src/gpu/cl/kernels/ClGemmLowpOffsetContributionKernel.cpp - platform/external/ComputeLibrary - Git at Google


            
            /*

            
             * Copyright (c) 2017-2022 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/ClGemmLowpOffsetContributionKernel.h"


            #include "arm_compute/core/CL/ICLTensor.h"

            
            #include "arm_compute/core/Helpers.h"

            
            #include "arm_compute/core/TensorInfo.h"

            
            #include "arm_compute/core/Utils.h"

            
            #include "arm_compute/core/Validate.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(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, const ITensorInfo *bias,

            
                                      int32_t a_offset, int32_t b_offset)

            
            {

            
                ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(mm_result, 1, DataType::S32);


                if(bias != nullptr)

            
                {

            
                    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32);

            
                    ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1);

            
                    ARM_COMPUTE_RETURN_ERROR_ON(mm_result->dimension(0) != bias->dimension(0));

            
                }


                // If a_offset == 0, vector_sum_col can be a nullptr

            
                if(a_offset != 0)

            
                {

            
                    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_col, 1, DataType::S32);

            
                    ARM_COMPUTE_RETURN_ERROR_ON(vector_sum_col->dimension(0) != mm_result->dimension(0));

            
                }


                // If b_offset == 0, vector_sum_row can be a nullptr

            
                if(b_offset != 0)

            
                {

            
                    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_row, 1, DataType::S32);


                    // Check if input is a 3D reinterpretation

            
                    const bool reinterpret_as_3d = mm_result->num_dimensions() > 1 && mm_result->tensor_shape().y() != vector_sum_row->tensor_shape().x();


                    // Validate input

            
                    ARM_COMPUTE_RETURN_ERROR_ON(reinterpret_as_3d && vector_sum_row->dimension(0) != (mm_result->dimension(1) * mm_result->dimension(2)));

            
                    ARM_COMPUTE_RETURN_ERROR_ON(!reinterpret_as_3d && vector_sum_row->dimension(0) != mm_result->dimension(1));


                    TensorShape output_shape = mm_result->tensor_shape();

            
                    if(output_shape.num_dimensions() > 1)

            
                    {

            
                        const unsigned int output_batch_idx = reinterpret_as_3d ? 3 : 2;


                        TensorShape vector_sum_row_shape = vector_sum_row->tensor_shape();

            
                        vector_sum_row_shape.collapse_from(1);

            
                        output_shape.collapse_from(output_batch_idx);


                        ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_row_shape[1] != output_shape[output_batch_idx],

            
                                                        "mm_result tensor must have the same number of batches of output tensor");


                        if(a_offset != 0)

            
                        {

            
                            TensorShape vector_sum_col_shape = vector_sum_col->tensor_shape();

            
                            vector_sum_col_shape.collapse_from(1);


                            ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_col_shape[1] != 1 && vector_sum_col_shape[1] != vector_sum_row_shape[1],

            
                                                            "vector_sum_col tensor must have the same number of batches of vector_sum_row_shape or the number of batches must be set to 1");

            
                        }

            
                    }

            
                }


                return Status{};

            
            }

            
            } // namespace


            ClGemmLowpOffsetContributionKernel::ClGemmLowpOffsetContributionKernel()

            
            {

            
                _type = CLKernelType::ELEMENTWISE;

            
            }


            void ClGemmLowpOffsetContributionKernel::configure(const CLCompileContext &compile_context,

            
                                                               const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, const ITensorInfo *bias,

            
                                                               int32_t k, int32_t a_offset, int32_t b_offset)

            
            {

            
                // Perform validate step

            
                ARM_COMPUTE_ERROR_ON_NULLPTR(mm_result);

            
                ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(mm_result, vector_sum_col, vector_sum_row, bias, a_offset, b_offset));


                auto padding_info = get_padding_info({ mm_result, vector_sum_col, vector_sum_row, bias });


                // Check if input is a 3D reinterpretation

            
                const bool reinterpret_as_3d = vector_sum_row != nullptr

            
                                               && mm_result->num_dimensions() > 1

            
                                               && mm_result->tensor_shape().y() != vector_sum_row->tensor_shape().x();


                const unsigned int num_elems_processed_per_iteration = adjust_vec_size(4, mm_result->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(mm_result->dimension(0) % num_elems_processed_per_iteration));


                // If a_offset == 0, vector_sum_col can be a nullptr

            
                if(a_offset != 0)

            
                {

            
                    build_opts.add_option("-DA_OFFSET=" + support::cpp11::to_string(a_offset));

            
                    build_opts.add_option_if(vector_sum_col->tensor_shape().num_dimensions() > 1, "-DSUM_COL_HAS_BATCHES");

            
                }

            
                // If b_offset == 0, vector_sum_row can be a nullptr

            
                build_opts.add_option_if(b_offset != 0, "-DB_OFFSET=" + support::cpp11::to_string(b_offset));

            
                build_opts.add_option("-DK_OFFSET=" + support::cpp11::to_string(a_offset * b_offset * k));

            
                build_opts.add_option_if(reinterpret_as_3d, "-DHEIGHT_INPUT3D=" + support::cpp11::to_string(mm_result->dimension(1)));

            
                build_opts.add_option_if(reinterpret_as_3d, "-DDEPTH_INPUT3D=" + support::cpp11::to_string(mm_result->dimension(2)));

            
                build_opts.add_option_if(bias != nullptr, "-DADD_BIAS");


                std::string kernel_name("gemmlowp_offset_contribution");


                // 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(*mm_result, Steps(num_elems_processed_per_iteration));

            
                IClKernel::configure_internal(win);


                // Set config_id for enabling LWS tuning

            
                _config_id = kernel_name + "_";

            
                _config_id += support::cpp11::to_string(mm_result->dimension(0));

            
                _config_id += "_";

            
                _config_id += support::cpp11::to_string(mm_result->dimension(1));

            
                _config_id += "_";

            
                _config_id += support::cpp11::to_string(mm_result->dimension(2));


                ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));

            
            }


            Status ClGemmLowpOffsetContributionKernel::validate(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, const ITensorInfo *bias,

            
                                                                int32_t a_offset, int32_t b_offset)

            
            {

            
                ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(mm_result, vector_sum_col, vector_sum_row, bias, a_offset, b_offset));

            
                return Status{};

            
            }


            void ClGemmLowpOffsetContributionKernel::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 vector_sum_col = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_VEC_COL_SUM));

            
                const auto vector_sum_row = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_VEC_ROW_SUM));

            
                const auto bias           = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_BIAS));

            
                const auto mm_result      = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_SRC_DST));


                Window collapsed = window.collapse_if_possible(IClKernel::window(), Window::DimZ);

            
                Window slice     = collapsed.first_slice_window_3D();


                // Set window for vector_sum_col

            
                Window win_vector_sum_col = slice;

            
                win_vector_sum_col.set(Window::DimY, Window::Dimension(0, 0, 0));

            
                win_vector_sum_col.set(Window::DimZ, Window::Dimension(0, 0, 0));


                // Set window for vector_sum_row

            
                Window win_vector_sum_row = slice;

            
                win_vector_sum_row.set(Window::DimX, Window::Dimension(0, 0, 0));

            
                win_vector_sum_row.set(Window::DimY, Window::Dimension(0, 0, 0));

            
                win_vector_sum_col.set(Window::DimZ, Window::Dimension(0, 0, 0));


                Window biases_slice = slice;

            
                biases_slice.set(Window::DimY, Window::Dimension(0, 1, 1));

            
                biases_slice.set(Window::DimZ, Window::Dimension(0, 1, 1));


                do

            
                {

            
                    unsigned int idx = 0;

            
                    add_3D_tensor_argument(idx, mm_result, slice);

            
                    add_2D_tensor_argument_if((vector_sum_col != nullptr), idx, vector_sum_col, win_vector_sum_col);

            
                    add_2D_tensor_argument_if((vector_sum_row != nullptr), idx, vector_sum_row, win_vector_sum_row);

            
                    add_1D_tensor_argument_if((bias != nullptr), idx, bias, biases_slice);


                    enqueue(queue, *this, slice, lws_hint());

            
                }

            
                while(collapsed.slide_window_slice_3D(slice));

            
            }

            
            } // namespace kernels

            
            } // namespace opencl

            
            } // namespace arm_compute
	/*
	* Copyright (c) 2017-2022 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/ClGemmLowpOffsetContributionKernel.h"

	#include "arm_compute/core/CL/ICLTensor.h"
	#include "arm_compute/core/Helpers.h"
	#include "arm_compute/core/TensorInfo.h"
	#include "arm_compute/core/Utils.h"
	#include "arm_compute/core/Validate.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(const ITensorInfo mm_result, const ITensorInfo vector_sum_col, const ITensorInfo vector_sum_row, const ITensorInfo bias,
	int32_t a_offset, int32_t b_offset)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(mm_result, 1, DataType::S32);

	if(bias != nullptr)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32);
	ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1);
	ARM_COMPUTE_RETURN_ERROR_ON(mm_result->dimension(0) != bias->dimension(0));
	}

	// If a_offset == 0, vector_sum_col can be a nullptr
	if(a_offset != 0)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_col, 1, DataType::S32);
	ARM_COMPUTE_RETURN_ERROR_ON(vector_sum_col->dimension(0) != mm_result->dimension(0));
	}

	// If b_offset == 0, vector_sum_row can be a nullptr
	if(b_offset != 0)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_row, 1, DataType::S32);

	// Check if input is a 3D reinterpretation
	const bool reinterpret_as_3d = mm_result->num_dimensions() > 1 && mm_result->tensor_shape().y() != vector_sum_row->tensor_shape().x();

	// Validate input
	ARM_COMPUTE_RETURN_ERROR_ON(reinterpret_as_3d && vector_sum_row->dimension(0) != (mm_result->dimension(1) * mm_result->dimension(2)));
	ARM_COMPUTE_RETURN_ERROR_ON(!reinterpret_as_3d && vector_sum_row->dimension(0) != mm_result->dimension(1));

	TensorShape output_shape = mm_result->tensor_shape();
	if(output_shape.num_dimensions() > 1)
	{
	const unsigned int output_batch_idx = reinterpret_as_3d ? 3 : 2;

	TensorShape vector_sum_row_shape = vector_sum_row->tensor_shape();
	vector_sum_row_shape.collapse_from(1);
	output_shape.collapse_from(output_batch_idx);

	ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_row_shape[1] != output_shape[output_batch_idx],
	"mm_result tensor must have the same number of batches of output tensor");

	if(a_offset != 0)
	{
	TensorShape vector_sum_col_shape = vector_sum_col->tensor_shape();
	vector_sum_col_shape.collapse_from(1);

	ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_col_shape[1] != 1 && vector_sum_col_shape[1] != vector_sum_row_shape[1],
	"vector_sum_col tensor must have the same number of batches of vector_sum_row_shape or the number of batches must be set to 1");
	}
	}
	}

	return Status{};
	}
	} // namespace

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

	void ClGemmLowpOffsetContributionKernel::configure(const CLCompileContext &compile_context,
	const ITensorInfo mm_result, const ITensorInfo vector_sum_col, const ITensorInfo vector_sum_row, const ITensorInfo bias,
	int32_t k, int32_t a_offset, int32_t b_offset)
	{
	// Perform validate step
	ARM_COMPUTE_ERROR_ON_NULLPTR(mm_result);
	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(mm_result, vector_sum_col, vector_sum_row, bias, a_offset, b_offset));

	auto padding_info = get_padding_info({ mm_result, vector_sum_col, vector_sum_row, bias });

	// Check if input is a 3D reinterpretation
	const bool reinterpret_as_3d = vector_sum_row != nullptr
	&& mm_result->num_dimensions() > 1
	&& mm_result->tensor_shape().y() != vector_sum_row->tensor_shape().x();

	const unsigned int num_elems_processed_per_iteration = adjust_vec_size(4, mm_result->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(mm_result->dimension(0) % num_elems_processed_per_iteration));

	// If a_offset == 0, vector_sum_col can be a nullptr
	if(a_offset != 0)
	{
	build_opts.add_option("-DA_OFFSET=" + support::cpp11::to_string(a_offset));
	build_opts.add_option_if(vector_sum_col->tensor_shape().num_dimensions() > 1, "-DSUM_COL_HAS_BATCHES");
	}
	// If b_offset == 0, vector_sum_row can be a nullptr
	build_opts.add_option_if(b_offset != 0, "-DB_OFFSET=" + support::cpp11::to_string(b_offset));
	build_opts.add_option("-DK_OFFSET=" + support::cpp11::to_string(a_offset * b_offset * k));
	build_opts.add_option_if(reinterpret_as_3d, "-DHEIGHT_INPUT3D=" + support::cpp11::to_string(mm_result->dimension(1)));
	build_opts.add_option_if(reinterpret_as_3d, "-DDEPTH_INPUT3D=" + support::cpp11::to_string(mm_result->dimension(2)));
	build_opts.add_option_if(bias != nullptr, "-DADD_BIAS");

	std::string kernel_name("gemmlowp_offset_contribution");

	// 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(*mm_result, Steps(num_elems_processed_per_iteration));
	IClKernel::configure_internal(win);

	// Set config_id for enabling LWS tuning
	_config_id = kernel_name + "_";
	_config_id += support::cpp11::to_string(mm_result->dimension(0));
	_config_id += "_";
	_config_id += support::cpp11::to_string(mm_result->dimension(1));
	_config_id += "_";
	_config_id += support::cpp11::to_string(mm_result->dimension(2));

	ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
	}

	Status ClGemmLowpOffsetContributionKernel::validate(const ITensorInfo mm_result, const ITensorInfo vector_sum_col, const ITensorInfo vector_sum_row, const ITensorInfo bias,
	int32_t a_offset, int32_t b_offset)
	{
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(mm_result, vector_sum_col, vector_sum_row, bias, a_offset, b_offset));
	return Status{};
	}

	void ClGemmLowpOffsetContributionKernel::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 vector_sum_col = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_VEC_COL_SUM));
	const auto vector_sum_row = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_VEC_ROW_SUM));
	const auto bias = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_BIAS));
	const auto mm_result = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_SRC_DST));

	Window collapsed = window.collapse_if_possible(IClKernel::window(), Window::DimZ);
	Window slice = collapsed.first_slice_window_3D();

	// Set window for vector_sum_col
	Window win_vector_sum_col = slice;
	win_vector_sum_col.set(Window::DimY, Window::Dimension(0, 0, 0));
	win_vector_sum_col.set(Window::DimZ, Window::Dimension(0, 0, 0));

	// Set window for vector_sum_row
	Window win_vector_sum_row = slice;
	win_vector_sum_row.set(Window::DimX, Window::Dimension(0, 0, 0));
	win_vector_sum_row.set(Window::DimY, Window::Dimension(0, 0, 0));
	win_vector_sum_col.set(Window::DimZ, Window::Dimension(0, 0, 0));

	Window biases_slice = slice;
	biases_slice.set(Window::DimY, Window::Dimension(0, 1, 1));
	biases_slice.set(Window::DimZ, Window::Dimension(0, 1, 1));

	do
	{
	unsigned int idx = 0;
	add_3D_tensor_argument(idx, mm_result, slice);
	add_2D_tensor_argument_if((vector_sum_col != nullptr), idx, vector_sum_col, win_vector_sum_col);
	add_2D_tensor_argument_if((vector_sum_row != nullptr), idx, vector_sum_row, win_vector_sum_row);
	add_1D_tensor_argument_if((bias != nullptr), idx, bias, biases_slice);

	enqueue(queue, *this, slice, lws_hint());
	}
	while(collapsed.slide_window_slice_3D(slice));
	}
	} // namespace kernels
	} // namespace opencl
	} // namespace arm_compute