| /* |
| * Copyright (c) 2017-2018 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/NEON/kernels/NEReductionOperationKernel.h" |
| |
| #include "arm_compute/core/Coordinates.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/IAccessWindow.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/NEON/INEKernel.h" |
| #include "arm_compute/core/NEON/NEMath.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Validate.h" |
| |
| #include "arm_compute/core/NEON/wrapper/wrapper.h" |
| #include <arm_neon.h> |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| template <class F> |
| class Reducer |
| { |
| public: |
| static void reduceX(const Window &window, const ITensor *input, ITensor *output, F f) |
| { |
| // Set out window |
| Window out_window(window); |
| out_window.set(Window::DimX, Window::Dimension(0, 0, 0)); |
| |
| // Get first input and output slices |
| Window in_slice = window.first_slice_window_1D(); |
| Window out_slice = out_window.first_slice_window_1D(); |
| |
| do |
| { |
| Iterator in(input, in_slice); |
| Iterator out(output, out_slice); |
| |
| f(in, out, in_slice, out_slice, *input->info()); |
| } |
| while(window.slide_window_slice_1D(in_slice) && out_window.slide_window_slice_1D(out_slice)); |
| } |
| static void reduceY(const Window &window, const ITensor *input, ITensor *output, F f) |
| { |
| // Set in window |
| Window in_window(window); |
| Window out_window(window); |
| |
| in_window.set(Window::DimY, Window::Dimension(0, 1, 1)); |
| out_window.set(Window::DimY, Window::Dimension(0, output->info()->dimension(1), output->info()->dimension(1))); |
| |
| // Get first input and output slices |
| Window in_slice = in_window.first_slice_window_2D(); |
| Window out_slice = out_window.first_slice_window_2D(); |
| |
| do |
| { |
| Iterator in(input, in_slice); |
| Iterator out(output, out_slice); |
| |
| f(in, out, in_slice, out_slice, *input->info(), 1); |
| } |
| while(in_window.slide_window_slice_2D(in_slice) && out_window.slide_window_slice_2D(out_slice)); |
| } |
| static void reduceZ(const Window &window, const ITensor *input, ITensor *output, F f) |
| { |
| // Set in window |
| Window in_window(window); |
| Window out_window(window); |
| |
| in_window.set(Window::DimZ, Window::Dimension(0, 1, 1)); |
| out_window.set(Window::DimZ, Window::Dimension(0, output->info()->dimension(2), output->info()->dimension(2))); |
| |
| // Get first input and output slices |
| Window in_slice = in_window.first_slice_window_3D(); |
| Window out_slice = out_window.first_slice_window_3D(); |
| |
| do |
| { |
| Iterator in(input, in_slice); |
| Iterator out(output, out_slice); |
| |
| f(in, out, in_slice, out_slice, *input->info(), 2); |
| } |
| while(in_window.slide_window_slice_3D(in_slice) && out_window.slide_window_slice_3D(out_slice)); |
| } |
| static void reduceW(const Window &window, const ITensor *input, ITensor *output, F f) |
| { |
| // Set in/out window |
| Window in_window(window); |
| Window out_window(window); |
| |
| in_window.set(3, Window::Dimension(0, 1, 1)); |
| out_window.set(3, Window::Dimension(0, 1, 1)); |
| |
| // Get first input and output slices |
| Window in_slice = in_window.first_slice_window_4D(); |
| Window out_slice = out_window.first_slice_window_4D(); |
| |
| do |
| { |
| Iterator in(input, in_slice); |
| Iterator out(output, out_slice); |
| |
| f(in, out, in_slice, out_slice, *input->info(), 3); |
| } |
| while(in_window.slide_window_slice_4D(in_slice) && out_window.slide_window_slice_4D(out_slice)); |
| } |
| }; |
| |
| template <typename T, int S, ReductionOperation op> |
| struct RedOpX |
| { |
| /** NEON vector tag type. */ |
| using ExactTagType = typename wrapper::traits::neon_vector<T, S>::tag_type; |
| |
| inline void operator()(Iterator &input, Iterator &output, Window &in_slice, Window &out_slice, const TensorInfo &in_info) |
| { |
| ARM_COMPUTE_UNUSED(out_slice); |
| auto vec_sum_value = wrapper::vdup_n(static_cast<T>(0.f), ExactTagType{}); |
| |
| execute_window_loop(in_slice, [&](const Coordinates & id) |
| { |
| const auto in_ptr = reinterpret_cast<const T *>(input.ptr()); |
| const auto vec_elements = wrapper::vloadq(in_ptr); |
| |
| if(op == ReductionOperation::SUM_SQUARE) |
| { |
| vec_sum_value = wrapper::vadd(wrapper::vmul(vec_elements, vec_elements), vec_sum_value); |
| } |
| else |
| { |
| vec_sum_value = wrapper::vadd(vec_elements, vec_sum_value); |
| } |
| }, |
| input); |
| |
| auto carry_addition = wrapper::vpadd(wrapper::vgethigh(vec_sum_value), wrapper::vgetlow(vec_sum_value)); |
| for(int i = 0; i < S / 4; ++i) |
| { |
| carry_addition = wrapper::vpadd(carry_addition, carry_addition); |
| } |
| |
| auto res = wrapper::vgetlane(carry_addition, 0); |
| if(op == ReductionOperation::MEAN_SUM) |
| { |
| res /= in_info.dimension(0); |
| } |
| |
| *(reinterpret_cast<T *>(output.ptr())) = res; |
| } |
| }; |
| |
| template <ReductionOperation op> |
| struct RedOpX_qasymm8 |
| { |
| inline void operator()(Iterator &input, Iterator &output, Window &in_slice, Window &out_slice, const TensorInfo &in_info) |
| { |
| ARM_COMPUTE_UNUSED(out_slice); |
| auto vec_sum_value1 = vdupq_n_u32(static_cast<uint32_t>(0.f)); |
| auto vec_sum_value2 = vdupq_n_u32(static_cast<uint32_t>(0.f)); |
| auto vec_sum_value3 = vdupq_n_u32(static_cast<uint32_t>(0.f)); |
| auto vec_sum_value4 = vdupq_n_u32(static_cast<uint32_t>(0.f)); |
| |
| execute_window_loop(in_slice, [&](const Coordinates & id) |
| { |
| const auto vec_elements = wrapper::vloadq(input.ptr()); |
| |
| const auto temp16x8t_1 = wrapper::vmovl(wrapper::vgetlow(vec_elements)); |
| const auto temp16x8t_2 = wrapper::vmovl(wrapper::vgethigh(vec_elements)); |
| |
| const auto temp32x4t_1 = wrapper::vmovl(wrapper::vgetlow(temp16x8t_1)); |
| const auto temp32x4t_2 = wrapper::vmovl(wrapper::vgethigh(temp16x8t_1)); |
| const auto temp32x4t_3 = wrapper::vmovl(wrapper::vgetlow(temp16x8t_2)); |
| const auto temp32x4t_4 = wrapper::vmovl(wrapper::vgethigh(temp16x8t_2)); |
| |
| vec_sum_value1 = wrapper::vadd(temp32x4t_1, vec_sum_value1); |
| vec_sum_value2 = wrapper::vadd(temp32x4t_2, vec_sum_value2); |
| vec_sum_value3 = wrapper::vadd(temp32x4t_3, vec_sum_value3); |
| vec_sum_value4 = wrapper::vadd(temp32x4t_4, vec_sum_value4); |
| }, |
| input); |
| |
| auto carry_addition = wrapper::vadd(vec_sum_value1, vec_sum_value2); |
| carry_addition = wrapper::vadd(carry_addition, vec_sum_value3); |
| carry_addition = wrapper::vadd(carry_addition, vec_sum_value4); |
| |
| auto carry_paddition = wrapper::vpadd(wrapper::vgethigh(carry_addition), wrapper::vgetlow(carry_addition)); |
| carry_paddition = wrapper::vpadd(carry_paddition, carry_paddition); |
| auto res = wrapper::vgetlane(carry_paddition, 0); |
| |
| if(op == ReductionOperation::MEAN_SUM) |
| { |
| res /= in_info.dimension(0); |
| } |
| |
| *(output.ptr()) = static_cast<uint8_t>(res); |
| } |
| }; |
| |
| template <typename T, int S, ReductionOperation op> |
| struct RedOpYZW |
| { |
| /** NEON vector tag type. */ |
| using ExactTagType = typename wrapper::traits::neon_vector<T, S>::tag_type; |
| |
| inline void operator()(Iterator &input, Iterator &output, Window &in_slice, Window &out_slice, const TensorInfo &in_info, int axis) |
| { |
| ARM_COMPUTE_UNUSED(out_slice); |
| |
| execute_window_loop(in_slice, [&](const Coordinates & id) |
| { |
| auto vec_sum_value = wrapper::vdup_n(static_cast<T>(0.f), ExactTagType{}); |
| for(unsigned int dim = 0; dim < in_info.dimension(axis); ++dim) |
| { |
| T *in_ptr; |
| switch(axis) |
| { |
| case 1: |
| in_ptr = reinterpret_cast<T *>(input.ptr() + in_info.offset_element_in_bytes(Coordinates(0, dim))); |
| break; |
| case 2: |
| in_ptr = reinterpret_cast<T *>(input.ptr() + in_info.offset_element_in_bytes(Coordinates(0, 0, dim))); |
| break; |
| case 3: |
| in_ptr = reinterpret_cast<T *>(input.ptr() + in_info.offset_element_in_bytes(Coordinates(0, 0, 0, dim))); |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| const auto vec_elements = wrapper::vloadq(in_ptr); |
| |
| if(op == ReductionOperation::SUM_SQUARE) |
| { |
| vec_sum_value = wrapper::vadd(wrapper::vmul(vec_elements, vec_elements), vec_sum_value); |
| } |
| else |
| { |
| vec_sum_value = wrapper::vadd(vec_elements, vec_sum_value); |
| } |
| } |
| |
| if(op == ReductionOperation::MEAN_SUM) |
| { |
| auto vec_width_inv = wrapper::vinv(wrapper::vdup_n(static_cast<T>(in_info.dimension(axis)), ExactTagType{})); |
| vec_sum_value = wrapper::vmul(vec_sum_value, vec_width_inv); |
| } |
| |
| wrapper::vstore(reinterpret_cast<T *>(output.ptr()), vec_sum_value); |
| }, |
| input, output); |
| } |
| }; |
| |
| template <ReductionOperation op> |
| struct RedOpYZW_qasymm8 |
| { |
| inline void operator()(Iterator &input, Iterator &output, Window &in_slice, Window &out_slice, const TensorInfo &in_info, int axis) |
| { |
| ARM_COMPUTE_UNUSED(out_slice); |
| |
| execute_window_loop(in_slice, [&](const Coordinates & id) |
| { |
| auto vec_sum_value1 = vdupq_n_u32(static_cast<uint32_t>(0.f)); |
| auto vec_sum_value2 = vdupq_n_u32(static_cast<uint32_t>(0.f)); |
| auto vec_sum_value3 = vdupq_n_u32(static_cast<uint32_t>(0.f)); |
| auto vec_sum_value4 = vdupq_n_u32(static_cast<uint32_t>(0.f)); |
| for(unsigned int dim = 0; dim < in_info.dimension(axis); ++dim) |
| { |
| uint8_t *in_ptr; |
| switch(axis) |
| { |
| case 1: |
| in_ptr = input.ptr() + in_info.offset_element_in_bytes(Coordinates(0, dim)); |
| break; |
| case 2: |
| in_ptr = input.ptr() + in_info.offset_element_in_bytes(Coordinates(0, 0, dim)); |
| break; |
| case 3: |
| in_ptr = input.ptr() + in_info.offset_element_in_bytes(Coordinates(0, 0, 0, dim)); |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| const auto vec_elements = wrapper::vloadq(in_ptr); |
| |
| const auto temp16x8t_1 = wrapper::vmovl(wrapper::vgetlow(vec_elements)); |
| const auto temp16x8t_2 = wrapper::vmovl(wrapper::vgethigh(vec_elements)); |
| |
| const auto temp32x4t_1 = wrapper::vmovl(wrapper::vgetlow(temp16x8t_1)); |
| const auto temp32x4t_2 = wrapper::vmovl(wrapper::vgethigh(temp16x8t_1)); |
| const auto temp32x4t_3 = wrapper::vmovl(wrapper::vgetlow(temp16x8t_2)); |
| const auto temp32x4t_4 = wrapper::vmovl(wrapper::vgethigh(temp16x8t_2)); |
| |
| vec_sum_value1 = wrapper::vadd(temp32x4t_1, vec_sum_value1); |
| vec_sum_value2 = wrapper::vadd(temp32x4t_2, vec_sum_value2); |
| vec_sum_value3 = wrapper::vadd(temp32x4t_3, vec_sum_value3); |
| vec_sum_value4 = wrapper::vadd(temp32x4t_4, vec_sum_value4); |
| } |
| |
| if(op == ReductionOperation::MEAN_SUM) |
| { |
| const auto vec_width_inv = wrapper::vinv(vdupq_n_f32(in_info.dimension(axis))); |
| const auto vec_sum_value1_f = wrapper::vmul(vcvtq_f32_u32(vec_sum_value1), vec_width_inv); |
| const auto vec_sum_value2_f = wrapper::vmul(vcvtq_f32_u32(vec_sum_value2), vec_width_inv); |
| const auto vec_sum_value3_f = wrapper::vmul(vcvtq_f32_u32(vec_sum_value3), vec_width_inv); |
| const auto vec_sum_value4_f = wrapper::vmul(vcvtq_f32_u32(vec_sum_value4), vec_width_inv); |
| |
| vec_sum_value1 = vcvtq_u32_f32(vec_sum_value1_f); |
| vec_sum_value2 = vcvtq_u32_f32(vec_sum_value2_f); |
| vec_sum_value3 = vcvtq_u32_f32(vec_sum_value3_f); |
| vec_sum_value4 = vcvtq_u32_f32(vec_sum_value4_f); |
| } |
| |
| const auto temp16x8t_1 = vcombine_u16(wrapper::vqmovn(vec_sum_value1), wrapper::vqmovn(vec_sum_value2)); |
| const auto temp16x8t_2 = vcombine_u16(wrapper::vqmovn(vec_sum_value3), wrapper::vqmovn(vec_sum_value4)); |
| auto res = vcombine_u8(wrapper::vqmovn(temp16x8t_1), wrapper::vqmovn(temp16x8t_2)); |
| wrapper::vstore(output.ptr(), res); |
| }, |
| input, output); |
| } |
| }; |
| |
| void reduce_sumsq(const Window &window, const ITensor *input, ITensor *output, unsigned int axis) |
| { |
| switch(axis) |
| { |
| case 0: |
| switch(input->info()->data_type()) |
| { |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| return Reducer<RedOpX<float16_t, 8, ReductionOperation::SUM_SQUARE>>::reduceX(window, input, output, RedOpX<float16_t, 8, ReductionOperation::SUM_SQUARE>()); |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F32: |
| return Reducer<RedOpX<float, 4, ReductionOperation::SUM_SQUARE>>::reduceX(window, input, output, RedOpX<float, 4, ReductionOperation::SUM_SQUARE>()); |
| case DataType::QASYMM8: |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| case 1: |
| switch(input->info()->data_type()) |
| { |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| return Reducer<RedOpYZW<float16_t, 8, ReductionOperation::SUM_SQUARE>>::reduceY(window, input, output, RedOpYZW<float16_t, 8, ReductionOperation::SUM_SQUARE>()); |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F32: |
| return Reducer<RedOpYZW<float, 4, ReductionOperation::SUM_SQUARE>>::reduceY(window, input, output, RedOpYZW<float, 4, ReductionOperation::SUM_SQUARE>()); |
| case DataType::QASYMM8: |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| case 2: |
| switch(input->info()->data_type()) |
| { |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| return Reducer<RedOpYZW<float16_t, 8, ReductionOperation::SUM_SQUARE>>::reduceZ(window, input, output, RedOpYZW<float16_t, 8, ReductionOperation::SUM_SQUARE>()); |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F32: |
| return Reducer<RedOpYZW<float, 4, ReductionOperation::SUM_SQUARE>>::reduceZ(window, input, output, RedOpYZW<float, 4, ReductionOperation::SUM_SQUARE>()); |
| case DataType::QASYMM8: |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| case 3: |
| switch(input->info()->data_type()) |
| { |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| return Reducer<RedOpYZW<float16_t, 8, ReductionOperation::SUM_SQUARE>>::reduceW(window, input, output, RedOpYZW<float16_t, 8, ReductionOperation::SUM_SQUARE>()); |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F32: |
| return Reducer<RedOpYZW<float, 4, ReductionOperation::SUM_SQUARE>>::reduceW(window, input, output, RedOpYZW<float, 4, ReductionOperation::SUM_SQUARE>()); |
| case DataType::QASYMM8: |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| default: |
| ARM_COMPUTE_ERROR("Unsupported reduction axis"); |
| } |
| } |
| |
| void reduce_sum(const Window &window, const ITensor *input, ITensor *output, unsigned int axis) |
| { |
| switch(axis) |
| { |
| case 0: |
| switch(input->info()->data_type()) |
| { |
| case DataType::QASYMM8: |
| return Reducer<RedOpX_qasymm8<ReductionOperation::SUM>>::reduceX(window, input, output, RedOpX_qasymm8<ReductionOperation::SUM>()); |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| return Reducer<RedOpX<float16_t, 8, ReductionOperation::SUM>>::reduceX(window, input, output, RedOpX<float16_t, 8, ReductionOperation::SUM>()); |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F32: |
| return Reducer<RedOpX<float, 4, ReductionOperation::SUM>>::reduceX(window, input, output, RedOpX<float, 4, ReductionOperation::SUM>()); |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| case 1: |
| switch(input->info()->data_type()) |
| { |
| case DataType::QASYMM8: |
| return Reducer<RedOpYZW_qasymm8<ReductionOperation::SUM>>::reduceY(window, input, output, RedOpYZW_qasymm8<ReductionOperation::SUM>()); |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| return Reducer<RedOpYZW<float16_t, 8, ReductionOperation::SUM>>::reduceY(window, input, output, RedOpYZW<float16_t, 8, ReductionOperation::SUM>()); |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F32: |
| return Reducer<RedOpYZW<float, 4, ReductionOperation::SUM>>::reduceY(window, input, output, RedOpYZW<float, 4, ReductionOperation::SUM>()); |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| case 2: |
| switch(input->info()->data_type()) |
| { |
| case DataType::QASYMM8: |
| return Reducer<RedOpYZW_qasymm8<ReductionOperation::SUM>>::reduceZ(window, input, output, RedOpYZW_qasymm8<ReductionOperation::SUM>()); |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| return Reducer<RedOpYZW<float16_t, 8, ReductionOperation::SUM>>::reduceZ(window, input, output, RedOpYZW<float16_t, 8, ReductionOperation::SUM>()); |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F32: |
| return Reducer<RedOpYZW<float, 4, ReductionOperation::SUM>>::reduceZ(window, input, output, RedOpYZW<float, 4, ReductionOperation::SUM>()); |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| case 3: |
| switch(input->info()->data_type()) |
| { |
| case DataType::QASYMM8: |
| return Reducer<RedOpYZW_qasymm8<ReductionOperation::SUM>>::reduceW(window, input, output, RedOpYZW_qasymm8<ReductionOperation::SUM>()); |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| return Reducer<RedOpYZW<float16_t, 8, ReductionOperation::SUM>>::reduceW(window, input, output, RedOpYZW<float16_t, 8, ReductionOperation::SUM>()); |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F32: |
| return Reducer<RedOpYZW<float, 4, ReductionOperation::SUM>>::reduceW(window, input, output, RedOpYZW<float, 4, ReductionOperation::SUM>()); |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| default: |
| ARM_COMPUTE_ERROR("Unsupported reduction axis"); |
| } |
| } |
| void reduce_mean_sum(const Window &window, const ITensor *input, ITensor *output, unsigned int axis) |
| { |
| switch(axis) |
| { |
| case 0: |
| switch(input->info()->data_type()) |
| { |
| case DataType::QASYMM8: |
| return Reducer<RedOpX_qasymm8<ReductionOperation::MEAN_SUM>>::reduceX(window, input, output, RedOpX_qasymm8<ReductionOperation::MEAN_SUM>()); |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| return Reducer<RedOpX<float16_t, 8, ReductionOperation::MEAN_SUM>>::reduceX(window, input, output, RedOpX<float16_t, 8, ReductionOperation::MEAN_SUM>()); |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F32: |
| return Reducer<RedOpX<float, 4, ReductionOperation::MEAN_SUM>>::reduceX(window, input, output, RedOpX<float, 4, ReductionOperation::MEAN_SUM>()); |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| case 1: |
| switch(input->info()->data_type()) |
| { |
| case DataType::QASYMM8: |
| return Reducer<RedOpYZW_qasymm8<ReductionOperation::MEAN_SUM>>::reduceY(window, input, output, RedOpYZW_qasymm8<ReductionOperation::MEAN_SUM>()); |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| return Reducer<RedOpYZW<float16_t, 8, ReductionOperation::MEAN_SUM>>::reduceY(window, input, output, RedOpYZW<float16_t, 8, ReductionOperation::MEAN_SUM>()); |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F32: |
| return Reducer<RedOpYZW<float, 4, ReductionOperation::MEAN_SUM>>::reduceY(window, input, output, RedOpYZW<float, 4, ReductionOperation::MEAN_SUM>()); |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| case 2: |
| switch(input->info()->data_type()) |
| { |
| case DataType::QASYMM8: |
| return Reducer<RedOpYZW_qasymm8<ReductionOperation::MEAN_SUM>>::reduceZ(window, input, output, RedOpYZW_qasymm8<ReductionOperation::MEAN_SUM>()); |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| return Reducer<RedOpYZW<float16_t, 8, ReductionOperation::MEAN_SUM>>::reduceZ(window, input, output, RedOpYZW<float16_t, 8, ReductionOperation::MEAN_SUM>()); |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F32: |
| return Reducer<RedOpYZW<float, 4, ReductionOperation::MEAN_SUM>>::reduceZ(window, input, output, RedOpYZW<float, 4, ReductionOperation::MEAN_SUM>()); |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| case 3: |
| switch(input->info()->data_type()) |
| { |
| case DataType::QASYMM8: |
| return Reducer<RedOpYZW_qasymm8<ReductionOperation::MEAN_SUM>>::reduceW(window, input, output, RedOpYZW_qasymm8<ReductionOperation::MEAN_SUM>()); |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| return Reducer<RedOpYZW<float16_t, 8, ReductionOperation::MEAN_SUM>>::reduceW(window, input, output, RedOpYZW<float16_t, 8, ReductionOperation::MEAN_SUM>()); |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F32: |
| return Reducer<RedOpYZW<float, 4, ReductionOperation::MEAN_SUM>>::reduceW(window, input, output, RedOpYZW<float, 4, ReductionOperation::MEAN_SUM>()); |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| default: |
| ARM_COMPUTE_ERROR("Unsupported reduction axis"); |
| } |
| } |
| |
| TensorShape calculate_output_shape(const TensorShape &input_shape, unsigned int axis) |
| { |
| TensorShape output_shape{ input_shape }; |
| output_shape.set(axis, 1); |
| |
| return output_shape; |
| } |
| |
| Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, unsigned int axis, ReductionOperation op) |
| { |
| ARM_COMPUTE_UNUSED(op); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis >= TensorShape::num_max_dimensions, "Reduction axis greater than max number of dimensions"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis > 3, "Unsupported reduction axis"); |
| |
| if(output->total_size() != 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output); |
| |
| const TensorShape output_shape = calculate_output_shape(input->tensor_shape(), axis); |
| const TensorInfo tensor_info_reshaped = input->clone()->set_tensor_shape(output_shape); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_reshaped); |
| } |
| |
| return Status{}; |
| } |
| |
| std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, unsigned int axis) |
| { |
| // Calculate output shape and set if empty |
| const TensorShape output_shape = calculate_output_shape(input->tensor_shape(), axis); |
| |
| // Output auto initialization if not yet initialized |
| auto_init_if_empty(*output, output_shape, 1, input->data_type()); |
| |
| unsigned int num_elems_processed_per_iteration = 16 / data_size_from_type(input->data_type()); |
| |
| // Configure kernel window |
| Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration)); |
| AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration); |
| AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration); |
| |
| bool window_changed = update_window_and_padding(win, input_access, output_access); |
| output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); |
| |
| Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; |
| |
| return std::make_tuple(err, win); |
| } |
| } // namespace |
| |
| NEReductionOperationKernel::NEReductionOperationKernel() |
| : _input(nullptr), _output(nullptr), _reduction_axis(0), _op(ReductionOperation::SUM_SQUARE), _border_size() |
| { |
| } |
| |
| BorderSize NEReductionOperationKernel::border_size() const |
| { |
| return _border_size; |
| } |
| |
| void NEReductionOperationKernel::configure(const ITensor *input, ITensor *output, unsigned int axis, ReductionOperation op) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); |
| |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), axis, op)); |
| |
| unsigned int num_elems_processed_per_iteration = 16 / data_size_from_type(input->info()->data_type()); |
| |
| _input = input; |
| _output = output; |
| _border_size = (axis == 0) ? BorderSize(0, num_elems_processed_per_iteration - (input->info()->dimension(0) % num_elems_processed_per_iteration), 0, 0) : BorderSize(); |
| _op = op; |
| _reduction_axis = axis; |
| |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(_input->info(), _output->info(), axis); |
| |
| ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config)); |
| |
| INEKernel::configure(std::get<1>(win_config)); |
| } |
| |
| Status NEReductionOperationKernel::validate(const ITensorInfo *input, const ITensorInfo *output, unsigned int axis, ReductionOperation op) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, axis, op)); |
| ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), output->clone().get(), axis))); |
| |
| return Status{}; |
| } |
| |
| void NEReductionOperationKernel::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); |
| |
| switch(_op) |
| { |
| case ReductionOperation::SUM_SQUARE: |
| reduce_sumsq(window, _input, _output, _reduction_axis); |
| break; |
| case ReductionOperation::MEAN_SUM: |
| reduce_mean_sum(window, _input, _output, _reduction_axis); |
| break; |
| case ReductionOperation::SUM: |
| reduce_sum(window, _input, _output, _reduction_axis); |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Unsupported reduction operation."); |
| } |
| } |
| } // namespace arm_compute |