From 164a2727d3bbce0e575d24b7db787c85e2e2c203 Mon Sep 17 00:00:00 2001 From: giuros01 Date: Tue, 20 Nov 2018 18:34:46 +0000 Subject: COMPMID-1717: CL: Implement Maximum, Minimum, SquaredDifference Change-Id: Ice653e48211053bd3cd20a693bd76de6b4efc370 Reviewed-on: https://review.mlplatform.org/270 Reviewed-by: Georgios Pinitas Tested-by: Arm Jenkins --- .../CL/kernels/CLElementwiseOperationKernel.cpp | 337 +++++++++++++++++++++ 1 file changed, 337 insertions(+) create mode 100644 src/core/CL/kernels/CLElementwiseOperationKernel.cpp (limited to 'src/core/CL/kernels/CLElementwiseOperationKernel.cpp') diff --git a/src/core/CL/kernels/CLElementwiseOperationKernel.cpp b/src/core/CL/kernels/CLElementwiseOperationKernel.cpp new file mode 100644 index 0000000000..5dc5b7e13f --- /dev/null +++ b/src/core/CL/kernels/CLElementwiseOperationKernel.cpp @@ -0,0 +1,337 @@ +/* + * 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/CLElementwiseOperationKernel.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLValidate.h" +#include "arm_compute/core/CL/ICLTensor.h" +#include + +namespace arm_compute +{ +namespace +{ +constexpr unsigned int num_elems_processed_per_iteration = 16; + +std::map supported_arithmetic_ops = +{ + { ArithmeticOperation::ADD, "ADD" }, + { ArithmeticOperation::SUB, "SUB" }, + { ArithmeticOperation::DIV, "DIV" }, + { ArithmeticOperation::SQUARED_DIFF, "SQUARED_DIFF" }, + { ArithmeticOperation::MIN, "MIN" }, + { ArithmeticOperation::MAX, "MAX" }, +}; + +std::map supported_sat_arithmetic_ops = +{ + { ArithmeticOperation::ADD, "ADD" }, + { ArithmeticOperation::SUB, "SUB" }, +}; + +std::string generate_id_for_tuning_common(const std::string &kernel_name, const ITensorInfo &input1, const ITensorInfo &output) +{ + std::string config_id; + // Set config_id for enabling LWS tuning + config_id = kernel_name; + config_id += "_"; + config_id += lower_string(string_from_data_type(input1.data_type())); + config_id += "_"; + config_id += support::cpp11::to_string(output.dimension(0)); + config_id += "_"; + config_id += support::cpp11::to_string(output.dimension(1)); + return config_id; +} + +Status validate_arguments_with_arithmetic_rules(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output) +{ + ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input1); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input2); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input2, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32); + + const bool is_qasymm = is_data_type_quantized_asymmetric(input1.data_type()) || is_data_type_quantized_asymmetric(input2.data_type()); + if(is_qasymm) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &input2); + } + + 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_F16_UNSUPPORTED(&output); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&output, 1, DataType::U8, DataType::QASYMM8, 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"); + if(is_qasymm) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &output); + } + } + return Status{}; +} + +CLBuildOptions generate_build_options_with_arithmetic_rules(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output, const std::string &operation_string) +{ + CLBuildOptions build_opts; + + build_opts.add_option("-DDATA_TYPE_IN1=" + get_cl_type_from_data_type(input1.data_type())); + build_opts.add_option("-DDATA_TYPE_IN2=" + get_cl_type_from_data_type(input2.data_type())); + build_opts.add_option("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output.data_type())); + build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration)); + build_opts.add_option("-DOP=" + operation_string); + if(is_data_type_quantized_asymmetric(input1.data_type())) + { + build_opts.add_option("-DOFFSET_IN1=" + support::cpp11::to_string(input1.quantization_info().offset)); + build_opts.add_option("-DOFFSET_IN2=" + support::cpp11::to_string(input2.quantization_info().offset)); + build_opts.add_option("-DOFFSET_OUT=" + support::cpp11::to_string(output.quantization_info().offset)); + build_opts.add_option("-DSCALE_IN1=" + float_to_string_with_full_precision(input1.quantization_info().scale)); + build_opts.add_option("-DSCALE_IN2=" + float_to_string_with_full_precision(input2.quantization_info().scale)); + build_opts.add_option("-DSCALE_OUT=" + float_to_string_with_full_precision(output.quantization_info().scale)); + } + return build_opts; +} + +std::pair validate_and_configure_window_for_arithmetic_operators(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output) +{ + const std::pair broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(input1, input2); + const TensorShape &out_shape = broadcast_pair.first; + const ValidRegion &valid_region = broadcast_pair.second; + + 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::F16 && input2.data_type() == DataType::F16) + { + set_format_if_unknown(output, Format::F16); + } + 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 + +CLElementwiseOperationKernel::CLElementwiseOperationKernel() + : _input1(nullptr), _input2(nullptr), _output(nullptr) +{ +} + +void CLElementwiseOperationKernel::configure_common(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*input1->info(), *input2->info(), *output->info())); + + // 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; + + std::string kernel_name = "elementwise_operation_" + name(); + if(is_data_type_quantized_asymmetric(input1->info()->data_type())) + { + kernel_name += "_quantized"; + } + + // Set kernel build options + CLBuildOptions build_opts = generate_build_options(*input1->info(), *input2->info(), *output->info()); + + // Create kernel + _kernel = static_cast(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); + + ICLKernel::configure_internal(win_config.second); + + _config_id = generate_id_for_tuning(kernel_name, *input1->info(), *output->info()); +} + +void CLElementwiseOperationKernel::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 CLElementwiseOperationKernel::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(num_elems_processed_per_iteration - 1U, replicateSize); + return BorderSize(0, border, 0, 0); +} + +/** Arithmetic operations with saturation*/ + +void CLSaturatedArithmeticOperationKernel::configure(ArithmeticOperation op, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, const ConvertPolicy &policy) +{ + _policy = policy; + _op = op; + configure_common(input1, input2, output); +} + +Status CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation op, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, const ConvertPolicy &policy) +{ + ARM_COMPUTE_UNUSED(op, policy); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_with_arithmetic_rules(*input1, *input2, *output)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_for_arithmetic_operators(*input1->clone(), *input2->clone(), *output->clone()).first); + + return Status{}; +} + +std::pair CLSaturatedArithmeticOperationKernel::validate_and_configure_window(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output) +{ + return validate_and_configure_window_for_arithmetic_operators(input1, input2, output); +} + +Status CLSaturatedArithmeticOperationKernel::validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output) +{ + return validate_arguments_with_arithmetic_rules(input1, input2, output); +} + +CLBuildOptions CLSaturatedArithmeticOperationKernel::generate_build_options(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output) +{ + const bool has_float_out = is_data_type_float(output.data_type()); + auto build_options = generate_build_options_with_arithmetic_rules(input1, input2, output, name()); + build_options.add_option((_policy == ConvertPolicy::WRAP || has_float_out) ? "-DWRAP" : "-DSATURATE"); + return build_options; +} +std::string CLSaturatedArithmeticOperationKernel::generate_id_for_tuning(const std::string &kernel_name, const ITensorInfo &input1, const ITensorInfo &output) +{ + auto config_id = generate_id_for_tuning_common(kernel_name, input1, output); + config_id += (_policy == ConvertPolicy::WRAP) ? "_wrap_" : "_saturate_"; + config_id += lower_string(string_from_data_layout(input1.data_layout())); + return config_id; +} + +std::string CLSaturatedArithmeticOperationKernel::name() +{ + return supported_sat_arithmetic_ops[_op]; +} + +/** Arithmetic operations*/ + +void CLArithmeticOperationKernel::configure(ArithmeticOperation op, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output) +{ + _op = op; + configure_common(input1, input2, output); +} + +Status CLArithmeticOperationKernel::validate(ArithmeticOperation op, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output) +{ + ARM_COMPUTE_UNUSED(op); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_with_arithmetic_rules(*input1, *input2, *output)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_for_arithmetic_operators(*input1->clone(), *input2->clone(), *output->clone()).first); + return Status{}; +} +std::pair CLArithmeticOperationKernel::validate_and_configure_window(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output) +{ + return validate_and_configure_window_for_arithmetic_operators(input1, input2, output); +} +Status CLArithmeticOperationKernel::validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output) +{ + return validate_arguments_with_arithmetic_rules(input1, input2, output); +} + +CLBuildOptions CLArithmeticOperationKernel::generate_build_options(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output) +{ + return generate_build_options_with_arithmetic_rules(input1, input2, output, name()); +} +std::string CLArithmeticOperationKernel::generate_id_for_tuning(const std::string &kernel_name, const ITensorInfo &input1, const ITensorInfo &output) +{ + return generate_id_for_tuning_common(kernel_name, input1, output); +} + +std::string CLArithmeticOperationKernel::name() +{ + return supported_arithmetic_ops[_op]; +} +} // namespace arm_compute -- cgit v1.2.1