| /* |
| * Copyright (c) 2018-2021 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/core/NEON/kernels/NESelectKernel.h" |
| |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/Validate.h" |
| #include "src/core/CPP/Validate.h" |
| #include "src/core/NEON/wrapper/wrapper.h" |
| #include "src/core/helpers/AutoConfiguration.h" |
| #include "src/core/helpers/WindowHelpers.h" |
| |
| #include <arm_neon.h> |
| #include <map> |
| #include <string> |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| template <typename ScalarType, typename VectorType> |
| void select_op(const ITensor *cond, const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, |
| const int window_step_x, const int window_start_x, const int window_end_x, const int limit, VectorType (*condition_conversion)(const uint8_t *)) |
| { |
| Window win = window; |
| win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| Iterator condition(cond, win); |
| Iterator input1(in1, win); |
| Iterator input2(in2, win); |
| Iterator output(out, win); |
| |
| execute_window_loop(win, [&](const Coordinates &) |
| { |
| auto output_ptr = reinterpret_cast<ScalarType *>(output.ptr()); |
| const auto condition_ptr = reinterpret_cast<const uint8_t *>(condition.ptr()); |
| const auto input1_ptr = reinterpret_cast<const ScalarType *>(input1.ptr()); |
| const auto input2_ptr = reinterpret_cast<const ScalarType *>(input2.ptr()); |
| |
| int x = window_start_x; |
| for(; x <= limit; x += window_step_x) |
| { |
| const auto c = (*condition_conversion)(condition_ptr + x); |
| const auto a = wrapper::vloadq(input1_ptr + x); |
| const auto b = wrapper::vloadq(input2_ptr + x); |
| wrapper::vstore(output_ptr + x, wrapper::vbsl(c, a, b)); |
| } |
| for(; x < window_end_x; ++x) |
| { |
| const auto c = *(condition_ptr + x); |
| const auto a = *(input1_ptr + x); |
| const auto b = *(input2_ptr + x); |
| *(output_ptr + x) = static_cast<bool>(c) ? a : b; |
| } |
| }, |
| condition, input1, input2, output); |
| } |
| |
| template <typename ScalarType, typename VectorType> |
| void select_op_8(const ITensor *cond, const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) |
| { |
| const auto window_step_x = 16 / sizeof(ScalarType); |
| const auto window_start_x = static_cast<int>(window.x().start()); |
| const auto window_end_x = static_cast<int>(window.x().end()); |
| |
| select_op<ScalarType, VectorType>(cond, in1, in2, out, window, window_step_x, window_start_x, window_end_x, window_end_x - window_step_x, [](const uint8_t *condition_ptr) -> VectorType |
| { |
| static const auto zero = wrapper::vdup_n(static_cast<uint8_t>(0), arm_compute::wrapper::traits::vector_128_tag()); |
| return wrapper::vcgt(wrapper::vloadq(condition_ptr), zero); |
| }); |
| } |
| |
| template <typename ScalarType, typename VectorType> |
| void select_op_16(const ITensor *cond, const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) |
| { |
| const auto window_step_x = 16 / sizeof(ScalarType); |
| const auto window_start_x = static_cast<int>(window.x().start()); |
| const auto window_end_x = static_cast<int>(window.x().end()); |
| |
| select_op<ScalarType, VectorType>(cond, in1, in2, out, window, window_step_x, window_start_x, window_end_x, window_end_x - window_step_x, [](const uint8_t *condition_ptr) -> VectorType |
| { |
| static const auto zero = wrapper::vdup_n(static_cast<uint16_t>(0), arm_compute::wrapper::traits::vector_128_tag()); |
| return wrapper::vcgt(wrapper::vmovl(wrapper::vload(condition_ptr)), zero); |
| }); |
| } |
| |
| template <typename ScalarType, typename VectorType> |
| void select_op_32(const ITensor *cond, const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) |
| { |
| const auto window_step_x = 16 / sizeof(ScalarType); |
| const auto window_start_x = static_cast<int>(window.x().start()); |
| const auto window_end_x = static_cast<int>(window.x().end()); |
| |
| select_op<ScalarType, VectorType>(cond, in1, in2, out, window, window_step_x, window_start_x, window_end_x, window_end_x - window_step_x, [](const uint8_t *condition_ptr) -> VectorType |
| { |
| static const auto zero = wrapper::vdup_n(static_cast<uint32_t>(0), arm_compute::wrapper::traits::vector_128_tag()); |
| return wrapper::vcgt(wrapper::vmovl(wrapper::vgetlow(wrapper::vmovl(wrapper::vload(condition_ptr)))), zero); |
| }); |
| } |
| |
| template <typename ScalarType> |
| void select_op_not_same_rank(const ITensor *cond, const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) |
| { |
| ARM_COMPUTE_UNUSED(window); |
| |
| auto output_ptr = reinterpret_cast<ScalarType *>(out->buffer()); |
| const auto condition_ptr = reinterpret_cast<const uint8_t *>(cond->buffer()); |
| const auto input1_ptr = reinterpret_cast<const ScalarType *>(in1->buffer()); |
| const auto input2_ptr = reinterpret_cast<const ScalarType *>(in2->buffer()); |
| |
| const int outer_size = cond->info()->total_size() / cond->info()->element_size(); |
| const int inner_size = (in1->info()->total_size() / in1->info()->element_size()) / outer_size; |
| int offset = 0; |
| const int step = 16 / in1->info()->element_size(); |
| |
| for(int i = 0; i < outer_size; ++i) |
| { |
| int x = offset; |
| const auto input_ptr = static_cast<bool>(*(condition_ptr + i)) ? input1_ptr : input2_ptr; |
| for(; x <= offset + inner_size - step; x += step) |
| { |
| wrapper::vstore(output_ptr + x, wrapper::vloadq(input_ptr + x)); |
| } |
| if(x <= offset + inner_size - (step / 2)) |
| { |
| wrapper::vstore(output_ptr + x, wrapper::vload(input_ptr + x)); |
| x += step / 2; |
| } |
| for(; x < offset + inner_size; ++x) |
| { |
| *(output_ptr + x) = *(input_ptr + x); |
| } |
| offset += inner_size; |
| } |
| } |
| } // namespace |
| |
| NESelectKernel::NESelectKernel() |
| : _function(nullptr), _c(nullptr), _x(nullptr), _y(nullptr), _output(nullptr), _has_same_rank(false) |
| { |
| } |
| |
| void NESelectKernel::configure(const ITensor *c, const ITensor *x, const ITensor *y, ITensor *output) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(c, x, y, output); |
| |
| // Auto initialize output if not initialized |
| auto_init_if_empty(*output->info(), x->info()->tensor_shape(), 1, x->info()->data_type()); |
| ARM_COMPUTE_ERROR_THROW_ON(validate(c->info(), x->info(), y->info(), output->info())); |
| |
| _c = c; |
| _x = x; |
| _y = y; |
| _output = output; |
| _has_same_rank = (c->info()->tensor_shape().num_dimensions() == x->info()->tensor_shape().num_dimensions()); |
| |
| std::string function_to_call("op_"); |
| function_to_call += string_from_data_type(x->info()->data_type()); |
| |
| static std::map<std::string, SelectFunction *> map_function; |
| |
| if(_has_same_rank) |
| { |
| map_function = |
| { |
| { "op_S8", &select_op_8<int8_t, uint8x16_t> }, |
| { "op_S16", &select_op_16<int16_t, uint16x8_t> }, |
| { "op_S32", &select_op_32<int32_t, uint32x4_t> }, |
| { "op_U8", &select_op_8<uint8_t, uint8x16_t> }, |
| { "op_U16", &select_op_16<uint16_t, uint16x8_t> }, |
| { "op_U32", &select_op_32<uint32_t, uint32x4_t> }, |
| { "op_F32", &select_op_32<float, uint32x4_t> } |
| }; |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| map_function["op_F16"] = &select_op_16<float16_t, uint16x8_t>; |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| } |
| else |
| { |
| map_function = |
| { |
| { "op_S8", &select_op_not_same_rank<int8_t> }, |
| { "op_S16", &select_op_not_same_rank<int16_t> }, |
| { "op_S32", &select_op_not_same_rank<int32_t> }, |
| { "op_U8", &select_op_not_same_rank<uint8_t> }, |
| { "op_U16", &select_op_not_same_rank<uint16_t> }, |
| { "op_U32", &select_op_not_same_rank<uint32_t> }, |
| { "op_F32", &select_op_not_same_rank<float> } |
| }; |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| map_function["op_F16"] = &select_op_not_same_rank<float16_t>; |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| } |
| |
| auto it = map_function.find(function_to_call); |
| |
| if(it != map_function.end()) |
| { |
| _function = it->second; |
| } |
| |
| Window win = calculate_max_window(*x->info()); |
| INEKernel::configure(win); |
| } |
| |
| Status NESelectKernel::validate(const ITensorInfo *c, const ITensorInfo *x, const ITensorInfo *y, const ITensorInfo *output) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(c, x, y); |
| ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(x); |
| ARM_COMPUTE_RETURN_ERROR_ON(x->data_type() == DataType::UNKNOWN); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(x, y); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(x, y); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(c, 1, DataType::U8); |
| |
| const bool is_same_rank = (c->tensor_shape().num_dimensions() == x->tensor_shape().num_dimensions()); |
| ARM_COMPUTE_RETURN_ERROR_ON(is_same_rank && (x->tensor_shape() != c->tensor_shape())); |
| ARM_COMPUTE_RETURN_ERROR_ON(!is_same_rank && ((c->tensor_shape().num_dimensions() > 1) || (c->tensor_shape().x() != x->tensor_shape()[x->tensor_shape().num_dimensions() - 1]))); |
| |
| if(output != nullptr && output->total_size() != 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(x, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(x, output); |
| } |
| |
| return Status{}; |
| } |
| |
| void NESelectKernel::run(const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); |
| ARM_COMPUTE_ERROR_ON(_function == nullptr); |
| _function(_c, _x, _y, _output, window); |
| } |
| } // namespace arm_compute |