| /* |
| * 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/CL/kernels/CLGEMMMatrixMultiplyKernel.h" |
| |
| #include "arm_compute/core/AccessWindowStatic.h" |
| #include "arm_compute/core/AccessWindowTranspose.h" |
| #include "arm_compute/core/CL/CLHelpers.h" |
| #include "arm_compute/core/CL/CLKernelLibrary.h" |
| #include "arm_compute/core/CL/ICLTensor.h" |
| #include "arm_compute/core/CL/OpenCL.h" |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/FixedPoint.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_RETURN_ERROR_ON_NULLPTR(input0, input1, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input0, input1); |
| |
| 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); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(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, GPUTarget gpu_target, |
| ElementsProcessed &num_elements_processed) |
| { |
| 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 kernel window |
| num_elems_processed_per_iteration_x = max_cl_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); |
| |
| window_changed = update_window_and_padding(win, input0_access, input1_access, output_access); |
| |
| output_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), 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_x is set up for the default case. |
| num_elems_processed_per_iteration_x = max_cl_vector_width / data_size_from_type(data_type); |
| num_elems_processed_per_iteration_y = std::min(static_cast<int>(output->dimension(1)), 4); |
| |
| // Create kernels according to the architecture, data type and input size. |
| if(gpu_target == GPUTarget::BIFROST && data_type == DataType::F32) |
| { |
| num_elems_processed_per_iteration_x = (input1->dimension(0) <= 1000 && input0->num_dimensions() == 1) ? 2 : 4; |
| } |
| |
| // Configure window |
| win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); |
| |
| AccessWindowStatic input0_access(input0, 0, 0, input0->dimension(0), 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); |
| |
| window_changed = 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())); |
| } |
| |
| // Collapse along the Z direction |
| // This collapse needs to be here in order to tune the Z dimension of LWS |
| Window collapsed = win; |
| if(input1->num_dimensions() > 1) |
| { |
| const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(input1->num_dimensions() - 1), 2u); |
| collapsed = win.collapse(win, dimension_to_collapse); |
| } |
| |
| Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; |
| return std::make_pair(err, collapsed); |
| } |
| } // namespace |
| |
| CLGEMMMatrixMultiplyKernel::CLGEMMMatrixMultiplyKernel() |
| : _input0(nullptr), _input1(nullptr), _output(nullptr) |
| { |
| } |
| |
| void CLGEMMMatrixMultiplyKernel::configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, float alpha, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output); |
| |
| // Output tensor auto inizialitation if not yet initialized |
| TensorShape tensor_shape{ input0->info()->tensor_shape() }; |
| tensor_shape.set(0, is_interleaved_transposed ? reshape_info.n() : input1->info()->dimension(0)); |
| tensor_shape.set(1, is_interleaved_transposed ? reshape_info.m() : input0->info()->dimension(1)); |
| |
| auto_init_if_empty(*output->info(), input0->info()->clone()->set_tensor_shape(tensor_shape)); |
| |
| // 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; |
| |
| const DataType data_type = input0->info()->data_type(); |
| const int fp_pos = input0->info()->fixed_point_position(); |
| |
| // Get target architecture |
| GPUTarget arch_target = get_arch_from_target(get_target()); |
| |
| // Configure LWS hint |
| if(arch_target == GPUTarget::BIFROST && input1->info()->dimension(1) == 24) |
| { |
| // LWS optimized for the 11x11 AlexNet convolution on Bifrost. |
| _lws_hint = cl::NDRange(2, 2); |
| } |
| else if(output->info()->dimension(1) == 196) |
| { |
| _lws_hint = cl::NDRange(1, 7); |
| } |
| else |
| { |
| _lws_hint = cl::NDRange(8, 8); |
| } |
| |
| ElementsProcessed num_elements_processed{}; |
| |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info(), is_interleaved_transposed, arch_target, num_elements_processed); |
| ARM_COMPUTE_ERROR_THROW_ON(win_config.first); |
| ICLKernel::configure(win_config.second); |
| |
| // Create build options |
| CLBuildOptions build_opts; |
| build_opts.add_option_if(is_data_type_fixed_point(data_type), "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(fp_pos)); |
| |
| // Only define ALPHA when alpha is not 1.0f. This avoids performing unnecessary multiplications. |
| if(std::abs(1.0f - alpha) > 0.00001f) |
| { |
| build_opts.add_option_if_else(is_data_type_fixed_point(data_type), |
| "-DALPHA=" + support::cpp11::to_string((data_type == DataType::QS8 ? sqcvt_qs8_f32(alpha, fp_pos) : sqcvt_qs16_f32(alpha, fp_pos))), |
| "-DALPHA=" + float_to_string_with_full_precision(alpha)); |
| } |
| |
| std::string kernel_name; |
| 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.add_option("-DCOLS_B=" + support::cpp11::to_string(input1->info()->dimension(0))); |
| build_opts.add_option("-DMULT_TRANSPOSE1XW_WIDTH=" + support::cpp11::to_string(mult_transpose1xW_width)); |
| build_opts.add_option("-DMULT_INTERLEAVE4X4_HEIGHT=" + support::cpp11::to_string(mult_interleave4x4_height)); |
| |
| if(data_type == DataType::F32) |
| { |
| kernel_name = "gemm_mm_interleaved_transposed_f32_" + string_from_target(arch_target); |
| } |
| else |
| { |
| kernel_name = "gemm_mm_interleaved_transposed_" + lower_string(string_from_data_type(data_type)); |
| } |
| } |
| else // The input tensors have not been reshaped |
| { |
| build_opts.add_option("-DCOLS_A=" + support::cpp11::to_string(input0->info()->dimension(0))); |
| |
| // Create kernels according to the architecture, data type and input size. |
| if(arch_target == GPUTarget::BIFROST && data_type == DataType::F32) |
| { |
| // The first kernel is optimized for the case of 1000 or less output elements (e.g. FC8 of AlexNet and VGG-16, and |
| // FC1 of Inception v3). The second kernel is optimized for the case of greater than 1000 output elements (e.g. |
| // FC6 and FC7 of AlexNet and VGG-16). |
| kernel_name = (input1->info()->dimension(0) <= 1000 && input0->info()->num_dimensions() == 1) ? "gemm_mm_floating_point_f32_bifrost_1000" : "gemm_mm_floating_point_f32_bifrost"; |
| |
| // The work-group size equal to the Bifrost quad size has been proved to be optimal for these kernels |
| // via exhaustive autotuning over a range of representative layer configurations. |
| _lws_hint = cl::NDRange(4); |
| } |
| else if(is_data_type_fixed_point(data_type)) |
| { |
| kernel_name = "gemm_mm_" + lower_string(string_from_data_type(data_type)); |
| } |
| else // (MIDGARD and F32) or (F16) |
| { |
| build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)); |
| kernel_name = "gemm_mm_floating_point"; |
| } |
| build_opts.add_option("-DNUM_ELEMS_PROCESSED_PER_THREAD_Y=" + support::cpp11::to_string(num_elements_processed.y())); |
| build_opts.add_option("-DNUM_ELEMS_PROCESSED_PER_THREAD_X=" + support::cpp11::to_string(num_elements_processed.x())); |
| } |
| |
| // Create kernel |
| _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); |
| |
| // Set config_id for enabling LWS tuning |
| _config_id = "gemm_"; |
| _config_id += (is_interleaved_transposed ? "reshaped_" : ""); |
| _config_id += lower_string(string_from_data_type(input0->info()->data_type())); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(output->info()->dimension(1)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(output->info()->dimension(0)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(output->info()->dimension(2)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(output->info()->dimension(3)); |
| _config_id += "_"; |
| _config_id += (is_interleaved_transposed ? support::cpp11::to_string(input1->info()->dimension(0)) : support::cpp11::to_string(input1->info()->dimension(1))); |
| } |
| |
| Status CLGEMMMatrixMultiplyKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, float alpha, bool is_interleaved_transposed, |
| const GEMMReshapeInfo &reshape_info, GPUTarget gpu_target) |
| { |
| // Note: num_elements_processed will be set in validate_and_configure_window() |
| ElementsProcessed num_elements_processed{}; |
| ARM_COMPUTE_UNUSED(alpha); |
| 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, |
| gpu_target, |
| num_elements_processed) |
| .first); |
| |
| return Status{}; |
| } |
| |
| void CLGEMMMatrixMultiplyKernel::run(const Window &window, cl::CommandQueue &queue) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); |
| |
| if(_input1->info()->num_dimensions() < 3) |
| { |
| // The stride_z for matrix B must be zero if we do not slice |
| ARM_COMPUTE_ERROR_ON(_input1->info()->strides_in_bytes()[3] != 0); |
| } |
| |
| Window slice = window.first_slice_window_3D(); |
| 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 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, slice); |
| add_2D_tensor_argument(idx, _input1, slice_b); |
| add_2D_tensor_argument(idx, _output, slice); |
| _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input0->info()->strides_in_bytes()[3])); |
| _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input1->info()->strides_in_bytes()[3])); |
| _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_output->info()->strides_in_bytes()[3])); |
| enqueue(queue, *this, slice, _lws_hint); |
| } |
| while(window.slide_window_slice_3D(slice)); |
| } |