/* * Copyright (c) 2022-2023 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 "src/gpu/cl/kernels/ClPool3dKernel.h" #include "arm_compute/core/CL/ICLTensor.h" #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/utils/helpers/AdjustVecSize.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/core/utils/StringUtils.h" #include "src/core/CL/CLValidate.h" #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/WindowHelpers.h" #include "support/Cast.h" #include "utils/TypePrinter.h" namespace arm_compute { namespace opencl { namespace kernels { using namespace arm_compute::misc::shape_calculator; namespace { Status validate_arguments(const ITensorInfo *src, const ITensorInfo *dst, const Pooling3dLayerInfo &pool_info) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst); ARM_COMPUTE_RETURN_ERROR_ON_MSG(src->data_layout() != DataLayout::NDHWC, "Only NDHWC layout supported"); ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src); ARM_COMPUTE_RETURN_ERROR_ON_MSG( (pool_info.stride.x() == 0 || pool_info.stride.y() == 0 || pool_info.stride.z() == 0), "Strides cannot be zero."); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F16, DataType::F32, DataType::QASYMM8_SIGNED, DataType::QASYMM8); ARM_COMPUTE_RETURN_ERROR_ON_MSG((!is_data_type_float(src->data_type())) && (!pool_info.exclude_padding && (pool_info.pool_type == PoolingType::AVG)), "Exclude padding is unsupported for non-float types for Avg op"); const auto data_layout = src->data_layout(); const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); const int idx_depth = get_data_layout_dimension_index(data_layout, DataLayoutDimension::DEPTH); const bool is_global_pooling = pool_info.is_global_pooling; const unsigned int pool_size_x = is_global_pooling ? src->dimension(idx_width) : pool_info.pool_size.width; const unsigned int pool_size_y = is_global_pooling ? src->dimension(idx_height) : pool_info.pool_size.height; const unsigned int pool_size_z = is_global_pooling ? src->dimension(idx_depth) : pool_info.pool_size.depth; int output_width = 0; int output_height = 0; int output_depth = 0; bool round_type_ceil_with_asymm_padding = (pool_info.round_type == DimensionRoundingType::CEIL) && (!is_symmetric(pool_info.padding)); ARM_COMPUTE_RETURN_ERROR_ON_MSG(round_type_ceil_with_asymm_padding, "Cannot use dimension round type CEIL when padding is asymmetric."); ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_pool_3d_region_entirely_outside_input(pool_info), "Pooling region that is entirely outside input tensor is unsupported"); std::tie(output_width, output_height, output_depth) = scaled_3d_dimensions_signed(src->tensor_shape()[idx_width], src->tensor_shape()[idx_height], src->tensor_shape()[idx_depth], pool_size_x, pool_size_y, pool_size_z, pool_info); ARM_COMPUTE_RETURN_ERROR_ON_MSG((output_width < 1 || output_height < 1 || output_depth < 1), "Calculated output dimension size is invalid"); // Checks performed when dst is configured if (dst->total_size() != 0) { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, dst); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, dst); TensorInfo out_info(TensorInfo(compute_pool3d_shape(src->tensor_shape(), pool_info), 1, dst->data_type())); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &out_info); } return Status{}; } } // namespace ClPool3dKernel::ClPool3dKernel() { _type = CLKernelType::POOL; } void ClPool3dKernel::configure(const ClCompileContext &compile_context, const ITensorInfo *src, ITensorInfo *dst, const Pooling3dLayerInfo &pool_info) { ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst); ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst, pool_info)); auto padding_info = get_padding_info({src, dst}); // Auto init if empty TensorShape out_shape = compute_pool3d_shape(src->tensor_shape(), pool_info); auto_init_if_empty(*dst, src->clone()->set_tensor_shape(out_shape)); // Set instance variables _pool_info = pool_info; _data_layout = src->data_layout(); _num_elems_processed_per_iteration = (dst->data_type() == DataType::F32) ? 2 : 4; _num_elems_processed_per_iteration = adjust_vec_size(_num_elems_processed_per_iteration, dst->dimension(0)); const PoolingType pool_type = pool_info.pool_type; const int idx_width = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH); const int idx_height = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT); const int idx_depth = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::DEPTH); const int idx_channel = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL); const int idx_batch_size = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::BATCHES); const int pool_size_x = pool_info.is_global_pooling ? src->dimension(idx_width) : pool_info.pool_size.width; const int pool_size_y = pool_info.is_global_pooling ? src->dimension(idx_height) : pool_info.pool_size.height; const int pool_size_z = pool_info.is_global_pooling ? src->dimension(idx_depth) : pool_info.pool_size.depth; const bool exclude_padding = pool_info.exclude_padding; const int pool_stride_x = pool_info.stride.x(); const int pool_stride_y = pool_info.stride.y(); const int pool_stride_z = pool_info.stride.z(); const int pool_pad_top = pool_info.padding.top; const int pool_pad_left = pool_info.padding.left; const int pool_pad_front = pool_info.padding.front; const DataType data_type = src->data_type(); // Set build options CLBuildOptions build_opts; build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(_num_elems_processed_per_iteration)); 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("-DSTRIDE_X=" + support::cpp11::to_string(pool_stride_x)); build_opts.add_option("-DSTRIDE_Y=" + support::cpp11::to_string(pool_stride_y)); build_opts.add_option("-DSTRIDE_Z=" + support::cpp11::to_string(pool_stride_z)); build_opts.add_option("-DPAD_X=" + support::cpp11::to_string(pool_pad_left)); build_opts.add_option("-DPAD_Y=" + support::cpp11::to_string(pool_pad_top)); build_opts.add_option("-DPAD_Z=" + support::cpp11::to_string(pool_pad_front)); 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("-DPOOL_SIZE_Z=" + support::cpp11::to_string(pool_size_z)); build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(src->dimension(idx_width))); build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(src->dimension(idx_height))); build_opts.add_option("-DSRC_DEPTH=" + support::cpp11::to_string(src->dimension(idx_depth))); // If datatype is quantized add relevant parameters if (is_data_type_quantized_asymmetric(data_type) && src->quantization_info() != dst->quantization_info()) { const UniformQuantizationInfo iq_info = src->quantization_info().uniform(); const UniformQuantizationInfo oq_info = dst->quantization_info().uniform(); build_opts.add_option("-DOFFSET_IN1=" + float_to_string_with_full_precision(iq_info.offset)); build_opts.add_option("-DOFFSET_OUT=" + float_to_string_with_full_precision(oq_info.offset)); build_opts.add_option("-DSCALE_IN1=" + float_to_string_with_full_precision(iq_info.scale)); build_opts.add_option("-DSCALE_OUT=" + float_to_string_with_full_precision(oq_info.scale)); } // Set the initial value for the pooling operation accordingly with the data type if (pool_type == PoolingType::MAX) { if (is_data_type_quantized(data_type)) { PixelValue type_min{}; std::tie(type_min, std::ignore) = get_min_max(data_type); build_opts.add_option("-DINITIAL_VALUE=" + support::cpp11::to_string(type_min.get())); } else { build_opts.add_option("-DINITIAL_VALUE=" + float_to_string_with_full_precision(std::numeric_limits::lowest())); } } else { // Pool AVG and Pool L2 initial value build_opts.add_option("-DINITIAL_VALUE=0"); } // Create kernel // Floating point mixed precision is support on F16 only const auto use_fp_mixed_precision = (data_type == DataType::F16) && pool_info.fp_mixed_precision && pool_type != PoolingType::MAX; // Wider accumulation is required to avoid accuracy loss // Case 1: Floating point mixed precision (fp16 src data and fp32 accumulation) DataType acc_data_type = data_type; if (use_fp_mixed_precision) { acc_data_type = DataType::F32; } else if (is_data_type_quantized(data_type) && pool_type != PoolingType::MAX) // Use S32 for avg pooling to allow for integer division { acc_data_type = DataType::S32; } build_opts.add_option("-DACC_DATA_TYPE=" + get_cl_type_from_data_type(acc_data_type)); build_opts.add_option_if(use_fp_mixed_precision, "-DFP_MIXED_PRECISION"); build_opts.add_option_if(exclude_padding, "-DEXCLUDE_PADDING"); build_opts.add_option("-DDST_HEIGHT=" + support::cpp11::to_string(dst->dimension(idx_height))); build_opts.add_option("-DDST_DEPTH=" + support::cpp11::to_string(dst->dimension(idx_depth))); build_opts.add_option("-DDST_CHANNELS=" + support::cpp11::to_string(dst->dimension(idx_channel))); build_opts.add_option("-DDST_BATCH_SIZE=" + support::cpp11::to_string(dst->dimension(idx_batch_size))); build_opts.add_option("-DVEC_SIZE_LEFTOVER=" + support::cpp11::to_string(src->dimension(0) % _num_elems_processed_per_iteration)); // if datatype is quantized use quantized kernel function std::string kernel_name = (is_data_type_quantized_asymmetric(data_type) ? "pooling_3d_layer_MxN_ndhwc_quantized" : "pooling_3d_layer_MxN_ndhwc"); _kernel = create_kernel(compile_context, kernel_name, build_opts.options()); // Configure kernel window Window win = calculate_max_window(*dst, Steps(_num_elems_processed_per_iteration)); ICLKernel::configure_internal(win); // Set config_id for enabling LWS tuning _config_id = "pooling_layer_3d"; _config_id += lower_string(string_from_data_type(data_type)); _config_id += "_"; _config_id += lower_string(string_from_data_layout(_data_layout)); _config_id += "_"; _config_id += support::cpp11::to_string(dst->dimension(idx_width)); _config_id += "_"; _config_id += support::cpp11::to_string(dst->dimension(idx_height)); _config_id += "_"; _config_id += support::cpp11::to_string(dst->dimension(idx_channel)); _config_id += "_"; _config_id += lower_string(string_from_data_layout(src->data_layout())); ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info)); } Status ClPool3dKernel::validate(const ITensorInfo *src, const ITensorInfo *dst, const Pooling3dLayerInfo &pool_info) { ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst, pool_info)); return Status{}; } void ClPool3dKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) { ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); const auto src = utils::cast::polymorphic_downcast(tensors.get_const_tensor(TensorType::ACL_SRC)); auto dst = utils::cast::polymorphic_downcast(tensors.get_tensor(TensorType::ACL_DST_0)); // Collapse 3D window Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ); // Set CL kernel arguments unsigned int idx = 0; // Passing of the window not needed, as the steps are not used for the pool3d kernel add_5D_tensor_argument(idx, src, window); add_5D_tensor_argument(idx, dst, window); enqueue(queue, *this, window_collapsed, lws_hint()); } } // namespace kernels } // namespace opencl } // namespace arm_compute