| /* |
| * Copyright (c) 2017-2023 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/NEReductionOperationKernel.h" |
| |
| #include "arm_compute/core/Coordinates.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "src/core/CPP/Validate.h" |
| #include "src/core/NEON/INEKernel.h" |
| #include "src/core/NEON/NEMath.h" |
| #include "src/core/helpers/AutoConfiguration.h" |
| #include "src/core/helpers/WindowHelpers.h" |
| #include "support/SaturateCast.h" |
| |
| #include "src/core/NEON/wrapper/wrapper.h" |
| #include <arm_neon.h> |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| // Helper function that calls vqmovun/vqmvn, vcombine and vstore, allows templating of RedOpYZW_quantized |
| template <typename T> |
| void combine_and_store(int16x8_t t1, int16x8_t t2, Iterator &output, int offset = 0) |
| { |
| if(std::is_same<T, uint8_t>::value) |
| { |
| auto res = wrapper::vcombine(wrapper::vqmovun(t1), wrapper::vqmovun(t2)); |
| wrapper::vstore(output.ptr() + offset, res); |
| } |
| else |
| { |
| auto res = wrapper::vcombine(wrapper::vqmovn(t1), wrapper::vqmovn(t2)); |
| wrapper::vstore(reinterpret_cast<int8_t *>(output.ptr() + offset), res); |
| } |
| } |
| |
| template <typename T> |
| uint32x4x4_t calculate_index(uint32_t idx, T a, T b, uint32x4x4_t c, ReductionOperation op, int axis) |
| { |
| uint32x4_t mask{ 0 }; |
| if(op == ReductionOperation::ARG_IDX_MIN) |
| { |
| mask = wrapper::vcgt(b, a); |
| } |
| else |
| { |
| mask = wrapper::vclt(b, a); |
| } |
| |
| uint32x4_t vec_idx = { idx, idx + 1, idx + 2, idx + 3 }; |
| if(axis != 0) |
| { |
| vec_idx = wrapper::vdup_n(idx, wrapper::traits::vector_128_tag{}); |
| } |
| uint32x4x4_t res = { { wrapper::vbsl(mask, vec_idx, c.val[0]), 0, 0, 0 } }; |
| |
| return res; |
| } |
| |
| template <typename T> |
| uint32x4x4_t calculate_index_quantized(uint32_t idx, T a, T b, uint32x4x4_t c, ReductionOperation op, int axis) |
| { |
| uint32x4x4_t mask{ { 0 } }; |
| uint8x16_t mask_u8{ 0 }; |
| if(op == ReductionOperation::ARG_IDX_MIN) |
| { |
| mask_u8 = wrapper::vcgt(b, a); |
| } |
| else |
| { |
| mask_u8 = wrapper::vclt(b, a); |
| } |
| auto wide_u16_1 = wrapper::vorr(vshll_n_u8(wrapper::vgetlow(mask_u8), 8), wrapper::vmovl(wrapper::vgetlow(mask_u8))); |
| auto wide_u16_2 = wrapper::vorr(vshll_n_u8(wrapper::vgethigh(mask_u8), 8), wrapper::vmovl(wrapper::vgethigh(mask_u8))); |
| mask.val[0] = wrapper::vorr(vshll_n_u16(wrapper::vgetlow(wide_u16_1), 16), wrapper::vmovl(wrapper::vgetlow(wide_u16_1))); |
| mask.val[1] = wrapper::vorr(vshll_n_u16(wrapper::vgethigh(wide_u16_1), 16), wrapper::vmovl(wrapper::vgethigh(wide_u16_1))); |
| mask.val[2] = wrapper::vorr(vshll_n_u16(wrapper::vgetlow(wide_u16_2), 16), wrapper::vmovl(wrapper::vgetlow(wide_u16_2))); |
| mask.val[3] = wrapper::vorr(vshll_n_u16(wrapper::vgethigh(wide_u16_2), 16), wrapper::vmovl(wrapper::vgethigh(wide_u16_2))); |
| |
| uint32x4x4_t vec_idx = { { { idx + 0, idx + 1, idx + 2, idx + 3 }, |
| { idx + 4, idx + 5, idx + 6, idx + 7 }, |
| { idx + 8, idx + 9, idx + 10, idx + 11 }, |
| { idx + 12, idx + 13, idx + 14, idx + 15 } |
| } |
| }; |
| if(axis != 0) |
| { |
| vec_idx.val[0] = wrapper::vdup_n(idx, wrapper::traits::vector_128_tag{}); |
| vec_idx.val[1] = wrapper::vdup_n(idx, wrapper::traits::vector_128_tag{}); |
| vec_idx.val[2] = wrapper::vdup_n(idx, wrapper::traits::vector_128_tag{}); |
| vec_idx.val[3] = wrapper::vdup_n(idx, wrapper::traits::vector_128_tag{}); |
| } |
| uint32x4x4_t res = |
| { |
| { |
| vbslq_u32(mask.val[0], vec_idx.val[0], c.val[0]), |
| vbslq_u32(mask.val[1], vec_idx.val[1], c.val[1]), |
| vbslq_u32(mask.val[2], vec_idx.val[2], c.val[2]), |
| vbslq_u32(mask.val[3], vec_idx.val[3], c.val[3]) |
| } |
| }; |
| |
| return res; |
| } |
| |
| // Helper function to calculate the minimum value of the input vector. All the elements in the output vector contain the min value. |
| template <typename T> |
| inline typename std::enable_if < std::is_same<T, float32x4_t>::value || std::is_same<T, int32x4_t>::value, |
| typename std::conditional<std::is_same<T, float32x4_t>::value, float32x2_t, int32x2_t>::type >::type |
| calculate_min(T in) |
| { |
| auto pmin = wrapper::vpmin(wrapper::vgethigh(in), wrapper::vgetlow(in)); |
| return wrapper::vpmin(pmin, pmin); |
| } |
| |
| // Helper function to calculate the minimum value of the input vector. All the elements in the output vector contain the min value. |
| template <typename T> |
| inline typename std::enable_if < std::is_same<T, uint8x16_t>::value || std::is_same<T, int8x16_t>::value, |
| typename std::conditional<std::is_same<T, uint8x16_t>::value, uint8x8_t, int8x8_t>::type >::type |
| calculate_min(T in) |
| { |
| auto pmin = wrapper::vpmin(wrapper::vgethigh(in), wrapper::vgetlow(in)); |
| pmin = wrapper::vpmin(pmin, pmin); |
| pmin = wrapper::vpmin(pmin, pmin); |
| return wrapper::vpmin(pmin, pmin); |
| } |
| |
| // Helper function to calculate the maximum value of the input vector. All the elements in the output vector contain the max value. |
| template <typename T> |
| inline typename std::enable_if < std::is_same<T, float32x4_t>::value || std::is_same<T, int32x4_t>::value, |
| typename std::conditional<std::is_same<T, float32x4_t>::value, float32x2_t, int32x2_t>::type >::type |
| calculate_max(T in) |
| { |
| auto pmax = wrapper::vpmax(wrapper::vgethigh(in), wrapper::vgetlow(in)); |
| return wrapper::vpmax(pmax, pmax); |
| } |
| |
| // Helper function to calculate the maximum value of the input vector. All the elements in the output vector contain the max value. |
| template <typename T> |
| inline typename std::enable_if < std::is_same<T, uint8x16_t>::value || std::is_same<T, int8x16_t>::value, |
| typename std::conditional<std::is_same<T, uint8x16_t>::value, uint8x8_t, int8x8_t>::type >::type |
| calculate_max(T in) |
| { |
| auto pmax = wrapper::vpmax(wrapper::vgethigh(in), wrapper::vgetlow(in)); |
| pmax = wrapper::vpmax(pmax, pmax); |
| pmax = wrapper::vpmax(pmax, pmax); |
| return wrapper::vpmax(pmax, pmax); |
| } |
| |
| template <typename T> |
| uint32_t calculate_vector_index(uint32x4x4_t vec_res_idx, T vec_res_value, ReductionOperation op) |
| { |
| uint32x4_t res_idx_mask{ 0 }; |
| uint32x4_t mask_ones = vdupq_n_u32(0xFFFFFFFF); |
| |
| if(op == ReductionOperation::ARG_IDX_MIN) |
| { |
| auto pmin = calculate_min(vec_res_value); |
| auto mask = wrapper::vceq(vec_res_value, wrapper::vcombine(pmin, pmin)); |
| res_idx_mask = wrapper::vand(vec_res_idx.val[0], mask); |
| } |
| else |
| { |
| auto pmax = calculate_max(vec_res_value); |
| auto mask = wrapper::vceq(vec_res_value, wrapper::vcombine(pmax, pmax)); |
| res_idx_mask = wrapper::vand(vec_res_idx.val[0], mask); |
| } |
| |
| res_idx_mask = wrapper::vadd(res_idx_mask, mask_ones); |
| auto pmin = wrapper::vpmin(wrapper::vgethigh(res_idx_mask), wrapper::vgetlow(res_idx_mask)); |
| pmin = wrapper::vpmin(pmin, pmin); |
| uint32_t res = wrapper::vgetlane(pmin, 0); |
| |
| return (res - 0xFFFFFFFF); |
| } |
| |
| template <typename T> |
| uint32_t calculate_vector_index_quantized(uint32x4x4_t vec_res_idx, T vec_res_value, ReductionOperation op) |
| { |
| uint32x4x4_t res_idx_mask{ { 0 } }; |
| uint32x4_t mask_ones = vdupq_n_u32(0xFFFFFFFF); |
| uint8x16_t mask_u8{ 0 }; |
| if(op == ReductionOperation::ARG_IDX_MIN) |
| { |
| auto pmin = calculate_min(vec_res_value); |
| mask_u8 = wrapper::vceq(vec_res_value, wrapper::vcombine(pmin, pmin)); |
| } |
| else |
| { |
| auto pmax = calculate_max(vec_res_value); |
| mask_u8 = wrapper::vceq(vec_res_value, wrapper::vcombine(pmax, pmax)); |
| } |
| |
| // Widen vectors |
| auto wide_u16_1 = wrapper::vorr(vshll_n_u8(wrapper::vgetlow(mask_u8), 8), wrapper::vmovl(wrapper::vgetlow(mask_u8))); |
| auto wide_u16_2 = wrapper::vorr(vshll_n_u8(wrapper::vgethigh(mask_u8), 8), wrapper::vmovl(wrapper::vgethigh(mask_u8))); |
| auto wide_u32_1 = wrapper::vorr(vshll_n_u16(wrapper::vgetlow(wide_u16_1), 16), wrapper::vmovl(wrapper::vgetlow(wide_u16_1))); |
| auto wide_u32_2 = wrapper::vorr(vshll_n_u16(wrapper::vgethigh(wide_u16_1), 16), wrapper::vmovl(wrapper::vgethigh(wide_u16_1))); |
| auto wide_u32_3 = wrapper::vorr(vshll_n_u16(wrapper::vgetlow(wide_u16_2), 16), wrapper::vmovl(wrapper::vgetlow(wide_u16_2))); |
| auto wide_u32_4 = wrapper::vorr(vshll_n_u16(wrapper::vgethigh(wide_u16_2), 16), wrapper::vmovl(wrapper::vgethigh(wide_u16_2))); |
| res_idx_mask.val[0] = wrapper::vand(vec_res_idx.val[0], wide_u32_1); |
| res_idx_mask.val[1] = wrapper::vand(vec_res_idx.val[1], wide_u32_2); |
| res_idx_mask.val[2] = wrapper::vand(vec_res_idx.val[2], wide_u32_3); |
| res_idx_mask.val[3] = wrapper::vand(vec_res_idx.val[3], wide_u32_4); |
| res_idx_mask.val[0] = wrapper::vadd(res_idx_mask.val[0], mask_ones); |
| res_idx_mask.val[1] = wrapper::vadd(res_idx_mask.val[1], mask_ones); |
| res_idx_mask.val[2] = wrapper::vadd(res_idx_mask.val[2], mask_ones); |
| res_idx_mask.val[3] = wrapper::vadd(res_idx_mask.val[3], mask_ones); |
| |
| uint32_t res = 0xFFFFFFFF; |
| int iter = 0; |
| do |
| { |
| auto pmin = wrapper::vpmin(wrapper::vgethigh(res_idx_mask.val[iter]), wrapper::vgetlow(res_idx_mask.val[iter])); |
| pmin = wrapper::vpmin(pmin, pmin); |
| res = std::min(wrapper::vgetlane(pmin, 0), res); |
| iter++; |
| } |
| while(iter < 4); |
| |
| return (res - 0xFFFFFFFF); |
| } |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| template <> |
| uint32x4x4_t calculate_index(uint32_t idx, float16x8_t a, float16x8_t b, uint32x4x4_t c, ReductionOperation op, int axis) |
| { |
| uint32x4x2_t mask{ 0 }; |
| uint16x8_t mask_u16{ 0 }; |
| if(op == ReductionOperation::ARG_IDX_MIN) |
| { |
| mask_u16 = wrapper::vcgt(b, a); |
| } |
| else |
| { |
| mask_u16 = wrapper::vclt(b, a); |
| } |
| mask.val[0] = wrapper::vmovl(wrapper::vgetlow(mask_u16)); |
| mask.val[1] = wrapper::vmovl(wrapper::vgethigh(mask_u16)); |
| uint32x4x2_t vec_idx = { { { idx + 0, idx + 1, idx + 2, idx + 3 }, |
| { idx + 4, idx + 5, idx + 6, idx + 7 } |
| } |
| }; |
| if(axis != 0) |
| { |
| vec_idx.val[0] = wrapper::vdup_n(idx, wrapper::traits::vector_128_tag{}); |
| vec_idx.val[1] = wrapper::vdup_n(idx, wrapper::traits::vector_128_tag{}); |
| } |
| uint32x4x4_t res = { wrapper::vbsl(mask.val[0], vec_idx.val[0], c.val[0]), |
| wrapper::vbsl(mask.val[1], vec_idx.val[1], c.val[1]), |
| 0, 0 |
| }; |
| |
| return res; |
| } |
| |
| // Helper function to calculate the minimum value of the input vector. All the elements in the output vector contain the min value. |
| inline float16x4_t calculate_min(float16x8_t in) |
| { |
| auto pmin = wrapper::vpmin(wrapper::vgethigh(in), wrapper::vgetlow(in)); |
| pmin = wrapper::vpmin(pmin, pmin); |
| return wrapper::vpmin(pmin, pmin); |
| } |
| // Helper function to calculate the maximum value of the input vector. All the elements in the output vector contain the max value. |
| inline float16x4_t calculate_max(float16x8_t in) |
| { |
| auto pmax = wrapper::vpmax(wrapper::vgethigh(in), wrapper::vgetlow(in)); |
| pmax = wrapper::vpmax(pmax, pmax); |
| return wrapper::vpmax(pmax, pmax); |
| } |
| |
| template <> |
| uint32_t calculate_vector_index(uint32x4x4_t vec_res_idx, float16x8_t vec_res_value, ReductionOperation op) |
| { |
| uint32x4x2_t res_idx_mask{ 0 }; |
| uint32x4_t mask_ones = vdupq_n_u32(0xFFFFFFFF); |
| uint16x8_t mask_u16; |
| if(op == ReductionOperation::ARG_IDX_MIN) |
| { |
| auto pmin = calculate_min(vec_res_value); |
| mask_u16 = wrapper::vceq(vec_res_value, wrapper::vcombine(pmin, pmin)); |
| } |
| else |
| { |
| auto pmax = calculate_max(vec_res_value); |
| mask_u16 = wrapper::vceq(vec_res_value, wrapper::vcombine(pmax, pmax)); |
| } |
| |
| // Widen vectors |
| auto wide_u32_1 = wrapper::vorr(vshll_n_u16(wrapper::vgetlow(mask_u16), 8), wrapper::vmovl(wrapper::vgetlow(mask_u16))); |
| auto wide_u32_2 = wrapper::vorr(vshll_n_u16(wrapper::vgethigh(mask_u16), 8), wrapper::vmovl(wrapper::vgethigh(mask_u16))); |
| res_idx_mask.val[0] = wrapper::vand(vec_res_idx.val[0], wide_u32_1); |
| res_idx_mask.val[1] = wrapper::vand(vec_res_idx.val[1], wide_u32_2); |
| res_idx_mask.val[0] = wrapper::vadd(res_idx_mask.val[0], mask_ones); |
| res_idx_mask.val[1] = wrapper::vadd(res_idx_mask.val[1], mask_ones); |
| |
| uint32_t res = 0xFFFFFFFF; |
| uint32_t iter = 0; |
| do |
| { |
| auto pmin = wrapper::vpmin(wrapper::vgethigh(res_idx_mask.val[iter]), wrapper::vgetlow(res_idx_mask.val[iter])); |
| pmin = wrapper::vpmin(pmin, pmin); |
| res = std::min(wrapper::vgetlane(pmin, 0), res); |
| iter++; |
| } |
| while(iter < 2); |
| |
| return (res - 0xFFFFFFFF); |
| } |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| |
| template <class F> |
| class Reducer |
| { |
| public: |
| static void reduceX(const Window &window, const ITensor *input, ITensor *output, F f, const ReductionOperation op) |
| { |
| // Set out window |
| Window out_window(window); |
| out_window.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| f(window, out_window, input, output, op); |
| } |
| static void reduceY(const Window &window, const ITensor *input, ITensor *output, F f, const ReductionOperation op) |
| { |
| // 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))); |
| |
| f(in_window, out_window, input, output, 1, op); |
| } |
| static void reduceZ(const Window &window, const ITensor *input, ITensor *output, F f, const ReductionOperation op) |
| { |
| // 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))); |
| |
| f(in_window, out_window, input, output, 2, op); |
| } |
| static void reduceW(const Window &window, const ITensor *input, ITensor *output, F f, const ReductionOperation op) |
| { |
| // 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)); |
| |
| f(in_window, out_window, input, output, 3, op); |
| } |
| }; |
| |
| template <typename T, int S> |
| struct RedOpX |
| { |
| /** SIMD vector tag type. */ |
| using ExactTagType = typename wrapper::traits::neon_vector<T, S>::tag_type; |
| |
| inline void operator()(const Window &in_window, Window &out_window, const ITensor *in, ITensor *out, const ReductionOperation op) |
| { |
| const size_t input_dim_0 = in->info()->dimension(0); |
| const int window_step_x = 16 / sizeof(T); |
| const auto window_start_x = static_cast<int>(in_window.x().start()); |
| const auto window_end_x = static_cast<int>(in_window.x().end()); |
| |
| Window in_win_no_pad = in_window; |
| in_win_no_pad.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| Iterator input(in, in_win_no_pad); |
| Iterator output(out, out_window); |
| |
| execute_window_loop( |
| in_win_no_pad, [&](const Coordinates &) |
| { |
| const auto input_ptr = reinterpret_cast<const T *>(input.ptr()); |
| |
| auto init_res_value = static_cast<T>(0.f); |
| switch(op) |
| { |
| case ReductionOperation::ARG_IDX_MAX: |
| case ReductionOperation::ARG_IDX_MIN: |
| case ReductionOperation::MIN: |
| case ReductionOperation::MAX: |
| { |
| init_res_value = static_cast<T>(*input_ptr); |
| break; |
| } |
| case ReductionOperation::PROD: |
| { |
| init_res_value = static_cast<T>(1.f); |
| break; |
| } |
| default: |
| break; |
| } |
| auto vec_res_value = wrapper::vdup_n(init_res_value, ExactTagType{}); |
| uint32x4x4_t vec_res_idx{ { 0 } }; |
| |
| // Compute window_step_x elements per iteration |
| int x = window_start_x; |
| for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| const auto vec_elements = wrapper::vloadq(input_ptr + x); |
| switch(op) |
| { |
| case ReductionOperation::SUM_SQUARE: |
| vec_res_value = wrapper::vadd(wrapper::vmul(vec_elements, vec_elements), vec_res_value); |
| break; |
| case ReductionOperation::MEAN_SUM: |
| case ReductionOperation::SUM: |
| vec_res_value = wrapper::vadd(vec_elements, vec_res_value); |
| break; |
| case ReductionOperation::PROD: |
| vec_res_value = wrapper::vmul(vec_elements, vec_res_value); |
| break; |
| case ReductionOperation::ARG_IDX_MIN: |
| { |
| auto temp_vec_res_value = wrapper::vmin(vec_elements, vec_res_value); |
| vec_res_idx = calculate_index<decltype(vec_res_value)>(x, temp_vec_res_value, vec_res_value, vec_res_idx, op, 0); |
| vec_res_value = temp_vec_res_value; |
| break; |
| } |
| case ReductionOperation::ARG_IDX_MAX: |
| { |
| auto temp_vec_res_value = wrapper::vmax(vec_elements, vec_res_value); |
| vec_res_idx = calculate_index<decltype(vec_res_value)>(x, temp_vec_res_value, vec_res_value, vec_res_idx, op, 0); |
| vec_res_value = temp_vec_res_value; |
| break; |
| } |
| case ReductionOperation::MIN: |
| { |
| vec_res_value = wrapper::vmin(vec_elements, vec_res_value); |
| break; |
| } |
| case ReductionOperation::MAX: |
| { |
| vec_res_value = wrapper::vmax(vec_elements, vec_res_value); |
| break; |
| } |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| } |
| |
| switch(op) |
| { |
| case ReductionOperation::SUM: |
| case ReductionOperation::MEAN_SUM: |
| case ReductionOperation::SUM_SQUARE: |
| { |
| #ifdef ARM_COMPUTE_DEBUG_ENABLED |
| auto res = static_cast<T>(0.f); |
| for(int i = 0; i < S; ++i) |
| { |
| res += wrapper::vgetlane(vec_res_value, i); |
| } |
| #else // ARM_COMPUTE_DEBUG_ENABLED |
| auto carry_res = wrapper::vpadd(wrapper::vgethigh(vec_res_value), wrapper::vgetlow(vec_res_value)); |
| for(int i = 0; i < S / 4; ++i) |
| { |
| carry_res = wrapper::vpadd(carry_res, carry_res); |
| } |
| auto res = wrapper::vgetlane(carry_res, 0); |
| #endif // ARM_COMPUTE_DEBUG_ENABLED |
| if(op == ReductionOperation::SUM_SQUARE) |
| { |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| res += (*(input_ptr + x)) * (*(input_ptr + x)); |
| } |
| } |
| else |
| { |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| res += *(input_ptr + x); |
| } |
| } |
| |
| if(op == ReductionOperation::MEAN_SUM) |
| { |
| res /= input_dim_0; |
| } |
| |
| *(reinterpret_cast<T *>(output.ptr())) = res; |
| break; |
| } |
| case ReductionOperation::PROD: |
| { |
| auto carry_res = wrapper::vmul(wrapper::vgethigh(vec_res_value), wrapper::vgetlow(vec_res_value)); |
| T res = 1; |
| for(int i = 0; i < S / 2; ++i) |
| { |
| res *= wrapper::vgetlane(carry_res, i); |
| } |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| res *= *(input_ptr + x); |
| } |
| |
| *(reinterpret_cast<T *>(output.ptr())) = res; |
| break; |
| } |
| case ReductionOperation::ARG_IDX_MIN: |
| { |
| auto idx = calculate_vector_index<decltype(vec_res_value)>(vec_res_idx, vec_res_value, op); |
| auto res = static_cast<T>(wrapper::vgetlane(calculate_min(vec_res_value), 0)); |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| if(*(input_ptr + x) < res) |
| { |
| idx = x; |
| res = *(input_ptr + x); |
| } |
| } |
| *(reinterpret_cast<uint32_t *>(output.ptr())) = idx; |
| break; |
| } |
| case ReductionOperation::ARG_IDX_MAX: |
| { |
| auto idx = calculate_vector_index<decltype(vec_res_value)>(vec_res_idx, vec_res_value, op); |
| auto res = static_cast<T>(wrapper::vgetlane(calculate_max(vec_res_value), 0)); |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| if(*(input_ptr + x) > res) |
| { |
| idx = x; |
| res = *(input_ptr + x); |
| } |
| } |
| *(reinterpret_cast<uint32_t *>(output.ptr())) = idx; |
| break; |
| } |
| case ReductionOperation::MIN: |
| { |
| auto res = static_cast<T>(wrapper::vgetlane(calculate_min(vec_res_value), 0)); |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| res = *(input_ptr + x) < res ? *(input_ptr + x) : res; |
| } |
| *(reinterpret_cast<T *>(output.ptr())) = res; |
| break; |
| } |
| case ReductionOperation::MAX: |
| { |
| auto res = static_cast<T>(wrapper::vgetlane(calculate_max(vec_res_value), 0)); |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| res = *(input_ptr + x) > res ? *(input_ptr + x) : res; |
| } |
| *(reinterpret_cast<T *>(output.ptr())) = res; |
| break; |
| } |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| }, |
| input, output); |
| } |
| }; |
| |
| template <typename T> |
| struct RedOpX_quantized |
| { |
| inline void operator()(const Window &in_window, Window &out_window, const ITensor *in, ITensor *out, const ReductionOperation op) |
| { |
| using PromotedType = typename wrapper::traits::promote<typename wrapper::traits::promote<T>::type>::type; |
| |
| const auto oq_info = out->info()->quantization_info().uniform(); |
| |
| const TensorInfo in_info = *(in->info()); |
| const UniformQuantizationInfo iq_info = in_info.quantization_info().uniform(); |
| |
| const int window_step_x = 16 / sizeof(T); |
| const auto window_start_x = static_cast<int>(in_window.x().start()); |
| const auto window_end_x = static_cast<int>(in_window.x().end()); |
| |
| Window in_win_no_pad = in_window; |
| in_win_no_pad.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| Iterator input(in, in_win_no_pad); |
| Iterator output(out, out_window); |
| |
| const auto in_offset = static_cast<float>(iq_info.offset); |
| const float in_scale = iq_info.scale; |
| |
| const auto out_offset = static_cast<float>(oq_info.offset); |
| const float out_scale = oq_info.scale; |
| |
| const auto num_elements = static_cast<float>(in_info.dimension(0)); |
| |
| const float A = in_scale / (out_scale * num_elements); |
| const float B = out_offset - (in_scale * in_offset) / (out_scale); |
| |
| execute_window_loop( |
| in_win_no_pad, [&](const Coordinates &) |
| { |
| const auto input_ptr = reinterpret_cast<T *>(input.ptr()); |
| |
| auto vec_res_value1 = wrapper::vdup_n(static_cast<PromotedType>(0.f), wrapper::traits::vector_128_tag{}); |
| auto vec_res_value2 = wrapper::vdup_n(static_cast<PromotedType>(0.f), wrapper::traits::vector_128_tag{}); |
| auto vec_res_value3 = wrapper::vdup_n(static_cast<PromotedType>(0.f), wrapper::traits::vector_128_tag{}); |
| auto vec_res_value4 = wrapper::vdup_n(static_cast<PromotedType>(0.f), wrapper::traits::vector_128_tag{}); |
| |
| auto vec_res_value1_f = vdupq_n_f32(static_cast<float>(1.f)); |
| auto vec_res_value2_f = vdupq_n_f32(static_cast<float>(1.f)); |
| auto vec_res_value3_f = vdupq_n_f32(static_cast<float>(1.f)); |
| auto vec_res_value4_f = vdupq_n_f32(static_cast<float>(1.f)); |
| |
| typename wrapper::traits::neon_vector<T, 16>::type vec_res_value = { 0 }; |
| |
| if(op == ReductionOperation::ARG_IDX_MAX || op == ReductionOperation::ARG_IDX_MIN || op == ReductionOperation::MIN || op == ReductionOperation::MAX) |
| { |
| vec_res_value = wrapper::vdup_n(*input_ptr, wrapper::traits::vector_128_tag{}); |
| } |
| |
| uint32x4x4_t vec_res_idx{ { 0 } }; |
| // Compute window_step_x elements per iteration |
| int x = window_start_x; |
| for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| const auto vec_elements = wrapper::vloadq(input_ptr + x); |
| switch(op) |
| { |
| case ReductionOperation::SUM: |
| case ReductionOperation::MEAN_SUM: |
| { |
| 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_res_value1 = wrapper::vadd(temp32x4t_1, vec_res_value1); |
| vec_res_value2 = wrapper::vadd(temp32x4t_2, vec_res_value2); |
| vec_res_value3 = wrapper::vadd(temp32x4t_3, vec_res_value3); |
| vec_res_value4 = wrapper::vadd(temp32x4t_4, vec_res_value4); |
| break; |
| } |
| case ReductionOperation::PROD: |
| { |
| const auto offset32x4f_4 = vdupq_n_f32(iq_info.offset); |
| const auto scale32x4f_4 = vdupq_n_f32(iq_info.scale); |
| |
| 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)); |
| |
| auto temp32x4f_1 = wrapper::vcvt<float>(temp32x4t_1); |
| auto temp32x4f_2 = wrapper::vcvt<float>(temp32x4t_2); |
| auto temp32x4f_3 = wrapper::vcvt<float>(temp32x4t_3); |
| auto temp32x4f_4 = wrapper::vcvt<float>(temp32x4t_4); |
| |
| //de-quantize vec_elements |
| temp32x4f_1 = vmulq_f32(vsubq_f32(temp32x4f_1, offset32x4f_4), scale32x4f_4); |
| temp32x4f_2 = vmulq_f32(vsubq_f32(temp32x4f_2, offset32x4f_4), scale32x4f_4); |
| temp32x4f_3 = vmulq_f32(vsubq_f32(temp32x4f_3, offset32x4f_4), scale32x4f_4); |
| temp32x4f_4 = vmulq_f32(vsubq_f32(temp32x4f_4, offset32x4f_4), scale32x4f_4); |
| |
| vec_res_value1_f = vmulq_f32(temp32x4f_1, vec_res_value1_f); |
| vec_res_value2_f = vmulq_f32(temp32x4f_2, vec_res_value2_f); |
| vec_res_value3_f = vmulq_f32(temp32x4f_3, vec_res_value3_f); |
| vec_res_value4_f = vmulq_f32(temp32x4f_4, vec_res_value4_f); |
| break; |
| } |
| case ReductionOperation::ARG_IDX_MIN: |
| { |
| auto temp_vec_res_value = wrapper::vmin(vec_elements, vec_res_value); |
| vec_res_idx = calculate_index_quantized<decltype(vec_res_value)>(x, temp_vec_res_value, vec_res_value, vec_res_idx, op, 0); |
| vec_res_value = temp_vec_res_value; |
| break; |
| } |
| case ReductionOperation::ARG_IDX_MAX: |
| { |
| auto temp_vec_res_value = wrapper::vmax(vec_elements, vec_res_value); |
| vec_res_idx = calculate_index_quantized<decltype(vec_res_value)>(x, temp_vec_res_value, vec_res_value, vec_res_idx, op, 0); |
| vec_res_value = temp_vec_res_value; |
| break; |
| } |
| case ReductionOperation::MIN: |
| { |
| vec_res_value = wrapper::vmin(vec_elements, vec_res_value); |
| break; |
| } |
| case ReductionOperation::MAX: |
| { |
| vec_res_value = wrapper::vmax(vec_elements, vec_res_value); |
| break; |
| } |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| } |
| |
| switch(op) |
| { |
| case ReductionOperation::ARG_IDX_MIN: |
| { |
| auto idx = calculate_vector_index_quantized<decltype(vec_res_value)>(vec_res_idx, vec_res_value, op); |
| auto res = static_cast<T>(wrapper::vgetlane(calculate_min(vec_res_value), 0)); |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| if(*(input_ptr + x) < res) |
| { |
| idx = x; |
| res = *(input_ptr + x); |
| } |
| } |
| *(reinterpret_cast<uint32_t *>(output.ptr())) = idx; |
| break; |
| } |
| case ReductionOperation::ARG_IDX_MAX: |
| { |
| auto idx = calculate_vector_index_quantized<decltype(vec_res_value)>(vec_res_idx, vec_res_value, op); |
| auto res = static_cast<T>(wrapper::vgetlane(calculate_max(vec_res_value), 0)); |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| if(*(input_ptr + x) > res) |
| { |
| idx = x; |
| res = *(input_ptr + x); |
| } |
| } |
| *(reinterpret_cast<uint32_t *>(output.ptr())) = idx; |
| break; |
| } |
| case ReductionOperation::MIN: |
| { |
| auto res = static_cast<T>(wrapper::vgetlane(calculate_min(vec_res_value), 0)); |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| res = *(input_ptr + x) < res ? *(input_ptr + x) : res; |
| } |
| *(reinterpret_cast<T *>(output.ptr())) = res; |
| break; |
| } |
| case ReductionOperation::MAX: |
| { |
| auto res = static_cast<T>(wrapper::vgetlane(calculate_max(vec_res_value), 0)); |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| res = *(input_ptr + x) > res ? *(input_ptr + x) : res; |
| } |
| *(reinterpret_cast<T *>(output.ptr())) = res; |
| break; |
| } |
| case ReductionOperation::PROD: |
| { |
| auto carry_res = wrapper::vmul(vec_res_value1_f, vec_res_value2_f); |
| carry_res = wrapper::vmul(carry_res, vec_res_value3_f); |
| carry_res = wrapper::vmul(carry_res, vec_res_value4_f); |
| |
| float res = wrapper::vgetlane(carry_res, 0); |
| res *= wrapper::vgetlane(carry_res, 1); |
| res *= wrapper::vgetlane(carry_res, 2); |
| res *= wrapper::vgetlane(carry_res, 3); |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| //de-quantize input |
| if(std::is_same<T, uint8_t>::value) |
| { |
| res *= dequantize_qasymm8(*(input_ptr + x), iq_info); |
| } |
| else |
| { |
| res *= dequantize_qasymm8_signed(*(input_ptr + x), iq_info); |
| } |
| } |
| |
| //re-quantize result |
| if(std::is_same<T, uint8_t>::value) |
| { |
| res = quantize_qasymm8(res, iq_info); |
| } |
| else |
| { |
| res = quantize_qasymm8_signed(res, iq_info); |
| } |
| |
| *reinterpret_cast<T *>(output.ptr()) = static_cast<T>(res); |
| break; |
| } |
| case ReductionOperation::SUM: |
| case ReductionOperation::MEAN_SUM: |
| { |
| auto carry_res = wrapper::vadd(vec_res_value1, vec_res_value2); |
| carry_res = wrapper::vadd(carry_res, vec_res_value3); |
| carry_res = wrapper::vadd(carry_res, vec_res_value4); |
| |
| auto carry_paddition = wrapper::vpadd(wrapper::vgethigh(carry_res), wrapper::vgetlow(carry_res)); |
| carry_paddition = wrapper::vpadd(carry_paddition, carry_paddition); |
| auto res = static_cast<int32_t>(wrapper::vgetlane(carry_paddition, 0)); |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| res += *(input_ptr + x); |
| } |
| |
| if(op == ReductionOperation::MEAN_SUM) |
| { |
| const int32_t resFinal = A * (static_cast<float>(res)) + B; |
| |
| *reinterpret_cast<T *>(output.ptr()) = utils::cast::saturate_cast<T>(resFinal); |
| } |
| else |
| { |
| // Subtract accumulated offsets |
| res -= (in_info.dimension(0) - 1) * iq_info.offset; |
| *reinterpret_cast<T *>(output.ptr()) = utils::cast::saturate_cast<T>(res); |
| } |
| |
| break; |
| } |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| }, |
| input, output); |
| } |
| }; |
| |
| template <typename T, int S> |
| struct RedOpYZW |
| { |
| /** SIMD vector tag type. */ |
| using ExactTagType = typename wrapper::traits::neon_vector<T, S>::tag_type; |
| using neon_vector = typename wrapper::traits::neon_vector<T, S>::type; |
| |
| inline void operator()(const Window &in_window, Window &out_window, const ITensor *in, ITensor *out, int axis, const ReductionOperation op) |
| { |
| const TensorInfo in_info = *(in->info()); |
| const int window_step_x = 16 / sizeof(T); |
| const auto window_start_x_tmp = static_cast<int>(in_window.x().start()); |
| const auto window_end_x_tmp = static_cast<int>(in_window.x().end()); |
| // As it split over x-axis, need to set the correct spiltted window start and end. |
| const auto window_start_x = static_cast<int>(0); |
| const auto window_end_x = static_cast<int>(in_window.shape().x()); |
| |
| Window in_win_no_pad = in_window; |
| in_win_no_pad.set(Window::DimX, Window::Dimension(window_start_x_tmp, window_end_x_tmp, in_window.shape().x())); |
| Window out_win_no_pad = out_window; |
| out_win_no_pad.set(Window::DimX, Window::Dimension(window_start_x_tmp, window_end_x_tmp, out_window.shape().x())); |
| |
| Iterator input(in, in_win_no_pad); |
| Iterator output(out, out_win_no_pad); |
| |
| execute_window_loop( |
| in_win_no_pad, [&](const Coordinates &) |
| { |
| const auto input_ptr = reinterpret_cast<T *>(input.ptr()); |
| |
| // Compute window_step_x elements per iteration |
| int x = window_start_x; |
| for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| neon_vector vec_res_value = { 0 }; |
| switch(op) |
| { |
| case ReductionOperation::ARG_IDX_MAX: |
| case ReductionOperation::ARG_IDX_MIN: |
| case ReductionOperation::MIN: |
| case ReductionOperation::MAX: |
| { |
| vec_res_value = wrapper::vloadq(input_ptr + x); |
| break; |
| } |
| case ReductionOperation::PROD: |
| { |
| vec_res_value = wrapper::vdup_n(static_cast<T>(1.f), ExactTagType{}); |
| break; |
| } |
| default: |
| { |
| vec_res_value = wrapper::vdup_n(static_cast<T>(0.f), ExactTagType{}); |
| break; |
| } |
| } |
| uint32x4x4_t vec_res_idx{ { 0 } }; |
| |
| for(unsigned int dim = 0; dim < in_info.dimension(axis); ++dim) |
| { |
| const T *in_ptr = reinterpret_cast<T *>(input.ptr() + x * sizeof(T) + in_info.strides_in_bytes()[axis] * dim); |
| const auto vec_elements = wrapper::vloadq(in_ptr); |
| switch(op) |
| { |
| case ReductionOperation::SUM: |
| case ReductionOperation::MEAN_SUM: |
| vec_res_value = wrapper::vadd(vec_elements, vec_res_value); |
| break; |
| case ReductionOperation::SUM_SQUARE: |
| vec_res_value = wrapper::vadd(wrapper::vmul(vec_elements, vec_elements), vec_res_value); |
| break; |
| case ReductionOperation::PROD: |
| vec_res_value = wrapper::vmul(vec_elements, vec_res_value); |
| break; |
| case ReductionOperation::ARG_IDX_MIN: |
| { |
| auto temp_vec_res_value = wrapper::vmin(vec_elements, vec_res_value); |
| vec_res_idx = calculate_index(dim, temp_vec_res_value, vec_res_value, vec_res_idx, op, axis); |
| vec_res_value = temp_vec_res_value; |
| break; |
| } |
| case ReductionOperation::ARG_IDX_MAX: |
| { |
| auto temp_vec_res_value = wrapper::vmax(vec_elements, vec_res_value); |
| vec_res_idx = calculate_index(dim, temp_vec_res_value, vec_res_value, vec_res_idx, op, axis); |
| vec_res_value = temp_vec_res_value; |
| break; |
| } |
| case ReductionOperation::MIN: |
| { |
| vec_res_value = wrapper::vmin(vec_elements, vec_res_value); |
| break; |
| } |
| case ReductionOperation::MAX: |
| { |
| vec_res_value = wrapper::vmax(vec_elements, vec_res_value); |
| break; |
| } |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| } |
| |
| if(op == ReductionOperation::MEAN_SUM) |
| { |
| auto vec_width_inv = wrapper::vinv(wrapper::vdup_n(static_cast<T>(in_info.dimension(axis)), ExactTagType{})); |
| vec_res_value = wrapper::vmul(vec_res_value, vec_width_inv); |
| } |
| |
| if(op == ReductionOperation::ARG_IDX_MIN || op == ReductionOperation::ARG_IDX_MAX) |
| { |
| wrapper::vstore(reinterpret_cast<uint32_t *>(output.ptr()) + x, vec_res_idx.val[0]); |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| if(std::is_same<T, float16_t>::value) |
| { |
| wrapper::vstore(reinterpret_cast<uint32_t *>(output.ptr()) + x + 4, vec_res_idx.val[1]); |
| } |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| } |
| else |
| { |
| wrapper::vstore(reinterpret_cast<T *>(output.ptr() + x * sizeof(T)), vec_res_value); |
| } |
| } |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| auto res_value = 0.f; |
| switch(op) |
| { |
| case ReductionOperation::ARG_IDX_MAX: |
| case ReductionOperation::ARG_IDX_MIN: |
| case ReductionOperation::MIN: |
| case ReductionOperation::MAX: |
| { |
| res_value = *(input_ptr + x); |
| break; |
| } |
| case ReductionOperation::PROD: |
| { |
| res_value = static_cast<T>(1.f); |
| break; |
| } |
| default: |
| { |
| res_value = static_cast<T>(0.f); |
| break; |
| } |
| } |
| |
| uint32_t res_idx = 0; |
| for(unsigned int dim = 0; dim < in_info.dimension(axis); ++dim) |
| { |
| const T *in_ptr = reinterpret_cast<T *>(input.ptr() + x * sizeof(T) + in_info.strides_in_bytes()[axis] * dim); |
| |
| switch(op) |
| { |
| case ReductionOperation::SUM: |
| case ReductionOperation::MEAN_SUM: |
| res_value += *in_ptr; |
| break; |
| case ReductionOperation::SUM_SQUARE: |
| res_value += *in_ptr * *in_ptr; |
| break; |
| case ReductionOperation::PROD: |
| res_value *= *in_ptr; |
| break; |
| case ReductionOperation::ARG_IDX_MIN: |
| { |
| if(*in_ptr < res_value) |
| { |
| res_value = *in_ptr; |
| res_idx = dim; |
| } |
| break; |
| } |
| case ReductionOperation::ARG_IDX_MAX: |
| { |
| if(*in_ptr > res_value) |
| { |
| res_value = *in_ptr; |
| res_idx = dim; |
| } |
| break; |
| } |
| case ReductionOperation::MIN: |
| { |
| res_value = *in_ptr < res_value ? *in_ptr : res_value; |
| break; |
| } |
| case ReductionOperation::MAX: |
| { |
| res_value = *in_ptr > res_value ? *in_ptr : res_value; |
| break; |
| } |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| } |
| |
| if(op == ReductionOperation::MEAN_SUM) |
| { |
| res_value /= in_info.dimension(axis); |
| } |
| |
| if(op == ReductionOperation::ARG_IDX_MIN || op == ReductionOperation::ARG_IDX_MAX) |
| { |
| *(reinterpret_cast<uint32_t *>(output.ptr()) + x) = res_idx; |
| } |
| else |
| { |
| *(reinterpret_cast<T *>(output.ptr() + x * sizeof(T))) = res_value; |
| } |
| } |
| }, |
| input, output); |
| } |
| }; |
| |
| template <typename T, int S, int axis, ReductionOperation op> |
| struct RedOpYZW_complex |
| { |
| /** SIMD vector tag type. */ |
| using ExactTagType = typename wrapper::traits::neon_vector<T, S>::tag_type; |
| using neon_vector = typename wrapper::traits::neon_vector<T, S>::type; |
| |
| inline void operator()(const Window &in_window, Window &out_window, const ITensor *in, ITensor *out, int, const ReductionOperation) |
| { |
| ARM_COMPUTE_ERROR_ON(axis != 2); |
| ARM_COMPUTE_ERROR_ON(op != ReductionOperation::SUM); |
| |
| const TensorInfo in_info = *(in->info()); |
| const size_t stride_z = in_info.strides_in_bytes()[axis]; |
| const int window_step_x = 16 / sizeof(T); |
| const auto window_start_x_tmp = static_cast<int>(in_window.x().start()); |
| const auto window_end_x_tmp = static_cast<int>(in_window.x().end()); |
| // As it split over x-axis, need to set the correct spiltted window start and end. |
| const auto window_start_x = static_cast<int>(0); |
| const auto window_end_x = static_cast<int>(in_window.shape().x()); |
| |
| Window in_win_no_pad = in_window; |
| in_win_no_pad.set(Window::DimX, Window::Dimension(window_start_x_tmp, window_end_x_tmp, in_window.shape().x())); |
| Window out_win_no_pad = out_window; |
| out_win_no_pad.set(Window::DimX, Window::Dimension(window_start_x_tmp, window_end_x_tmp, out_window.shape().x())); |
| |
| Iterator input(in, in_win_no_pad); |
| Iterator output(out, out_win_no_pad); |
| |
| execute_window_loop( |
| in_win_no_pad, [&](const Coordinates &) |
| { |
| // Compute window_step_x elements per iteration |
| int x = window_start_x; |
| for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| neon_vector vec_res_value_0 = { 0 }; |
| neon_vector vec_res_value_1 = { 0 }; |
| |
| vec_res_value_0 = wrapper::vdup_n(static_cast<T>(0.f), ExactTagType{}); |
| vec_res_value_1 = wrapper::vdup_n(static_cast<T>(0.f), ExactTagType{}); |
| |
| T *out_ptr = reinterpret_cast<T *>(output.ptr() + 2 * x * sizeof(T)); |
| for(unsigned int dim = 0; dim < in_info.dimension(axis); ++dim) |
| { |
| T *in_ptr_0 = reinterpret_cast<T *>(input.ptr() + 2 * x * sizeof(T) + stride_z * dim); |
| T *in_ptr_1 = reinterpret_cast<T *>(input.ptr() + 2 * x * sizeof(T) + 16 + stride_z * dim); |
| |
| const auto vec_elements_0 = wrapper::vloadq(in_ptr_0); |
| const auto vec_elements_1 = wrapper::vloadq(in_ptr_1); |
| |
| vec_res_value_0 = wrapper::vadd(vec_elements_0, vec_res_value_0); |
| vec_res_value_1 = wrapper::vadd(vec_elements_1, vec_res_value_1); |
| } |
| |
| wrapper::vstore(out_ptr, vec_res_value_0); |
| wrapper::vstore(out_ptr + 4, vec_res_value_1); |
| } |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| auto res_value_0 = 0.f; |
| auto res_value_1 = 0.f; |
| |
| T *out_ptr = reinterpret_cast<T *>(output.ptr() + 2 * x * sizeof(T)); |
| for(unsigned int dim = 0; dim < in_info.dimension(axis); ++dim) |
| { |
| T *in_ptr = reinterpret_cast<T *>(input.ptr() + 2 * x * sizeof(T) + stride_z * dim); |
| res_value_0 += *in_ptr; |
| res_value_1 += *(in_ptr + 1); |
| } |
| *out_ptr = res_value_0; |
| *(out_ptr + 1) = res_value_1; |
| } |
| }, |
| input, output); |
| } |
| }; |
| |
| template <typename T> |
| struct RedOpYZW_quantized |
| { |
| inline void operator()(const Window &in_window, Window &out_window, const ITensor *in, ITensor *out, int axis, const ReductionOperation op) |
| { |
| const TensorInfo in_info = *(in->info()); |
| const UniformQuantizationInfo iq_info = in_info.quantization_info().uniform(); |
| using PromotedType = typename wrapper::traits::promote<typename wrapper::traits::promote<T>::type>::type; |
| |
| const auto oq_info = out->info()->quantization_info().uniform(); |
| |
| const int window_step_x = 16 / sizeof(T); |
| const auto window_start_x_tmp = static_cast<int>(in_window.x().start()); |
| const auto window_end_x_tmp = static_cast<int>(in_window.x().end()); |
| // As it split over x-axis, need to set the correct spiltted window start and end. |
| const auto window_start_x = static_cast<int>(0); |
| const auto window_end_x = static_cast<int>(in_window.shape().x()); |
| |
| Window in_win_no_pad = in_window; |
| in_win_no_pad.set(Window::DimX, Window::Dimension(window_start_x_tmp, window_end_x_tmp, in_window.shape().x())); |
| Window out_win_no_pad = out_window; |
| out_win_no_pad.set(Window::DimX, Window::Dimension(window_start_x_tmp, window_end_x_tmp, out_window.shape().x())); |
| |
| Iterator input(in, in_win_no_pad); |
| Iterator output(out, out_win_no_pad); |
| |
| using vector_type = typename wrapper::traits::neon_bitvector<PromotedType, wrapper::traits::BitWidth::W128>::type; |
| using vector_type_f = typename wrapper::traits::neon_vector<float, 4>::type; |
| |
| vector_type vec_res_value1{}; |
| vector_type vec_res_value2{}; |
| vector_type vec_res_value3{}; |
| vector_type vec_res_value4{}; |
| |
| vector_type_f vec_res_value1_f{}; |
| vector_type_f vec_res_value2_f{}; |
| vector_type_f vec_res_value3_f{}; |
| vector_type_f vec_res_value4_f{}; |
| |
| const float in_offset = static_cast<float>(iq_info.offset); |
| const float in_scale = iq_info.scale; |
| |
| const float out_offset = static_cast<float>(oq_info.offset); |
| const float out_scale = oq_info.scale; |
| |
| const float num_elements = static_cast<float>(in_info.dimension(axis)); |
| |
| const float A = in_scale / (out_scale * num_elements); |
| const float B = out_offset - (in_scale * in_offset) / (out_scale); |
| |
| const auto vec_A = wrapper::vdup_n(static_cast<float>(A), wrapper::traits::vector_128_tag{}); |
| const auto vec_B = wrapper::vdup_n(static_cast<float>(B), wrapper::traits::vector_128_tag{}); |
| |
| execute_window_loop( |
| in_win_no_pad, [&](const Coordinates &) |
| { |
| const auto input_ptr = reinterpret_cast<T *>(input.ptr()); |
| |
| // Compute window_step_x elements per iteration |
| int x = window_start_x; |
| for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| uint32x4x4_t vec_res_idx{ { 0 } }; |
| vec_res_value1 = wrapper::vdup_n(static_cast<PromotedType>(0), wrapper::traits::vector_128_tag{}); |
| vec_res_value2 = wrapper::vdup_n(static_cast<PromotedType>(0), wrapper::traits::vector_128_tag{}); |
| vec_res_value3 = wrapper::vdup_n(static_cast<PromotedType>(0), wrapper::traits::vector_128_tag{}); |
| vec_res_value4 = wrapper::vdup_n(static_cast<PromotedType>(0), wrapper::traits::vector_128_tag{}); |
| |
| vec_res_value1_f = wrapper::vdup_n(static_cast<float>(1), wrapper::traits::vector_128_tag{}); |
| vec_res_value2_f = wrapper::vdup_n(static_cast<float>(1), wrapper::traits::vector_128_tag{}); |
| vec_res_value3_f = wrapper::vdup_n(static_cast<float>(1), wrapper::traits::vector_128_tag{}); |
| vec_res_value4_f = wrapper::vdup_n(static_cast<float>(1), wrapper::traits::vector_128_tag{}); |
| |
| auto vec_res_value = wrapper::vloadq(input_ptr + x); |
| |
| for(unsigned int index_dim = 0; index_dim < in_info.dimension(axis); ++index_dim) |
| { |
| const T *in_ptr = input_ptr + x + in_info.strides_in_bytes()[axis] * index_dim; |
| const auto vec_elements = wrapper::vloadq(in_ptr); |
| switch(op) |
| { |
| case ReductionOperation::SUM: |
| case ReductionOperation::MEAN_SUM: |
| { |
| 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_res_value1 = wrapper::vadd(temp32x4t_1, vec_res_value1); |
| vec_res_value2 = wrapper::vadd(temp32x4t_2, vec_res_value2); |
| vec_res_value3 = wrapper::vadd(temp32x4t_3, vec_res_value3); |
| vec_res_value4 = wrapper::vadd(temp32x4t_4, vec_res_value4); |
| break; |
| } |
| case ReductionOperation::PROD: |
| { |
| const auto offset32x4f_4 = wrapper::vdup_n(static_cast<float>(iq_info.offset), wrapper::traits::vector_128_tag{}); |
| const auto scale32x4f_4 = wrapper::vdup_n(iq_info.scale, wrapper::traits::vector_128_tag{}); |
| |
| 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)); |
| |
| auto temp32x4f_1 = wrapper::vcvt<float>(temp32x4t_1); |
| auto temp32x4f_2 = wrapper::vcvt<float>(temp32x4t_2); |
| auto temp32x4f_3 = wrapper::vcvt<float>(temp32x4t_3); |
| auto temp32x4f_4 = wrapper::vcvt<float>(temp32x4t_4); |
| |
| //de-quantize vec_elements |
| temp32x4f_1 = wrapper::vmul(wrapper::vsub(temp32x4f_1, offset32x4f_4), scale32x4f_4); |
| temp32x4f_2 = wrapper::vmul(wrapper::vsub(temp32x4f_2, offset32x4f_4), scale32x4f_4); |
| temp32x4f_3 = wrapper::vmul(wrapper::vsub(temp32x4f_3, offset32x4f_4), scale32x4f_4); |
| temp32x4f_4 = wrapper::vmul(wrapper::vsub(temp32x4f_4, offset32x4f_4), scale32x4f_4); |
| |
| vec_res_value1_f = wrapper::vmul(temp32x4f_1, vec_res_value1_f); |
| vec_res_value2_f = wrapper::vmul(temp32x4f_2, vec_res_value2_f); |
| vec_res_value3_f = wrapper::vmul(temp32x4f_3, vec_res_value3_f); |
| vec_res_value4_f = wrapper::vmul(temp32x4f_4, vec_res_value4_f); |
| break; |
| } |
| case ReductionOperation::ARG_IDX_MIN: |
| { |
| auto temp_vec_res_value = wrapper::vmin(vec_elements, vec_res_value); |
| vec_res_idx = calculate_index_quantized(index_dim, temp_vec_res_value, vec_res_value, vec_res_idx, op, axis); |
| vec_res_value = temp_vec_res_value; |
| break; |
| } |
| case ReductionOperation::ARG_IDX_MAX: |
| { |
| auto temp_vec_res_value = wrapper::vmax(vec_elements, vec_res_value); |
| vec_res_idx = calculate_index_quantized(index_dim, temp_vec_res_value, vec_res_value, vec_res_idx, op, axis); |
| vec_res_value = temp_vec_res_value; |
| break; |
| } |
| case ReductionOperation::MIN: |
| { |
| vec_res_value = wrapper::vmin(vec_elements, vec_res_value); |
| break; |
| } |
| case ReductionOperation::MAX: |
| { |
| vec_res_value = wrapper::vmax(vec_elements, vec_res_value); |
| break; |
| } |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| } |
| |
| switch(op) |
| { |
| case ReductionOperation::ARG_IDX_MIN: |
| case ReductionOperation::ARG_IDX_MAX: |
| { |
| wrapper::vstore(reinterpret_cast<uint32_t *>(output.ptr() + 4 * x), vec_res_idx.val[0]); |
| wrapper::vstore(reinterpret_cast<uint32_t *>(output.ptr() + 4 * x) + 4, vec_res_idx.val[1]); |
| wrapper::vstore(reinterpret_cast<uint32_t *>(output.ptr() + 4 * x) + 8, vec_res_idx.val[2]); |
| wrapper::vstore(reinterpret_cast<uint32_t *>(output.ptr() + 4 * x) + 12, vec_res_idx.val[3]); |
| break; |
| } |
| case ReductionOperation::MIN: |
| case ReductionOperation::MAX: |
| { |
| wrapper::vstore(reinterpret_cast<T *>(output.ptr() + x), vec_res_value); |
| break; |
| } |
| case ReductionOperation::SUM: |
| { |
| // Subtract offsets |
| auto offsets = vdupq_n_s32((in_info.dimension(axis) - 1) * iq_info.offset); |
| |
| auto vec_res_s_value1 = wrapper::vreinterpret(vec_res_value1); |
| auto vec_res_s_value2 = wrapper::vreinterpret(vec_res_value2); |
| auto vec_res_s_value3 = wrapper::vreinterpret(vec_res_value3); |
| auto vec_res_s_value4 = wrapper::vreinterpret(vec_res_value4); |
| |
| vec_res_s_value1 = wrapper::vsub(vec_res_s_value1, offsets); |
| vec_res_s_value2 = wrapper::vsub(vec_res_s_value2, offsets); |
| vec_res_s_value3 = wrapper::vsub(vec_res_s_value3, offsets); |
| vec_res_s_value4 = wrapper::vsub(vec_res_s_value4, offsets); |
| |
| const auto temp16x8t_1 = wrapper::vcombine(wrapper::vqmovn(vec_res_s_value1), wrapper::vqmovn(vec_res_s_value2)); |
| const auto temp16x8t_2 = wrapper::vcombine(wrapper::vqmovn(vec_res_s_value3), wrapper::vqmovn(vec_res_s_value4)); |
| |
| combine_and_store<T>(temp16x8t_1, temp16x8t_2, output, x); |
| break; |
| } |
| case ReductionOperation::MEAN_SUM: |
| { |
| vec_res_value1_f = wrapper::vmla(vec_B, wrapper::vcvt<float>(vec_res_value1), vec_A); |
| vec_res_value2_f = wrapper::vmla(vec_B, wrapper::vcvt<float>(vec_res_value2), vec_A); |
| vec_res_value3_f = wrapper::vmla(vec_B, wrapper::vcvt<float>(vec_res_value3), vec_A); |
| vec_res_value4_f = wrapper::vmla(vec_B, wrapper::vcvt<float>(vec_res_value4), vec_A); |
| |
| #ifdef __aarch64__ |
| vec_res_value1 = wrapper::vcvta<PromotedType>(vec_res_value1_f); |
| vec_res_value2 = wrapper::vcvta<PromotedType>(vec_res_value2_f); |
| vec_res_value3 = wrapper::vcvta<PromotedType>(vec_res_value3_f); |
| vec_res_value4 = wrapper::vcvta<PromotedType>(vec_res_value4_f); |
| #else // defined(__aarch64__) |
| vec_res_value1 = wrapper::vcvt<PromotedType>(vec_res_value1_f); |
| vec_res_value2 = wrapper::vcvt<PromotedType>(vec_res_value2_f); |
| vec_res_value3 = wrapper::vcvt<PromotedType>(vec_res_value3_f); |
| vec_res_value4 = wrapper::vcvt<PromotedType>(vec_res_value4_f); |
| #endif // __aarch64__ |
| |
| const auto temp16x8t_1 = wrapper::vcombine(wrapper::vqmovn(vec_res_value1), wrapper::vqmovn(vec_res_value2)); |
| const auto temp16x8t_2 = wrapper::vcombine(wrapper::vqmovn(vec_res_value3), wrapper::vqmovn(vec_res_value4)); |
| auto res = wrapper::vcombine(wrapper::vqmovn(temp16x8t_1), wrapper::vqmovn(temp16x8t_2)); |
| |
| wrapper::vstore(reinterpret_cast<T *>(output.ptr() + x), res); |
| break; |
| } |
| case ReductionOperation::PROD: |
| { |
| const auto offset32x4f_4 = wrapper::vdup_n(static_cast<float>(iq_info.offset), wrapper::traits::vector_128_tag{}); |
| const auto iscale32x4f_4 = vinvq_f32(vdupq_n_f32(iq_info.scale)); |
| |
| //re-quantize |
| vec_res_value1_f = wrapper::vadd(wrapper::vmul(vec_res_value1_f, iscale32x4f_4), offset32x4f_4); |
| vec_res_value2_f = wrapper::vadd(wrapper::vmul(vec_res_value2_f, iscale32x4f_4), offset32x4f_4); |
| vec_res_value3_f = wrapper::vadd(wrapper::vmul(vec_res_value3_f, iscale32x4f_4), offset32x4f_4); |
| vec_res_value4_f = wrapper::vadd(wrapper::vmul(vec_res_value4_f, iscale32x4f_4), offset32x4f_4); |
| |
| vec_res_value1 = wrapper::vcvt<T>(vec_res_value1_f); |
| vec_res_value2 = wrapper::vcvt<T>(vec_res_value2_f); |
| vec_res_value3 = wrapper::vcvt<T>(vec_res_value3_f); |
| vec_res_value4 = wrapper::vcvt<T>(vec_res_value4_f); |
| |
| const auto temp16x8t_1 = wrapper::vcombine(wrapper::vqmovn(vec_res_value1), wrapper::vqmovn(vec_res_value2)); |
| const auto temp16x8t_2 = wrapper::vcombine(wrapper::vqmovn(vec_res_value3), wrapper::vqmovn(vec_res_value4)); |
| auto res = wrapper::vcombine(wrapper::vqmovn(temp16x8t_1), wrapper::vqmovn(temp16x8t_2)); |
| |
| wrapper::vstore(reinterpret_cast<T *>(output.ptr() + x), res); |
| break; |
| } |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| } |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| float res_value = 0.f; |
| int32_t res_value_q = 0; |
| |
| switch(op) |
| { |
| case ReductionOperation::ARG_IDX_MAX: |
| case ReductionOperation::ARG_IDX_MIN: |
| case ReductionOperation::MIN: |
| case ReductionOperation::MAX: |
| { |
| res_value = *(input_ptr + x); |
| break; |
| } |
| case ReductionOperation::PROD: |
| { |
| res_value = static_cast<T>(1.0f); |
| break; |
| } |
| default: |
| { |
| res_value = static_cast<T>(0.0f); |
| break; |
| } |
| } |
| uint32_t res_idx = 0; |
| |
| for(unsigned int dim = 0; dim < in_info.dimension(axis); ++dim) |
| { |
| const T *in_ptr = reinterpret_cast<T *>(input.ptr() + x + in_info.strides_in_bytes()[axis] * dim); |
| switch(op) |
| { |
| case ReductionOperation::SUM: |
| { |
| res_value += *in_ptr; |
| break; |
| } |
| case ReductionOperation::MEAN_SUM: |
| { |
| res_value_q += *in_ptr; |
| break; |
| } |
| case ReductionOperation::SUM_SQUARE: |
| { |
| res_value += *in_ptr * *in_ptr; |
| break; |
| } |
| case ReductionOperation::PROD: |
| { |
| //de-quantize input |
| if(std::is_same<T, uint8_t>::value) |
| { |
| res_value *= dequantize_qasymm8(*in_ptr, iq_info); |
| } |
| else |
| { |
| res_value *= dequantize_qasymm8_signed(*in_ptr, iq_info); |
| } |
| break; |
| } |
| case ReductionOperation::ARG_IDX_MIN: |
| { |
| if(*in_ptr < res_value) |
| { |
| res_value = *in_ptr; |
| res_idx = dim; |
| } |
| break; |
| } |
| case ReductionOperation::ARG_IDX_MAX: |
| { |
| if(*in_ptr > res_value) |
| { |
| res_value = *in_ptr; |
| res_idx = dim; |
| } |
| break; |
| } |
| case ReductionOperation::MIN: |
| { |
| res_value = *in_ptr < res_value ? *in_ptr : res_value; |
| break; |
| } |
| case ReductionOperation::MAX: |
| { |
| res_value = *in_ptr > res_value ? *in_ptr : res_value; |
| break; |
| } |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| } |
| |
| switch(op) |
| { |
| case ReductionOperation::MEAN_SUM: |
| { |
| // Apply previously calculated coefficients (with rounding on aarch64) |
| #ifdef __aarch64__ |
| const int32_t res = arm_compute::support::cpp11::round(A * (static_cast<float>(res_value_q)) + B); |
| #else // defined(__aarch64__) |
| const int32_t res = A * (static_cast<float>(res_value_q)) + B; |
| #endif // __aarch64__ |
| *reinterpret_cast<T *>(output.ptr() + x) = utils::cast::saturate_cast<T>(res); |
| break; |
| } |
| case ReductionOperation::SUM: |
| { |
| // Subtract accumulated offsets |
| res_value -= (in_info.dimension(axis) - 1) * iq_info.offset; |
| *reinterpret_cast<T *>(output.ptr() + x) = utils::cast::saturate_cast<T>(res_value); |
| break; |
| } |
| case ReductionOperation::PROD: |
| { |
| //re-quantize result |
| T res = 0; |
| if(std::is_same<T, uint8_t>::value) |
| { |
| res = quantize_qasymm8(res_value, iq_info); |
| } |
| else |
| { |
| res = quantize_qasymm8_signed(res_value, iq_info); |
| } |
| *(reinterpret_cast<T *>(output.ptr() + x)) = res; |
| break; |
| } |
| case ReductionOperation::ARG_IDX_MIN: |
| case ReductionOperation::ARG_IDX_MAX: |
| { |
| *(reinterpret_cast<uint32_t *>(output.ptr() + x * 4)) = res_idx; |
| break; |
| } |
| default: |
| *(reinterpret_cast<T *>(output.ptr() + x)) = res_value; |
| } |
| } |
| }, |
| input, output); |
| } |
| }; |
| |
| void reduce_op(const Window &window, const ITensor *input, ITensor *output, unsigned int axis, const ReductionOperation op) |
| { |
| const bool is_complex = (input->info()->num_channels() == 2); |
| |
| if(is_complex) |
| { |
| switch(axis) |
| { |
| case 2: |
| switch(input->info()->data_type()) |
| { |
| case DataType::F32: |
| switch(op) |
| { |
| case ReductionOperation::SUM: |
| return Reducer<RedOpYZW_complex<float, 4, 2, ReductionOperation::SUM>>::reduceZ(window, input, output, RedOpYZW_complex<float, 4, 2, ReductionOperation::SUM>(), op); |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| return; |
| } |
| |
| switch(axis) |
| { |
| case 0: |
| { |
| switch(input->info()->data_type()) |
| { |
| case DataType::QASYMM8: |
| { |
| return Reducer<RedOpX_quantized<uint8_t>>::reduceX(window, input, output, RedOpX_quantized<uint8_t>(), op); |
| } |
| case DataType::QASYMM8_SIGNED: |
| { |
| return Reducer<RedOpX_quantized<int8_t>>::reduceX(window, input, output, RedOpX_quantized<int8_t>(), op); |
| } |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| return Reducer<RedOpX<float16_t, 8>>::reduceX(window, input, output, RedOpX<float16_t, 8>(), op); |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F32: |
| { |
| return Reducer<RedOpX<float, 4>>::reduceX(window, input, output, RedOpX<float, 4>(), op); |
| } |
| case DataType::S32: |
| { |
| return Reducer<RedOpX<int32_t, 4>>::reduceX(window, input, output, RedOpX<int32_t, 4>(), op); |
| } |
| default: |
| { |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| } |
| } |
| case 1: |
| switch(input->info()->data_type()) |
| { |
| case DataType::QASYMM8: |
| { |
| return Reducer<RedOpYZW_quantized<uint8_t>>::reduceY(window, input, output, RedOpYZW_quantized<uint8_t>(), op); |
| } |
| case DataType::QASYMM8_SIGNED: |
| { |
| return Reducer<RedOpYZW_quantized<int8_t>>::reduceY(window, input, output, RedOpYZW_quantized<int8_t>(), op); |
| } |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| return Reducer<RedOpYZW<float16_t, 8>>::reduceY(window, input, output, RedOpYZW<float16_t, 8>(), op); |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F32: |
| return Reducer<RedOpYZW<float, 4>>::reduceY(window, input, output, RedOpYZW<float, 4>(), op); |
| case DataType::S32: |
| return Reducer<RedOpYZW<int32_t, 4>>::reduceY(window, input, output, RedOpYZW<int32_t, 4>(), op); |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| case 2: |
| switch(input->info()->data_type()) |
| { |
| case DataType::QASYMM8: |
| return Reducer<RedOpYZW_quantized<uint8_t>>::reduceZ(window, input, output, RedOpYZW_quantized<uint8_t>(), op); |
| case DataType::QASYMM8_SIGNED: |
| return Reducer<RedOpYZW_quantized<int8_t>>::reduceZ(window, input, output, RedOpYZW_quantized<int8_t>(), op); |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| return Reducer<RedOpYZW<float16_t, 8>>::reduceZ(window, input, output, RedOpYZW<float16_t, 8>(), op); |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F32: |
| return Reducer<RedOpYZW<float, 4>>::reduceZ(window, input, output, RedOpYZW<float, 4>(), op); |
| case DataType::S32: |
| return Reducer<RedOpYZW<int32_t, 4>>::reduceZ(window, input, output, RedOpYZW<int32_t, 4>(), op); |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| case 3: |
| switch(input->info()->data_type()) |
| { |
| case DataType::QASYMM8: |
| return Reducer<RedOpYZW_quantized<uint8_t>>::reduceW(window, input, output, RedOpYZW_quantized<uint8_t>(), op); |
| case DataType::QASYMM8_SIGNED: |
| return Reducer<RedOpYZW_quantized<int8_t>>::reduceW(window, input, output, RedOpYZW_quantized<int8_t>(), op); |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| return Reducer<RedOpYZW<float16_t, 8>>::reduceW(window, input, output, RedOpYZW<float16_t, 8>(), op); |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F32: |
| return Reducer<RedOpYZW<float, 4>>::reduceW(window, input, output, RedOpYZW<float, 4>(), op); |
| case DataType::S32: |
| return Reducer<RedOpYZW<int32_t, 4>>::reduceW(window, input, output, RedOpYZW<int32_t, 4>(), op); |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| default: |
| ARM_COMPUTE_ERROR("Unsupported reduction axis"); |
| } |
| } |
| |
| 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_CPU_F16_UNSUPPORTED(input); |
| |
| if(input->num_channels() == 1) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8_SIGNED, DataType::QASYMM8, DataType::S32, DataType::F16, DataType::F32); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 2, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON(op != ReductionOperation::SUM); |
| ARM_COMPUTE_RETURN_ERROR_ON(axis != 2); |
| } |
| |
| 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) |
| { |
| bool is_arg_min_max = (op == ReductionOperation::ARG_IDX_MAX || op == ReductionOperation::ARG_IDX_MIN); |
| if(!is_arg_min_max) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON(input->num_channels() != output->num_channels()); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U32, DataType::S32); |
| } |
| |
| const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_reduced_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{}; |
| } |
| } // namespace |
| |
| NEReductionOperationKernel::NEReductionOperationKernel() |
| : _input(nullptr), _output(nullptr), _reduction_axis(0), _op(ReductionOperation::SUM_SQUARE) |
| { |
| } |
| |
| 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)); |
| |
| _input = input; |
| _output = output; |
| _op = op; |
| _reduction_axis = axis; |
| |
| // Configure kernel window |
| Window win = calculate_max_window(*input->info(), Steps()); |
| INEKernel::configure(win); |
| |
| // Calculate output shape and set if empty |
| const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_reduced_shape(input->info()->tensor_shape(), axis); |
| // Output auto initialization if not yet initialized |
| const bool is_arg_min_max = (op == ReductionOperation::ARG_IDX_MIN || op == ReductionOperation::ARG_IDX_MAX); |
| DataType output_data_type = is_arg_min_max ? DataType::S32 : input->info()->data_type(); |
| auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape).set_data_type(output_data_type).reset_padding().set_is_resizable(true)); |
| } |
| |
| 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)); |
| |
| 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); |
| |
| reduce_op(window, _input, _output, _reduction_axis, _op); |
| } |
| } // namespace arm_compute |