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diff --git a/src/gpu/cl/kernels/ClPool3dKernel.cpp b/src/gpu/cl/kernels/ClPool3dKernel.cpp
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+++ b/src/gpu/cl/kernels/ClPool3dKernel.cpp
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+/*
+ * 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<int32_t>()));
+ }
+ else
+ {
+ build_opts.add_option("-DINITIAL_VALUE=" +
+ float_to_string_with_full_precision(std::numeric_limits<float>::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<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC));
+ auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(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