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Diffstat (limited to 'src/cpu/kernels/reduction_layer/generic/neon/impl.h')
| -rw-r--r-- | src/cpu/kernels/reduction_layer/generic/neon/impl.h | 1633 |
1 files changed, 1633 insertions, 0 deletions
diff --git a/src/cpu/kernels/reduction_layer/generic/neon/impl.h b/src/cpu/kernels/reduction_layer/generic/neon/impl.h new file mode 100644 index 0000000000..3fa821d3a4 --- /dev/null +++ b/src/cpu/kernels/reduction_layer/generic/neon/impl.h @@ -0,0 +1,1633 @@ +/* + * Copyright (c) 2024 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. + */ +#ifndef ACL_SRC_CPU_KERNELS_REDUCTION_LAYER_GENERIC_NEON_IMPL_H +#define ACL_SRC_CPU_KERNELS_REDUCTION_LAYER_GENERIC_NEON_IMPL_H + +#include "arm_compute/core/Coordinates.h" +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/TensorInfo.h" + +#include "src/core/NEON/NEMath.h" +#include "src/core/NEON/wrapper/wrapper.h" +#include "support/SaturateCast.h" + +#include <arm_neon.h> + +namespace arm_compute +{ +// 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 inline 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 <> +inline 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); + } +}; + +} // namespace arm_compute +#endif // ACL_SRC_CPU_KERNELS_REDUCTION_LAYER_GENERIC_NEON_IMPL_H |