diff options
Diffstat (limited to 'src/core/NEON/kernels/NEReductionOperationKernel.cpp')
| -rw-r--r-- | src/core/NEON/kernels/NEReductionOperationKernel.cpp | 2068 |
1 files changed, 1317 insertions, 751 deletions
diff --git a/src/core/NEON/kernels/NEReductionOperationKernel.cpp b/src/core/NEON/kernels/NEReductionOperationKernel.cpp index afe58ed07d..455d604b3b 100644 --- a/src/core/NEON/kernels/NEReductionOperationKernel.cpp +++ b/src/core/NEON/kernels/NEReductionOperationKernel.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2020 ARM Limited. + * Copyright (c) 2017-2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -21,22 +21,24 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#include "arm_compute/core/NEON/kernels/NEReductionOperationKernel.h" +#include "src/core/NEON/kernels/NEReductionOperationKernel.h" -#include "arm_compute/core/CPP/Validate.h" #include "arm_compute/core/Coordinates.h" #include "arm_compute/core/Helpers.h" -#include "arm_compute/core/IAccessWindow.h" #include "arm_compute/core/ITensor.h" -#include "arm_compute/core/NEON/INEKernel.h" -#include "arm_compute/core/NEON/NEMath.h" #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/Utils.h" -#include "arm_compute/core/Validate.h" -#include "arm_compute/core/utils/misc/SaturateCast.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "arm_compute/core/Validate.h" + +#include "src/core/CPP/Validate.h" +#include "src/core/helpers/AutoConfiguration.h" +#include "src/core/helpers/WindowHelpers.h" +#include "src/core/NEON/INEKernel.h" +#include "src/core/NEON/NEMath.h" +#include "src/core/NEON/wrapper/wrapper.h" +#include "support/SaturateCast.h" -#include "arm_compute/core/NEON/wrapper/wrapper.h" #include <arm_neon.h> namespace arm_compute @@ -45,25 +47,25 @@ 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) +void combine_and_store(int16x8_t t1, int16x8_t t2, Iterator &output, int offset = 0) { - if(std::is_same<T, uint8_t>::value) + if (std::is_same<T, uint8_t>::value) { auto res = wrapper::vcombine(wrapper::vqmovun(t1), wrapper::vqmovun(t2)); - wrapper::vstore(output.ptr(), res); + 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()), res); + 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) + uint32x4_t mask{0}; + if (op == ReductionOperation::ARG_IDX_MIN) { mask = wrapper::vcgt(b, a); } @@ -72,12 +74,12 @@ uint32x4x4_t calculate_index(uint32_t idx, T a, T b, uint32x4x4_t c, ReductionOp mask = wrapper::vclt(b, a); } - uint32x4_t vec_idx = { idx, idx + 1, idx + 2, idx + 3 }; - if(axis != 0) + 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 } }; + uint32x4x4_t res = {{wrapper::vbsl(mask, vec_idx, c.val[0]), 0, 0, 0}}; return res; } @@ -85,9 +87,9 @@ uint32x4x4_t calculate_index(uint32_t idx, T a, T b, uint32x4x4_t c, ReductionOp 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) + uint32x4x4_t mask{{0}}; + uint8x16_t mask_u8{0}; + if (op == ReductionOperation::ARG_IDX_MIN) { mask_u8 = wrapper::vcgt(b, a); } @@ -95,44 +97,43 @@ uint32x4x4_t calculate_index_quantized(uint32_t idx, T a, T b, uint32x4x4_t c, R { 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) + 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]) - } - }; + 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) +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); @@ -140,9 +141,10 @@ inline typename std::enable_if < std::is_same<T, float32x4_t>::value || std::is_ // 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) +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); @@ -152,9 +154,10 @@ inline typename std::enable_if < std::is_same<T, uint8x16_t>::value || std::is_s // 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) +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); @@ -162,9 +165,10 @@ inline typename std::enable_if < std::is_same<T, float32x4_t>::value || std::is_ // 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) +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); @@ -175,10 +179,10 @@ inline typename std::enable_if < std::is_same<T, uint8x16_t>::value || std::is_s 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 res_idx_mask{0}; uint32x4_t mask_ones = vdupq_n_u32(0xFFFFFFFF); - if(op == ReductionOperation::ARG_IDX_MIN) + if (op == ReductionOperation::ARG_IDX_MIN) { auto pmin = calculate_min(vec_res_value); auto mask = wrapper::vceq(vec_res_value, wrapper::vcombine(pmin, pmin)); @@ -202,10 +206,10 @@ uint32_t calculate_vector_index(uint32x4x4_t vec_res_idx, T vec_res_value, Reduc 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 } }; + 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) + 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)); @@ -217,12 +221,18 @@ uint32_t calculate_vector_index_quantized(uint32x4x4_t vec_res_idx, T vec_res_va } // 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))); + 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); @@ -240,19 +250,19 @@ uint32_t calculate_vector_index_quantized(uint32x4x4_t vec_res_idx, T vec_res_va pmin = wrapper::vpmin(pmin, pmin); res = std::min(wrapper::vgetlane(pmin, 0), res); iter++; - } - while(iter < 4); + } 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) +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) + uint32x4x2_t mask{0}; + uint16x8_t mask_u16{0}; + if (op == ReductionOperation::ARG_IDX_MIN) { mask_u16 = wrapper::vcgt(b, a); } @@ -262,19 +272,14 @@ uint32x4x4_t calculate_index(uint32_t idx, float16x8_t a, float16x8_t b, uint32x } 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) + 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 - }; + 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; } @@ -297,10 +302,10 @@ inline float16x4_t calculate_max(float16x8_t in) template <> uint32_t calculate_vector_index(uint32x4x4_t vec_res_idx, float16x8_t vec_res_value, ReductionOperation op) { - uint32x4x2_t res_idx_mask{ 0 }; + uint32x4x2_t res_idx_mask{0}; uint32x4_t mask_ones = vdupq_n_u32(0xFFFFFFFF); uint16x8_t mask_u16; - if(op == ReductionOperation::ARG_IDX_MIN) + if (op == ReductionOperation::ARG_IDX_MIN) { auto pmin = calculate_min(vec_res_value); mask_u16 = wrapper::vceq(vec_res_value, wrapper::vcombine(pmin, pmin)); @@ -312,23 +317,24 @@ uint32_t calculate_vector_index(uint32x4x4_t vec_res_idx, float16x8_t vec_res_va } // 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))); + 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; - int iter = 0; + 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); + } while (iter < 2); return (res - 0xFFFFFFFF); } @@ -342,20 +348,9 @@ public: { // Set out window Window out_window(window); - out_window.set(Window::DimX, Window::Dimension(0, 0, 0)); - - // Get first input and output slices - Window in_slice = window.first_slice_window_1D(); - Window out_slice = out_window.first_slice_window_1D(); + out_window.set(Window::DimX, Window::Dimension(0, 1, 1)); - do - { - Iterator in(input, in_slice); - Iterator out(output, out_slice); - - f(in, out, in_slice, out_slice, *input->info(), op); - } - while(window.slide_window_slice_1D(in_slice) && out_window.slide_window_slice_1D(out_slice)); + f(window, out_window, input, output, op); } static void reduceY(const Window &window, const ITensor *input, ITensor *output, F f, const ReductionOperation op) { @@ -366,18 +361,7 @@ public: in_window.set(Window::DimY, Window::Dimension(0, 1, 1)); out_window.set(Window::DimY, Window::Dimension(0, output->info()->dimension(1), output->info()->dimension(1))); - // Get first input and output slices - Window in_slice = in_window.first_slice_window_2D(); - Window out_slice = out_window.first_slice_window_2D(); - - do - { - Iterator in(input, in_slice); - Iterator out(output, out_slice); - - f(in, out, in_slice, out_slice, *input->info(), 1, op); - } - while(in_window.slide_window_slice_2D(in_slice) && out_window.slide_window_slice_2D(out_slice)); + 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) { @@ -388,18 +372,7 @@ public: in_window.set(Window::DimZ, Window::Dimension(0, 1, 1)); out_window.set(Window::DimZ, Window::Dimension(0, output->info()->dimension(2), output->info()->dimension(2))); - // Get first input and output slices - Window in_slice = in_window.first_slice_window_3D(); - Window out_slice = out_window.first_slice_window_3D(); - - do - { - Iterator in(input, in_slice); - Iterator out(output, out_slice); - - f(in, out, in_slice, out_slice, *input->info(), 2, op); - } - while(in_window.slide_window_slice_3D(in_slice) && out_window.slide_window_slice_3D(out_slice)); + 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) { @@ -410,699 +383,1280 @@ public: in_window.set(3, Window::Dimension(0, 1, 1)); out_window.set(3, Window::Dimension(0, 1, 1)); - // Get first input and output slices - Window in_slice = in_window.first_slice_window_4D(); - Window out_slice = out_window.first_slice_window_4D(); - - do - { - Iterator in(input, in_slice); - Iterator out(output, out_slice); - - f(in, out, in_slice, out_slice, *input->info(), 3, op); - } - while(in_window.slide_window_slice_4D(in_slice) && out_window.slide_window_slice_4D(out_slice)); + f(in_window, out_window, input, output, 3, op); } }; template <typename T, int S> struct RedOpX { - /** NEON vector tag type. */ + /** SIMD vector tag type. */ using ExactTagType = typename wrapper::traits::neon_vector<T, S>::tag_type; - inline void operator()(Iterator &input, Iterator &output, Window &in_slice, Window &out_slice, const TensorInfo &in_info, const ReductionOperation op) + inline void operator()( + const Window &in_window, Window &out_window, const ITensor *in, ITensor *out, const ReductionOperation op) { - ARM_COMPUTE_UNUSED(out_slice); - 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 = *reinterpret_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 } }; + 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()); - execute_window_loop(in_slice, [&](const Coordinates & id) - { - const auto in_ptr = reinterpret_cast<const T *>(input.ptr()); - const auto vec_elements = wrapper::vloadq(in_ptr); + Window in_win_no_pad = in_window; + in_win_no_pad.set(Window::DimX, Window::Dimension(0, 1, 1)); - 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)>(id.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)>(id.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"); - } - }, - input); + Iterator input(in, in_win_no_pad); + Iterator output(out, out_window); - switch(op) - { - case ReductionOperation::SUM: - case ReductionOperation::SUM_SQUARE: - case ReductionOperation::MEAN_SUM: + execute_window_loop( + in_win_no_pad, + [&](const Coordinates &) { - auto carry_res = wrapper::vpadd(wrapper::vgethigh(vec_res_value), wrapper::vgetlow(vec_res_value)); - for(int i = 0; i < S / 4; ++i) + const auto input_ptr = reinterpret_cast<const T *>(input.ptr()); + + auto init_res_value = static_cast<T>(0.f); + switch (op) { - carry_res = wrapper::vpadd(carry_res, carry_res); + 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 res = wrapper::vgetlane(carry_res, 0); + auto vec_res_value = wrapper::vdup_n(init_res_value, ExactTagType{}); + uint32x4x4_t vec_res_idx{{0}}; - if(op == ReductionOperation::MEAN_SUM) + // Compute window_step_x elements per iteration + int x = window_start_x; + for (; x <= (window_end_x - window_step_x); x += window_step_x) { - res /= in_info.dimension(0); + 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"); + } } - *(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) + switch (op) { - res *= wrapper::vgetlane(carry_res, i); + 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"); } - *(reinterpret_cast<T *>(output.ptr())) = res; - break; - } - case ReductionOperation::ARG_IDX_MIN: - case ReductionOperation::ARG_IDX_MAX: - { - auto res = calculate_vector_index<decltype(vec_res_value)>(vec_res_idx, vec_res_value, op); - *(reinterpret_cast<uint32_t *>(output.ptr())) = res; - break; - } - case ReductionOperation::MIN: - { - *(reinterpret_cast<T *>(output.ptr())) = wrapper::vgetlane(calculate_min(vec_res_value), 0); - break; - } - case ReductionOperation::MAX: - { - *(reinterpret_cast<T *>(output.ptr())) = wrapper::vgetlane(calculate_max(vec_res_value), 0); - break; - } - default: - ARM_COMPUTE_ERROR("Not supported"); - } + }, + input, output); } }; template <typename T> struct RedOpX_quantized { - inline void operator()(Iterator &input, Iterator &output, Window &in_slice, Window &out_slice, const TensorInfo &in_info, const ReductionOperation op) + inline void operator()( + const Window &in_window, Window &out_window, const ITensor *in, ITensor *out, const ReductionOperation op) { - ARM_COMPUTE_UNUSED(out_slice); - 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(); - 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{}); + 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()); - 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)); + Window in_win_no_pad = in_window; + in_win_no_pad.set(Window::DimX, Window::Dimension(0, 1, 1)); - typename wrapper::traits::neon_vector<T, 16>::type vec_res_value = { 0 }; + Iterator input(in, in_win_no_pad); + Iterator output(out, out_window); - if(op == ReductionOperation::ARG_IDX_MAX || op == ReductionOperation::ARG_IDX_MIN || op == ReductionOperation::MIN || op == ReductionOperation::MAX) - { - vec_res_value = wrapper::vdup_n(*reinterpret_cast<T *>(input.ptr()), wrapper::traits::vector_128_tag{}); - } + const auto in_offset = static_cast<float>(iq_info.offset); + const float in_scale = iq_info.scale; - uint32x4x4_t vec_res_idx{ { 0 } }; - execute_window_loop(in_slice, [&](const Coordinates & id) - { - const auto vec_elements = wrapper::vloadq(reinterpret_cast<T *>(input.ptr())); - switch(op) + 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 &) { - 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: + 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) { - auto temp_vec_res_value = wrapper::vmin(vec_elements, vec_res_value); - vec_res_idx = calculate_index_quantized(id.x(), temp_vec_res_value, vec_res_value, vec_res_idx, op, 0); - vec_res_value = temp_vec_res_value; - break; + vec_res_value = wrapper::vdup_n(*input_ptr, wrapper::traits::vector_128_tag{}); } - case ReductionOperation::ARG_IDX_MAX: - { - auto temp_vec_res_value = wrapper::vmax(vec_elements, vec_res_value); - vec_res_idx = calculate_index_quantized(id.x(), temp_vec_res_value, vec_res_value, vec_res_idx, op, 0); - vec_res_value = temp_vec_res_value; - break; - } - case ReductionOperation::MIN: + + 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) { - vec_res_value = wrapper::vmin(vec_elements, vec_res_value); - break; + 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"); + } } - case ReductionOperation::MAX: + + switch (op) { - vec_res_value = wrapper::vmax(vec_elements, vec_res_value); - break; - } - default: - ARM_COMPUTE_ERROR("Not supported"); - } - }, - input); + 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)); - switch(op) - { - case ReductionOperation::ARG_IDX_MIN: - case ReductionOperation::ARG_IDX_MAX: - { - auto res = calculate_vector_index_quantized(vec_res_idx, vec_res_value, op); - *(reinterpret_cast<PromotedType *>(output.ptr())) = res; - break; - } - case ReductionOperation::MIN: - { - *(output.ptr()) = static_cast<T>(wrapper::vgetlane(calculate_min(vec_res_value), 0)); - break; - } - case ReductionOperation::MAX: - { - *(output.ptr()) = static_cast<T>(wrapper::vgetlane(calculate_max(vec_res_value), 0)); - 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); + // 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)); - 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) + { + 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)); - //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); - } + // 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)); - *reinterpret_cast<T *>(output.ptr()) = static_cast<T>(res); - break; - } - default: - { - 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); + // 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); - 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)); + 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); - if(op == ReductionOperation::MEAN_SUM) - { - res /= static_cast<int32_t>(in_info.dimension(0)); - } - else - { - // Subtract accumulated offsets - res -= (in_info.dimension(0) - 1) * iq_info.offset; + // 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"); } - *reinterpret_cast<T *>(output.ptr()) = utils::cast::saturate_cast<T>(res); - } - } + }, + input, output); } }; template <typename T, int S> struct RedOpYZW { - /** NEON vector tag type. */ + /** 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()(Iterator &input, Iterator &output, Window &in_slice, Window &out_slice, const TensorInfo &in_info, int axis, const ReductionOperation op) + inline void operator()(const Window &in_window, + Window &out_window, + const ITensor *in, + ITensor *out, + int axis, + const ReductionOperation op) { - ARM_COMPUTE_UNUSED(out_slice); - - execute_window_loop(in_slice, [&](const Coordinates &) - { - neon_vector vec_res_value = { 0 }; - switch(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 &) { - case ReductionOperation::ARG_IDX_MAX: - case ReductionOperation::ARG_IDX_MIN: - case ReductionOperation::MIN: - case ReductionOperation::MAX: - { - vec_res_value = wrapper::vloadq(reinterpret_cast<T *>(input.ptr())); - 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 } }; + const auto input_ptr = reinterpret_cast<T *>(input.ptr()); - for(unsigned int dim = 0; dim < in_info.dimension(axis); ++dim) - { - const T *in_ptr = reinterpret_cast<T *>(input.ptr() + in_info.strides_in_bytes()[axis] * dim); - const auto vec_elements = wrapper::vloadq(in_ptr); - switch(op) + // Compute window_step_x elements per iteration + int x = window_start_x; + for (; x <= (window_end_x - window_step_x); x += window_step_x) { - 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: + neon_vector vec_res_value = {0}; + switch (op) { - 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: + 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; + } } - case ReductionOperation::ARG_IDX_MAX: + uint32x4x4_t vec_res_idx{{0}}; + + for (unsigned int dim = 0; dim < in_info.dimension(axis); ++dim) { - 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; + 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"); + } } - case ReductionOperation::MIN: + + if (op == ReductionOperation::MEAN_SUM) { - vec_res_value = wrapper::vmin(vec_elements, vec_res_value); - break; + 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); } - case ReductionOperation::MAX: + + if (op == ReductionOperation::ARG_IDX_MIN || op == ReductionOperation::ARG_IDX_MAX) { - vec_res_value = wrapper::vmax(vec_elements, vec_res_value); - break; + 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); } - 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()), vec_res_idx.val[0]); -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - if(std::is_same<T, float16_t>::value) + // Compute left-over elements + for (; x < window_end_x; ++x) { - wrapper::vstore(reinterpret_cast<uint32_t *>(output.ptr()) + 4, vec_res_idx.val[1]); + 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; + } } -#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - } - else - { - wrapper::vstore(reinterpret_cast<T *>(output.ptr()), vec_res_value); - } - }, - input, output); + }, + input, output); } }; template <typename T, int S, int axis, ReductionOperation op> struct RedOpYZW_complex { - /** NEON vector tag type. */ + /** 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()(Iterator &input, Iterator &output, Window &in_slice, Window &out_slice, const TensorInfo &in_info, int, const ReductionOperation) + inline void operator()( + const Window &in_window, Window &out_window, const ITensor *in, ITensor *out, int, const ReductionOperation) { - ARM_COMPUTE_UNUSED(out_slice); 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}; - const size_t stride_z = in_info.strides_in_bytes()[axis]; + 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{}); - execute_window_loop(in_slice, [&](const Coordinates &) - { - neon_vector vec_res_value_0 = { 0 }; - neon_vector vec_res_value_1 = { 0 }; + 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); - 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{}); + const auto vec_elements_0 = wrapper::vloadq(in_ptr_0); + const auto vec_elements_1 = wrapper::vloadq(in_ptr_1); - for(unsigned int dim = 0; dim < in_info.dimension(axis); ++dim) - { - T *in_ptr_0; - T *in_ptr_1; - switch(axis) - { - case 2: - in_ptr_0 = reinterpret_cast<T *>(input.ptr() + stride_z * dim); - in_ptr_1 = reinterpret_cast<T *>(input.ptr() + 16 + stride_z * dim); - break; - default: - ARM_COMPUTE_ERROR("Not supported"); - } - const auto vec_elements_0 = wrapper::vloadq(in_ptr_0); - const auto vec_elements_1 = wrapper::vloadq(in_ptr_1); - - switch(op) - { - case ReductionOperation::SUM: 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); - break; - default: - ARM_COMPUTE_ERROR("Not supported"); + } + + wrapper::vstore(out_ptr, vec_res_value_0); + wrapper::vstore(out_ptr + 4, vec_res_value_1); } - } - wrapper::vstore(reinterpret_cast<T *>(output.ptr()), vec_res_value_0); - wrapper::vstore(reinterpret_cast<T *>(output.ptr() + 16), 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; - }, - input, output); + 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()(Iterator &input, Iterator &output, Window &in_slice, Window &out_slice, const TensorInfo &in_info, int axis, const ReductionOperation op) + inline void operator()(const Window &in_window, + Window &out_window, + const ITensor *in, + ITensor *out, + int axis, + const ReductionOperation op) { - ARM_COMPUTE_UNUSED(out_slice); - + 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 UniformQuantizationInfo iq_info = in_info.quantization_info().uniform(); + const auto oq_info = out->info()->quantization_info().uniform(); - execute_window_loop(in_slice, [&](const Coordinates &) - { - uint32x4x4_t vec_res_idx{ { 0 } }; - auto vec_res_value1 = wrapper::vdup_n(static_cast<PromotedType>(0), wrapper::traits::vector_128_tag{}); - auto vec_res_value2 = wrapper::vdup_n(static_cast<PromotedType>(0), wrapper::traits::vector_128_tag{}); - auto vec_res_value3 = wrapper::vdup_n(static_cast<PromotedType>(0), wrapper::traits::vector_128_tag{}); - auto vec_res_value4 = wrapper::vdup_n(static_cast<PromotedType>(0), wrapper::traits::vector_128_tag{}); + 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)); - auto vec_res_value1_f = wrapper::vdup_n(static_cast<float>(1), wrapper::traits::vector_128_tag{}); - auto vec_res_value2_f = wrapper::vdup_n(static_cast<float>(1), wrapper::traits::vector_128_tag{}); - auto vec_res_value3_f = wrapper::vdup_n(static_cast<float>(1), wrapper::traits::vector_128_tag{}); - auto vec_res_value4_f = wrapper::vdup_n(static_cast<float>(1), wrapper::traits::vector_128_tag{}); + const float A = in_scale / (out_scale * num_elements); + const float B = out_offset - (in_scale * in_offset) / (out_scale); - auto vec_res_value = wrapper::vloadq(reinterpret_cast<T *>(input.ptr())); + 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{}); - for(unsigned int index_dim = 0; index_dim < in_info.dimension(axis); ++index_dim) + execute_window_loop( + in_win_no_pad, + [&](const Coordinates &) { - const T *in_ptr = reinterpret_cast<T *>(input.ptr()) + in_info.strides_in_bytes()[axis] * index_dim; - const auto vec_elements = wrapper::vloadq(in_ptr); - switch(op) + 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) { - 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: + 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) { - vec_res_value = wrapper::vmin(vec_elements, vec_res_value); - break; + 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"); + } } - case ReductionOperation::MAX: + + switch (op) { - vec_res_value = wrapper::vmax(vec_elements, vec_res_value); - break; + 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"); } - default: - ARM_COMPUTE_ERROR("Not supported"); } - } - - if(op == ReductionOperation::MEAN_SUM) - { - const auto vec_width_inv = wrapper::vinv(wrapper::vdup_n(static_cast<float>(in_info.dimension(axis)), wrapper::traits::vector_128_tag{})); - vec_res_value1_f = wrapper::vmul(wrapper::vcvt<float>(vec_res_value1), vec_width_inv); - vec_res_value2_f = wrapper::vmul(wrapper::vcvt<float>(vec_res_value2), vec_width_inv); - vec_res_value3_f = wrapper::vmul(wrapper::vcvt<float>(vec_res_value3), vec_width_inv); - vec_res_value4_f = wrapper::vmul(wrapper::vcvt<float>(vec_res_value4), vec_width_inv); - - 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); - } - else if(op == 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); - } - if(op == ReductionOperation::ARG_IDX_MIN || op == ReductionOperation::ARG_IDX_MAX) - { - wrapper::vstore(reinterpret_cast<uint32_t *>(output.ptr()), vec_res_idx.val[0]); - wrapper::vstore(reinterpret_cast<uint32_t *>(output.ptr()) + 4, vec_res_idx.val[1]); - wrapper::vstore(reinterpret_cast<uint32_t *>(output.ptr()) + 8, vec_res_idx.val[2]); - wrapper::vstore(reinterpret_cast<uint32_t *>(output.ptr()) + 12, vec_res_idx.val[3]); - } - else if(op == ReductionOperation::MIN || op == ReductionOperation::MAX) - { - wrapper::vstore(reinterpret_cast<T *>(output.ptr()), vec_res_value); - } - else - { - if(op == ReductionOperation::SUM) + // Compute left-over elements + for (; x < window_end_x; ++x) { - // Subtract offsets - auto offsets = vdupq_n_s32((in_info.dimension(axis) - 1) * iq_info.offset); + float res_value = 0.f; + int32_t res_value_q = 0; - 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)); + 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; - combine_and_store<T>(temp16x8t_1, temp16x8t_2, output); - } - else - { - 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)); + 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"); + } + } - wrapper::vstore(reinterpret_cast<T *>(output.ptr()), res); + 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); + }, + input, output); } }; -void reduce_op(const Window &window, const ITensor *input, ITensor *output, unsigned int axis, const ReductionOperation op) +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) + if (is_complex) { - switch(axis) + switch (axis) { case 2: - switch(input->info()->data_type()) + switch (input->info()->data_type()) { case DataType::F32: - switch(op) + 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); + 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"); } @@ -1112,38 +1666,60 @@ void reduce_op(const Window &window, const ITensor *input, ITensor *output, unsi default: ARM_COMPUTE_ERROR("Not supported"); } + return; } - switch(axis) + switch (axis) { case 0: - switch(input->info()->data_type()) + { + switch (input->info()->data_type()) { case DataType::QASYMM8: - return Reducer<RedOpX_quantized<uint8_t>>::reduceX(window, input, output, RedOpX_quantized<uint8_t>(), op); + { + 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); + { + 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()) + switch (input->info()->data_type()) { case DataType::QASYMM8: - return Reducer<RedOpYZW_quantized<uint8_t>>::reduceY(window, input, output, RedOpYZW_quantized<uint8_t>(), op); + { + 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); + { + 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); + 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); @@ -1153,15 +1729,18 @@ void reduce_op(const Window &window, const ITensor *input, ITensor *output, unsi ARM_COMPUTE_ERROR("Not supported"); } case 2: - switch(input->info()->data_type()) + switch (input->info()->data_type()) { case DataType::QASYMM8: - return Reducer<RedOpYZW_quantized<uint8_t>>::reduceZ(window, input, output, RedOpYZW_quantized<uint8_t>(), op); + 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); + 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); + 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); @@ -1171,15 +1750,18 @@ void reduce_op(const Window &window, const ITensor *input, ITensor *output, unsi ARM_COMPUTE_ERROR("Not supported"); } case 3: - switch(input->info()->data_type()) + switch (input->info()->data_type()) { case DataType::QASYMM8: - return Reducer<RedOpYZW_quantized<uint8_t>>::reduceW(window, input, output, RedOpYZW_quantized<uint8_t>(), op); + 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); + 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); + 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); @@ -1200,9 +1782,10 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, u ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input); - if(input->num_channels() == 1) + 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); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8_SIGNED, DataType::QASYMM8, + DataType::S32, DataType::F16, DataType::F32); } else { @@ -1211,16 +1794,16 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, u 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 >= 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) + 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) + if (!is_arg_min_max) { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output); ARM_COMPUTE_RETURN_ERROR_ON(input->num_channels() != output->num_channels()); } else @@ -1228,76 +1811,59 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, u 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); + 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{}; } - -std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, unsigned int axis, ReductionOperation op) -{ - // Calculate output shape and set if empty - const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_reduced_shape(input->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->data_type(); - auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape).set_data_type(output_data_type).reset_padding().set_is_resizable(true)); - - unsigned int num_elems_processed_per_iteration = 16 / data_size_from_type(input->data_type()); - - // Configure kernel window - Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration)); - AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration); - AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration); - - bool window_changed = update_window_and_padding(win, input_access, output_access); - output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); - - Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; - - return std::make_tuple(err, win); -} } // namespace NEReductionOperationKernel::NEReductionOperationKernel() - : _input(nullptr), _output(nullptr), _reduction_axis(0), _op(ReductionOperation::SUM_SQUARE), _border_size() + : _input(nullptr), _output(nullptr), _reduction_axis(0), _op(ReductionOperation::SUM_SQUARE) { } -BorderSize NEReductionOperationKernel::border_size() const -{ - return _border_size; -} - -void NEReductionOperationKernel::configure(const ITensor *input, ITensor *output, unsigned int axis, ReductionOperation op) +void NEReductionOperationKernel::configure(const ITensor *input, + ITensor *output, + unsigned int axis, + ReductionOperation op) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), axis, op)); - unsigned int num_elems_processed_per_iteration = 16 / data_size_from_type(input->info()->data_type()); - _input = input; _output = output; - _border_size = (axis == 0) ? BorderSize(0, num_elems_processed_per_iteration - (input->info()->dimension(0) % num_elems_processed_per_iteration), 0, 0) : BorderSize(); _op = op; _reduction_axis = axis; // Configure kernel window - auto win_config = validate_and_configure_window(_input->info(), _output->info(), axis, op); - - ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config)); + Window win = calculate_max_window(*input->info(), Steps()); + INEKernel::configure(win); - INEKernel::configure(std::get<1>(win_config)); + // 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) +Status NEReductionOperationKernel::validate(const ITensorInfo *input, + const ITensorInfo *output, + unsigned int axis, + ReductionOperation op) { ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, axis, op)); - ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), output->clone().get(), axis, op))); return Status{}; } |