_b->dimension(0)), 1, DataType::S32);

	auto padding_info = get_padding_info({mtx_b, vector_sum_col});

	const unsigned int num_elems_processed_per_iteration = adjust_vec_size(16, mtx_b->dimension(0));

	// Set the arguments to pass at compile time
	CLBuildOptions build_opts;
	build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration));
	build_opts.add_option("-DVEC_SIZE_LEFTOVER=" +
	support::cpp11::to_string(mtx_b->dimension(0) % num_elems_processed_per_iteration));
	build_opts.add_option("-DCOLS_B=" + support::cpp11::to_string(mtx_b->dimension(0)));
	build_opts.add_option("-DROWS_B=" + support::cpp11::to_string(mtx_b->dimension(1)));
	build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(mtx_b->data_type()));
	build_opts.add_option("-DACC_DATA_TYPE=" + get_cl_dot8_acc_type_from_data_type(mtx_b->data_type()));
	build_opts.add_option_if(info.mul_by_scalar, "-DSCALAR=" + support::cpp11::to_string(info.scalar));

	const std::string kernel_name = "gemmlowp_matrix_b_reduction";

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

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

	// Configure kernel window
	Window win = calculate_max_window(*vector_sum_col, Steps(num_elems_processed_per_iteration));
	IClKernel::configure_internal(win);

	ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
	}

	Status ClGemmLowpMatrixBReductionKernel::validate(const ITensorInfo *mtx_b,
	const ITensorInfo *vector_sum_col,
	const GEMMLowpReductionKernelInfo &info)
	{
	ARM_COMPUTE_UNUSED(info);
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_matrix_b_reduction(mtx_b, vector_sum_col));

	return Status{};
	}

	void ClGemmLowpMatrixBReductionKernel::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 auto src =
	utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC));
	auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));

	Window collapsed = window.collapse_if_possible(IKernel::window(), Window::DimY);

	Window slice_out = collapsed.first_slice_window_2D();
	Window slice_in = slice_out;

	slice_in.set(Window::DimY, Window::Dimension(0, 0, 0));
	slice_in.set(Window::DimZ, Window::Dimension(0, 0, 0));

	do
	{
	unsigned int idx = 0;
	add_3D_tensor_argument(idx, src, slice_in);
	add_2D_tensor_argument(idx, dst, slice_out);
	enqueue(queue, *this, slice_out, lws_hint());
	} while (collapsed.slide_window_slice_2D(slice_out));
	}
	} // namespace kernels
	} // namespace opencl
	} // namespace arm_compute