Fix ReductionLayer FP16 for armv8a multi_isa builds
- Enable FP16 code when building multi_isa for armv8a architecture in
order to run on higher architectures e.g. 8.2, 8.6.
- When running this build on v8 the validation will stop it flagging
that the arch does not support FP16.
Resolves: COMPMID-7013
Signed-off-by: Ramy Elgammal <ramy.elgammal@arm.com>
Change-Id: I0d445e2fade31c1156d7a6e142edf2a7f84d3622
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/11544
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Pablo Marquez Tello <pablo.tello@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>
diff --git a/Android.bp b/Android.bp
index ab554a8..c6e89b0 100644
--- a/Android.bp
+++ b/Android.bp
@@ -557,6 +557,11 @@
"src/cpu/kernels/range/generic/neon/fp16.cpp",
"src/cpu/kernels/range/generic/neon/fp32.cpp",
"src/cpu/kernels/range/generic/neon/integer.cpp",
+ "src/cpu/kernels/reduction_layer/generic/neon/fp16.cpp",
+ "src/cpu/kernels/reduction_layer/generic/neon/fp32.cpp",
+ "src/cpu/kernels/reduction_layer/generic/neon/integer.cpp",
+ "src/cpu/kernels/reduction_layer/generic/neon/qasymm8.cpp",
+ "src/cpu/kernels/reduction_layer/generic/neon/qasymm8_signed.cpp",
"src/cpu/kernels/roialign/generic/neon/fp16.cpp",
"src/cpu/kernels/roialign/generic/neon/fp32.cpp",
"src/cpu/kernels/roialign/generic/neon/qasymm8.cpp",
diff --git a/docs/user_guide/release_version_and_change_log.dox b/docs/user_guide/release_version_and_change_log.dox
index ce183d2..9c3eb8e 100644
--- a/docs/user_guide/release_version_and_change_log.dox
+++ b/docs/user_guide/release_version_and_change_log.dox
@@ -43,6 +43,7 @@
v24.05 Public major release
- Add @ref CLScatter operator for FP32/16, S32/16/8, U32/16/8 data types
+ - Fix @ref NEReductionOperationKernel FP16 for armv8a multi_isa builds
v24.04 Public major release
- Add Bfloat16 data type support for @ref NEMatMul.
diff --git a/filelist.json b/filelist.json
index eed9ccd..77656bc 100644
--- a/filelist.json
+++ b/filelist.json
@@ -2115,7 +2115,14 @@
"common": [
"src/core/NEON/kernels/NEReductionOperationKernel.cpp",
"src/runtime/NEON/functions/NEReductionOperation.cpp"
- ]
+ ],
+ "neon":{
+ "fp32":["src/cpu/kernels/reduction_layer/generic/neon/fp32.cpp"],
+ "fp16":["src/cpu/kernels/reduction_layer/generic/neon/fp16.cpp"],
+ "integer":["src/cpu/kernels/reduction_layer/generic/neon/integer.cpp"],
+ "qasymm8":["src/cpu/kernels/reduction_layer/generic/neon/qasymm8.cpp"],
+ "qasymm8_signed":["src/cpu/kernels/reduction_layer/generic/neon/qasymm8_signed.cpp"]
+ }
}
},
"Reorg": {
diff --git a/src/BUILD.bazel b/src/BUILD.bazel
index 8b8842d..2648a96 100644
--- a/src/BUILD.bazel
+++ b/src/BUILD.bazel
@@ -821,6 +821,11 @@
"cpu/kernels/range/generic/neon/fp16.cpp",
"cpu/kernels/range/generic/neon/fp32.cpp",
"cpu/kernels/range/generic/neon/integer.cpp",
+ "cpu/kernels/reduction_layer/generic/neon/fp16.cpp",
+ "cpu/kernels/reduction_layer/generic/neon/fp32.cpp",
+ "cpu/kernels/reduction_layer/generic/neon/integer.cpp",
+ "cpu/kernels/reduction_layer/generic/neon/qasymm8.cpp",
+ "cpu/kernels/reduction_layer/generic/neon/qasymm8_signed.cpp",
"cpu/kernels/roialign/generic/neon/fp16.cpp",
"cpu/kernels/roialign/generic/neon/fp32.cpp",
"cpu/kernels/roialign/generic/neon/qasymm8.cpp",
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 74be9e5..e47b5cb 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -812,6 +812,11 @@
cpu/kernels/range/generic/neon/fp16.cpp
cpu/kernels/range/generic/neon/fp32.cpp
cpu/kernels/range/generic/neon/integer.cpp
+ cpu/kernels/reduction_layer/generic/neon/fp16.cpp
+ cpu/kernels/reduction_layer/generic/neon/fp32.cpp
+ cpu/kernels/reduction_layer/generic/neon/integer.cpp
+ cpu/kernels/reduction_layer/generic/neon/qasymm8.cpp
+ cpu/kernels/reduction_layer/generic/neon/qasymm8_signed.cpp
cpu/kernels/roialign/generic/neon/fp16.cpp
cpu/kernels/roialign/generic/neon/fp32.cpp
cpu/kernels/roialign/generic/neon/qasymm8.cpp
diff --git a/src/core/NEON/kernels/NEReductionOperationKernel.cpp b/src/core/NEON/kernels/NEReductionOperationKernel.cpp
index 455d604..5380e6c 100644
--- a/src/core/NEON/kernels/NEReductionOperationKernel.cpp
+++ b/src/core/NEON/kernels/NEReductionOperationKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017-2023 Arm Limited.
+ * Copyright (c) 2017-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -31,1747 +31,221 @@
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/core/Validate.h"
+#include "src/core/common/Registrars.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_neon.h>
+#include "src/cpu/kernels/reduction_layer/generic/neon/list.h"
namespace arm_compute
{
-namespace
+
+void NEReductionOperationKernel::reduce_op()
{
-// Helper function that calls vqmovun/vqmvn, vcombine and vstore, allows templating of RedOpYZW_quantized
-template <typename T>
-void combine_and_store(int16x8_t t1, int16x8_t t2, Iterator &output, int offset = 0)
-{
- if (std::is_same<T, uint8_t>::value)
- {
- auto res = wrapper::vcombine(wrapper::vqmovun(t1), wrapper::vqmovun(t2));
- wrapper::vstore(output.ptr() + offset, res);
- }
- else
- {
- auto res = wrapper::vcombine(wrapper::vqmovn(t1), wrapper::vqmovn(t2));
- wrapper::vstore(reinterpret_cast<int8_t *>(output.ptr() + offset), res);
- }
-}
-
-template <typename T>
-uint32x4x4_t calculate_index(uint32_t idx, T a, T b, uint32x4x4_t c, ReductionOperation op, int axis)
-{
- uint32x4_t mask{0};
- if (op == ReductionOperation::ARG_IDX_MIN)
- {
- mask = wrapper::vcgt(b, a);
- }
- else
- {
- mask = wrapper::vclt(b, a);
- }
-
- uint32x4_t vec_idx = {idx, idx + 1, idx + 2, idx + 3};
- if (axis != 0)
- {
- vec_idx = wrapper::vdup_n(idx, wrapper::traits::vector_128_tag{});
- }
- uint32x4x4_t res = {{wrapper::vbsl(mask, vec_idx, c.val[0]), 0, 0, 0}};
-
- return res;
-}
-
-template <typename T>
-uint32x4x4_t calculate_index_quantized(uint32_t idx, T a, T b, uint32x4x4_t c, ReductionOperation op, int axis)
-{
- uint32x4x4_t mask{{0}};
- uint8x16_t mask_u8{0};
- if (op == ReductionOperation::ARG_IDX_MIN)
- {
- mask_u8 = wrapper::vcgt(b, a);
- }
- else
- {
- mask_u8 = wrapper::vclt(b, a);
- }
- auto wide_u16_1 =
- wrapper::vorr(vshll_n_u8(wrapper::vgetlow(mask_u8), 8), wrapper::vmovl(wrapper::vgetlow(mask_u8)));
- auto wide_u16_2 =
- wrapper::vorr(vshll_n_u8(wrapper::vgethigh(mask_u8), 8), wrapper::vmovl(wrapper::vgethigh(mask_u8)));
- mask.val[0] =
- wrapper::vorr(vshll_n_u16(wrapper::vgetlow(wide_u16_1), 16), wrapper::vmovl(wrapper::vgetlow(wide_u16_1)));
- mask.val[1] =
- wrapper::vorr(vshll_n_u16(wrapper::vgethigh(wide_u16_1), 16), wrapper::vmovl(wrapper::vgethigh(wide_u16_1)));
- mask.val[2] =
- wrapper::vorr(vshll_n_u16(wrapper::vgetlow(wide_u16_2), 16), wrapper::vmovl(wrapper::vgetlow(wide_u16_2)));
- mask.val[3] =
- wrapper::vorr(vshll_n_u16(wrapper::vgethigh(wide_u16_2), 16), wrapper::vmovl(wrapper::vgethigh(wide_u16_2)));
-
- uint32x4x4_t vec_idx = {{{idx + 0, idx + 1, idx + 2, idx + 3},
- {idx + 4, idx + 5, idx + 6, idx + 7},
- {idx + 8, idx + 9, idx + 10, idx + 11},
- {idx + 12, idx + 13, idx + 14, idx + 15}}};
- if (axis != 0)
- {
- vec_idx.val[0] = wrapper::vdup_n(idx, wrapper::traits::vector_128_tag{});
- vec_idx.val[1] = wrapper::vdup_n(idx, wrapper::traits::vector_128_tag{});
- vec_idx.val[2] = wrapper::vdup_n(idx, wrapper::traits::vector_128_tag{});
- vec_idx.val[3] = wrapper::vdup_n(idx, wrapper::traits::vector_128_tag{});
- }
- uint32x4x4_t res = {
- {vbslq_u32(mask.val[0], vec_idx.val[0], c.val[0]), vbslq_u32(mask.val[1], vec_idx.val[1], c.val[1]),
- vbslq_u32(mask.val[2], vec_idx.val[2], c.val[2]), vbslq_u32(mask.val[3], vec_idx.val[3], c.val[3])}};
-
- return res;
-}
-
-// Helper function to calculate the minimum value of the input vector. All the elements in the output vector contain the min value.
-template <typename T>
-inline typename std::enable_if<
- std::is_same<T, float32x4_t>::value || std::is_same<T, int32x4_t>::value,
- typename std::conditional<std::is_same<T, float32x4_t>::value, float32x2_t, int32x2_t>::type>::type
-calculate_min(T in)
-{
- auto pmin = wrapper::vpmin(wrapper::vgethigh(in), wrapper::vgetlow(in));
- return wrapper::vpmin(pmin, pmin);
-}
-
-// Helper function to calculate the minimum value of the input vector. All the elements in the output vector contain the min value.
-template <typename T>
-inline typename std::enable_if<
- std::is_same<T, uint8x16_t>::value || std::is_same<T, int8x16_t>::value,
- typename std::conditional<std::is_same<T, uint8x16_t>::value, uint8x8_t, int8x8_t>::type>::type
-calculate_min(T in)
-{
- auto pmin = wrapper::vpmin(wrapper::vgethigh(in), wrapper::vgetlow(in));
- pmin = wrapper::vpmin(pmin, pmin);
- pmin = wrapper::vpmin(pmin, pmin);
- return wrapper::vpmin(pmin, pmin);
-}
-
-// Helper function to calculate the maximum value of the input vector. All the elements in the output vector contain the max value.
-template <typename T>
-inline typename std::enable_if<
- std::is_same<T, float32x4_t>::value || std::is_same<T, int32x4_t>::value,
- typename std::conditional<std::is_same<T, float32x4_t>::value, float32x2_t, int32x2_t>::type>::type
-calculate_max(T in)
-{
- auto pmax = wrapper::vpmax(wrapper::vgethigh(in), wrapper::vgetlow(in));
- return wrapper::vpmax(pmax, pmax);
-}
-
-// Helper function to calculate the maximum value of the input vector. All the elements in the output vector contain the max value.
-template <typename T>
-inline typename std::enable_if<
- std::is_same<T, uint8x16_t>::value || std::is_same<T, int8x16_t>::value,
- typename std::conditional<std::is_same<T, uint8x16_t>::value, uint8x8_t, int8x8_t>::type>::type
-calculate_max(T in)
-{
- auto pmax = wrapper::vpmax(wrapper::vgethigh(in), wrapper::vgetlow(in));
- pmax = wrapper::vpmax(pmax, pmax);
- pmax = wrapper::vpmax(pmax, pmax);
- return wrapper::vpmax(pmax, pmax);
-}
-
-template <typename T>
-uint32_t calculate_vector_index(uint32x4x4_t vec_res_idx, T vec_res_value, ReductionOperation op)
-{
- uint32x4_t res_idx_mask{0};
- uint32x4_t mask_ones = vdupq_n_u32(0xFFFFFFFF);
-
- if (op == ReductionOperation::ARG_IDX_MIN)
- {
- auto pmin = calculate_min(vec_res_value);
- auto mask = wrapper::vceq(vec_res_value, wrapper::vcombine(pmin, pmin));
- res_idx_mask = wrapper::vand(vec_res_idx.val[0], mask);
- }
- else
- {
- auto pmax = calculate_max(vec_res_value);
- auto mask = wrapper::vceq(vec_res_value, wrapper::vcombine(pmax, pmax));
- res_idx_mask = wrapper::vand(vec_res_idx.val[0], mask);
- }
-
- res_idx_mask = wrapper::vadd(res_idx_mask, mask_ones);
- auto pmin = wrapper::vpmin(wrapper::vgethigh(res_idx_mask), wrapper::vgetlow(res_idx_mask));
- pmin = wrapper::vpmin(pmin, pmin);
- uint32_t res = wrapper::vgetlane(pmin, 0);
-
- return (res - 0xFFFFFFFF);
-}
-
-template <typename T>
-uint32_t calculate_vector_index_quantized(uint32x4x4_t vec_res_idx, T vec_res_value, ReductionOperation op)
-{
- uint32x4x4_t res_idx_mask{{0}};
- uint32x4_t mask_ones = vdupq_n_u32(0xFFFFFFFF);
- uint8x16_t mask_u8{0};
- if (op == ReductionOperation::ARG_IDX_MIN)
- {
- auto pmin = calculate_min(vec_res_value);
- mask_u8 = wrapper::vceq(vec_res_value, wrapper::vcombine(pmin, pmin));
- }
- else
- {
- auto pmax = calculate_max(vec_res_value);
- mask_u8 = wrapper::vceq(vec_res_value, wrapper::vcombine(pmax, pmax));
- }
-
- // Widen vectors
- auto wide_u16_1 =
- wrapper::vorr(vshll_n_u8(wrapper::vgetlow(mask_u8), 8), wrapper::vmovl(wrapper::vgetlow(mask_u8)));
- auto wide_u16_2 =
- wrapper::vorr(vshll_n_u8(wrapper::vgethigh(mask_u8), 8), wrapper::vmovl(wrapper::vgethigh(mask_u8)));
- auto wide_u32_1 =
- wrapper::vorr(vshll_n_u16(wrapper::vgetlow(wide_u16_1), 16), wrapper::vmovl(wrapper::vgetlow(wide_u16_1)));
- auto wide_u32_2 =
- wrapper::vorr(vshll_n_u16(wrapper::vgethigh(wide_u16_1), 16), wrapper::vmovl(wrapper::vgethigh(wide_u16_1)));
- auto wide_u32_3 =
- wrapper::vorr(vshll_n_u16(wrapper::vgetlow(wide_u16_2), 16), wrapper::vmovl(wrapper::vgetlow(wide_u16_2)));
- auto wide_u32_4 =
- wrapper::vorr(vshll_n_u16(wrapper::vgethigh(wide_u16_2), 16), wrapper::vmovl(wrapper::vgethigh(wide_u16_2)));
- res_idx_mask.val[0] = wrapper::vand(vec_res_idx.val[0], wide_u32_1);
- res_idx_mask.val[1] = wrapper::vand(vec_res_idx.val[1], wide_u32_2);
- res_idx_mask.val[2] = wrapper::vand(vec_res_idx.val[2], wide_u32_3);
- res_idx_mask.val[3] = wrapper::vand(vec_res_idx.val[3], wide_u32_4);
- res_idx_mask.val[0] = wrapper::vadd(res_idx_mask.val[0], mask_ones);
- res_idx_mask.val[1] = wrapper::vadd(res_idx_mask.val[1], mask_ones);
- res_idx_mask.val[2] = wrapper::vadd(res_idx_mask.val[2], mask_ones);
- res_idx_mask.val[3] = wrapper::vadd(res_idx_mask.val[3], mask_ones);
-
- uint32_t res = 0xFFFFFFFF;
- int iter = 0;
- do
- {
- auto pmin = wrapper::vpmin(wrapper::vgethigh(res_idx_mask.val[iter]), wrapper::vgetlow(res_idx_mask.val[iter]));
- pmin = wrapper::vpmin(pmin, pmin);
- res = std::min(wrapper::vgetlane(pmin, 0), res);
- iter++;
- } while (iter < 4);
-
- return (res - 0xFFFFFFFF);
-}
-
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-template <>
-uint32x4x4_t
-calculate_index(uint32_t idx, float16x8_t a, float16x8_t b, uint32x4x4_t c, ReductionOperation op, int axis)
-{
- uint32x4x2_t mask{0};
- uint16x8_t mask_u16{0};
- if (op == ReductionOperation::ARG_IDX_MIN)
- {
- mask_u16 = wrapper::vcgt(b, a);
- }
- else
- {
- mask_u16 = wrapper::vclt(b, a);
- }
- mask.val[0] = wrapper::vmovl(wrapper::vgetlow(mask_u16));
- mask.val[1] = wrapper::vmovl(wrapper::vgethigh(mask_u16));
- uint32x4x2_t vec_idx = {{{idx + 0, idx + 1, idx + 2, idx + 3}, {idx + 4, idx + 5, idx + 6, idx + 7}}};
- if (axis != 0)
- {
- vec_idx.val[0] = wrapper::vdup_n(idx, wrapper::traits::vector_128_tag{});
- vec_idx.val[1] = wrapper::vdup_n(idx, wrapper::traits::vector_128_tag{});
- }
- uint32x4x4_t res = {wrapper::vbsl(mask.val[0], vec_idx.val[0], c.val[0]),
- wrapper::vbsl(mask.val[1], vec_idx.val[1], c.val[1]), 0, 0};
-
- return res;
-}
-
-// Helper function to calculate the minimum value of the input vector. All the elements in the output vector contain the min value.
-inline float16x4_t calculate_min(float16x8_t in)
-{
- auto pmin = wrapper::vpmin(wrapper::vgethigh(in), wrapper::vgetlow(in));
- pmin = wrapper::vpmin(pmin, pmin);
- return wrapper::vpmin(pmin, pmin);
-}
-// Helper function to calculate the maximum value of the input vector. All the elements in the output vector contain the max value.
-inline float16x4_t calculate_max(float16x8_t in)
-{
- auto pmax = wrapper::vpmax(wrapper::vgethigh(in), wrapper::vgetlow(in));
- pmax = wrapper::vpmax(pmax, pmax);
- return wrapper::vpmax(pmax, pmax);
-}
-
-template <>
-uint32_t calculate_vector_index(uint32x4x4_t vec_res_idx, float16x8_t vec_res_value, ReductionOperation op)
-{
- uint32x4x2_t res_idx_mask{0};
- uint32x4_t mask_ones = vdupq_n_u32(0xFFFFFFFF);
- uint16x8_t mask_u16;
- if (op == ReductionOperation::ARG_IDX_MIN)
- {
- auto pmin = calculate_min(vec_res_value);
- mask_u16 = wrapper::vceq(vec_res_value, wrapper::vcombine(pmin, pmin));
- }
- else
- {
- auto pmax = calculate_max(vec_res_value);
- mask_u16 = wrapper::vceq(vec_res_value, wrapper::vcombine(pmax, pmax));
- }
-
- // Widen vectors
- auto wide_u32_1 =
- wrapper::vorr(vshll_n_u16(wrapper::vgetlow(mask_u16), 8), wrapper::vmovl(wrapper::vgetlow(mask_u16)));
- auto wide_u32_2 =
- wrapper::vorr(vshll_n_u16(wrapper::vgethigh(mask_u16), 8), wrapper::vmovl(wrapper::vgethigh(mask_u16)));
- res_idx_mask.val[0] = wrapper::vand(vec_res_idx.val[0], wide_u32_1);
- res_idx_mask.val[1] = wrapper::vand(vec_res_idx.val[1], wide_u32_2);
- res_idx_mask.val[0] = wrapper::vadd(res_idx_mask.val[0], mask_ones);
- res_idx_mask.val[1] = wrapper::vadd(res_idx_mask.val[1], mask_ones);
-
- uint32_t res = 0xFFFFFFFF;
- uint32_t iter = 0;
- do
- {
- auto pmin = wrapper::vpmin(wrapper::vgethigh(res_idx_mask.val[iter]), wrapper::vgetlow(res_idx_mask.val[iter]));
- pmin = wrapper::vpmin(pmin, pmin);
- res = std::min(wrapper::vgetlane(pmin, 0), res);
- iter++;
- } while (iter < 2);
-
- return (res - 0xFFFFFFFF);
-}
-#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-
-template <class F>
-class Reducer
-{
-public:
- static void reduceX(const Window &window, const ITensor *input, ITensor *output, F f, const ReductionOperation op)
- {
- // Set out window
- Window out_window(window);
- out_window.set(Window::DimX, Window::Dimension(0, 1, 1));
-
- f(window, out_window, input, output, op);
- }
- static void reduceY(const Window &window, const ITensor *input, ITensor *output, F f, const ReductionOperation op)
- {
- // Set in window
- Window in_window(window);
- Window out_window(window);
-
- in_window.set(Window::DimY, Window::Dimension(0, 1, 1));
- out_window.set(Window::DimY, Window::Dimension(0, output->info()->dimension(1), output->info()->dimension(1)));
-
- f(in_window, out_window, input, output, 1, op);
- }
- static void reduceZ(const Window &window, const ITensor *input, ITensor *output, F f, const ReductionOperation op)
- {
- // Set in window
- Window in_window(window);
- Window out_window(window);
-
- in_window.set(Window::DimZ, Window::Dimension(0, 1, 1));
- out_window.set(Window::DimZ, Window::Dimension(0, output->info()->dimension(2), output->info()->dimension(2)));
-
- f(in_window, out_window, input, output, 2, op);
- }
- static void reduceW(const Window &window, const ITensor *input, ITensor *output, F f, const ReductionOperation op)
- {
- // Set in/out window
- Window in_window(window);
- Window out_window(window);
-
- in_window.set(3, Window::Dimension(0, 1, 1));
- out_window.set(3, Window::Dimension(0, 1, 1));
-
- f(in_window, out_window, input, output, 3, op);
- }
-};
-
-template <typename T, int S>
-struct RedOpX
-{
- /** SIMD vector tag type. */
- using ExactTagType = typename wrapper::traits::neon_vector<T, S>::tag_type;
-
- inline void operator()(
- const Window &in_window, Window &out_window, const ITensor *in, ITensor *out, const ReductionOperation op)
- {
- const size_t input_dim_0 = in->info()->dimension(0);
- const int window_step_x = 16 / sizeof(T);
- const auto window_start_x = static_cast<int>(in_window.x().start());
- const auto window_end_x = static_cast<int>(in_window.x().end());
-
- Window in_win_no_pad = in_window;
- in_win_no_pad.set(Window::DimX, Window::Dimension(0, 1, 1));
-
- Iterator input(in, in_win_no_pad);
- Iterator output(out, out_window);
-
- execute_window_loop(
- in_win_no_pad,
- [&](const Coordinates &)
- {
- const auto input_ptr = reinterpret_cast<const T *>(input.ptr());
-
- auto init_res_value = static_cast<T>(0.f);
- switch (op)
- {
- case ReductionOperation::ARG_IDX_MAX:
- case ReductionOperation::ARG_IDX_MIN:
- case ReductionOperation::MIN:
- case ReductionOperation::MAX:
- {
- init_res_value = static_cast<T>(*input_ptr);
- break;
- }
- case ReductionOperation::PROD:
- {
- init_res_value = static_cast<T>(1.f);
- break;
- }
- default:
- break;
- }
- auto vec_res_value = wrapper::vdup_n(init_res_value, ExactTagType{});
- uint32x4x4_t vec_res_idx{{0}};
-
- // Compute window_step_x elements per iteration
- int x = window_start_x;
- for (; x <= (window_end_x - window_step_x); x += window_step_x)
- {
- const auto vec_elements = wrapper::vloadq(input_ptr + x);
- switch (op)
- {
- case ReductionOperation::SUM_SQUARE:
- vec_res_value = wrapper::vadd(wrapper::vmul(vec_elements, vec_elements), vec_res_value);
- break;
- case ReductionOperation::MEAN_SUM:
- case ReductionOperation::SUM:
- vec_res_value = wrapper::vadd(vec_elements, vec_res_value);
- break;
- case ReductionOperation::PROD:
- vec_res_value = wrapper::vmul(vec_elements, vec_res_value);
- break;
- case ReductionOperation::ARG_IDX_MIN:
- {
- auto temp_vec_res_value = wrapper::vmin(vec_elements, vec_res_value);
- vec_res_idx = calculate_index<decltype(vec_res_value)>(x, temp_vec_res_value, vec_res_value,
- vec_res_idx, op, 0);
- vec_res_value = temp_vec_res_value;
- break;
- }
- case ReductionOperation::ARG_IDX_MAX:
- {
- auto temp_vec_res_value = wrapper::vmax(vec_elements, vec_res_value);
- vec_res_idx = calculate_index<decltype(vec_res_value)>(x, temp_vec_res_value, vec_res_value,
- vec_res_idx, op, 0);
- vec_res_value = temp_vec_res_value;
- break;
- }
- case ReductionOperation::MIN:
- {
- vec_res_value = wrapper::vmin(vec_elements, vec_res_value);
- break;
- }
- case ReductionOperation::MAX:
- {
- vec_res_value = wrapper::vmax(vec_elements, vec_res_value);
- break;
- }
- default:
- ARM_COMPUTE_ERROR("Not supported");
- }
- }
-
- switch (op)
- {
- case ReductionOperation::SUM:
- case ReductionOperation::MEAN_SUM:
- case ReductionOperation::SUM_SQUARE:
- {
-#ifdef ARM_COMPUTE_DEBUG_ENABLED
- auto res = static_cast<T>(0.f);
- for (int i = 0; i < S; ++i)
- {
- res += wrapper::vgetlane(vec_res_value, i);
- }
-#else // ARM_COMPUTE_DEBUG_ENABLED
- auto carry_res =
- wrapper::vpadd(wrapper::vgethigh(vec_res_value), wrapper::vgetlow(vec_res_value));
- for (int i = 0; i < S / 4; ++i)
- {
- carry_res = wrapper::vpadd(carry_res, carry_res);
- }
- auto res = wrapper::vgetlane(carry_res, 0);
-#endif // ARM_COMPUTE_DEBUG_ENABLED
- if (op == ReductionOperation::SUM_SQUARE)
- {
- // Compute left-over elements
- for (; x < window_end_x; ++x)
- {
- res += (*(input_ptr + x)) * (*(input_ptr + x));
- }
- }
- else
- {
- // Compute left-over elements
- for (; x < window_end_x; ++x)
- {
- res += *(input_ptr + x);
- }
- }
-
- if (op == ReductionOperation::MEAN_SUM)
- {
- res /= input_dim_0;
- }
-
- *(reinterpret_cast<T *>(output.ptr())) = res;
- break;
- }
- case ReductionOperation::PROD:
- {
- auto carry_res =
- wrapper::vmul(wrapper::vgethigh(vec_res_value), wrapper::vgetlow(vec_res_value));
- T res = 1;
- for (int i = 0; i < S / 2; ++i)
- {
- res *= wrapper::vgetlane(carry_res, i);
- }
-
- // Compute left-over elements
- for (; x < window_end_x; ++x)
- {
- res *= *(input_ptr + x);
- }
-
- *(reinterpret_cast<T *>(output.ptr())) = res;
- break;
- }
- case ReductionOperation::ARG_IDX_MIN:
- {
- auto idx = calculate_vector_index<decltype(vec_res_value)>(vec_res_idx, vec_res_value, op);
- auto res = static_cast<T>(wrapper::vgetlane(calculate_min(vec_res_value), 0));
-
- // Compute left-over elements
- for (; x < window_end_x; ++x)
- {
- if (*(input_ptr + x) < res)
- {
- idx = x;
- res = *(input_ptr + x);
- }
- }
- *(reinterpret_cast<uint32_t *>(output.ptr())) = idx;
- break;
- }
- case ReductionOperation::ARG_IDX_MAX:
- {
- auto idx = calculate_vector_index<decltype(vec_res_value)>(vec_res_idx, vec_res_value, op);
- auto res = static_cast<T>(wrapper::vgetlane(calculate_max(vec_res_value), 0));
-
- // Compute left-over elements
- for (; x < window_end_x; ++x)
- {
- if (*(input_ptr + x) > res)
- {
- idx = x;
- res = *(input_ptr + x);
- }
- }
- *(reinterpret_cast<uint32_t *>(output.ptr())) = idx;
- break;
- }
- case ReductionOperation::MIN:
- {
- auto res = static_cast<T>(wrapper::vgetlane(calculate_min(vec_res_value), 0));
-
- // Compute left-over elements
- for (; x < window_end_x; ++x)
- {
- res = *(input_ptr + x) < res ? *(input_ptr + x) : res;
- }
- *(reinterpret_cast<T *>(output.ptr())) = res;
- break;
- }
- case ReductionOperation::MAX:
- {
- auto res = static_cast<T>(wrapper::vgetlane(calculate_max(vec_res_value), 0));
-
- // Compute left-over elements
- for (; x < window_end_x; ++x)
- {
- res = *(input_ptr + x) > res ? *(input_ptr + x) : res;
- }
- *(reinterpret_cast<T *>(output.ptr())) = res;
- break;
- }
- default:
- ARM_COMPUTE_ERROR("Not supported");
- }
- },
- input, output);
- }
-};
-
-template <typename T>
-struct RedOpX_quantized
-{
- inline void operator()(
- const Window &in_window, Window &out_window, const ITensor *in, ITensor *out, const ReductionOperation op)
- {
- using PromotedType = typename wrapper::traits::promote<typename wrapper::traits::promote<T>::type>::type;
-
- const auto oq_info = out->info()->quantization_info().uniform();
-
- const TensorInfo in_info = *(in->info());
- const UniformQuantizationInfo iq_info = in_info.quantization_info().uniform();
-
- const int window_step_x = 16 / sizeof(T);
- const auto window_start_x = static_cast<int>(in_window.x().start());
- const auto window_end_x = static_cast<int>(in_window.x().end());
-
- Window in_win_no_pad = in_window;
- in_win_no_pad.set(Window::DimX, Window::Dimension(0, 1, 1));
-
- Iterator input(in, in_win_no_pad);
- Iterator output(out, out_window);
-
- const auto in_offset = static_cast<float>(iq_info.offset);
- const float in_scale = iq_info.scale;
-
- const auto out_offset = static_cast<float>(oq_info.offset);
- const float out_scale = oq_info.scale;
-
- const auto num_elements = static_cast<float>(in_info.dimension(0));
-
- const float A = in_scale / (out_scale * num_elements);
- const float B = out_offset - (in_scale * in_offset) / (out_scale);
-
- execute_window_loop(
- in_win_no_pad,
- [&](const Coordinates &)
- {
- const auto input_ptr = reinterpret_cast<T *>(input.ptr());
-
- auto vec_res_value1 =
- wrapper::vdup_n(static_cast<PromotedType>(0.f), wrapper::traits::vector_128_tag{});
- auto vec_res_value2 =
- wrapper::vdup_n(static_cast<PromotedType>(0.f), wrapper::traits::vector_128_tag{});
- auto vec_res_value3 =
- wrapper::vdup_n(static_cast<PromotedType>(0.f), wrapper::traits::vector_128_tag{});
- auto vec_res_value4 =
- wrapper::vdup_n(static_cast<PromotedType>(0.f), wrapper::traits::vector_128_tag{});
-
- auto vec_res_value1_f = vdupq_n_f32(static_cast<float>(1.f));
- auto vec_res_value2_f = vdupq_n_f32(static_cast<float>(1.f));
- auto vec_res_value3_f = vdupq_n_f32(static_cast<float>(1.f));
- auto vec_res_value4_f = vdupq_n_f32(static_cast<float>(1.f));
-
- typename wrapper::traits::neon_vector<T, 16>::type vec_res_value = {0};
-
- if (op == ReductionOperation::ARG_IDX_MAX || op == ReductionOperation::ARG_IDX_MIN ||
- op == ReductionOperation::MIN || op == ReductionOperation::MAX)
- {
- vec_res_value = wrapper::vdup_n(*input_ptr, wrapper::traits::vector_128_tag{});
- }
-
- uint32x4x4_t vec_res_idx{{0}};
- // Compute window_step_x elements per iteration
- int x = window_start_x;
- for (; x <= (window_end_x - window_step_x); x += window_step_x)
- {
- const auto vec_elements = wrapper::vloadq(input_ptr + x);
- switch (op)
- {
- case ReductionOperation::SUM:
- case ReductionOperation::MEAN_SUM:
- {
- const auto temp16x8t_1 = wrapper::vmovl(wrapper::vgetlow(vec_elements));
- const auto temp16x8t_2 = wrapper::vmovl(wrapper::vgethigh(vec_elements));
-
- const auto temp32x4t_1 = wrapper::vmovl(wrapper::vgetlow(temp16x8t_1));
- const auto temp32x4t_2 = wrapper::vmovl(wrapper::vgethigh(temp16x8t_1));
- const auto temp32x4t_3 = wrapper::vmovl(wrapper::vgetlow(temp16x8t_2));
- const auto temp32x4t_4 = wrapper::vmovl(wrapper::vgethigh(temp16x8t_2));
-
- vec_res_value1 = wrapper::vadd(temp32x4t_1, vec_res_value1);
- vec_res_value2 = wrapper::vadd(temp32x4t_2, vec_res_value2);
- vec_res_value3 = wrapper::vadd(temp32x4t_3, vec_res_value3);
- vec_res_value4 = wrapper::vadd(temp32x4t_4, vec_res_value4);
- break;
- }
- case ReductionOperation::PROD:
- {
- const auto offset32x4f_4 = vdupq_n_f32(iq_info.offset);
- const auto scale32x4f_4 = vdupq_n_f32(iq_info.scale);
-
- const auto temp16x8t_1 = wrapper::vmovl(wrapper::vgetlow(vec_elements));
- const auto temp16x8t_2 = wrapper::vmovl(wrapper::vgethigh(vec_elements));
-
- const auto temp32x4t_1 = wrapper::vmovl(wrapper::vgetlow(temp16x8t_1));
- const auto temp32x4t_2 = wrapper::vmovl(wrapper::vgethigh(temp16x8t_1));
- const auto temp32x4t_3 = wrapper::vmovl(wrapper::vgetlow(temp16x8t_2));
- const auto temp32x4t_4 = wrapper::vmovl(wrapper::vgethigh(temp16x8t_2));
-
- auto temp32x4f_1 = wrapper::vcvt<float>(temp32x4t_1);
- auto temp32x4f_2 = wrapper::vcvt<float>(temp32x4t_2);
- auto temp32x4f_3 = wrapper::vcvt<float>(temp32x4t_3);
- auto temp32x4f_4 = wrapper::vcvt<float>(temp32x4t_4);
-
- //de-quantize vec_elements
- temp32x4f_1 = vmulq_f32(vsubq_f32(temp32x4f_1, offset32x4f_4), scale32x4f_4);
- temp32x4f_2 = vmulq_f32(vsubq_f32(temp32x4f_2, offset32x4f_4), scale32x4f_4);
- temp32x4f_3 = vmulq_f32(vsubq_f32(temp32x4f_3, offset32x4f_4), scale32x4f_4);
- temp32x4f_4 = vmulq_f32(vsubq_f32(temp32x4f_4, offset32x4f_4), scale32x4f_4);
-
- vec_res_value1_f = vmulq_f32(temp32x4f_1, vec_res_value1_f);
- vec_res_value2_f = vmulq_f32(temp32x4f_2, vec_res_value2_f);
- vec_res_value3_f = vmulq_f32(temp32x4f_3, vec_res_value3_f);
- vec_res_value4_f = vmulq_f32(temp32x4f_4, vec_res_value4_f);
- break;
- }
- case ReductionOperation::ARG_IDX_MIN:
- {
- auto temp_vec_res_value = wrapper::vmin(vec_elements, vec_res_value);
- vec_res_idx = calculate_index_quantized<decltype(vec_res_value)>(
- x, temp_vec_res_value, vec_res_value, vec_res_idx, op, 0);
- vec_res_value = temp_vec_res_value;
- break;
- }
- case ReductionOperation::ARG_IDX_MAX:
- {
- auto temp_vec_res_value = wrapper::vmax(vec_elements, vec_res_value);
- vec_res_idx = calculate_index_quantized<decltype(vec_res_value)>(
- x, temp_vec_res_value, vec_res_value, vec_res_idx, op, 0);
- vec_res_value = temp_vec_res_value;
- break;
- }
- case ReductionOperation::MIN:
- {
- vec_res_value = wrapper::vmin(vec_elements, vec_res_value);
- break;
- }
- case ReductionOperation::MAX:
- {
- vec_res_value = wrapper::vmax(vec_elements, vec_res_value);
- break;
- }
- default:
- ARM_COMPUTE_ERROR("Not supported");
- }
- }
-
- switch (op)
- {
- case ReductionOperation::ARG_IDX_MIN:
- {
- auto idx =
- calculate_vector_index_quantized<decltype(vec_res_value)>(vec_res_idx, vec_res_value, op);
- auto res = static_cast<T>(wrapper::vgetlane(calculate_min(vec_res_value), 0));
-
- // Compute left-over elements
- for (; x < window_end_x; ++x)
- {
- if (*(input_ptr + x) < res)
- {
- idx = x;
- res = *(input_ptr + x);
- }
- }
- *(reinterpret_cast<uint32_t *>(output.ptr())) = idx;
- break;
- }
- case ReductionOperation::ARG_IDX_MAX:
- {
- auto idx =
- calculate_vector_index_quantized<decltype(vec_res_value)>(vec_res_idx, vec_res_value, op);
- auto res = static_cast<T>(wrapper::vgetlane(calculate_max(vec_res_value), 0));
-
- // Compute left-over elements
- for (; x < window_end_x; ++x)
- {
- if (*(input_ptr + x) > res)
- {
- idx = x;
- res = *(input_ptr + x);
- }
- }
- *(reinterpret_cast<uint32_t *>(output.ptr())) = idx;
- break;
- }
- case ReductionOperation::MIN:
- {
- auto res = static_cast<T>(wrapper::vgetlane(calculate_min(vec_res_value), 0));
-
- // Compute left-over elements
- for (; x < window_end_x; ++x)
- {
- res = *(input_ptr + x) < res ? *(input_ptr + x) : res;
- }
- *(reinterpret_cast<T *>(output.ptr())) = res;
- break;
- }
- case ReductionOperation::MAX:
- {
- auto res = static_cast<T>(wrapper::vgetlane(calculate_max(vec_res_value), 0));
-
- // Compute left-over elements
- for (; x < window_end_x; ++x)
- {
- res = *(input_ptr + x) > res ? *(input_ptr + x) : res;
- }
- *(reinterpret_cast<T *>(output.ptr())) = res;
- break;
- }
- case ReductionOperation::PROD:
- {
- auto carry_res = wrapper::vmul(vec_res_value1_f, vec_res_value2_f);
- carry_res = wrapper::vmul(carry_res, vec_res_value3_f);
- carry_res = wrapper::vmul(carry_res, vec_res_value4_f);
-
- float res = wrapper::vgetlane(carry_res, 0);
- res *= wrapper::vgetlane(carry_res, 1);
- res *= wrapper::vgetlane(carry_res, 2);
- res *= wrapper::vgetlane(carry_res, 3);
-
- // Compute left-over elements
- for (; x < window_end_x; ++x)
- {
- //de-quantize input
- if (std::is_same<T, uint8_t>::value)
- {
- res *= dequantize_qasymm8(*(input_ptr + x), iq_info);
- }
- else
- {
- res *= dequantize_qasymm8_signed(*(input_ptr + x), iq_info);
- }
- }
-
- //re-quantize result
- if (std::is_same<T, uint8_t>::value)
- {
- res = quantize_qasymm8(res, iq_info);
- }
- else
- {
- res = quantize_qasymm8_signed(res, iq_info);
- }
-
- *reinterpret_cast<T *>(output.ptr()) = static_cast<T>(res);
- break;
- }
- case ReductionOperation::SUM:
- case ReductionOperation::MEAN_SUM:
- {
- auto carry_res = wrapper::vadd(vec_res_value1, vec_res_value2);
- carry_res = wrapper::vadd(carry_res, vec_res_value3);
- carry_res = wrapper::vadd(carry_res, vec_res_value4);
-
- auto carry_paddition =
- wrapper::vpadd(wrapper::vgethigh(carry_res), wrapper::vgetlow(carry_res));
- carry_paddition = wrapper::vpadd(carry_paddition, carry_paddition);
- auto res = static_cast<int32_t>(wrapper::vgetlane(carry_paddition, 0));
-
- // Compute left-over elements
- for (; x < window_end_x; ++x)
- {
- res += *(input_ptr + x);
- }
-
- if (op == ReductionOperation::MEAN_SUM)
- {
- const int32_t resFinal = A * (static_cast<float>(res)) + B;
-
- *reinterpret_cast<T *>(output.ptr()) = utils::cast::saturate_cast<T>(resFinal);
- }
- else
- {
- // Subtract accumulated offsets
- res -= (in_info.dimension(0) - 1) * iq_info.offset;
- *reinterpret_cast<T *>(output.ptr()) = utils::cast::saturate_cast<T>(res);
- }
-
- break;
- }
- default:
- ARM_COMPUTE_ERROR("Not supported");
- }
- },
- input, output);
- }
-};
-
-template <typename T, int S>
-struct RedOpYZW
-{
- /** SIMD vector tag type. */
- using ExactTagType = typename wrapper::traits::neon_vector<T, S>::tag_type;
- using neon_vector = typename wrapper::traits::neon_vector<T, S>::type;
-
- inline void operator()(const Window &in_window,
- Window &out_window,
- const ITensor *in,
- ITensor *out,
- int axis,
- const ReductionOperation op)
- {
- const TensorInfo in_info = *(in->info());
- const int window_step_x = 16 / sizeof(T);
- const auto window_start_x_tmp = static_cast<int>(in_window.x().start());
- const auto window_end_x_tmp = static_cast<int>(in_window.x().end());
- // As it split over x-axis, need to set the correct spiltted window start and end.
- const auto window_start_x = static_cast<int>(0);
- const auto window_end_x = static_cast<int>(in_window.shape().x());
-
- Window in_win_no_pad = in_window;
- in_win_no_pad.set(Window::DimX, Window::Dimension(window_start_x_tmp, window_end_x_tmp, in_window.shape().x()));
- Window out_win_no_pad = out_window;
- out_win_no_pad.set(Window::DimX,
- Window::Dimension(window_start_x_tmp, window_end_x_tmp, out_window.shape().x()));
-
- Iterator input(in, in_win_no_pad);
- Iterator output(out, out_win_no_pad);
-
- execute_window_loop(
- in_win_no_pad,
- [&](const Coordinates &)
- {
- const auto input_ptr = reinterpret_cast<T *>(input.ptr());
-
- // Compute window_step_x elements per iteration
- int x = window_start_x;
- for (; x <= (window_end_x - window_step_x); x += window_step_x)
- {
- neon_vector vec_res_value = {0};
- switch (op)
- {
- case ReductionOperation::ARG_IDX_MAX:
- case ReductionOperation::ARG_IDX_MIN:
- case ReductionOperation::MIN:
- case ReductionOperation::MAX:
- {
- vec_res_value = wrapper::vloadq(input_ptr + x);
- break;
- }
- case ReductionOperation::PROD:
- {
- vec_res_value = wrapper::vdup_n(static_cast<T>(1.f), ExactTagType{});
- break;
- }
- default:
- {
- vec_res_value = wrapper::vdup_n(static_cast<T>(0.f), ExactTagType{});
- break;
- }
- }
- uint32x4x4_t vec_res_idx{{0}};
-
- for (unsigned int dim = 0; dim < in_info.dimension(axis); ++dim)
- {
- const T *in_ptr =
- reinterpret_cast<T *>(input.ptr() + x * sizeof(T) + in_info.strides_in_bytes()[axis] * dim);
- const auto vec_elements = wrapper::vloadq(in_ptr);
- switch (op)
- {
- case ReductionOperation::SUM:
- case ReductionOperation::MEAN_SUM:
- vec_res_value = wrapper::vadd(vec_elements, vec_res_value);
- break;
- case ReductionOperation::SUM_SQUARE:
- vec_res_value = wrapper::vadd(wrapper::vmul(vec_elements, vec_elements), vec_res_value);
- break;
- case ReductionOperation::PROD:
- vec_res_value = wrapper::vmul(vec_elements, vec_res_value);
- break;
- case ReductionOperation::ARG_IDX_MIN:
- {
- auto temp_vec_res_value = wrapper::vmin(vec_elements, vec_res_value);
- vec_res_idx =
- calculate_index(dim, temp_vec_res_value, vec_res_value, vec_res_idx, op, axis);
- vec_res_value = temp_vec_res_value;
- break;
- }
- case ReductionOperation::ARG_IDX_MAX:
- {
- auto temp_vec_res_value = wrapper::vmax(vec_elements, vec_res_value);
- vec_res_idx =
- calculate_index(dim, temp_vec_res_value, vec_res_value, vec_res_idx, op, axis);
- vec_res_value = temp_vec_res_value;
- break;
- }
- case ReductionOperation::MIN:
- {
- vec_res_value = wrapper::vmin(vec_elements, vec_res_value);
- break;
- }
- case ReductionOperation::MAX:
- {
- vec_res_value = wrapper::vmax(vec_elements, vec_res_value);
- break;
- }
- default:
- ARM_COMPUTE_ERROR("Not supported");
- }
- }
-
- if (op == ReductionOperation::MEAN_SUM)
- {
- auto vec_width_inv =
- wrapper::vinv(wrapper::vdup_n(static_cast<T>(in_info.dimension(axis)), ExactTagType{}));
- vec_res_value = wrapper::vmul(vec_res_value, vec_width_inv);
- }
-
- if (op == ReductionOperation::ARG_IDX_MIN || op == ReductionOperation::ARG_IDX_MAX)
- {
- wrapper::vstore(reinterpret_cast<uint32_t *>(output.ptr()) + x, vec_res_idx.val[0]);
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
- if (std::is_same<T, float16_t>::value)
- {
- wrapper::vstore(reinterpret_cast<uint32_t *>(output.ptr()) + x + 4, vec_res_idx.val[1]);
- }
-#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
- }
- else
- {
- wrapper::vstore(reinterpret_cast<T *>(output.ptr() + x * sizeof(T)), vec_res_value);
- }
- }
-
- // Compute left-over elements
- for (; x < window_end_x; ++x)
- {
- auto res_value = 0.f;
- switch (op)
- {
- case ReductionOperation::ARG_IDX_MAX:
- case ReductionOperation::ARG_IDX_MIN:
- case ReductionOperation::MIN:
- case ReductionOperation::MAX:
- {
- res_value = *(input_ptr + x);
- break;
- }
- case ReductionOperation::PROD:
- {
- res_value = static_cast<T>(1.f);
- break;
- }
- default:
- {
- res_value = static_cast<T>(0.f);
- break;
- }
- }
-
- uint32_t res_idx = 0;
- for (unsigned int dim = 0; dim < in_info.dimension(axis); ++dim)
- {
- const T *in_ptr =
- reinterpret_cast<T *>(input.ptr() + x * sizeof(T) + in_info.strides_in_bytes()[axis] * dim);
-
- switch (op)
- {
- case ReductionOperation::SUM:
- case ReductionOperation::MEAN_SUM:
- res_value += *in_ptr;
- break;
- case ReductionOperation::SUM_SQUARE:
- res_value += *in_ptr * *in_ptr;
- break;
- case ReductionOperation::PROD:
- res_value *= *in_ptr;
- break;
- case ReductionOperation::ARG_IDX_MIN:
- {
- if (*in_ptr < res_value)
- {
- res_value = *in_ptr;
- res_idx = dim;
- }
- break;
- }
- case ReductionOperation::ARG_IDX_MAX:
- {
- if (*in_ptr > res_value)
- {
- res_value = *in_ptr;
- res_idx = dim;
- }
- break;
- }
- case ReductionOperation::MIN:
- {
- res_value = *in_ptr < res_value ? *in_ptr : res_value;
- break;
- }
- case ReductionOperation::MAX:
- {
- res_value = *in_ptr > res_value ? *in_ptr : res_value;
- break;
- }
- default:
- ARM_COMPUTE_ERROR("Not supported");
- }
- }
-
- if (op == ReductionOperation::MEAN_SUM)
- {
- res_value /= in_info.dimension(axis);
- }
-
- if (op == ReductionOperation::ARG_IDX_MIN || op == ReductionOperation::ARG_IDX_MAX)
- {
- *(reinterpret_cast<uint32_t *>(output.ptr()) + x) = res_idx;
- }
- else
- {
- *(reinterpret_cast<T *>(output.ptr() + x * sizeof(T))) = res_value;
- }
- }
- },
- input, output);
- }
-};
-
-template <typename T, int S, int axis, ReductionOperation op>
-struct RedOpYZW_complex
-{
- /** SIMD vector tag type. */
- using ExactTagType = typename wrapper::traits::neon_vector<T, S>::tag_type;
- using neon_vector = typename wrapper::traits::neon_vector<T, S>::type;
-
- inline void operator()(
- const Window &in_window, Window &out_window, const ITensor *in, ITensor *out, int, const ReductionOperation)
- {
- ARM_COMPUTE_ERROR_ON(axis != 2);
- ARM_COMPUTE_ERROR_ON(op != ReductionOperation::SUM);
-
- const TensorInfo in_info = *(in->info());
- const size_t stride_z = in_info.strides_in_bytes()[axis];
- const int window_step_x = 16 / sizeof(T);
- const auto window_start_x_tmp = static_cast<int>(in_window.x().start());
- const auto window_end_x_tmp = static_cast<int>(in_window.x().end());
- // As it split over x-axis, need to set the correct spiltted window start and end.
- const auto window_start_x = static_cast<int>(0);
- const auto window_end_x = static_cast<int>(in_window.shape().x());
-
- Window in_win_no_pad = in_window;
- in_win_no_pad.set(Window::DimX, Window::Dimension(window_start_x_tmp, window_end_x_tmp, in_window.shape().x()));
- Window out_win_no_pad = out_window;
- out_win_no_pad.set(Window::DimX,
- Window::Dimension(window_start_x_tmp, window_end_x_tmp, out_window.shape().x()));
-
- Iterator input(in, in_win_no_pad);
- Iterator output(out, out_win_no_pad);
-
- execute_window_loop(
- in_win_no_pad,
- [&](const Coordinates &)
- {
- // Compute window_step_x elements per iteration
- int x = window_start_x;
- for (; x <= (window_end_x - window_step_x); x += window_step_x)
- {
- neon_vector vec_res_value_0 = {0};
- neon_vector vec_res_value_1 = {0};
-
- vec_res_value_0 = wrapper::vdup_n(static_cast<T>(0.f), ExactTagType{});
- vec_res_value_1 = wrapper::vdup_n(static_cast<T>(0.f), ExactTagType{});
-
- T *out_ptr = reinterpret_cast<T *>(output.ptr() + 2 * x * sizeof(T));
- for (unsigned int dim = 0; dim < in_info.dimension(axis); ++dim)
- {
- T *in_ptr_0 = reinterpret_cast<T *>(input.ptr() + 2 * x * sizeof(T) + stride_z * dim);
- T *in_ptr_1 = reinterpret_cast<T *>(input.ptr() + 2 * x * sizeof(T) + 16 + stride_z * dim);
-
- const auto vec_elements_0 = wrapper::vloadq(in_ptr_0);
- const auto vec_elements_1 = wrapper::vloadq(in_ptr_1);
-
- vec_res_value_0 = wrapper::vadd(vec_elements_0, vec_res_value_0);
- vec_res_value_1 = wrapper::vadd(vec_elements_1, vec_res_value_1);
- }
-
- wrapper::vstore(out_ptr, vec_res_value_0);
- wrapper::vstore(out_ptr + 4, vec_res_value_1);
- }
-
- // Compute left-over elements
- for (; x < window_end_x; ++x)
- {
- auto res_value_0 = 0.f;
- auto res_value_1 = 0.f;
-
- T *out_ptr = reinterpret_cast<T *>(output.ptr() + 2 * x * sizeof(T));
- for (unsigned int dim = 0; dim < in_info.dimension(axis); ++dim)
- {
- T *in_ptr = reinterpret_cast<T *>(input.ptr() + 2 * x * sizeof(T) + stride_z * dim);
- res_value_0 += *in_ptr;
- res_value_1 += *(in_ptr + 1);
- }
- *out_ptr = res_value_0;
- *(out_ptr + 1) = res_value_1;
- }
- },
- input, output);
- }
-};
-
-template <typename T>
-struct RedOpYZW_quantized
-{
- inline void operator()(const Window &in_window,
- Window &out_window,
- const ITensor *in,
- ITensor *out,
- int axis,
- const ReductionOperation op)
- {
- const TensorInfo in_info = *(in->info());
- const UniformQuantizationInfo iq_info = in_info.quantization_info().uniform();
- using PromotedType = typename wrapper::traits::promote<typename wrapper::traits::promote<T>::type>::type;
-
- const auto oq_info = out->info()->quantization_info().uniform();
-
- const int window_step_x = 16 / sizeof(T);
- const auto window_start_x_tmp = static_cast<int>(in_window.x().start());
- const auto window_end_x_tmp = static_cast<int>(in_window.x().end());
- // As it split over x-axis, need to set the correct spiltted window start and end.
- const auto window_start_x = static_cast<int>(0);
- const auto window_end_x = static_cast<int>(in_window.shape().x());
-
- Window in_win_no_pad = in_window;
- in_win_no_pad.set(Window::DimX, Window::Dimension(window_start_x_tmp, window_end_x_tmp, in_window.shape().x()));
- Window out_win_no_pad = out_window;
- out_win_no_pad.set(Window::DimX,
- Window::Dimension(window_start_x_tmp, window_end_x_tmp, out_window.shape().x()));
-
- Iterator input(in, in_win_no_pad);
- Iterator output(out, out_win_no_pad);
-
- using vector_type =
- typename wrapper::traits::neon_bitvector<PromotedType, wrapper::traits::BitWidth::W128>::type;
- using vector_type_f = typename wrapper::traits::neon_vector<float, 4>::type;
-
- vector_type vec_res_value1{};
- vector_type vec_res_value2{};
- vector_type vec_res_value3{};
- vector_type vec_res_value4{};
-
- vector_type_f vec_res_value1_f{};
- vector_type_f vec_res_value2_f{};
- vector_type_f vec_res_value3_f{};
- vector_type_f vec_res_value4_f{};
-
- const float in_offset = static_cast<float>(iq_info.offset);
- const float in_scale = iq_info.scale;
-
- const float out_offset = static_cast<float>(oq_info.offset);
- const float out_scale = oq_info.scale;
-
- const float num_elements = static_cast<float>(in_info.dimension(axis));
-
- const float A = in_scale / (out_scale * num_elements);
- const float B = out_offset - (in_scale * in_offset) / (out_scale);
-
- const auto vec_A = wrapper::vdup_n(static_cast<float>(A), wrapper::traits::vector_128_tag{});
- const auto vec_B = wrapper::vdup_n(static_cast<float>(B), wrapper::traits::vector_128_tag{});
-
- execute_window_loop(
- in_win_no_pad,
- [&](const Coordinates &)
- {
- const auto input_ptr = reinterpret_cast<T *>(input.ptr());
-
- // Compute window_step_x elements per iteration
- int x = window_start_x;
- for (; x <= (window_end_x - window_step_x); x += window_step_x)
- {
- uint32x4x4_t vec_res_idx{{0}};
- vec_res_value1 = wrapper::vdup_n(static_cast<PromotedType>(0), wrapper::traits::vector_128_tag{});
- vec_res_value2 = wrapper::vdup_n(static_cast<PromotedType>(0), wrapper::traits::vector_128_tag{});
- vec_res_value3 = wrapper::vdup_n(static_cast<PromotedType>(0), wrapper::traits::vector_128_tag{});
- vec_res_value4 = wrapper::vdup_n(static_cast<PromotedType>(0), wrapper::traits::vector_128_tag{});
-
- vec_res_value1_f = wrapper::vdup_n(static_cast<float>(1), wrapper::traits::vector_128_tag{});
- vec_res_value2_f = wrapper::vdup_n(static_cast<float>(1), wrapper::traits::vector_128_tag{});
- vec_res_value3_f = wrapper::vdup_n(static_cast<float>(1), wrapper::traits::vector_128_tag{});
- vec_res_value4_f = wrapper::vdup_n(static_cast<float>(1), wrapper::traits::vector_128_tag{});
-
- auto vec_res_value = wrapper::vloadq(input_ptr + x);
-
- for (unsigned int index_dim = 0; index_dim < in_info.dimension(axis); ++index_dim)
- {
- const T *in_ptr = input_ptr + x + in_info.strides_in_bytes()[axis] * index_dim;
- const auto vec_elements = wrapper::vloadq(in_ptr);
- switch (op)
- {
- case ReductionOperation::SUM:
- case ReductionOperation::MEAN_SUM:
- {
- const auto temp16x8t_1 = wrapper::vmovl(wrapper::vgetlow(vec_elements));
- const auto temp16x8t_2 = wrapper::vmovl(wrapper::vgethigh(vec_elements));
-
- const auto temp32x4t_1 = wrapper::vmovl(wrapper::vgetlow(temp16x8t_1));
- const auto temp32x4t_2 = wrapper::vmovl(wrapper::vgethigh(temp16x8t_1));
- const auto temp32x4t_3 = wrapper::vmovl(wrapper::vgetlow(temp16x8t_2));
- const auto temp32x4t_4 = wrapper::vmovl(wrapper::vgethigh(temp16x8t_2));
-
- vec_res_value1 = wrapper::vadd(temp32x4t_1, vec_res_value1);
- vec_res_value2 = wrapper::vadd(temp32x4t_2, vec_res_value2);
- vec_res_value3 = wrapper::vadd(temp32x4t_3, vec_res_value3);
- vec_res_value4 = wrapper::vadd(temp32x4t_4, vec_res_value4);
- break;
- }
- case ReductionOperation::PROD:
- {
- const auto offset32x4f_4 = wrapper::vdup_n(static_cast<float>(iq_info.offset),
- wrapper::traits::vector_128_tag{});
- const auto scale32x4f_4 =
- wrapper::vdup_n(iq_info.scale, wrapper::traits::vector_128_tag{});
-
- const auto temp16x8t_1 = wrapper::vmovl(wrapper::vgetlow(vec_elements));
- const auto temp16x8t_2 = wrapper::vmovl(wrapper::vgethigh(vec_elements));
-
- const auto temp32x4t_1 = wrapper::vmovl(wrapper::vgetlow(temp16x8t_1));
- const auto temp32x4t_2 = wrapper::vmovl(wrapper::vgethigh(temp16x8t_1));
- const auto temp32x4t_3 = wrapper::vmovl(wrapper::vgetlow(temp16x8t_2));
- const auto temp32x4t_4 = wrapper::vmovl(wrapper::vgethigh(temp16x8t_2));
-
- auto temp32x4f_1 = wrapper::vcvt<float>(temp32x4t_1);
- auto temp32x4f_2 = wrapper::vcvt<float>(temp32x4t_2);
- auto temp32x4f_3 = wrapper::vcvt<float>(temp32x4t_3);
- auto temp32x4f_4 = wrapper::vcvt<float>(temp32x4t_4);
-
- //de-quantize vec_elements
- temp32x4f_1 = wrapper::vmul(wrapper::vsub(temp32x4f_1, offset32x4f_4), scale32x4f_4);
- temp32x4f_2 = wrapper::vmul(wrapper::vsub(temp32x4f_2, offset32x4f_4), scale32x4f_4);
- temp32x4f_3 = wrapper::vmul(wrapper::vsub(temp32x4f_3, offset32x4f_4), scale32x4f_4);
- temp32x4f_4 = wrapper::vmul(wrapper::vsub(temp32x4f_4, offset32x4f_4), scale32x4f_4);
-
- vec_res_value1_f = wrapper::vmul(temp32x4f_1, vec_res_value1_f);
- vec_res_value2_f = wrapper::vmul(temp32x4f_2, vec_res_value2_f);
- vec_res_value3_f = wrapper::vmul(temp32x4f_3, vec_res_value3_f);
- vec_res_value4_f = wrapper::vmul(temp32x4f_4, vec_res_value4_f);
- break;
- }
- case ReductionOperation::ARG_IDX_MIN:
- {
- auto temp_vec_res_value = wrapper::vmin(vec_elements, vec_res_value);
- vec_res_idx = calculate_index_quantized(index_dim, temp_vec_res_value, vec_res_value,
- vec_res_idx, op, axis);
- vec_res_value = temp_vec_res_value;
- break;
- }
- case ReductionOperation::ARG_IDX_MAX:
- {
- auto temp_vec_res_value = wrapper::vmax(vec_elements, vec_res_value);
- vec_res_idx = calculate_index_quantized(index_dim, temp_vec_res_value, vec_res_value,
- vec_res_idx, op, axis);
- vec_res_value = temp_vec_res_value;
- break;
- }
- case ReductionOperation::MIN:
- {
- vec_res_value = wrapper::vmin(vec_elements, vec_res_value);
- break;
- }
- case ReductionOperation::MAX:
- {
- vec_res_value = wrapper::vmax(vec_elements, vec_res_value);
- break;
- }
- default:
- ARM_COMPUTE_ERROR("Not supported");
- }
- }
-
- switch (op)
- {
- case ReductionOperation::ARG_IDX_MIN:
- case ReductionOperation::ARG_IDX_MAX:
- {
- wrapper::vstore(reinterpret_cast<uint32_t *>(output.ptr() + 4 * x), vec_res_idx.val[0]);
- wrapper::vstore(reinterpret_cast<uint32_t *>(output.ptr() + 4 * x) + 4, vec_res_idx.val[1]);
- wrapper::vstore(reinterpret_cast<uint32_t *>(output.ptr() + 4 * x) + 8, vec_res_idx.val[2]);
- wrapper::vstore(reinterpret_cast<uint32_t *>(output.ptr() + 4 * x) + 12,
- vec_res_idx.val[3]);
- break;
- }
- case ReductionOperation::MIN:
- case ReductionOperation::MAX:
- {
- wrapper::vstore(reinterpret_cast<T *>(output.ptr() + x), vec_res_value);
- break;
- }
- case ReductionOperation::SUM:
- {
- // Subtract offsets
- auto offsets = vdupq_n_s32((in_info.dimension(axis) - 1) * iq_info.offset);
-
- auto vec_res_s_value1 = wrapper::vreinterpret(vec_res_value1);
- auto vec_res_s_value2 = wrapper::vreinterpret(vec_res_value2);
- auto vec_res_s_value3 = wrapper::vreinterpret(vec_res_value3);
- auto vec_res_s_value4 = wrapper::vreinterpret(vec_res_value4);
-
- vec_res_s_value1 = wrapper::vsub(vec_res_s_value1, offsets);
- vec_res_s_value2 = wrapper::vsub(vec_res_s_value2, offsets);
- vec_res_s_value3 = wrapper::vsub(vec_res_s_value3, offsets);
- vec_res_s_value4 = wrapper::vsub(vec_res_s_value4, offsets);
-
- const auto temp16x8t_1 =
- wrapper::vcombine(wrapper::vqmovn(vec_res_s_value1), wrapper::vqmovn(vec_res_s_value2));
- const auto temp16x8t_2 =
- wrapper::vcombine(wrapper::vqmovn(vec_res_s_value3), wrapper::vqmovn(vec_res_s_value4));
-
- combine_and_store<T>(temp16x8t_1, temp16x8t_2, output, x);
- break;
- }
- case ReductionOperation::MEAN_SUM:
- {
- vec_res_value1_f = wrapper::vmla(vec_B, wrapper::vcvt<float>(vec_res_value1), vec_A);
- vec_res_value2_f = wrapper::vmla(vec_B, wrapper::vcvt<float>(vec_res_value2), vec_A);
- vec_res_value3_f = wrapper::vmla(vec_B, wrapper::vcvt<float>(vec_res_value3), vec_A);
- vec_res_value4_f = wrapper::vmla(vec_B, wrapper::vcvt<float>(vec_res_value4), vec_A);
-
-#ifdef __aarch64__
- vec_res_value1 = wrapper::vcvta<PromotedType>(vec_res_value1_f);
- vec_res_value2 = wrapper::vcvta<PromotedType>(vec_res_value2_f);
- vec_res_value3 = wrapper::vcvta<PromotedType>(vec_res_value3_f);
- vec_res_value4 = wrapper::vcvta<PromotedType>(vec_res_value4_f);
-#else // defined(__aarch64__)
- vec_res_value1 = wrapper::vcvt<PromotedType>(vec_res_value1_f);
- vec_res_value2 = wrapper::vcvt<PromotedType>(vec_res_value2_f);
- vec_res_value3 = wrapper::vcvt<PromotedType>(vec_res_value3_f);
- vec_res_value4 = wrapper::vcvt<PromotedType>(vec_res_value4_f);
-#endif // __aarch64__
-
- const auto temp16x8t_1 =
- wrapper::vcombine(wrapper::vqmovn(vec_res_value1), wrapper::vqmovn(vec_res_value2));
- const auto temp16x8t_2 =
- wrapper::vcombine(wrapper::vqmovn(vec_res_value3), wrapper::vqmovn(vec_res_value4));
- auto res = wrapper::vcombine(wrapper::vqmovn(temp16x8t_1), wrapper::vqmovn(temp16x8t_2));
-
- wrapper::vstore(reinterpret_cast<T *>(output.ptr() + x), res);
- break;
- }
- case ReductionOperation::PROD:
- {
- const auto offset32x4f_4 =
- wrapper::vdup_n(static_cast<float>(iq_info.offset), wrapper::traits::vector_128_tag{});
- const auto iscale32x4f_4 = vinvq_f32(vdupq_n_f32(iq_info.scale));
-
- //re-quantize
- vec_res_value1_f =
- wrapper::vadd(wrapper::vmul(vec_res_value1_f, iscale32x4f_4), offset32x4f_4);
- vec_res_value2_f =
- wrapper::vadd(wrapper::vmul(vec_res_value2_f, iscale32x4f_4), offset32x4f_4);
- vec_res_value3_f =
- wrapper::vadd(wrapper::vmul(vec_res_value3_f, iscale32x4f_4), offset32x4f_4);
- vec_res_value4_f =
- wrapper::vadd(wrapper::vmul(vec_res_value4_f, iscale32x4f_4), offset32x4f_4);
-
- vec_res_value1 = wrapper::vcvt<T>(vec_res_value1_f);
- vec_res_value2 = wrapper::vcvt<T>(vec_res_value2_f);
- vec_res_value3 = wrapper::vcvt<T>(vec_res_value3_f);
- vec_res_value4 = wrapper::vcvt<T>(vec_res_value4_f);
-
- const auto temp16x8t_1 =
- wrapper::vcombine(wrapper::vqmovn(vec_res_value1), wrapper::vqmovn(vec_res_value2));
- const auto temp16x8t_2 =
- wrapper::vcombine(wrapper::vqmovn(vec_res_value3), wrapper::vqmovn(vec_res_value4));
- auto res = wrapper::vcombine(wrapper::vqmovn(temp16x8t_1), wrapper::vqmovn(temp16x8t_2));
-
- wrapper::vstore(reinterpret_cast<T *>(output.ptr() + x), res);
- break;
- }
- default:
- ARM_COMPUTE_ERROR("Not supported");
- }
- }
-
- // Compute left-over elements
- for (; x < window_end_x; ++x)
- {
- float res_value = 0.f;
- int32_t res_value_q = 0;
-
- switch (op)
- {
- case ReductionOperation::ARG_IDX_MAX:
- case ReductionOperation::ARG_IDX_MIN:
- case ReductionOperation::MIN:
- case ReductionOperation::MAX:
- {
- res_value = *(input_ptr + x);
- break;
- }
- case ReductionOperation::PROD:
- {
- res_value = static_cast<T>(1.0f);
- break;
- }
- default:
- {
- res_value = static_cast<T>(0.0f);
- break;
- }
- }
- uint32_t res_idx = 0;
-
- for (unsigned int dim = 0; dim < in_info.dimension(axis); ++dim)
- {
- const T *in_ptr =
- reinterpret_cast<T *>(input.ptr() + x + in_info.strides_in_bytes()[axis] * dim);
- switch (op)
- {
- case ReductionOperation::SUM:
- {
- res_value += *in_ptr;
- break;
- }
- case ReductionOperation::MEAN_SUM:
- {
- res_value_q += *in_ptr;
- break;
- }
- case ReductionOperation::SUM_SQUARE:
- {
- res_value += *in_ptr * *in_ptr;
- break;
- }
- case ReductionOperation::PROD:
- {
- //de-quantize input
- if (std::is_same<T, uint8_t>::value)
- {
- res_value *= dequantize_qasymm8(*in_ptr, iq_info);
- }
- else
- {
- res_value *= dequantize_qasymm8_signed(*in_ptr, iq_info);
- }
- break;
- }
- case ReductionOperation::ARG_IDX_MIN:
- {
- if (*in_ptr < res_value)
- {
- res_value = *in_ptr;
- res_idx = dim;
- }
- break;
- }
- case ReductionOperation::ARG_IDX_MAX:
- {
- if (*in_ptr > res_value)
- {
- res_value = *in_ptr;
- res_idx = dim;
- }
- break;
- }
- case ReductionOperation::MIN:
- {
- res_value = *in_ptr < res_value ? *in_ptr : res_value;
- break;
- }
- case ReductionOperation::MAX:
- {
- res_value = *in_ptr > res_value ? *in_ptr : res_value;
- break;
- }
- default:
- ARM_COMPUTE_ERROR("Not supported");
- }
- }
-
- switch (op)
- {
- case ReductionOperation::MEAN_SUM:
- {
- // Apply previously calculated coefficients (with rounding on aarch64)
-#ifdef __aarch64__
- const int32_t res =
- arm_compute::support::cpp11::round(A * (static_cast<float>(res_value_q)) + B);
-#else // defined(__aarch64__)
- const int32_t res = A * (static_cast<float>(res_value_q)) + B;
-#endif // __aarch64__
- *reinterpret_cast<T *>(output.ptr() + x) = utils::cast::saturate_cast<T>(res);
- break;
- }
- case ReductionOperation::SUM:
- {
- // Subtract accumulated offsets
- res_value -= (in_info.dimension(axis) - 1) * iq_info.offset;
- *reinterpret_cast<T *>(output.ptr() + x) = utils::cast::saturate_cast<T>(res_value);
- break;
- }
- case ReductionOperation::PROD:
- {
- //re-quantize result
- T res = 0;
- if (std::is_same<T, uint8_t>::value)
- {
- res = quantize_qasymm8(res_value, iq_info);
- }
- else
- {
- res = quantize_qasymm8_signed(res_value, iq_info);
- }
- *(reinterpret_cast<T *>(output.ptr() + x)) = res;
- break;
- }
- case ReductionOperation::ARG_IDX_MIN:
- case ReductionOperation::ARG_IDX_MAX:
- {
- *(reinterpret_cast<uint32_t *>(output.ptr() + x * 4)) = res_idx;
- break;
- }
- default:
- *(reinterpret_cast<T *>(output.ptr() + x)) = res_value;
- }
- }
- },
- input, output);
- }
-};
-
-void reduce_op(
- const Window &window, const ITensor *input, ITensor *output, unsigned int axis, const ReductionOperation op)
-{
- const bool is_complex = (input->info()->num_channels() == 2);
+ const bool is_complex = (_input->info()->num_channels() == 2);
if (is_complex)
{
- switch (axis)
+ switch (_reduction_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);
+ _func = REGISTER_FP32_NEON(cpu::reduce_RedOpYZW_complex_reduceZ_float32_4_2_SUM);
+ break;
default:
ARM_COMPUTE_ERROR("Not supported");
+ break;
}
+ break;
+ }
default:
+ {
ARM_COMPUTE_ERROR("Not supported");
+ break;
+ }
}
+ break;
default:
+ {
ARM_COMPUTE_ERROR("Not supported");
+ break;
+ }
}
return;
}
- switch (axis)
+ switch (_reduction_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);
+ _func = REGISTER_QASYMM8_NEON(cpu::reduce_RedOpX_reduceX_qasymm8);
+ break;
}
case DataType::QASYMM8_SIGNED:
{
- return Reducer<RedOpX_quantized<int8_t>>::reduceX(window, input, output, RedOpX_quantized<int8_t>(),
- op);
+ _func = REGISTER_QASYMM8_SIGNED_NEON(cpu::reduce_RedOpX_reduceX_qasymm8_signed);
+ break;
}
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+#ifdef ARM_COMPUTE_ENABLE_FP16
case DataType::F16:
- return Reducer<RedOpX<float16_t, 8>>::reduceX(window, input, output, RedOpX<float16_t, 8>(), op);
-#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ {
+ _func = REGISTER_FP16_NEON(cpu::reduce_RedOpX_reduceX_float16_8);
+ break;
+ }
+#endif // ARM_COMPUTE_ENABLE_FP16
case DataType::F32:
{
- return Reducer<RedOpX<float, 4>>::reduceX(window, input, output, RedOpX<float, 4>(), op);
+ _func = REGISTER_FP32_NEON(cpu::reduce_RedOpX_reduceX_float32_4);
+ break;
}
case DataType::S32:
{
- return Reducer<RedOpX<int32_t, 4>>::reduceX(window, input, output, RedOpX<int32_t, 4>(), op);
+ _func = REGISTER_INTEGER_NEON(cpu::reduce_RedOpX_reduceX_S32_4);
+ break;
}
default:
{
ARM_COMPUTE_ERROR("Not supported");
+ break;
}
}
+ break;
}
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);
+ _func = REGISTER_QASYMM8_NEON(cpu::reduce_RedOpYZW_reduceY_qasymm8);
+ break;
}
case DataType::QASYMM8_SIGNED:
{
- return Reducer<RedOpYZW_quantized<int8_t>>::reduceY(window, input, output,
- RedOpYZW_quantized<int8_t>(), op);
+ _func = REGISTER_QASYMM8_SIGNED_NEON(cpu::reduce_RedOpYZW_reduceY_qasymm8_signed);
+ break;
}
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+#ifdef ARM_COMPUTE_ENABLE_FP16
case DataType::F16:
- return Reducer<RedOpYZW<float16_t, 8>>::reduceY(window, input, output, RedOpYZW<float16_t, 8>(),
- op);
-#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ {
+ _func = REGISTER_FP16_NEON(cpu::reduce_RedOpYZW_reduceY_float16_8);
+ break;
+ }
+#endif // ARM_COMPUTE_ENABLE_FP16
case DataType::F32:
- return Reducer<RedOpYZW<float, 4>>::reduceY(window, input, output, RedOpYZW<float, 4>(), op);
+ {
+ _func = REGISTER_FP32_NEON(cpu::reduce_RedOpYZW_reduceY_float32_4);
+ break;
+ }
case DataType::S32:
- return Reducer<RedOpYZW<int32_t, 4>>::reduceY(window, input, output, RedOpYZW<int32_t, 4>(), op);
+ {
+ _func = REGISTER_INTEGER_NEON(cpu::reduce_RedOpYZW_reduceY_S32_4);
+ break;
+ }
default:
+ {
ARM_COMPUTE_ERROR("Not supported");
+ break;
+ }
}
+ break;
+ }
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);
+ {
+ _func = REGISTER_QASYMM8_NEON(cpu::reduce_RedOpYZW_reduceZ_qasymm8);
+ break;
+ }
case DataType::QASYMM8_SIGNED:
- return Reducer<RedOpYZW_quantized<int8_t>>::reduceZ(window, input, output,
- RedOpYZW_quantized<int8_t>(), op);
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ {
+ _func = REGISTER_QASYMM8_SIGNED_NEON(cpu::reduce_RedOpYZW_reduceZ_qasymm8_signed);
+ break;
+ }
+#ifdef ARM_COMPUTE_ENABLE_FP16
case DataType::F16:
- return Reducer<RedOpYZW<float16_t, 8>>::reduceZ(window, input, output, RedOpYZW<float16_t, 8>(),
- op);
-#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ {
+ _func = REGISTER_FP16_NEON(cpu::reduce_RedOpYZW_reduceZ_float16_8);
+ break;
+ }
+#endif // ARM_COMPUTE_ENABLE_FP16
case DataType::F32:
- return Reducer<RedOpYZW<float, 4>>::reduceZ(window, input, output, RedOpYZW<float, 4>(), op);
+ {
+ _func = REGISTER_FP32_NEON(cpu::reduce_RedOpYZW_reduceZ_float32_4);
+ break;
+ }
case DataType::S32:
- return Reducer<RedOpYZW<int32_t, 4>>::reduceZ(window, input, output, RedOpYZW<int32_t, 4>(), op);
+ {
+ _func = REGISTER_INTEGER_NEON(cpu::reduce_RedOpYZW_reduceZ_S32_4);
+ break;
+ }
default:
+ {
+ std::cout << int(_input->info()->data_type()) << std::endl;
ARM_COMPUTE_ERROR("Not supported");
+ break;
+ }
}
+ break;
+ }
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);
+ {
+ _func = REGISTER_QASYMM8_NEON(cpu::reduce_RedOpYZW_reduceW_qasymm8);
+ break;
+ }
case DataType::QASYMM8_SIGNED:
- return Reducer<RedOpYZW_quantized<int8_t>>::reduceW(window, input, output,
- RedOpYZW_quantized<int8_t>(), op);
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ {
+ _func = REGISTER_QASYMM8_SIGNED_NEON(cpu::reduce_RedOpYZW_reduceW_qasymm8_signed);
+ break;
+ }
+#ifdef ARM_COMPUTE_ENABLE_FP16
case DataType::F16:
- return Reducer<RedOpYZW<float16_t, 8>>::reduceW(window, input, output, RedOpYZW<float16_t, 8>(),
- op);
-#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ {
+ _func = REGISTER_FP16_NEON(cpu::reduce_RedOpYZW_reduceW_float16_8);
+ break;
+ }
+#endif // ARM_COMPUTE_ENABLE_FP16
case DataType::F32:
- return Reducer<RedOpYZW<float, 4>>::reduceW(window, input, output, RedOpYZW<float, 4>(), op);
+ {
+ _func = REGISTER_FP32_NEON(cpu::reduce_RedOpYZW_reduceW_float32_4);
+ break;
+ }
case DataType::S32:
- return Reducer<RedOpYZW<int32_t, 4>>::reduceW(window, input, output, RedOpYZW<int32_t, 4>(), op);
+ {
+ _func = REGISTER_INTEGER_NEON(cpu::reduce_RedOpYZW_reduceW_S32_4);
+ break;
+ }
default:
+ {
ARM_COMPUTE_ERROR("Not supported");
+ break;
+ }
}
+ break;
+ }
default:
+ {
ARM_COMPUTE_ERROR("Unsupported reduction axis");
+ break;
+ }
}
}
@@ -1819,10 +293,9 @@
return Status{};
}
-} // namespace
NEReductionOperationKernel::NEReductionOperationKernel()
- : _input(nullptr), _output(nullptr), _reduction_axis(0), _op(ReductionOperation::SUM_SQUARE)
+ : _func(nullptr), _input(nullptr), _output(nullptr), _reduction_axis(0), _op(ReductionOperation::SUM_SQUARE)
{
}
@@ -1856,6 +329,8 @@
.set_data_type(output_data_type)
.reset_padding()
.set_is_resizable(true));
+ // Determine the reduction function
+ NEReductionOperationKernel::reduce_op();
}
Status NEReductionOperationKernel::validate(const ITensorInfo *input,
@@ -1874,6 +349,6 @@
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
- reduce_op(window, _input, _output, _reduction_axis, _op);
+ (*_func)(window, _input, _output, _op);
}
} // namespace arm_compute
diff --git a/src/core/NEON/kernels/NEReductionOperationKernel.h b/src/core/NEON/kernels/NEReductionOperationKernel.h
index 78bec62..407e5de 100644
--- a/src/core/NEON/kernels/NEReductionOperationKernel.h
+++ b/src/core/NEON/kernels/NEReductionOperationKernel.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017-2021 Arm Limited.
+ * Copyright (c) 2017-2021, 2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -21,8 +21,8 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#ifndef ARM_COMPUTE_NEREDUCTIONOPERATIONKERNEL_H
-#define ARM_COMPUTE_NEREDUCTIONOPERATIONKERNEL_H
+#ifndef ACL_SRC_CORE_NEON_KERNELS_NEREDUCTIONOPERATIONKERNEL_H
+#define ACL_SRC_CORE_NEON_KERNELS_NEREDUCTIONOPERATIONKERNEL_H
#include "src/core/NEON/INEKernel.h"
@@ -80,14 +80,24 @@
static Status
validate(const ITensorInfo *input, const ITensorInfo *output, unsigned int axis, ReductionOperation op);
+private:
// Inherited methods overridden:
void run(const Window &window, const ThreadInfo &info) override;
+ /** Common signature for all the specialized Reduction functions
+ *
+ * @param[in] window Region on which to execute the kernel.
+ */
+ using ReductionFunction = void (*)(const Window &window, const ITensor *in, ITensor *out, ReductionOperation op);
-private:
+ /** Populate the _func with the right reduction operation handler
+ */
+ void reduce_op();
+
+ ReductionFunction _func;
const ITensor *_input;
ITensor *_output;
unsigned int _reduction_axis;
ReductionOperation _op;
};
} // namespace arm_compute
-#endif /*ARM_COMPUTE_NEREDUCTIONOPERATIONKERNEL_H */
+#endif // ACL_SRC_CORE_NEON_KERNELS_NEREDUCTIONOPERATIONKERNEL_H
diff --git a/src/cpu/kernels/reduction_layer/generic/neon/fp16.cpp b/src/cpu/kernels/reduction_layer/generic/neon/fp16.cpp
new file mode 100644
index 0000000..41584e9
--- /dev/null
+++ b/src/cpu/kernels/reduction_layer/generic/neon/fp16.cpp
@@ -0,0 +1,65 @@
+/*
+ * 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.
+ */
+#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS)
+
+#include "src/cpu/kernels/reduction_layer/generic/neon/impl_fp16.h"
+
+namespace arm_compute
+{
+namespace cpu
+{
+void reduce_RedOpX_reduceX_float16_8(const Window &window,
+ const ITensor *input,
+ ITensor *output,
+ const ReductionOperation op)
+{
+ return Reducer<RedOpX<float16_t, 8>>::reduceX(window, input, output, RedOpX<float16_t, 8>(), op);
+}
+
+void reduce_RedOpYZW_reduceY_float16_8(const Window &window,
+ const ITensor *input,
+ ITensor *output,
+ const ReductionOperation op)
+{
+ return Reducer<RedOpYZW<float16_t, 8>>::reduceY(window, input, output, RedOpYZW<float16_t, 8>(), op);
+}
+
+void reduce_RedOpYZW_reduceZ_float16_8(const Window &window,
+ const ITensor *input,
+ ITensor *output,
+ const ReductionOperation op)
+{
+ return Reducer<RedOpYZW<float16_t, 8>>::reduceZ(window, input, output, RedOpYZW<float16_t, 8>(), op);
+}
+
+void reduce_RedOpYZW_reduceW_float16_8(const Window &window,
+ const ITensor *input,
+ ITensor *output,
+ const ReductionOperation op)
+{
+ return Reducer<RedOpYZW<float16_t, 8>>::reduceW(window, input, output, RedOpYZW<float16_t, 8>(), op);
+}
+} // namespace cpu
+} // namespace arm_compute
+#endif /* defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS) */
diff --git a/src/cpu/kernels/reduction_layer/generic/neon/fp32.cpp b/src/cpu/kernels/reduction_layer/generic/neon/fp32.cpp
new file mode 100644
index 0000000..6f5f13e
--- /dev/null
+++ b/src/cpu/kernels/reduction_layer/generic/neon/fp32.cpp
@@ -0,0 +1,73 @@
+/*
+ * 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.
+ */
+
+#include "src/cpu/kernels/reduction_layer/generic/neon/impl.h"
+
+namespace arm_compute
+{
+namespace cpu
+{
+void reduce_RedOpYZW_complex_reduceZ_float32_4_2_SUM(const Window &window,
+ const ITensor *input,
+ ITensor *output,
+ const ReductionOperation op)
+{
+ Reducer<RedOpYZW_complex<float, 4, 2, ReductionOperation::SUM>>::reduceZ(
+ window, input, output, RedOpYZW_complex<float, 4, 2, ReductionOperation::SUM>(), op);
+}
+
+void reduce_RedOpX_reduceX_float32_4(const Window &window,
+ const ITensor *input,
+ ITensor *output,
+ const ReductionOperation op)
+{
+ return Reducer<RedOpX<float, 4>>::reduceX(window, input, output, RedOpX<float, 4>(), op);
+}
+
+void reduce_RedOpYZW_reduceY_float32_4(const Window &window,
+ const ITensor *input,
+ ITensor *output,
+ const ReductionOperation op)
+{
+ return Reducer<RedOpYZW<float, 4>>::reduceY(window, input, output, RedOpYZW<float, 4>(), op);
+}
+
+void reduce_RedOpYZW_reduceZ_float32_4(const Window &window,
+ const ITensor *input,
+ ITensor *output,
+ const ReductionOperation op)
+{
+ return Reducer<RedOpYZW<float, 4>>::reduceZ(window, input, output, RedOpYZW<float, 4>(), op);
+}
+
+void reduce_RedOpYZW_reduceW_float32_4(const Window &window,
+ const ITensor *input,
+ ITensor *output,
+ const ReductionOperation op)
+{
+ return Reducer<RedOpYZW<float, 4>>::reduceW(window, input, output, RedOpYZW<float, 4>(), op);
+}
+
+} // namespace cpu
+} // namespace arm_compute
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 0000000..611d83c
--- /dev/null
+++ b/src/cpu/kernels/reduction_layer/generic/neon/impl.h
@@ -0,0 +1,1543 @@
+/*
+ * 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/ITensor.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);
+}
+
+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]);
+ }
+ 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
diff --git a/src/cpu/kernels/reduction_layer/generic/neon/impl_fp16.h b/src/cpu/kernels/reduction_layer/generic/neon/impl_fp16.h
new file mode 100644
index 0000000..c7ca36d
--- /dev/null
+++ b/src/cpu/kernels/reduction_layer/generic/neon/impl_fp16.h
@@ -0,0 +1,718 @@
+/*
+ * 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_FP16_H
+#define ACL_SRC_CPU_KERNELS_REDUCTION_LAYER_GENERIC_NEON_IMPL_FP16_H
+
+#include "arm_compute/core/Coordinates.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+
+#include "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
+void combine_and_store(int16x8_t t1, int16x8_t t2, Iterator &output, int offset = 0)
+{
+ auto res = wrapper::vcombine(wrapper::vqmovn(t1), wrapper::vqmovn(t2));
+ wrapper::vstore(reinterpret_cast<int8_t *>(output.ptr() + offset), res);
+}
+
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+uint32x4x4_t
+calculate_index(uint32_t idx, float16x8_t a, float16x8_t b, uint32x4x4_t c, ReductionOperation op, int axis)
+{
+ uint32x4x2_t mask{0};
+ uint16x8_t mask_u16{0};
+ if (op == ReductionOperation::ARG_IDX_MIN)
+ {
+ mask_u16 = wrapper::vcgt(b, a);
+ }
+ else
+ {
+ mask_u16 = wrapper::vclt(b, a);
+ }
+ mask.val[0] = wrapper::vmovl(wrapper::vgetlow(mask_u16));
+ mask.val[1] = wrapper::vmovl(wrapper::vgethigh(mask_u16));
+ uint32x4x2_t vec_idx = {{{idx + 0, idx + 1, idx + 2, idx + 3}, {idx + 4, idx + 5, idx + 6, idx + 7}}};
+ if (axis != 0)
+ {
+ vec_idx.val[0] = wrapper::vdup_n(idx, wrapper::traits::vector_128_tag{});
+ vec_idx.val[1] = wrapper::vdup_n(idx, wrapper::traits::vector_128_tag{});
+ }
+ uint32x4x4_t res = {wrapper::vbsl(mask.val[0], vec_idx.val[0], c.val[0]),
+ wrapper::vbsl(mask.val[1], vec_idx.val[1], c.val[1]), 0, 0};
+
+ return res;
+}
+
+// Helper function to calculate the minimum value of the input vector. All the elements in the output vector contain the min value.
+inline float16x4_t calculate_min(float16x8_t in)
+{
+ auto pmin = wrapper::vpmin(wrapper::vgethigh(in), wrapper::vgetlow(in));
+ pmin = wrapper::vpmin(pmin, pmin);
+ return wrapper::vpmin(pmin, pmin);
+}
+// Helper function to calculate the maximum value of the input vector. All the elements in the output vector contain the max value.
+inline float16x4_t calculate_max(float16x8_t in)
+{
+ auto pmax = wrapper::vpmax(wrapper::vgethigh(in), wrapper::vgetlow(in));
+ pmax = wrapper::vpmax(pmax, pmax);
+ return wrapper::vpmax(pmax, pmax);
+}
+
+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(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(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(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(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, 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);
+ }
+};
+
+} // namespace arm_compute
+#endif // ACL_SRC_CPU_KERNELS_REDUCTION_LAYER_GENERIC_NEON_IMPL_FP16_H
diff --git a/src/cpu/kernels/reduction_layer/generic/neon/integer.cpp b/src/cpu/kernels/reduction_layer/generic/neon/integer.cpp
new file mode 100644
index 0000000..ad66b45
--- /dev/null
+++ b/src/cpu/kernels/reduction_layer/generic/neon/integer.cpp
@@ -0,0 +1,62 @@
+/*
+ * 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.
+ */
+
+#include "src/cpu/kernels/reduction_layer/generic/neon/impl.h"
+
+namespace arm_compute
+{
+namespace cpu
+{
+void reduce_RedOpX_reduceX_S32_4(const Window &window,
+ const ITensor *input,
+ ITensor *output,
+ const ReductionOperation op)
+{
+ return Reducer<RedOpX<int32_t, 4>>::reduceX(window, input, output, RedOpX<int32_t, 4>(), op);
+}
+
+void reduce_RedOpYZW_reduceY_S32_4(const Window &window,
+ const ITensor *input,
+ ITensor *output,
+ const ReductionOperation op)
+{
+ return Reducer<RedOpYZW<int32_t, 4>>::reduceY(window, input, output, RedOpYZW<int32_t, 4>(), op);
+}
+void reduce_RedOpYZW_reduceZ_S32_4(const Window &window,
+ const ITensor *input,
+ ITensor *output,
+ const ReductionOperation op)
+{
+ return Reducer<RedOpYZW<int32_t, 4>>::reduceZ(window, input, output, RedOpYZW<int32_t, 4>(), op);
+}
+
+void reduce_RedOpYZW_reduceW_S32_4(const Window &window,
+ const ITensor *input,
+ ITensor *output,
+ const ReductionOperation op)
+{
+ return Reducer<RedOpYZW<int32_t, 4>>::reduceW(window, input, output, RedOpYZW<int32_t, 4>(), op);
+}
+} // namespace cpu
+} // namespace arm_compute
diff --git a/src/cpu/kernels/reduction_layer/generic/neon/list.h b/src/cpu/kernels/reduction_layer/generic/neon/list.h
new file mode 100644
index 0000000..947c28a
--- /dev/null
+++ b/src/cpu/kernels/reduction_layer/generic/neon/list.h
@@ -0,0 +1,66 @@
+/*
+ * 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_LIST_H
+#define ACL_SRC_CPU_KERNELS_REDUCTION_LAYER_GENERIC_NEON_LIST_H
+
+#include "arm_compute/core/Helpers.h"
+
+namespace arm_compute
+{
+namespace cpu
+{
+
+#define DECLARE_REDUCTION_KERNEL(func_name) \
+ void func_name(const Window &window, const ITensor *in, ITensor *out, const ReductionOperation op)
+
+DECLARE_REDUCTION_KERNEL(reduce_RedOpYZW_complex_reduceZ_float32_4_2_SUM);
+DECLARE_REDUCTION_KERNEL(reduce_RedOpX_reduceX_float32_4);
+DECLARE_REDUCTION_KERNEL(reduce_RedOpYZW_reduceY_float32_4);
+DECLARE_REDUCTION_KERNEL(reduce_RedOpYZW_reduceZ_float32_4);
+DECLARE_REDUCTION_KERNEL(reduce_RedOpYZW_reduceW_float32_4);
+
+DECLARE_REDUCTION_KERNEL(reduce_RedOpX_reduceX_float16_8);
+DECLARE_REDUCTION_KERNEL(reduce_RedOpYZW_reduceY_float16_8);
+DECLARE_REDUCTION_KERNEL(reduce_RedOpYZW_reduceZ_float16_8);
+DECLARE_REDUCTION_KERNEL(reduce_RedOpYZW_reduceW_float16_8);
+
+DECLARE_REDUCTION_KERNEL(reduce_RedOpX_reduceX_S32_4);
+DECLARE_REDUCTION_KERNEL(reduce_RedOpYZW_reduceY_S32_4);
+DECLARE_REDUCTION_KERNEL(reduce_RedOpYZW_reduceZ_S32_4);
+DECLARE_REDUCTION_KERNEL(reduce_RedOpYZW_reduceW_S32_4);
+
+DECLARE_REDUCTION_KERNEL(reduce_RedOpX_reduceX_qasymm8);
+DECLARE_REDUCTION_KERNEL(reduce_RedOpYZW_reduceY_qasymm8);
+DECLARE_REDUCTION_KERNEL(reduce_RedOpYZW_reduceZ_qasymm8);
+DECLARE_REDUCTION_KERNEL(reduce_RedOpYZW_reduceW_qasymm8);
+
+DECLARE_REDUCTION_KERNEL(reduce_RedOpX_reduceX_qasymm8_signed);
+DECLARE_REDUCTION_KERNEL(reduce_RedOpYZW_reduceY_qasymm8_signed);
+DECLARE_REDUCTION_KERNEL(reduce_RedOpYZW_reduceZ_qasymm8_signed);
+DECLARE_REDUCTION_KERNEL(reduce_RedOpYZW_reduceW_qasymm8_signed);
+
+#undef DECLARE_REDUCTION_KERNEL
+} // namespace cpu
+} // namespace arm_compute
+#endif // ACL_SRC_CPU_KERNELS_REDUCTION_LAYER_GENERIC_NEON_LIST_H
diff --git a/src/cpu/kernels/reduction_layer/generic/neon/qasymm8.cpp b/src/cpu/kernels/reduction_layer/generic/neon/qasymm8.cpp
new file mode 100644
index 0000000..bc711c6
--- /dev/null
+++ b/src/cpu/kernels/reduction_layer/generic/neon/qasymm8.cpp
@@ -0,0 +1,63 @@
+/*
+ * 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.
+ */
+
+#include "src/cpu/kernels/reduction_layer/generic/neon/impl.h"
+
+namespace arm_compute
+{
+namespace cpu
+{
+void reduce_RedOpX_reduceX_qasymm8(const Window &window,
+ const ITensor *input,
+ ITensor *output,
+ const ReductionOperation op)
+{
+ return Reducer<RedOpX_quantized<uint8_t>>::reduceX(window, input, output, RedOpX_quantized<uint8_t>(), op);
+}
+
+void reduce_RedOpYZW_reduceY_qasymm8(const Window &window,
+ const ITensor *input,
+ ITensor *output,
+ const ReductionOperation op)
+{
+ return Reducer<RedOpYZW_quantized<uint8_t>>::reduceY(window, input, output, RedOpYZW_quantized<uint8_t>(), op);
+}
+
+void reduce_RedOpYZW_reduceZ_qasymm8(const Window &window,
+ const ITensor *input,
+ ITensor *output,
+ const ReductionOperation op)
+{
+ return Reducer<RedOpYZW_quantized<uint8_t>>::reduceZ(window, input, output, RedOpYZW_quantized<uint8_t>(), op);
+}
+
+void reduce_RedOpYZW_reduceW_qasymm8(const Window &window,
+ const ITensor *input,
+ ITensor *output,
+ const ReductionOperation op)
+{
+ return Reducer<RedOpYZW_quantized<uint8_t>>::reduceW(window, input, output, RedOpYZW_quantized<uint8_t>(), op);
+}
+} // namespace cpu
+} // namespace arm_compute
diff --git a/src/cpu/kernels/reduction_layer/generic/neon/qasymm8_signed.cpp b/src/cpu/kernels/reduction_layer/generic/neon/qasymm8_signed.cpp
new file mode 100644
index 0000000..10ac3d6
--- /dev/null
+++ b/src/cpu/kernels/reduction_layer/generic/neon/qasymm8_signed.cpp
@@ -0,0 +1,63 @@
+/*
+ * 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.
+ */
+
+#include "src/cpu/kernels/reduction_layer/generic/neon/impl.h"
+
+namespace arm_compute
+{
+namespace cpu
+{
+void reduce_RedOpX_reduceX_qasymm8_signed(const Window &window,
+ const ITensor *input,
+ ITensor *output,
+ const ReductionOperation op)
+{
+ return Reducer<RedOpX_quantized<int8_t>>::reduceX(window, input, output, RedOpX_quantized<int8_t>(), op);
+}
+
+void reduce_RedOpYZW_reduceY_qasymm8_signed(const Window &window,
+ const ITensor *input,
+ ITensor *output,
+ const ReductionOperation op)
+{
+ return Reducer<RedOpYZW_quantized<int8_t>>::reduceY(window, input, output, RedOpYZW_quantized<int8_t>(), op);
+}
+
+void reduce_RedOpYZW_reduceZ_qasymm8_signed(const Window &window,
+ const ITensor *input,
+ ITensor *output,
+ const ReductionOperation op)
+{
+ return Reducer<RedOpYZW_quantized<int8_t>>::reduceZ(window, input, output, RedOpYZW_quantized<int8_t>(), op);
+}
+
+void reduce_RedOpYZW_reduceW_qasymm8_signed(const Window &window,
+ const ITensor *input,
+ ITensor *output,
+ const ReductionOperation op)
+{
+ return Reducer<RedOpYZW_quantized<int8_t>>::reduceW(window, input, output, RedOpYZW_quantized<int8_t>(), op);
+}
+} // namespace cpu
+} // namespace arm_compute