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Diffstat (limited to 'libs/ARMComputeEx/src/core/CL/kernels/CLPixelWiseDivisionKernel.cpp')
| -rw-r--r-- | libs/ARMComputeEx/src/core/CL/kernels/CLPixelWiseDivisionKernel.cpp | 322 |
1 files changed, 322 insertions, 0 deletions
diff --git a/libs/ARMComputeEx/src/core/CL/kernels/CLPixelWiseDivisionKernel.cpp b/libs/ARMComputeEx/src/core/CL/kernels/CLPixelWiseDivisionKernel.cpp new file mode 100644 index 000000000..a3e0163de --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/kernels/CLPixelWiseDivisionKernel.cpp @@ -0,0 +1,322 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/core/CL/kernels/CLPixelWiseDivisionKernel.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibrary.h" +#include "arm_compute/core/CL/CLKernelLibraryEx.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/TensorInfo.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/core/Window.h" + +#include <cmath> +#include <cstdlib> +#include <set> +#include <string> + +using namespace arm_compute; + +namespace +{ +constexpr unsigned int num_elems_processed_per_iteration = 16; + +Status validate_arguments(const ITensorInfo *input1, const ITensorInfo *input2, + const ITensorInfo *output, float scale, ConvertPolicy overflow_policy, + RoundingPolicy rounding_policy) +{ + ARM_COMPUTE_UNUSED(overflow_policy); + ARM_COMPUTE_UNUSED(rounding_policy); + + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::U8, DataType::QS8, + DataType::QS16, DataType::S16, DataType::F16, + DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::U8, DataType::QS8, + DataType::QS16, DataType::S16, DataType::F16, + DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(scale < 0, "Scale cannot be negative."); + + const TensorShape &out_shape = + TensorShape::broadcast_shape(input1->tensor_shape(), input2->tensor_shape()); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, + "Inputs are not broadcast compatible"); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input1, input2); + + if (is_data_type_fixed_point(input1->data_type())) + { + // All data types must be all QS8 or all QS16 + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input1, input2); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(scale != 1, + "Unsupported scaling factor for QS8/QS16. Scale must be 1."); + } + + // Validate in case of configured output + if (output->total_size() > 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::QS8, + DataType::QS16, DataType::S16, + DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MSG( + output->data_type() == DataType::U8 && + (input1->data_type() != DataType::U8 || input2->data_type() != DataType::U8), + "Output can only be U8 if both inputs are U8"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG( + detail::have_different_dimensions(out_shape, output->tensor_shape(), 0), + "Wrong shape for output"); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input1, output); + if (is_data_type_fixed_point(input1->data_type())) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input1, output); + } + } + + return Status{}; +} + +std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input1, ITensorInfo *input2, + ITensorInfo *output) +{ + const std::pair<TensorShape, ValidRegion> broadcast_pair = + ITensorInfo::broadcast_shape_and_valid_region(*input1, *input2); + const TensorShape &out_shape = broadcast_pair.first; + const ValidRegion &valid_region = broadcast_pair.second; + + // Auto initialize output if not initialized + { + set_shape_if_empty(*output, out_shape); + + if (input1->data_type() == DataType::S16 || input2->data_type() == DataType::S16) + { + set_format_if_unknown(*output, Format::S16); + } + else if (input1->data_type() == DataType::F32 || input2->data_type() == DataType::F32) + { + set_format_if_unknown(*output, Format::F32); + } + } + + Window win = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration)); + Window win_input1 = win.broadcast_if_dimension_le_one(*input1); + Window win_input2 = win.broadcast_if_dimension_le_one(*input2); + + AccessWindowHorizontal input1_access(input1, 0, num_elems_processed_per_iteration); + AccessWindowHorizontal input2_access(input2, 0, num_elems_processed_per_iteration); + AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration); + + bool window_changed = update_window_and_padding(win_input1, input1_access) || + update_window_and_padding(win_input2, input2_access) || + update_window_and_padding(win, output_access); + + output_access.set_valid_region(win, valid_region); + + Status err = (window_changed) + ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") + : Status{}; + return std::make_pair(err, win); +} +} // namespace + +CLPixelWiseDivisionKernel::CLPixelWiseDivisionKernel() + : _input1(nullptr), _input2(nullptr), _output(nullptr) +{ +} + +void CLPixelWiseDivisionKernel::configure(const ICLTensor *input1, const ICLTensor *input2, + ICLTensor *output, float scale, + ConvertPolicy overflow_policy, + RoundingPolicy rounding_policy) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input1->info(), input2->info(), output->info(), + scale, overflow_policy, rounding_policy)); + + // Configure kernel window + auto win_config = validate_and_configure_window(input1->info(), input2->info(), output->info()); + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); + + _input1 = input1; + _input2 = input2; + _output = output; + + int scale_int = -1; + // Extract sign, exponent and mantissa + int exponent = 0; + float normalized_mantissa = std::frexp(scale, &exponent); + // Use int scaling if factor is equal to 1/2^n for 0 <= n <= 15 + // frexp returns 0.5 as mantissa which means that the exponent will be in the range of -1 <= e <= + // 14 + // Moreover, it will be negative as we deal with 1/2^n + if ((normalized_mantissa == 0.5f) && (-14 <= exponent) && (exponent <= 1)) + { + // Store the positive exponent. We know that we compute 1/2^n + // Additionally we need to subtract 1 to compensate that frexp used a mantissa of 0.5 + scale_int = std::abs(exponent - 1); + } + + std::string data_type; + std::string compute_type; + // Check if it has float inputs and output + if (is_data_type_float(input1->info()->data_type()) || + is_data_type_float(input2->info()->data_type())) + { + scale_int = -1; + compute_type = (input1->info()->data_type() == DataType::F32 || + input2->info()->data_type() == DataType::F32) + ? "float" + : "half"; + data_type = "DATA_TYPE_FLOAT"; + } + else + { + if (input1->info()->data_type() == DataType::S16 || + input2->info()->data_type() == DataType::S16) + { + compute_type = "int"; + } + else if (input1->info()->data_type() == DataType::QS8) + { + compute_type = "qs8"; + } + else if (input1->info()->data_type() == DataType::QS16) + { + compute_type = "qs16"; + } + else + { + compute_type = "ushort"; + } + data_type = "DATA_TYPE_INT"; + } + + // Construct kernel name + std::string kernel_name = "pixelwise_div"; + kernel_name += (scale_int >= 0) ? "_int" : "_float"; + + // Set kernel build options + std::set<std::string> build_opts; + build_opts.emplace( + (overflow_policy == ConvertPolicy::WRAP || is_data_type_float(output->info()->data_type())) + ? "-DWRAP" + : "-DSATURATE"); + build_opts.emplace((rounding_policy == RoundingPolicy::TO_ZERO) ? "-DROUND=_rtz" + : "-DROUND=_rte"); + if (is_data_type_fixed_point(input1->info()->data_type())) + { + build_opts.emplace("-DFIXED_POINT_POSITION=" + + support::cpp11::to_string(input1->info()->fixed_point_position())); + } + build_opts.emplace("-DDATA_TYPE_IN1=" + get_cl_type_from_data_type(input1->info()->data_type())); + build_opts.emplace("-DDATA_TYPE_IN2=" + get_cl_type_from_data_type(input2->info()->data_type())); + build_opts.emplace("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output->info()->data_type())); + build_opts.emplace("-DDATA_TYPE_RES=" + compute_type); + build_opts.emplace("-D" + data_type); + + // Create kernel + _kernel = + static_cast<cl::Kernel>(CLKernelLibraryEx::get().create_kernel(kernel_name, build_opts)); + + // Set scale argument + unsigned int idx = 3 * num_arguments_per_3D_tensor(); // Skip the inputs and output parameters + + if (scale_int >= 0) + { + _kernel.setArg(idx++, scale_int); + } + else + { + _kernel.setArg(idx++, scale); + } + + ICLKernel::configure(win_config.second); +} + +Status CLPixelWiseDivisionKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, + const ITensorInfo *output, float scale, + ConvertPolicy overflow_policy, + RoundingPolicy rounding_policy) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output); + ARM_COMPUTE_RETURN_ON_ERROR( + validate_arguments(input1, input2, output, scale, overflow_policy, rounding_policy)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input1->clone().get(), + input2->clone().get(), + output->clone().get()) + .first); + + return Status{}; +} + +void CLPixelWiseDivisionKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); + + const TensorShape &in_shape1 = _input1->info()->tensor_shape(); + const TensorShape &in_shape2 = _input2->info()->tensor_shape(); + const TensorShape &out_shape = _output->info()->tensor_shape(); + + bool can_collapse = true; + if (std::min(in_shape1.total_size(), in_shape2.total_size()) > 1) + { + can_collapse = + (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ); + for (size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); ++d) + { + can_collapse = (in_shape1[d] == in_shape2[d]); + } + } + + bool has_collapsed = false; + Window collapsed = + can_collapse ? window.collapse_if_possible(ICLKernel::window(), Window::DimZ, &has_collapsed) + : window; + + const TensorShape &in_shape1_collapsed = + has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1; + const TensorShape &in_shape2_collapsed = + has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2; + + Window slice = collapsed.first_slice_window_3D(); + Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed); + Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed); + + do + { + unsigned int idx = 0; + add_3D_tensor_argument(idx, _input1, slice_input1); + add_3D_tensor_argument(idx, _input2, slice_input2); + add_3D_tensor_argument(idx, _output, slice); + enqueue(queue, *this, slice); + + collapsed.slide_window_slice_3D(slice_input1); + collapsed.slide_window_slice_3D(slice_input2); + } while (collapsed.slide_window_slice_3D(slice)); +} + +BorderSize CLPixelWiseDivisionKernel::border_size() const +{ + const unsigned int replicateSize = + _output->info()->dimension(0) - + std::min(_input1->info()->dimension(0), _input2->info()->dimension(0)); + const unsigned int border = + std::min<unsigned int>(num_elems_processed_per_iteration - 1U, replicateSize); + return BorderSize(0, border, 0, 0); +} |