| /* |
| * 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/CLPoolingLayerKernel.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/ICLKernel.h" |
| #include "arm_compute/core/CL/ICLTensor.h" |
| #include "arm_compute/core/CL/OpenCL.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 "arm_compute/core/Window.h" |
| |
| #include <set> |
| #include <string> |
| #include <tuple> |
| |
| using namespace arm_compute; |
| |
| namespace |
| { |
| // Internal window config info |
| using CLPoolingConfig = std::pair<unsigned int, BorderSize>; //num_elems_processed_per_iteration, border_size |
| |
| void auto_init(const ITensorInfo *input, ITensorInfo *output, unsigned int pooled_w, unsigned int pooled_h) |
| { |
| TensorShape output_shape{ input->tensor_shape() }; |
| output_shape.set(0, pooled_w); |
| output_shape.set(1, pooled_h); |
| |
| auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape)); |
| } |
| |
| Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG((is_data_type_quantized_asymmetric(input->data_type()) && pool_info.pool_type() == PoolingType::L2), |
| "Unsupported combination of parameters!"); |
| ARM_COMPUTE_RETURN_ERROR_ON(!pool_info.pad_stride_info().padding_is_symmetric()); |
| |
| const bool is_global_pooling = pool_info.is_global_pooling(); |
| const unsigned int pool_size_x = is_global_pooling ? input->tensor_shape().x() : pool_info.pool_size().width; |
| const unsigned int pool_size_y = is_global_pooling ? input->tensor_shape().y() : pool_info.pool_size().height; |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_global_pooling && ((pool_info.pad_stride_info().pad().first >= pool_size_x) || (pool_info.pad_stride_info().pad().second >= pool_size_y)), |
| "Invalid pool size and pool pad combination!"); |
| |
| // Checks performed when output is configured |
| if(output->total_size() != 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output); |
| |
| unsigned int pooled_w = 0; |
| unsigned int pooled_h = 0; |
| std::tie(pooled_w, pooled_h) = scaled_dimensions(input->dimension(0), |
| input->dimension(1), |
| pool_size_x, |
| pool_size_y, |
| pool_info.pad_stride_info()); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG((output->dimension(0) != pooled_w) || (output->dimension(1) != pooled_h), |
| "Invalid output pooling dimensions!"); |
| } |
| |
| return Status{}; |
| } |
| |
| std::tuple<Status, Window, CLPoolingConfig> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, const PoolingLayerInfo &pool_info) |
| { |
| int pool_pad_x = 0; |
| int pool_pad_y = 0; |
| int pool_stride_x = 0; |
| int pool_stride_y = 0; |
| unsigned int pooled_w = 0; |
| unsigned int pooled_h = 0; |
| int pool_size_x = pool_info.is_global_pooling() ? input->dimension(0) : pool_info.pool_size().width; |
| int pool_size_y = pool_info.is_global_pooling() ? input->dimension(1) : pool_info.pool_size().height; |
| const PadStrideInfo pad_stride_info = pool_info.pad_stride_info(); |
| std::tie(pool_pad_x, pool_pad_y) = pad_stride_info.pad(); |
| std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride(); |
| |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); |
| |
| // Check output dimensions |
| std::tie(pooled_w, pooled_h) = scaled_dimensions(input->dimension(0), |
| input->dimension(1), |
| pool_size_x, |
| pool_size_y, |
| pad_stride_info); |
| |
| auto_init(input, output, pooled_w, pooled_h); |
| |
| BorderSize border_size = BorderSize(pool_pad_y, pool_pad_x); |
| const DataType data_type = input->data_type(); |
| |
| const int input_width = input->dimension(0); |
| const int input_height = input->dimension(1); |
| |
| // Change the number of elements processed per iteration |
| // for pooling 3x3 with stride less equal than 3 |
| const bool can_optimize = (pool_size_x == 3) && (pool_size_y == 3) && (pool_stride_x <= 3) && !is_data_type_quantized(data_type); |
| const unsigned int num_elems_processed_per_iteration = can_optimize ? 4 : 1; |
| const int num_elems_read_per_iteration = (num_elems_processed_per_iteration - 1) * pool_stride_x + pool_size_x; |
| |
| // Number of iterations in X dimension |
| const int num_iterations_x = (pooled_w + num_elems_processed_per_iteration - 1) / num_elems_processed_per_iteration; |
| |
| // Upper limit for the number of right/bottom border elements that are accessed |
| const int upper_bound_w = ((num_iterations_x - 1) * num_elems_processed_per_iteration * pool_stride_x - pool_pad_x + num_elems_read_per_iteration) - input_width; |
| const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size_y) - input_height; |
| |
| border_size.right = std::max(upper_bound_w, pool_pad_x); |
| border_size.bottom = std::max(upper_bound_h, pool_pad_y); |
| |
| Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration)); |
| |
| AccessWindowRectangle input_access(input, -pool_pad_x, -pool_pad_y, num_elems_read_per_iteration, pool_size_y, |
| pool_stride_x * num_elems_processed_per_iteration, pool_stride_y); |
| AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration); |
| bool window_changed = update_window_and_padding(win, input_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, CLPoolingConfig(num_elems_processed_per_iteration, border_size)); |
| } |
| } // namespace |
| |
| CLPoolingLayerKernel::CLPoolingLayerKernel() |
| : _input(nullptr), _output(nullptr), _pool_info(), _border_size(0), _num_elems_processed_per_iteration(1) |
| { |
| } |
| |
| BorderSize CLPoolingLayerKernel::border_size() const |
| { |
| return _border_size; |
| } |
| |
| void CLPoolingLayerKernel::configure(const ICLTensor *input, ICLTensor *output, const PoolingLayerInfo &pool_info) |
| { |
| int pool_pad_x = 0; |
| int pool_pad_y = 0; |
| int pool_stride_x = 0; |
| int pool_stride_y = 0; |
| unsigned int pooled_w = 0; |
| unsigned int pooled_h = 0; |
| const PoolingType pool_type = pool_info.pool_type(); |
| const int pool_size_x = pool_info.is_global_pooling() ? input->info()->dimension(0) : pool_info.pool_size().width; |
| const int pool_size_y = pool_info.is_global_pooling() ? input->info()->dimension(1) : pool_info.pool_size().height; |
| const PadStrideInfo pad_stride_info = pool_info.pad_stride_info(); |
| const bool exclude_padding = pool_info.exclude_padding(); |
| std::tie(pool_pad_x, pool_pad_y) = pad_stride_info.pad(); |
| std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride(); |
| |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); |
| |
| // Check output dimensions |
| std::tie(pooled_w, pooled_h) = scaled_dimensions(input->info()->dimension(0), |
| input->info()->dimension(1), |
| pool_size_x, |
| pool_size_y, |
| pad_stride_info); |
| |
| auto_init(input->info(), output->info(), pooled_w, pooled_h); |
| |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), pool_info)); |
| |
| // Set instance variables |
| _input = input; |
| _output = output; |
| _pool_info = pool_info; |
| |
| const GPUTarget gpu_target = get_arch_from_target(get_target()); |
| const DataType data_type = input->info()->data_type(); |
| |
| // Set build options |
| CLBuildOptions build_opts; |
| build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)); |
| build_opts.add_option("-DPOOL_" + string_from_pooling_type(pool_type)); |
| build_opts.add_option_if(is_data_type_fixed_point(data_type), |
| "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position())); |
| build_opts.add_option("-DSTRIDE_X=" + support::cpp11::to_string(pool_stride_x)); |
| if(pool_type != PoolingType::MAX) |
| { |
| build_opts.add_option_if(exclude_padding, "-DEXCLUDE_PADDING"); |
| build_opts.add_option("-DMAX_WIDTH=" + support::cpp11::to_string(input->info()->dimension(0) + (exclude_padding ? 0 : pool_pad_x))); |
| build_opts.add_option("-DMAX_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_y))); |
| build_opts.add_option("-DSTRIDE_Y=" + support::cpp11::to_string(pool_stride_y)); |
| build_opts.add_option("-DPAD_X=" + support::cpp11::to_string(pool_pad_x)); |
| build_opts.add_option("-DPAD_Y=" + support::cpp11::to_string(pool_pad_y)); |
| } |
| |
| // Create kernel |
| if((pool_size_x == 3) && (pool_size_y == 3) && !is_data_type_quantized_asymmetric(data_type)) |
| { |
| // Check if we have pool3x3 with stride_x less equal than 3. In these cases, run an optimized OpenCL kernel where |
| // each thread computes 4 output elements |
| const bool is_pool3x3_stride_le3 = (pool_size_x == 3) && (pool_size_y == 3) && (pool_stride_x <= 3) && !is_data_type_fixed_point(data_type); |
| |
| std::string kernel_name = ((is_pool3x3_stride_le3) ? "pooling_layer_optimized_" : "pooling_layer_") |
| + support::cpp11::to_string(pool_size_x); |
| _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); |
| } |
| else // Run general case |
| { |
| build_opts.add_option("-DPOOL_SIZE_X=" + support::cpp11::to_string(pool_size_x)); |
| build_opts.add_option("-DPOOL_SIZE_Y=" + support::cpp11::to_string(pool_size_y)); |
| build_opts.add_option_if(data_type == DataType::F16, "-DFP16"); |
| |
| std::string kernel_name = is_data_type_quantized_asymmetric(data_type) ? "pooling_layer_MxN_quantized" : "pooling_layer_MxN"; |
| _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); |
| } |
| |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(input->info(), output->info(), pool_info); |
| |
| ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config)); |
| |
| // Configure the local work size (hint) from the first two dimensions of the global work size. |
| // On Bifrost, this works for up to 35x35xC filters, for which the pooling_layer_3_optimized |
| // kernel is launched with gws=(9, 33, C). In any case, the hint will be ignored if it is |
| // invalid (e.g. exceeds the maximum workgroup size that the kernel can be launched with). |
| if(gpu_target == GPUTarget::BIFROST) |
| { |
| cl::NDRange gws = ICLKernel::gws_from_window(std::get<1>(win_config)); |
| _lws_hint = cl::NDRange(gws[0], gws[1], 1); |
| } |
| |
| ICLKernel::configure(std::get<1>(win_config)); |
| |
| CLPoolingConfig pooling_config = std::get<2>(win_config); |
| _num_elems_processed_per_iteration = pooling_config.first; |
| _border_size = pooling_config.second; |
| |
| // Set config_id for enabling LWS tuning |
| _config_id = "pooling_layer_"; |
| _config_id += lower_string(string_from_data_type(data_type)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(output->info()->dimension(0)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(output->info()->dimension(1)); |
| } |
| |
| Status CLPoolingLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, pool_info)); |
| ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), output->clone().get(), pool_info))); |
| |
| return Status{}; |
| } |
| |
| void CLPoolingLayerKernel::run(const Window &window, cl::CommandQueue &queue) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); |
| |
| unsigned int pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y = 0; |
| std::tie(pool_pad_x, pool_pad_y) = _pool_info.pad_stride_info().pad(); |
| std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride(); |
| |
| Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ); |
| Window slice = window_collapsed.first_slice_window_3D(); |
| |
| do |
| { |
| // Upsample input by pool size |
| Window in_slice(slice); |
| in_slice.set(Window::DimX, Window::Dimension(in_slice.x().start() - pool_pad_x, |
| (in_slice.x().end() - pool_pad_x) * pool_stride_x, |
| pool_stride_x * _num_elems_processed_per_iteration)); |
| in_slice.set(Window::DimY, Window::Dimension(in_slice.y().start() - pool_pad_y, |
| (in_slice.y().end() - pool_pad_y) * pool_stride_y, |
| pool_stride_y)); |
| |
| // Set inputs |
| unsigned int idx = 0; |
| add_3D_tensor_argument(idx, _input, in_slice); |
| add_3D_tensor_argument(idx, _output, slice); |
| enqueue(queue, *this, slice, _lws_hint); |
| } |
| while(window_collapsed.slide_window_slice_3D(slice)); |
| } |