| /* |
| * Copyright (c) 2018-2019 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/CLWidthConcatenate4TensorsKernel.h" |
| |
| #include "arm_compute/core/AccessWindowStatic.h" |
| #include "arm_compute/core/CL/CLHelpers.h" |
| #include "arm_compute/core/CL/CLKernelLibrary.h" |
| #include "arm_compute/core/CL/CLValidate.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/Helpers.h" |
| #include "arm_compute/core/IAccessWindow.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Window.h" |
| #include "arm_compute/core/utils/helpers/tensor_info.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| |
| #include "support/ToolchainSupport.h" |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| constexpr unsigned int num_elems_processed_per_iteration = 8; |
| |
| std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *input3, ITensorInfo *input4, ITensorInfo *output) |
| { |
| const unsigned int input1_width = input1->dimension(0); |
| const unsigned int input2_width = input2->dimension(0); |
| const unsigned int input3_width = input3->dimension(0); |
| const unsigned int input4_width = input4->dimension(0); |
| |
| // The window needs to be based on the output |
| Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration)); |
| AccessWindowStatic input1_access(input1, 0, 0, ceil_to_multiple(input1_width, num_elems_processed_per_iteration), input1->dimension(1)); |
| |
| const unsigned int input2_left_padding = input1_width % num_elems_processed_per_iteration; |
| const unsigned int input2_right_padding = ((input1_width + input2_width) / num_elems_processed_per_iteration) * num_elems_processed_per_iteration - input1_width + num_elems_processed_per_iteration - |
| input2_width; |
| AccessWindowStatic input2_access(input2, -input2_left_padding, 0, input2_width + input2_right_padding, input2->dimension(1)); |
| |
| const unsigned int input3_left_padding = (input1_width + input2_width) % num_elems_processed_per_iteration; |
| const unsigned int input3_right_padding = ((input1_width + input2_width + input3_width) / num_elems_processed_per_iteration) * num_elems_processed_per_iteration - input1_width - input2_width + |
| num_elems_processed_per_iteration - input3_width; |
| AccessWindowStatic input3_access(input3, -input3_left_padding, 0, input3_width + input3_right_padding, input3->dimension(1)); |
| |
| const unsigned int input4_left_padding = (input1_width + input2_width + input3_width) % num_elems_processed_per_iteration; |
| const unsigned int input4_right_padding = (output->dimension(0) / num_elems_processed_per_iteration) * num_elems_processed_per_iteration + num_elems_processed_per_iteration - output->dimension(0); |
| AccessWindowStatic input4_access(input4, -input4_left_padding, 0, input4_width + input4_right_padding, input4->dimension(1)); |
| |
| AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration); |
| bool window_changed = update_window_and_padding(win, input1_access, input2_access, input3_access, input4_access, output_access); |
| |
| Window win_collapsed = win.collapse(win, Window::DimZ); |
| |
| Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; |
| return std::make_pair(err, win_collapsed); |
| } |
| Status validate_arguments(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *input3, const ITensorInfo *input4, const ITensorInfo *output) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, input3, input4, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input1); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::U8, DataType::S8, DataType::QASYMM8, DataType::U16, DataType::S16, DataType::F16, DataType::U32, |
| DataType::S32, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input1, input2, input3, input4, output); |
| ARM_COMPUTE_RETURN_ERROR_ON(input1->dimension(0) + input2->dimension(0) + input3->dimension(0) + input4->dimension(0) > output->dimension(0)); |
| |
| for(size_t i = 1; i < Coordinates::num_max_dimensions; ++i) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(input1->dimension(i) != output->dimension(i)); |
| ARM_COMPUTE_RETURN_ERROR_ON(input2->dimension(i) != output->dimension(i)); |
| ARM_COMPUTE_RETURN_ERROR_ON(input3->dimension(i) != output->dimension(i)); |
| ARM_COMPUTE_RETURN_ERROR_ON(input4->dimension(i) != output->dimension(i)); |
| } |
| ARM_COMPUTE_RETURN_ERROR_ON(input1->num_dimensions() > 4); |
| |
| return Status{}; |
| } |
| } // namespace |
| |
| CLWidthConcatenate4TensorsKernel::CLWidthConcatenate4TensorsKernel() |
| : _input1(nullptr), _input2(nullptr), _input3(nullptr), _input4(nullptr), _output(nullptr) |
| { |
| } |
| |
| Status CLWidthConcatenate4TensorsKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *input3, const ITensorInfo *input4, const ITensorInfo *output) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input1, input2, input3, input4, output)); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input1->clone().get(), input2->clone().get(), input3->clone().get(), input4->clone().get(), output->clone().get()).first); |
| return Status{}; |
| } |
| |
| void CLWidthConcatenate4TensorsKernel::configure(const ICLTensor *input1, const ICLTensor *input2, const ICLTensor *input3, const ICLTensor *input4, ICLTensor *output) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, input3, input4, output); |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input1->info(), input2->info(), input3->info(), input4->info(), output->info())); |
| |
| _input1 = input1; |
| _input2 = input2; |
| _input3 = input3; |
| _input4 = input4; |
| _output = output; |
| |
| // Add build options |
| CLBuildOptions build_opts; |
| build_opts.add_option("-DDATA_TYPE=" + get_underlying_cl_type_from_data_type(input1->info()->data_type())); |
| build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration)); |
| build_opts.add_option("-DDEPTH=" + support::cpp11::to_string(input1->info()->dimension(2))); |
| build_opts.add_option("-DINPUT1_WIDTH=" + support::cpp11::to_string(input1->info()->dimension(0))); |
| build_opts.add_option("-DINPUT2_WIDTH=" + support::cpp11::to_string(input2->info()->dimension(0))); |
| build_opts.add_option("-DINPUT3_WIDTH=" + support::cpp11::to_string(input3->info()->dimension(0))); |
| build_opts.add_option("-DELEMENT_SIZE=" + support::cpp11::to_string(input1->info()->element_size())); |
| |
| // If input have different quantization info set quantization parameters needed for the re-quantization process |
| const bool have_different_qinfo = helpers::tensor_info::tensors_have_different_quantization_info(output->info(), input1->info(), input2->info(), input3->info(), input4->info()); |
| if(is_data_type_quantized_asymmetric(input1->info()->data_type()) && have_different_qinfo) |
| { |
| const UniformQuantizationInfo iq1_info = input1->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo iq2_info = input2->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo iq3_info = input3->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo iq4_info = input4->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo oq_info = output->info()->quantization_info().uniform(); |
| |
| build_opts.add_option("-DOFFSET_IN1=" + float_to_string_with_full_precision(iq1_info.offset)); |
| build_opts.add_option("-DSCALE_IN1=" + float_to_string_with_full_precision(iq1_info.scale)); |
| build_opts.add_option("-DOFFSET_IN2=" + float_to_string_with_full_precision(iq2_info.offset)); |
| build_opts.add_option("-DSCALE_IN2=" + float_to_string_with_full_precision(iq2_info.scale)); |
| build_opts.add_option("-DOFFSET_IN3=" + float_to_string_with_full_precision(iq3_info.offset)); |
| build_opts.add_option("-DSCALE_IN3=" + float_to_string_with_full_precision(iq3_info.scale)); |
| build_opts.add_option("-DOFFSET_IN4=" + float_to_string_with_full_precision(iq4_info.offset)); |
| build_opts.add_option("-DSCALE_IN4=" + float_to_string_with_full_precision(iq4_info.scale)); |
| build_opts.add_option("-DOFFSET_OUT=" + float_to_string_with_full_precision(oq_info.offset)); |
| build_opts.add_option("-DSCALE_OUT=" + float_to_string_with_full_precision(oq_info.scale)); |
| } |
| |
| // Create kernel |
| _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("concatenate_width_x4", build_opts.options())); |
| |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(input1->info(), input2->info(), input3->info(), input4->info(), output->info()); |
| ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config)); |
| |
| ICLKernel::configure_internal(std::get<1>(win_config)); |
| |
| // Set output valid region |
| output->info()->set_valid_region(ValidRegion(Coordinates(), output->info()->tensor_shape())); |
| |
| // Pass paddings as arguments to the kernel |
| const unsigned int input1_width = input1->info()->dimension(0); |
| const unsigned int input2_width = input2->info()->dimension(0); |
| const unsigned int input3_width = input3->info()->dimension(0); |
| |
| const unsigned int input1_right_padding = ceil_to_multiple(input1_width, num_elems_processed_per_iteration) - input1_width; |
| const unsigned int input2_left_padding = input1_width % num_elems_processed_per_iteration; |
| const unsigned int input2_right_padding = ((input1_width + input2_width) / num_elems_processed_per_iteration) * num_elems_processed_per_iteration - input1_width + num_elems_processed_per_iteration - |
| input2_width; |
| const unsigned int input3_left_padding = (input1_width + input2_width) % num_elems_processed_per_iteration; |
| const unsigned int input3_right_padding = ((input1_width + input2_width + input3_width) / num_elems_processed_per_iteration) * num_elems_processed_per_iteration - input1_width - input2_width + |
| num_elems_processed_per_iteration - input3_width; |
| const unsigned int input4_left_padding = (input1_width + input2_width + input3_width) % num_elems_processed_per_iteration; |
| unsigned int idx0 = 5 * num_arguments_per_4D_tensor(); |
| _kernel.setArg<cl_uint>(idx0++, input1_right_padding); |
| _kernel.setArg<cl_uint>(idx0++, input2_left_padding); |
| _kernel.setArg<cl_uint>(idx0++, input2_right_padding); |
| _kernel.setArg<cl_uint>(idx0++, input3_left_padding); |
| _kernel.setArg<cl_uint>(idx0++, input3_right_padding); |
| _kernel.setArg<cl_uint>(idx0++, input4_left_padding); |
| |
| // Set config_id for enabling LWS tuning |
| _config_id = "concatenate_width_x4_"; |
| _config_id += lower_string(string_from_data_type(input1->info()->data_type())); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(input1->info()->dimension(0)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(input1->info()->dimension(1)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(input2->info()->dimension(0)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(input2->info()->dimension(1)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(input3->info()->dimension(0)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(input3->info()->dimension(1)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(input4->info()->dimension(0)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(input4->info()->dimension(1)); |
| } |
| |
| void CLWidthConcatenate4TensorsKernel::run(const Window &window, cl::CommandQueue &queue) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); |
| |
| Window slice = window.first_slice_window_4D(); |
| |
| do |
| { |
| unsigned int idx = 0; |
| add_4D_tensor_argument(idx, _input1, slice); |
| add_4D_tensor_argument(idx, _input2, slice); |
| add_4D_tensor_argument(idx, _input3, slice); |
| add_4D_tensor_argument(idx, _input4, slice); |
| add_4D_tensor_argument(idx, _output, slice); |
| enqueue(queue, *this, window, lws_hint()); |
| } |
| while(window.slide_window_slice_4D(slice)); |
| } |
| } // namespace arm_compute |