| /* |
| * Copyright (c) 2017-2020 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/CLLocallyConnectedMatrixMultiplyKernel.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/Types.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Window.h" |
| |
| #include <set> |
| #include <sstream> |
| #include <string> |
| |
| using namespace arm_compute; |
| |
| CLLocallyConnectedMatrixMultiplyKernel::CLLocallyConnectedMatrixMultiplyKernel() |
| : _input0(nullptr), _input1(nullptr), _output(nullptr) |
| { |
| } |
| |
| namespace |
| { |
| Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input0); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1, output); |
| ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != input1->dimension(1)); |
| |
| return Status{}; |
| } |
| |
| std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output) |
| { |
| const unsigned int num_elems_processed_per_iteration_x = max_cl_vector_width / data_size_from_type(input0->data_type()); |
| |
| Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x)); |
| |
| AccessWindowHorizontal input0_access(input0, 0, num_elems_processed_per_iteration_x); |
| AccessWindowHorizontal input1_access(input1, 0, num_elems_processed_per_iteration_x); |
| AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration_x); |
| |
| bool window_changed = update_window_and_padding(win, input0_access, input1_access, output_access); |
| |
| output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); |
| |
| Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; |
| |
| return std::make_tuple(err, win); |
| } |
| } // namespace |
| |
| void CLLocallyConnectedMatrixMultiplyKernel::configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output) |
| { |
| configure(CLKernelLibrary::get().get_compile_context(), input0, input1, output); |
| } |
| |
| void CLLocallyConnectedMatrixMultiplyKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output); |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info())); |
| |
| _input0 = input0; |
| _input1 = input1; |
| _output = output; |
| |
| cl::NDRange lws_hint; |
| if(output->info()->dimension(1) == 196) |
| { |
| lws_hint = cl::NDRange(1, 7); |
| } |
| else |
| { |
| lws_hint = cl::NDRange(8, 8); |
| } |
| |
| std::ostringstream mm_arguments; |
| std::set<std::string> build_opts; |
| |
| mm_arguments << "-DWIDTH_VECTOR_A=" << input0->info()->dimension(0) << " "; |
| build_opts.emplace(mm_arguments.str()); |
| |
| // Create kernel |
| std::string data_type_name = lower_string(string_from_data_type(input0->info()->data_type())); |
| _kernel = create_kernel(compile_context, ("gemm_lc_vm_" + data_type_name), build_opts); |
| |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info()); |
| |
| ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config)); |
| |
| ICLKernel::configure_internal(std::get<1>(win_config), lws_hint); |
| } |
| |
| Status CLLocallyConnectedMatrixMultiplyKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output)); |
| ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input0->clone().get(), input1->clone().get(), output->clone().get()))); |
| |
| return Status{}; |
| } |
| |
| void CLLocallyConnectedMatrixMultiplyKernel::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_2D(); |
| |
| Window matrix_b_window; |
| matrix_b_window.use_tensor_dimensions(_input1->info()->tensor_shape()); |
| Window slice_matrix_b = matrix_b_window.first_slice_window_3D(); |
| |
| do |
| { |
| unsigned int idx = 0; |
| add_2D_tensor_argument(idx, _input0, slice); |
| add_3D_tensor_argument(idx, _input1, slice_matrix_b); |
| add_2D_tensor_argument(idx, _output, slice); |
| enqueue(queue, *this, slice, lws_hint()); |
| } |
| while(window.slide_window_slice_2D(slice)); |
| } |