| /* |
| * Copyright (c) 2017 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/CLDirectConvolutionLayerKernel.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/ICLTensor.h" |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/IAccessWindow.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "support/ToolchainSupport.h" |
| |
| using namespace arm_compute; |
| |
| CLDirectConvolutionLayerKernel::CLDirectConvolutionLayerKernel() |
| : _input(nullptr), _biases(nullptr), _weights(nullptr), _output(nullptr), _border_size(0), _conv_pad_x(0), _conv_pad_y(0), _conv_stride_x(0), _conv_stride_y(0) |
| { |
| } |
| |
| BorderSize CLDirectConvolutionLayerKernel::border_size() const |
| { |
| return _border_size; |
| } |
| |
| void CLDirectConvolutionLayerKernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); |
| ARM_COMPUTE_ERROR_ON_MSG(weights->info()->dimension(0) != weights->info()->dimension(1), |
| "Weights should have same width as length"); |
| ARM_COMPUTE_ERROR_ON_MSG(weights->info()->dimension(0) != 1 && weights->info()->dimension(0) != 3 && weights->info()->dimension(0) != 5, |
| "Kernel sizes other than 1x1, 3x3 or 5x5 are not supported"); |
| ARM_COMPUTE_ERROR_ON(weights->info()->dimension(2) != input->info()->dimension(2)); |
| ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != weights->info()->dimension(1)); |
| ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4); |
| ARM_COMPUTE_ERROR_ON_MSG((weights->info()->dimension(0) == 1) && std::get<0>(conv_info.stride()) > 3, "Strides larger than 3 not supported for 1x1 convolution."); |
| ARM_COMPUTE_ERROR_ON_MSG((weights->info()->dimension(0) == 3 || weights->info()->dimension(0) == 5) && std::get<0>(conv_info.stride()) > 2, "Strides larger than 2 not supported for 3x3 convolution."); |
| |
| if(biases != nullptr) |
| { |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); |
| ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3)); |
| ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1); |
| } |
| |
| const unsigned int kernel_size = weights->info()->dimension(0); |
| |
| // Get convolved dimensions |
| unsigned int output_width = 0; |
| unsigned int output_height = 0; |
| std::tie(output_width, output_height) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_size, kernel_size, conv_info); |
| |
| TensorShape output_shape = input->info()->tensor_shape(); |
| output_shape.set(0, output_width); |
| output_shape.set(1, output_height); |
| output_shape.set(2, weights->info()->dimension(3)); |
| |
| // Output auto inizialitation if not yet initialized |
| auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position()); |
| |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output); |
| |
| _conv_stride_x = std::get<0>(conv_info.stride()); |
| _conv_stride_y = std::get<1>(conv_info.stride()); |
| _conv_pad_x = std::min(std::get<0>(conv_info.pad()), kernel_size / 2); |
| _conv_pad_y = std::min(std::get<1>(conv_info.pad()), kernel_size / 2); |
| |
| _input = input; |
| _weights = weights; |
| _output = output; |
| _biases = biases; |
| _border_size = BorderSize(_conv_pad_y, _conv_pad_x); |
| |
| std::set<std::string> options; |
| |
| const GPUTarget gpu_target = get_arch_from_target(get_target()); |
| |
| if(_biases != nullptr) |
| { |
| options.emplace("-DHAS_BIAS"); |
| } |
| |
| if((gpu_target == GPUTarget::BIFROST) && (kernel_size <= 5) && (_conv_stride_x == 1) && (_conv_stride_y == 1) && (input->info()->data_type() == DataType::F32)) |
| { |
| options.emplace("-DWEIGHTS_DEPTH=" + support::cpp11::to_string(_weights->info()->dimension(2))); |
| |
| std::string kernel_name = "direct_convolution" + support::cpp11::to_string(kernel_size) + "x" + support::cpp11::to_string(kernel_size) + "_f32_bifrost"; |
| _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, options)); |
| |
| // Configure kernel window |
| Window win = calculate_max_window(*output->info()); |
| |
| unsigned int num_elems_read_per_iteration_x = 0; |
| unsigned int num_elems_read_per_iteration_y = 0; |
| unsigned int num_elems_written_per_iteration_x = 0; |
| unsigned int num_elems_written_per_iteration_y = 0; |
| |
| switch(kernel_size) |
| { |
| case 1: |
| { |
| num_elems_read_per_iteration_x = 4; |
| num_elems_read_per_iteration_y = 4; |
| num_elems_written_per_iteration_x = 4; |
| num_elems_written_per_iteration_y = 4; |
| break; |
| } |
| case 3: |
| { |
| num_elems_read_per_iteration_x = 6; |
| num_elems_read_per_iteration_y = 5; |
| num_elems_written_per_iteration_x = 4; |
| num_elems_written_per_iteration_y = 3; |
| break; |
| } |
| case 5: |
| { |
| num_elems_read_per_iteration_x = 8; |
| num_elems_read_per_iteration_y = 6; |
| num_elems_written_per_iteration_x = 4; |
| num_elems_written_per_iteration_y = 2; |
| break; |
| } |
| default: |
| { |
| ARM_COMPUTE_ERROR("Kernel size not optimized for Bifrost"); |
| } |
| } |
| |
| // Calculate right and bottom border |
| const int input_width = input->info()->dimension(0) - kernel_size / 2 + _conv_pad_x; |
| const int input_height = input->info()->dimension(1) - kernel_size / 2 + _conv_pad_y; |
| |
| // Create window and update padding |
| win = calculate_max_window(*output->info(), Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y)); |
| |
| AccessWindowStatic input_access(input->info(), -_conv_pad_x, -_conv_pad_y, input_width + num_elems_read_per_iteration_x, input_height + num_elems_read_per_iteration_y); |
| AccessWindowStatic weights_access(weights->info(), 0, 0, kernel_size, kernel_size); |
| AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_written_per_iteration_x, num_elems_written_per_iteration_y); |
| |
| update_window_and_padding(win, input_access, weights_access, output_access); |
| |
| output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape())); |
| |
| ICLKernel::configure(win); |
| } |
| else |
| { |
| std::stringstream kernel_name; |
| kernel_name << "direct_convolution" << kernel_size << "x" << kernel_size; |
| DataType promoted_type = input->info()->data_type(); |
| |
| options.emplace("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); |
| options.emplace("-DDATA_SIZE=" + get_data_size_from_data_type(input->info()->data_type())); |
| options.emplace("-DWEIGHTS_DEPTH=" + support::cpp11::to_string(_weights->info()->dimension(2))); |
| options.emplace("-DSTRIDE_X=" + support::cpp11::to_string(_conv_stride_x)); |
| |
| if(is_data_type_fixed_point(input->info()->data_type())) |
| { |
| options.emplace("-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position())); |
| |
| switch(input->info()->data_type()) |
| { |
| case DataType::QS8: |
| promoted_type = DataType::QS16; |
| break; |
| case DataType::QS16: |
| promoted_type = DataType::QS32; |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Datatype not supported"); |
| } |
| } |
| |
| options.emplace("-DDATA_TYPE_PROMOTED=" + get_cl_type_from_data_type(promoted_type)); |
| |
| _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name.str(), options)); |
| |
| // Configure kernel window |
| |
| bool is_stride2 = ((kernel_size != 1) && (_conv_stride_x == 2)); |
| |
| const unsigned int num_elems_read_per_iteration_x = 8 + 2 * (kernel_size / 2) + (is_stride2 ? 6 + kernel_size / 2 : 0); |
| const unsigned int num_elems_read_per_iteration_y = kernel_size; |
| const unsigned int num_elems_written_per_iteration_x = 8; |
| const unsigned int num_elems_written_per_iteration_y = 1; |
| |
| // Calculate right and bottom border |
| const int input_width = input->info()->dimension(0) - kernel_size / 2 + _conv_pad_x; |
| const int input_height = input->info()->dimension(1) - kernel_size / 2 + _conv_pad_y; |
| |
| // Create window and update padding |
| Window win = calculate_max_window(*output->info(), Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y)); |
| |
| AccessWindowStatic input_access(input->info(), -_conv_pad_x, -_conv_pad_y, input_width + num_elems_read_per_iteration_x, input_height + num_elems_read_per_iteration_y); |
| AccessWindowStatic weights_access(weights->info(), 0, 0, kernel_size, kernel_size); |
| AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_written_per_iteration_x, num_elems_written_per_iteration_y); |
| |
| update_window_and_padding(win, input_access, weights_access, output_access); |
| |
| output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape())); |
| |
| ICLKernel::configure(win); |
| } |
| |
| // Set config_id for enabling LWS tuning |
| _config_id = "direct_convolution_"; |
| _config_id += lower_string(string_from_data_type(input->info()->data_type())); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(kernel_size); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(_conv_pad_x); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(_conv_pad_y); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(_conv_stride_x); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(_conv_stride_y); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(output->info()->dimension(0)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(output->info()->dimension(1)); |
| } |
| |
| void CLDirectConvolutionLayerKernel::run(const Window &window, cl::CommandQueue &queue) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); |
| |
| // Get initial windows |
| Window slice = window.first_slice_window_3D(); |
| Window win_in = window; |
| |
| win_in.adjust(Window::DimX, -_conv_pad_x, true); |
| win_in.adjust(Window::DimY, -_conv_pad_y, true); |
| win_in.set_dimension_step(Window::DimX, window.x().step() * _conv_stride_x); |
| win_in.set_dimension_step(Window::DimY, window.y().step() * _conv_stride_y); |
| |
| Window slice_in = win_in.first_slice_window_3D(); |
| |
| unsigned int idx1 = 2 * num_arguments_per_3D_tensor(); |
| add_3D_tensor_argument(idx1, _weights, slice); |
| |
| if(_biases != nullptr) |
| { |
| Window slice_biases; |
| slice_biases.use_tensor_dimensions(_biases->info()->tensor_shape()); |
| add_1D_tensor_argument(idx1, _biases, slice_biases); |
| } |
| |
| _kernel.setArg(idx1++, static_cast<unsigned int>(_weights->info()->strides_in_bytes()[3])); |
| |
| do |
| { |
| unsigned int idx = 0; |
| add_3D_tensor_argument(idx, _input, slice_in); |
| add_3D_tensor_argument(idx, _output, slice); |
| |
| enqueue(queue, *this, slice, _lws_hint); |
| } |
| while(window.slide_window_slice_3D(slice) && win_in.slide_window_slice_3D(slice_in)); |
| } |