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review.mlplatform.org / ml / ComputeLibrary / 7891a73ef36f4ad7b71069b3c57694f85bb79454 / . / src / cpu / kernels / pool2d / neon / quantized.h
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/*
* Copyright (c) 2021 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 SRC_CORE_NEON_KERNELS_QUANTIZED_H
#define SRC_CORE_NEON_KERNELS_QUANTIZED_H
#include "arm_compute/core/Types.h"
#include "arm_compute/core/utils/misc/Traits.h"
#include "src/core/NEON/NEAsymm.h"
#include "src/core/NEON/NEFixedPoint.h"
#include "src/core/NEON/NEMath.h"
#include "src/core/NEON/wrapper/wrapper.h"
#include <arm_neon.h>
namespace arm_compute
{
namespace cpu
{
template <typename T>
inline typename std::enable_if<std::is_same<T, int8_t>::value, int8_t>::type
quantize(float val, const UniformQuantizationInfo &info)
{
return quantize_qasymm8_signed(val, info);
}
template <typename T>
inline typename std::enable_if<std::is_same<T, uint8_t>::value, uint8_t>::type
quantize(float val, const UniformQuantizationInfo &info)
{
return quantize_qasymm8(val, info);
}
template <typename T>
inline T vcvtq_q32_f32(float32x4_t values);
template <>
inline uint32x4_t vcvtq_q32_f32(float32x4_t values)
{
return vcvtq_u32_f32(values);
}
template <>
inline int32x4_t vcvtq_q32_f32(float32x4_t values)
{
return vcvtq_s32_f32(values);
}
template <typename T>
inline float32x4_t vcvtq_f32_q32(T values);
template <>
inline float32x4_t vcvtq_f32_q32(uint32x4_t values)
{
return vcvtq_f32_u32(values);
}
template <>
inline float32x4_t vcvtq_f32_q32(int32x4_t values)
{
return vcvtq_f32_s32(values);
}
template <typename Tout>
inline Tout vrequantize_pooling_with_scale(const float32x4x4_t &acc, const float quant_rescale, const float scale_pooling, const int32_t new_offset);
template <>
inline uint8x16_t vrequantize_pooling_with_scale(const float32x4x4_t &acc, const float quant_rescale, const float scale_pooling, const int32_t new_offset)
{
const float new_scale = quant_rescale / scale_pooling;
return vquantize(acc, UniformQuantizationInfo(new_scale, new_offset));
}
template <>
inline int8x16_t vrequantize_pooling_with_scale(const float32x4x4_t &acc, const float quant_rescale, const float scale_pooling, const int32_t new_offset)
{
const float new_scale = quant_rescale / scale_pooling;
return vquantize_signed(acc, UniformQuantizationInfo(new_scale, new_offset));
}
template <typename Tin, typename Tout>
inline Tout vrequantize_pooling(Tin vec1, Tin vec2, const UniformQuantizationInfo &requant_qinfo);
template <>
inline uint8x16_t vrequantize_pooling(uint8x8_t vec1, uint8x8_t vec2, const UniformQuantizationInfo &requant_qinfo)
{
const float32x4x4_t acc =
{
{
vcvtq_f32_u32(vmovl_u16(vget_low_u16(vmovl_u8((vec1))))),
vcvtq_f32_u32(vmovl_u16(vget_high_u16(vmovl_u8((vec1))))),
vcvtq_f32_u32(vmovl_u16(vget_low_u16(vmovl_u8((vec2))))),
vcvtq_f32_u32(vmovl_u16(vget_high_u16(vmovl_u8((vec2))))),
}
};
return vquantize(acc, requant_qinfo);
}
template <>
inline int8x16_t vrequantize_pooling(int8x8_t vec1, int8x8_t vec2, const UniformQuantizationInfo &requant_qinfo)
{
const float32x4x4_t acc =
{
{
vcvtq_f32_s32(vmovl_s16(vget_low_s16(vmovl_s8((vec1))))),
vcvtq_f32_s32(vmovl_s16(vget_high_s16(vmovl_s8((vec1))))),
vcvtq_f32_s32(vmovl_s16(vget_low_s16(vmovl_s8((vec2))))),
vcvtq_f32_s32(vmovl_s16(vget_high_s16(vmovl_s8((vec2))))),
}
};
return vquantize_signed(acc, requant_qinfo);
}
template <typename T>
inline T vrequantize_pooling(T &vec, const UniformQuantizationInfo &requant_qinfo);
template <>
inline uint8x8_t vrequantize_pooling(uint8x8_t &vec, const UniformQuantizationInfo &requant_qinfo)
{
const float32x4x2_t acc =
{
{
vcvtq_f32_u32(vmovl_u16(vget_low_u16(vmovl_u8((vec))))),
vcvtq_f32_u32(vmovl_u16(vget_high_u16(vmovl_u8((vec))))),
}
};
return vquantize(acc, requant_qinfo);
}
template <>
inline int8x8_t vrequantize_pooling(int8x8_t &vec, const UniformQuantizationInfo &requant_qinfo)
{
const float32x4x2_t acc =
{
{
vcvtq_f32_s32(vmovl_s16(vget_low_s16(vmovl_s8((vec))))),
vcvtq_f32_s32(vmovl_s16(vget_high_s16(vmovl_s8((vec))))),
}
};
return vquantize_signed(acc, requant_qinfo);
}
inline float calculate_avg_scale(bool exclude_padding, DataLayout data_layout, const Coordinates &id, const int pool_size_x, const int pool_size_y, const int upper_bound_w, const int upper_bound_h,
const int pad_x, const int pad_y, const int stride_x, const int stride_y)
{
const unsigned int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
const unsigned int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
int start_x = id[idx_width] * stride_x - pad_x;
int start_y = id[idx_height] * stride_y - pad_y;
const int end_x = std::min(start_x + pool_size_x, upper_bound_w);
const int end_y = std::min(start_y + pool_size_y, upper_bound_h);
if(exclude_padding)
{
start_x = std::max(0, start_x);
start_y = std::max(0, start_y);
}
return 1.f / ((end_y - start_y) * (end_x - start_x));
}
template <typename T>
void poolingMxN_q8_neon_nhwc(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window)
{
ARM_COMPUTE_UNUSED(dst1);
const int window_start_x = window.x().start();
const int window_end_x = window.x().end();
const int window_step_x = 16;
const int window_half_step_x = window_step_x / 2;
Window window_out = window;
window_out.set(Window::DimX, Window::Dimension(0, 1, 1));
Iterator in(src, window_src);
Iterator out(dst0, window_out);
using q8x8_t = typename wrapper::traits::neon_vector<T, 8>::type;
using q8x16_t = typename wrapper::traits::neon_vector<T, 16>::type;
using q16_t = typename wrapper::traits::promote_t<T>;
using q16x8_t = typename wrapper::traits::neon_vector<q16_t, 8>::type;
using q32_t = typename wrapper::traits::promote_t<q16_t>;
using q32x4_t = typename wrapper::traits::neon_vector<q32_t, 4>::type;
const int pool_size_x = pool_info.is_global_pooling ? src->info()->tensor_shape().y() : pool_info.pool_size.width;
const int pool_size_y = pool_info.is_global_pooling ? src->info()->tensor_shape().z() : pool_info.pool_size.height;
const int pool_pad_right = pool_info.pad_stride_info.pad_right();
const int pool_pad_top = pool_info.pad_stride_info.pad_top();
const int pool_pad_left = pool_info.pad_stride_info.pad_left();
const int pool_pad_bottom = pool_info.pad_stride_info.pad_bottom();
int pool_stride_x = 0;
int pool_stride_y = 0;
std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride();
const int upper_bound_w = src->info()->dimension(1) + (pool_info.exclude_padding ? 0 : pool_pad_right);
const int upper_bound_h = src->info()->dimension(2) + (pool_info.exclude_padding ? 0 : pool_pad_bottom);
const float32x4_t half_scale_v = vdupq_n_f32(0.5f);
const UniformQuantizationInfo src_qinfo = src->info()->quantization_info().uniform();
const UniformQuantizationInfo dst_qinfo = dst0->info()->quantization_info().uniform();
const float quant_rescale = dst_qinfo.scale / src_qinfo.scale;
// "new_offset" doesn't have to consider the "half_scale_v" in its computation
// With a requantization performed in a single step there won't be uncertainties introduced
const int32_t new_offset = dst_qinfo.offset - static_cast<int32_t>(static_cast<float>(src_qinfo.offset) / quant_rescale);
const float requant_scale = dst_qinfo.scale / src_qinfo.scale;
const int32_t requant_offset = dst_qinfo.offset - static_cast<int32_t>(static_cast<float>(src_qinfo.offset) / requant_scale);
const UniformQuantizationInfo requant_qinfo = UniformQuantizationInfo(requant_scale, requant_offset);
execute_window_loop(window_out, [&](const Coordinates & id)
{
const int idx_width = id.y() * pool_stride_x;
const int idx_height = id.z() * pool_stride_y;
const int pool_limit_y = pool_pad_top - idx_height;
const int pool_limit_x = pool_pad_left - idx_width;
const int pool_start_y = std::max(0, window_src.z().start() + pool_limit_y);
const int pool_end_y = std::min(pool_size_y, window_src.z().end() + pool_limit_y);
const int pool_start_x = std::max(0, window_src.y().start() + pool_limit_x);
const int pool_end_x = std::min(pool_size_x, window_src.y().end() + pool_limit_x);
int x_off = window_start_x;
for(; x_off <= (window_end_x - window_step_x); x_off += window_step_x)
{
if(pool_info.pool_type != PoolingType::MAX)
{
q32x4_t vres1 = wrapper::vdup_n(static_cast<q32_t>(0.f), wrapper::traits::vector_128_tag{});
q32x4_t vres2 = wrapper::vdup_n(static_cast<q32_t>(0.f), wrapper::traits::vector_128_tag{});
q32x4_t vres3 = wrapper::vdup_n(static_cast<q32_t>(0.f), wrapper::traits::vector_128_tag{});
q32x4_t vres4 = wrapper::vdup_n(static_cast<q32_t>(0.f), wrapper::traits::vector_128_tag{});
// Calculate scale
const float scale = calculate_avg_scale(pool_info.exclude_padding, DataLayout::NHWC, id, pool_size_x, pool_size_y, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x,
pool_stride_y);
// Perform pooling
for(int y = pool_start_y; y < pool_end_y; ++y)
{
for(int x = pool_start_x; x < pool_end_x; ++x)
{
const q8x16_t data = wrapper::vloadq(reinterpret_cast<const T *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int>
(src->info()->strides_in_bytes().z())) + x_off);
const q16x8_t data_q16 = wrapper::vmovl(wrapper::vgetlow(data));
const q16x8_t data2_q16 = wrapper::vmovl(wrapper::vgethigh(data));
vres1 = wrapper::vadd(vres1, wrapper::vmovl(wrapper::vgetlow(data_q16)));
vres2 = wrapper::vadd(vres2, wrapper::vmovl(wrapper::vgethigh(data_q16)));
vres3 = wrapper::vadd(vres3, wrapper::vmovl(wrapper::vgetlow(data2_q16)));
vres4 = wrapper::vadd(vres4, wrapper::vmovl(wrapper::vgethigh(data2_q16)));
}
}
if(src_qinfo != dst_qinfo)
{
const float32x4x4_t vres =
{
{
vcvtq_f32_q32(vres1),
vcvtq_f32_q32(vres2),
vcvtq_f32_q32(vres3),
vcvtq_f32_q32(vres4),
}
};
const auto requantized_dst = vrequantize_pooling_with_scale<q8x16_t>(vres, quant_rescale, scale, new_offset);
// Store result
wrapper::vstore(reinterpret_cast<T *>(out.ptr()) + x_off, wrapper::vgetlow(requantized_dst));
wrapper::vstore(reinterpret_cast<T *>(out.ptr()) + x_off + 8, wrapper::vgethigh(requantized_dst));
}
else
{
const float32x4_t scale_v = vdupq_n_f32(scale);
// Divide by scale and add 0.5f to round to nearest instead of rounding towards zero
vres1 = vcvtq_q32_f32<q32x4_t>(wrapper::vmla(half_scale_v, vcvtq_f32_q32(vres1), scale_v));
vres2 = vcvtq_q32_f32<q32x4_t>(wrapper::vmla(half_scale_v, vcvtq_f32_q32(vres2), scale_v));
vres3 = vcvtq_q32_f32<q32x4_t>(wrapper::vmla(half_scale_v, vcvtq_f32_q32(vres3), scale_v));
vres4 = vcvtq_q32_f32<q32x4_t>(wrapper::vmla(half_scale_v, vcvtq_f32_q32(vres4), scale_v));
const q8x8_t res1 = wrapper::vmovn(wrapper::vcombine(wrapper::vmovn(vres1), wrapper::vmovn(vres2)));
const q8x8_t res2 = wrapper::vmovn(wrapper::vcombine(wrapper::vmovn(vres3), wrapper::vmovn(vres4)));
// Store result
wrapper::vstore(reinterpret_cast<T *>(out.ptr()) + x_off, res1);
wrapper::vstore(reinterpret_cast<T *>(out.ptr()) + x_off + 8, res2);
}
}
else
{
q8x16_t vres = wrapper::vdup_n(std::numeric_limits<T>::min(), wrapper::traits::vector_128_tag{});
for(int y = pool_start_y; y < pool_end_y; ++y)
{
for(int x = pool_start_x; x < pool_end_x; ++x)
{
const q8x16_t data = wrapper::vloadq(reinterpret_cast<const T *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int>
(src->info()->strides_in_bytes().z())) + x_off);
vres = wrapper::vmax(vres, data);
}
}
// Store result
wrapper::vstore(reinterpret_cast<T *>(out.ptr()) + x_off, (src_qinfo != dst_qinfo) ? vrequantize_pooling<q8x8_t, q8x16_t>(wrapper::vgetlow(vres), wrapper::vgethigh(vres),
requant_qinfo) :
vres);
}
}
if(pool_info.pool_type == PoolingType::MAX)
{
for(; x_off <= (window_end_x - window_half_step_x); x_off += window_half_step_x)
{
q8x8_t vres = wrapper::vdup_n(std::numeric_limits<T>::min(), wrapper::traits::vector_64_tag{});
for(int y = pool_start_y; y < pool_end_y; ++y)
{
for(int x = pool_start_x; x < pool_end_x; ++x)
{
const q8x8_t data = wrapper::vload(reinterpret_cast<const T *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int>
(src->info()->strides_in_bytes().z())) + x_off);
vres = wrapper::vmax(vres, data);
}
}
// Store result
wrapper::vstore(reinterpret_cast<T *>(out.ptr()) + x_off,
(src_qinfo != dst_qinfo) ? vrequantize_pooling<q8x8_t>(vres, requant_qinfo) : vres);
}
}
// Left-overs loop
for(; x_off < window_end_x; ++x_off)
{
if(pool_info.pool_type != PoolingType::MAX)
{
q32_t res = static_cast<q32_t>(0.f);
// Calculate scale
const float scale = calculate_avg_scale(pool_info.exclude_padding, DataLayout::NHWC, id, pool_size_x, pool_size_y, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x,
pool_stride_y);
// Perform pooling
for(int y = pool_start_y; y < pool_end_y; ++y)
{
for(int x = pool_start_x; x < pool_end_x; ++x)
{
const T data = *(reinterpret_cast<const T *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int>
(src->info()->strides_in_bytes().z())) + x_off);
res += data;
}
}
if(src_qinfo != dst_qinfo)
{
const float res_f = static_cast<float>(res);
const float new_scale = quant_rescale / scale;
const auto requantized_dst = quantize<T>(res_f, UniformQuantizationInfo(new_scale, new_offset));
// Store result
*(reinterpret_cast<T *>(out.ptr()) + x_off) = requantized_dst;
}
else
{
// Divide by scale and add 0.5f to round to nearest instead of rounding towards zero
res = static_cast<T>(0.5f + static_cast<float>(res) * scale);
// Store result
*(reinterpret_cast<T *>(out.ptr()) + x_off) = res;
}
}
else
{
T res = std::numeric_limits<T>::min();
for(int y = pool_start_y; y < pool_end_y; ++y)
{
for(int x = pool_start_x; x < pool_end_x; ++x)
{
const T data = *(reinterpret_cast<const T *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int>
(src->info()->strides_in_bytes().z())) + x_off);
res = std::max(res, data);
}
}
// Store result
if(src_qinfo != dst_qinfo)
{
const float res_f = static_cast<float>(res);
*(reinterpret_cast<T *>(out.ptr()) + x_off) = quantize<T>(res_f, requant_qinfo);
}
else
{
*(reinterpret_cast<T *>(out.ptr()) + x_off) = res;
}
}
}
},
in, out);
}
#if defined(ENABLE_NCHW_KERNELS)
template <typename T, typename TVec>
inline void scale_vector_q16x8(bool exclude_padding, TVec &v, const Coordinates &id, int id_offset, int step,
const int pool_size, const int upper_bound_w, const int upper_bound_h,
const int pad_x, const int pad_y, const int stride_x, const int stride_y)
{
int start_x = (id.x() + id_offset) * stride_x - pad_x;
int start_y = id.y() * stride_y - pad_y;
const int end_y = std::min(start_y + pool_size, upper_bound_h);
if(exclude_padding)
{
start_y = std::max(0, start_y);
}
std::array<T, 8> elems =
{
{
wrapper::vgetlane(v, 0),
wrapper::vgetlane(v, 1),
wrapper::vgetlane(v, 2),
wrapper::vgetlane(v, 3),
wrapper::vgetlane(v, 4),
wrapper::vgetlane(v, 5),
wrapper::vgetlane(v, 6),
wrapper::vgetlane(v, 7),
}
};
for(auto &el : elems)
{
int c_start_x = start_x;
const int end_x = std::min(c_start_x + pool_size, upper_bound_w);
if(exclude_padding)
{
c_start_x = std::max(0, c_start_x);
}
float scale = 1.f / ((end_y - start_y) * (end_x - c_start_x));
el *= scale;
start_x += step * stride_x;
}
v = wrapper::vsetlane(elems[0], v, 0);
v = wrapper::vsetlane(elems[1], v, 1);
v = wrapper::vsetlane(elems[2], v, 2);
v = wrapper::vsetlane(elems[3], v, 3);
v = wrapper::vsetlane(elems[4], v, 4);
v = wrapper::vsetlane(elems[5], v, 5);
v = wrapper::vsetlane(elems[6], v, 6);
v = wrapper::vsetlane(elems[7], v, 7);
}
template <typename T>
void pooling2_quantized_neon_nchw(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window)
{
ARM_COMPUTE_UNUSED(dst1);
Iterator in(src, window_src);
Iterator out(dst0, window);
/** SIMD vector types */
using q8x8_t = typename wrapper::traits::neon_vector<T, 8>::type;
using q8x16_t = typename wrapper::traits::neon_vector<T, 16>::type;
using q8x8x2_t = typename std::conditional<std::is_same<T, uint8_t>::value, uint8x8x2_t, int8x8x2_t>::type;
using q16_t = typename wrapper::traits::promote_t<T>;
using q16x4_t = typename wrapper::traits::neon_vector<q16_t, 4>::type;
using q16x8_t = typename wrapper::traits::neon_vector<q16_t, 8>::type;
using q16x8x2_t = typename wrapper::traits::neon_vector<q16_t, 16>::type;
constexpr int pool_size = 2;
int pool_stride_x = 0;
int pool_stride_y = 0;
const int pool_pad_right = pool_info.pad_stride_info.pad_right();
const int pool_pad_top = pool_info.pad_stride_info.pad_top();
const int pool_pad_left = pool_info.pad_stride_info.pad_left();
const int pool_pad_bottom = pool_info.pad_stride_info.pad_bottom();
std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride();
const int upper_bound_w = src->info()->dimension(0) + (pool_info.exclude_padding ? 0 : pool_pad_right);
const int upper_bound_h = src->info()->dimension(1) + (pool_info.exclude_padding ? 0 : pool_pad_bottom);
const T *const src_top_ptr = reinterpret_cast<const T *>(src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top))));
const T *const src_bottom_ptr = reinterpret_cast<const T *>(src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 1)));
const int scale_step_x = (pool_stride_x == 1) ? 2 : 1;
const UniformQuantizationInfo src_qinfo = src->info()->quantization_info().uniform();
const UniformQuantizationInfo dst_qinfo = dst0->info()->quantization_info().uniform();
const bool have_different_qinfo = src_qinfo != dst_qinfo;
const float requant_scale = dst_qinfo.scale / src_qinfo.scale;
const int32_t requant_offset = dst_qinfo.offset - static_cast<int32_t>(static_cast<float>(src_qinfo.offset) / requant_scale);
const UniformQuantizationInfo requant_qinfo = UniformQuantizationInfo(requant_scale, requant_offset);
execute_window_loop(window, [&](const Coordinates & id)
{
const auto top_data = wrapper::vloadq(src_top_ptr + in.offset());
const auto bottom_data = wrapper::vloadq(src_bottom_ptr + in.offset());
q8x8_t lower_res = {};
q8x8_t upper_res = {};
if(pool_info.pool_type != PoolingType::MAX)
{
const q16x8x2_t top_data_q16 = { { wrapper::vmovl(wrapper::vgetlow(top_data)), wrapper::vmovl(wrapper::vgethigh(top_data)) } };
const q16x8x2_t bottom_data_q16 = { { wrapper::vmovl(wrapper::vgetlow(bottom_data)), wrapper::vmovl(wrapper::vgethigh(bottom_data)) } };
// Add rows
const q16x8x2_t vrsum =
{
{
wrapper::vadd(top_data_q16.val[0], bottom_data_q16.val[0]),
wrapper::vadd(top_data_q16.val[1], bottom_data_q16.val[1]),
}
};
// Pair-wise add row data
const q16x4_t vpsum_1 = wrapper::vpadd(wrapper::vgetlow(vrsum.val[0]), wrapper::vgethigh(vrsum.val[0]));
const q16x4_t vpsum_2 = wrapper::vpadd(wrapper::vgetlow(vrsum.val[1]), wrapper::vgethigh(vrsum.val[1]));
q16x8_t res_lower = wrapper::vcombine(vpsum_1, vpsum_2);
// Scale lower result
scale_vector_q16x8<q16_t, q16x8_t>(pool_info.exclude_padding, res_lower, id, 0, scale_step_x,
pool_size, upper_bound_w, upper_bound_h,
pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y);
lower_res = wrapper::vmovn(res_lower);
// Compute upper result for stride_x == 1
if(pool_stride_x == 1)
{
// Shifted row sum
const q16x8x2_t vrsum_shifted =
{
{
wrapper::vext_1(vrsum.val[0], vrsum.val[1]),
wrapper::vext_1(vrsum.val[1], vrsum.val[1])
}
};
// Pair-wise add shifted row
q16x8_t res_upper = wrapper::vcombine(
wrapper::vpadd(wrapper::vgetlow(vrsum_shifted.val[0]), wrapper::vgethigh(vrsum_shifted.val[0])),
wrapper::vpadd(wrapper::vgetlow(vrsum_shifted.val[1]), wrapper::vgethigh(vrsum_shifted.val[1])));
// Scale upper result
scale_vector_q16x8<q16_t, q16x8_t>(pool_info.exclude_padding, res_upper, id, 1, 2,
pool_size, upper_bound_w, upper_bound_h,
pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y);
upper_res = wrapper::vmovn(res_upper);
}
}
else
{
const q8x16_t max_data = wrapper::vmax(top_data, bottom_data);
lower_res = wrapper::vpmax(wrapper::vgetlow(max_data), wrapper::vgethigh(max_data));
if(pool_stride_x == 1)
{
const q8x16_t max_data_shifted = wrapper::vext_1(max_data, max_data);
upper_res = wrapper::vpmax(wrapper::vgetlow(max_data_shifted), wrapper::vgethigh(max_data_shifted));
}
}
if(have_different_qinfo)
{
const auto requantized_dst = vrequantize_pooling<q8x8_t, q8x16_t>(lower_res, upper_res, requant_qinfo);
lower_res = wrapper::vgetlow(requantized_dst);
upper_res = wrapper::vgethigh(requantized_dst);
}
// Store result
if(pool_stride_x == 1)
{
const q8x8x2_t res = { { lower_res, upper_res } };
wrapper::vstore(reinterpret_cast<T *>(out.ptr()), res);
}
else
{
wrapper::vstore(reinterpret_cast<T *>(out.ptr()), lower_res);
}
},
in, out);
}
template <typename T>
void pooling3_quantized_neon_nchw(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window)
{
ARM_COMPUTE_UNUSED(dst1);
Iterator in(src, window_src);
Iterator out(dst0, window);
/** SIMD vector types */
using q8x8_t = typename wrapper::traits::neon_vector<T, 8>::type;
using q8x16_t = typename wrapper::traits::neon_vector<T, 16>::type;
using q8x8x2_t = typename std::conditional<std::is_same<T, uint8_t>::value, uint8x8x2_t, int8x8x2_t>::type;
using q16_t = typename wrapper::traits::promote_t<T>;
using q16x8_t = typename wrapper::traits::neon_vector<q16_t, 8>::type;
using q16x8x2_t = typename wrapper::traits::neon_vector<q16_t, 16>::type;
constexpr int pool_size = 3;
const int pool_pad_right = pool_info.pad_stride_info.pad_right();
const int pool_pad_top = pool_info.pad_stride_info.pad_top();
const int pool_pad_left = pool_info.pad_stride_info.pad_left();
const int pool_pad_bottom = pool_info.pad_stride_info.pad_bottom();
int pool_stride_x = 0;
int pool_stride_y = 0;
std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride();
const int upper_bound_w = src->info()->dimension(0) + (pool_info.exclude_padding ? 0 : pool_pad_right);
const int upper_bound_h = src->info()->dimension(1) + (pool_info.exclude_padding ? 0 : pool_pad_bottom);
const UniformQuantizationInfo &src_qinfo = src->info()->quantization_info().uniform();
const UniformQuantizationInfo &dst_qinfo = dst0->info()->quantization_info().uniform();
const float requant_scale = dst_qinfo.scale / src_qinfo.scale;
const int32_t requant_offset = dst_qinfo.offset - static_cast<int32_t>(static_cast<float>(src_qinfo.offset) / requant_scale);
const UniformQuantizationInfo requant_qinfo = UniformQuantizationInfo(requant_scale, requant_offset);
const T *const src_top_ptr = reinterpret_cast<const T *>(src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top))));
const T *const src_middle_ptr = reinterpret_cast<const T *>(src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 1)));
const T *const src_bottom_ptr = reinterpret_cast<const T *>(src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 2)));
execute_window_loop(window, [&](const Coordinates & id)
{
const auto top_data = wrapper::vloadq(src_top_ptr + in.offset());
const auto middle_data = wrapper::vloadq(src_middle_ptr + in.offset());
const auto bottom_data = wrapper::vloadq(src_bottom_ptr + in.offset());
q8x8_t fres = {};
q8x16_t fqres = {};
if(pool_info.pool_type == PoolingType::AVG)
{
// Convert data to u16
const q16x8x2_t top_data_q16 = { { wrapper::vmovl(wrapper::vgetlow(top_data)), wrapper::vmovl(wrapper::vgethigh(top_data)) } };
const q16x8x2_t middle_data_q16 = { { wrapper::vmovl(wrapper::vgetlow(middle_data)), wrapper::vmovl(wrapper::vgethigh(middle_data)) } };
const q16x8x2_t bottom_data_q16 = { { wrapper::vmovl(wrapper::vgetlow(bottom_data)), wrapper::vmovl(wrapper::vgethigh(bottom_data)) } };
// Calculate row sums
const q16x8x2_t vrsum =
{
{
wrapper::vadd(wrapper::vadd(top_data_q16.val[0], bottom_data_q16.val[0]), middle_data_q16.val[0]),
wrapper::vadd(wrapper::vadd(top_data_q16.val[1], bottom_data_q16.val[1]), middle_data_q16.val[1]),
}
};
const q16x8x2_t vrsum_shifted_1 =
{
{
wrapper::vext_1(vrsum.val[0], vrsum.val[1]),
wrapper::vext_1(vrsum.val[1], vrsum.val[1])
}
};
const q16x8x2_t vrsum_shifted_2 =
{
{
wrapper::vext_2(vrsum.val[0], vrsum.val[1]),
wrapper::vext_2(vrsum.val[1], vrsum.val[1])
}
};
// Calculate final sum
q16x8x2_t final_sum =
{
{
wrapper::vadd(wrapper::vadd(vrsum.val[0], vrsum_shifted_1.val[0]), vrsum_shifted_2.val[0]),
wrapper::vadd(wrapper::vadd(vrsum.val[1], vrsum_shifted_1.val[1]), vrsum_shifted_2.val[1]),
}
};
if(pool_stride_x == 2)
{
q16x8_t res =
{
wrapper::vgetlane(final_sum.val[0], 0),
wrapper::vgetlane(final_sum.val[0], 2),
wrapper::vgetlane(final_sum.val[0], 4),
wrapper::vgetlane(final_sum.val[0], 6),
wrapper::vgetlane(final_sum.val[1], 0),
wrapper::vgetlane(final_sum.val[1], 2),
wrapper::vgetlane(final_sum.val[1], 4),
wrapper::vgetlane(final_sum.val[1], 6),
};
scale_vector_q16x8<q16_t, q16x8_t>(pool_info.exclude_padding, res, id, 0, 1,
pool_size, upper_bound_w, upper_bound_h,
pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y);
fres = wrapper::vmovn(res);
}
else
{
// Scale lower result
scale_vector_q16x8<q16_t, q16x8_t>(pool_info.exclude_padding, final_sum.val[0], id, 0, 1,
pool_size, upper_bound_w, upper_bound_h,
pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y);
// Scale lower result
scale_vector_q16x8<q16_t, q16x8_t>(pool_info.exclude_padding, final_sum.val[1], id, 8, 1,
pool_size, upper_bound_w, upper_bound_h,
pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y);
fqres = wrapper::vcombine(wrapper::vmovn(final_sum.val[0]), wrapper::vmovn(final_sum.val[1]));
}
}
else
{
const q8x16_t max_data = wrapper::vmax(wrapper::vmax(top_data, bottom_data), middle_data);
const q8x16_t max_data_shift1 = wrapper::vext_1(max_data, max_data);
const q8x16_t max_data_shift2 = wrapper::vext_2(max_data, max_data);
const q8x16_t final_max = wrapper::vmax(wrapper::vmax(max_data, max_data_shift1), max_data_shift2);
if(pool_stride_x == 2)
{
const q8x8x2_t table = { { wrapper::vgetlow(final_max), wrapper::vgethigh(final_max) } };
static const q8x8_t lookup_val = { 0, 2, 4, 6, 8, 10, 12, 14 };
fres = wrapper::vtbl(table, lookup_val);
}
else
{
fqres = final_max;
}
}
// Store result
if(pool_stride_x == 1)
{
if(src_qinfo != dst_qinfo)
{
fqres = vrequantize_pooling<q8x8_t, q8x16_t>(wrapper::vgetlow(fqres), wrapper::vgethigh(fqres), requant_qinfo);
}
wrapper::vstore(reinterpret_cast<T *>(out.ptr()), fqres);
}
else
{
if(src_qinfo != dst_qinfo)
{
fres = vrequantize_pooling<q8x8_t>(fres, requant_qinfo);
}
wrapper::vstore(reinterpret_cast<T *>(out.ptr()), fres);
}
},
in, out);
}
template <typename T>
void poolingMxN_quantized_neon_nchw(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window)
{
ARM_COMPUTE_UNUSED(dst1);
Iterator in(src, window_src);
Iterator out(dst0, window);
/** SIMD vector types */
using q8x8_t = typename wrapper::traits::neon_vector<T, 8>::type;
using q16_t = typename wrapper::traits::promote_t<T>;
using q16x8_t = typename wrapper::traits::neon_vector<q16_t, 8>::type;
using q32_t = typename wrapper::traits::promote_t<q16_t>;
using q32x4_t = typename wrapper::traits::neon_vector<q32_t, 4>::type;
const int pool_size_x = pool_info.is_global_pooling ? src->info()->tensor_shape().x() : pool_info.pool_size.width;
const int pool_size_y = pool_info.is_global_pooling ? src->info()->tensor_shape().y() : pool_info.pool_size.height;
const int pool_pad_right = pool_info.pad_stride_info.pad_right();
const int pool_pad_top = pool_info.pad_stride_info.pad_top();
const int pool_pad_left = pool_info.pad_stride_info.pad_left();
const int pool_pad_bottom = pool_info.pad_stride_info.pad_bottom();
int pool_stride_x = 0;
int pool_stride_y = 0;
std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride();
const int upper_bound_w = src->info()->dimension(0) + (pool_info.exclude_padding ? 0 : pool_pad_right);
const int upper_bound_h = src->info()->dimension(1) + (pool_info.exclude_padding ? 0 : pool_pad_bottom);
const UniformQuantizationInfo &src_qinfo = src->info()->quantization_info().uniform();
const UniformQuantizationInfo &dst_qinfo = dst0->info()->quantization_info().uniform();
execute_window_loop(window, [&](const Coordinates & id)
{
T res = std::numeric_limits<T>::min();
if(pool_info.pool_type != PoolingType::MAX)
{
q32x4_t vres = wrapper::vdup_n(static_cast<q32_t>(0.f), wrapper::traits::vector_128_tag{});
q32_t sres = 0;
// Calculate scale
const float scale = calculate_avg_scale(pool_info.exclude_padding, DataLayout::NCHW, id, pool_size_x, pool_size_y, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x,
pool_stride_y);
// Perform pooling
for(int y = 0; y < pool_size_y; ++y)
{
int x = 0;
for(; x <= (pool_size_x - 8); x += 8)
{
const q8x8_t data = wrapper::vload(reinterpret_cast<const T *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int>
(src->info()->strides_in_bytes().y())));
const q16x8_t data_q16 = wrapper::vmovl(data);
vres = wrapper::vadd(vres, wrapper::vaddl(wrapper::vgethigh(data_q16), wrapper::vgetlow(data_q16)));
}
// Leftover for loop
for(; x < pool_size_x; ++x)
{
T data = *(reinterpret_cast<const T *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int>
(src->info()->strides_in_bytes().y())));
sres += data;
}
}
// Reduction
const auto tmp = wrapper::vpadd(wrapper::vgethigh(vres), wrapper::vgetlow(vres));
sres += wrapper::vgetlane(tmp, 0) + wrapper::vgetlane(tmp, 1);
// Divide by scale
res = static_cast<T>(support::cpp11::round(sres * scale));
}
else
{
q8x8_t vres = wrapper::vdup_n(std::numeric_limits<T>::min(), wrapper::traits::vector_64_tag{});
for(int y = 0; y < pool_size_y; ++y)
{
int x = 0;
for(; x <= (pool_size_x - 8); x += 8)
{
const q8x8_t data = wrapper::vload(reinterpret_cast<const T *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int>
(src->info()->strides_in_bytes().y())));
vres = wrapper::vmax(vres, data);
}
// Leftover for loop
for(; x < pool_size_x; ++x)
{
const T data = *(reinterpret_cast<const T *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int>
(src->info()->strides_in_bytes().y())));
res = std::max(res, data);
}
}
// Reduce max
vres = wrapper::vpmax(vres, vres);
vres = wrapper::vpmax(vres, vres);
vres = wrapper::vpmax(vres, vres);
// Get max value
res = std::max(res, wrapper::vgetlane(vres, 0));
}
// Store result
res = (src_qinfo != dst_qinfo) ? Qasymm8QuantizationHelper<T>::quantize(Qasymm8QuantizationHelper<T>::dequantize(res, src_qinfo), dst_qinfo) : res;
*(reinterpret_cast<T *>(out.ptr())) = res;
},
in, out);
}
#endif /* defined(ENABLE_NCHW_KERNELS) */
} // namespace cpu
} // namespace arm_compute
#endif // SRC_CORE_NEON_KERNELS_QUANTIZED_H