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Diffstat (limited to 'libs/ARMComputeEx/src/core/CL/kernels/CLComparisonOpKernel.cpp')
| -rw-r--r-- | libs/ARMComputeEx/src/core/CL/kernels/CLComparisonOpKernel.cpp | 212 |
1 files changed, 212 insertions, 0 deletions
diff --git a/libs/ARMComputeEx/src/core/CL/kernels/CLComparisonOpKernel.cpp b/libs/ARMComputeEx/src/core/CL/kernels/CLComparisonOpKernel.cpp new file mode 100644 index 000000000..5af5b16ea --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/kernels/CLComparisonOpKernel.cpp @@ -0,0 +1,212 @@ +/* + * 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/CLComparisonOpKernel.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibraryEx.h" +#include "arm_compute/core/CL/ICLTensor.h" + +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) +{ + const TensorShape &out_shape = + TensorShape::broadcast_shape(input1->tensor_shape(), input2->tensor_shape()); + + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::U8, DataType::U16, + DataType::S16, DataType::F16, DataType::S32, + DataType::F32, DataType::QASYMM8); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::U8, DataType::U16, + DataType::S16, DataType::F16, DataType::S32, + DataType::F32, DataType::QASYMM8); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, + "Inputs are not broadcast compatible"); + // Validate in case of configured output + if (output->total_size() > 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8); + ARM_COMPUTE_RETURN_ERROR_ON_MSG( + detail::have_different_dimensions(out_shape, output->tensor_shape(), 0), + "Wrong shape for output"); + } + return Status{}; +} +} // namespace + +CLComparisonOpKernel::CLComparisonOpKernel() : _input1(nullptr), _input2(nullptr), _output(nullptr) +{ +} + +void CLComparisonOpKernel::configure(const ICLTensor *input1, const ICLTensor *input2, + ICLTensor *output, const ComparisonOperation &op) +{ + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input1, input2); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input1->info(), input2->info(), output->info())); + + _input1 = input1; + _input2 = input2; + _output = output; + + // Create kernel + std::string kernel_name = "comparison_op"; + int op_code = 0; + + switch (op) + { + case ComparisonOperation::EQUAL: + op_code = 1; + break; + case ComparisonOperation::NOT_EQUAL: + op_code = 2; + break; + default: + throw std::runtime_error(" Operation not supported, yet"); + } + + std::set<std::string> build_opts; + build_opts.emplace(("-DOP_CODE=" + support::cpp11::to_string(op_code))); + build_opts.emplace(("-DDATA_TYPE_IN=" + get_cl_type_from_data_type(input1->info()->data_type()))); + build_opts.emplace( + ("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output->info()->data_type()))); + build_opts.emplace( + ("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration))); + + if (is_data_type_quantized_asymmetric(input1->info()->data_type()) && + ((input1->info()->quantization_info().offset != input2->info()->quantization_info().offset) || + (input1->info()->quantization_info().scale != input2->info()->quantization_info().scale))) + { + build_opts.emplace("-DOFFSET_IN1=" + + support::cpp11::to_string(input1->info()->quantization_info().offset)); + build_opts.emplace("-DOFFSET_IN2=" + + support::cpp11::to_string(input2->info()->quantization_info().offset)); + build_opts.emplace("-DSCALE_IN1=" + + support::cpp11::to_string(input1->info()->quantization_info().scale)); + build_opts.emplace("-DSCALE_IN2=" + + support::cpp11::to_string(input2->info()->quantization_info().scale)); + kernel_name += "_qasymm8"; + } + + _kernel = + static_cast<cl::Kernel>(CLKernelLibraryEx::get().create_kernel(kernel_name, build_opts)); + + const std::pair<TensorShape, ValidRegion> broadcast_pair = + ITensorInfo::broadcast_shape_and_valid_region(*input1->info(), *input2->info()); + + 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->info(), out_shape); + + if (input1->info()->data_type() == DataType::S16 || + input2->info()->data_type() == DataType::S16) + { + set_format_if_unknown(*output->info(), Format::S16); + } + else if (input1->info()->data_type() == DataType::F16 && + input2->info()->data_type() == DataType::F16) + { + set_format_if_unknown(*output->info(), Format::F16); + } + else if (input1->info()->data_type() == DataType::F32 || + input2->info()->data_type() == DataType::F32) + { + set_format_if_unknown(*output->info(), 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->info()); + Window win_input2 = win.broadcast_if_dimension_le_one(*input2->info()); + + AccessWindowHorizontal input1_access(input1->info(), 0, num_elems_processed_per_iteration); + AccessWindowHorizontal input2_access(input2->info(), 0, num_elems_processed_per_iteration); + AccessWindowHorizontal output_access(output->info(), 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); + + ICLKernel::configure_internal(win); +} + +void CLComparisonOpKernel::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 CLComparisonOpKernel::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); +} |