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Diffstat (limited to 'src/core/CL/kernels/CLComparisonKernel.cpp')
| -rw-r--r-- | src/core/CL/kernels/CLComparisonKernel.cpp | 224 |
1 files changed, 224 insertions, 0 deletions
diff --git a/src/core/CL/kernels/CLComparisonKernel.cpp b/src/core/CL/kernels/CLComparisonKernel.cpp new file mode 100644 index 0000000000..f5f5a0fbd6 --- /dev/null +++ b/src/core/CL/kernels/CLComparisonKernel.cpp @@ -0,0 +1,224 @@ +/* + * Copyright (c) 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/CLComparisonKernel.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLValidate.h" +#include "arm_compute/core/CL/ICLTensor.h" + +#include <map> + +namespace arm_compute +{ +namespace +{ +// Create supported comparisons map +const std::map<ComparisonOperation, std::string> supported_comparison_ops = +{ + { ComparisonOperation::Equal, "EQUAL" }, + { ComparisonOperation::NotEqual, "NOTEQUAL" }, + { ComparisonOperation::Greater, "GREATER" }, + { ComparisonOperation::GreaterEqual, "GREATEREQUAL" }, + { ComparisonOperation::Less, "LESS" }, + { ComparisonOperation::LessEqual, "LESSEQUAL" }, +}; + +int calculate_num_elems_processed_per_iteration(const ITensorInfo &input) +{ + return 16 / input.element_size(); +} + +Status validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output, ComparisonOperation operation) +{ + ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input1); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, + 1, + DataType::U8, DataType::S8, DataType::QASYMM8, + DataType::U16, DataType::S16, + DataType::U32, DataType::S32, + DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &input2); + ARM_COMPUTE_RETURN_ERROR_ON(supported_comparison_ops.count(operation) == 0); + + 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"); + + // 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); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output.tensor_shape(), 0), + "Wrong shape for 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; + + const unsigned int num_elems_processed_per_iteration = calculate_num_elems_processed_per_iteration(input1); + + // Auto initialize output if not initialized + auto_init_if_empty(output, out_shape, 1, DataType::U8, QuantizationInfo()); + + 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 + +CLComparisonKernel::CLComparisonKernel() + : _input1(nullptr), _input2(nullptr), _output(nullptr) +{ +} + +void CLComparisonKernel::configure(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, ComparisonOperation operation) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*input1->info(), *input2->info(), *output->info(), operation)); + + // 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; + + const std::string &operation_name = supported_comparison_ops.at(operation); + std::string kernel_name = "compare_" + lower_string(operation_name); + + // Set kernel build options + std::set<std::string> build_opts; + build_opts.emplace("-DDATA_TYPE=" + get_cl_type_from_data_type(input1->info()->data_type())); + build_opts.emplace("-DVEC_SIZE=" + support::cpp11::to_string(calculate_num_elems_processed_per_iteration(*input1->info()))); + build_opts.emplace("-DOP=" + operation_name); + build_opts.emplace("-DOP_NAME=" + lower_string(operation_name)); + if(is_data_type_quantized_asymmetric(input1->info()->data_type())) + { + 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=" + float_to_string_with_full_precision(input1->info()->quantization_info().scale)); + build_opts.emplace("-DSCALE_IN2=" + float_to_string_with_full_precision(input2->info()->quantization_info().scale)); + kernel_name += "_quantized"; + } + + // Create kernel + _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts)); + + ICLKernel::configure_internal(win_config.second); + + // Set config_id for enabling LWS tuning + _config_id = kernel_name; + _config_id += "_"; + _config_id += lower_string(string_from_data_type(input1->info()->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)); + _config_id += lower_string(string_from_data_layout(input1->info()->data_layout())); +} + +Status CLComparisonKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ComparisonOperation operation) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output); + + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(*input1, *input2, *output, operation)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(*input1->clone(), *input2->clone(), *output->clone()).first); + + return Status{}; +} + +void CLComparisonKernel::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; + const bool is_vector = in_shape1.num_dimensions() == 1 || in_shape2.num_dimensions() == 1; + if(std::min(in_shape1.total_size(), in_shape2.total_size()) > 1 && !is_vector) + { + 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, lws_hint()); + + collapsed.slide_window_slice_3D(slice_input1); + collapsed.slide_window_slice_3D(slice_input2); + } + while(collapsed.slide_window_slice_3D(slice)); +} + +BorderSize CLComparisonKernel::border_size() const +{ + const int num_elems_processed_per_iteration = calculate_num_elems_processed_per_iteration(*_input1->info()); + + 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); +} +} // namespace arm_compute |