// Copyright 2015 Google Inc. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// pack_neon.h: optimized NEON specializations of the templates in pack.h.
#ifndef GEMMLOWP_INTERNAL_PACK_NEON_H_
#define GEMMLOWP_INTERNAL_PACK_NEON_H_
#include "pack.h"
#include <arm_neon.h>
namespace gemmlowp {
template <RoundingMode tRoundingMode>
class NEONRoundingOffsetGenerator {
public:
uint8x16_t get() {
assert(false); // This generic path should never be called.
return vdupq_n_u8(0);
}
};
// A RoundingOffsetGenerator for rounding-to-nearest, always returning
// the midpoint value 127.
template <>
class NEONRoundingOffsetGenerator<RoundingMode::Nearest> {
public:
uint8x16_t get() { return vdupq_n_u8(127); }
};
// Variant of NEONRoundingOffsetGenerator that produces
// random NEON 128-bit vectors using a 8-bit Xorshift.
template <>
class NEONRoundingOffsetGenerator<RoundingMode::ProbabilisticXorshift> {
public:
NEONRoundingOffsetGenerator() {
uint8_t s = 128;
std::uint8_t a[16];
for (int i = 0; i < 16; i++) {
a[i] = s;
// Xorshift8(7,7,1). Very important to choose a different
// xorshift than we do in get(), otherwise lanes would contain
// the same values!
s ^= s << 7;
s ^= s >> 7;
s ^= s << 1;
}
x_ = vld1q_u8(a);
}
uint8x16_t get() {
// Xorshift produces values in [1..255], we want [0..254].
uint8x16_t result = vsubq_u8(x_, vdupq_n_u8(1));
// Xorshift8(7,5,3)
x_ = veorq_u8(x_, vshlq_n_u8(x_, 7));
x_ = veorq_u8(x_, vshrq_n_u8(x_, 5));
x_ = veorq_u8(x_, vshlq_n_u8(x_, 3));
return result;
}
private:
// State
uint8x16_t x_;
};
// Variant of NEONRoundingOffsetGenerator that produces
// rounding vectors using an 8-bit add/mod low-discrepancy sequence.
template <>
class NEONRoundingOffsetGenerator<RoundingMode::ProbabilisticAddmod> {
public:
NEONRoundingOffsetGenerator() {
uint8_t s = 128;
std::uint8_t a[16];
// The initial offset is set by offsetting each lane to one
// more iteration of the sequence (s0...s15) Then, upon iteration,
// each lane moves ahead by 16.
for (int i = 0; i < 16; i++) {
a[i] = s;
s += (97 + (s >= 158));
}
x_ = vld1q_u8(a);
}
uint8x16_t get() {
// Get moves the lane ahead by 16 iterations of the sequence
// x_ = (x + (16*97)) % 255. (16*97)%255 = 22. 255-22=233,
// so x_ += (22 + (x >= 233)).
// There's an excessively opaque bit hack here:
// A "true" compare on NEON produces an all-1s result (0xff).
// So instead of adding in the comparison result, we subtract it
// to get the same effect as adding 1.
uint8x16_t extra_one = vcgeq_u8(x_, vdupq_n_u8(233));
x_ = vaddq_u8(x_, vdupq_n_u8(22));
x_ = vsubq_u8(x_, extra_one);
return x_;
}
private:
// State
uint8x16_t x_;
};
// Requantizes source uint8 values in [0..255] range
// to the range specified by BitDepth, [0..((2^bits)-1)].
// Bias must be avoided. Currently this is achieved
// by probabilistic rounding.
template <typename QuantizationParams>
uint8x16_t Requantize(
uint8x16_t raw_src_data,
NEONRoundingOffsetGenerator<QuantizationParams::kRoundingMode>*
rounding_offset_generator) {
static const int kBits = QuantizationParams::BitDepth::kBits;
static const std::uint8_t kMaxVal = (1 << kBits) - 1;
if (kBits == 8) {
return raw_src_data;
}
uint8x16_t rounding_offset = rounding_offset_generator->get();
// Compute:
// x = maxval * src + rounding_offset
uint16x8_t x[2];
const uint8x8_t maxval_dup = vdup_n_u8(kMaxVal);
x[0] = vmlal_u8(vmovl_u8(vget_low_u8(rounding_offset)), maxval_dup,
vget_low_u8(raw_src_data));
x[1] = vmlal_u8(vmovl_u8(vget_high_u8(rounding_offset)), maxval_dup,
vget_high_u8(raw_src_data));
// Divide by 255 (truncating).
//
// Here we use the following formula, valid for all integers y in 0..65534
// (which is more than we need since we've already early-returned
// if kBits==8).
//
// y/255 = (y + 1 + (y >> 8)) >> 8.
uint8x8_t result[2];
for (int i = 0; i < 2; i++) {
result[i] = vshrn_n_u16(
vaddq_u16(vaddq_u16(x[i], vdupq_n_u16(1)), vshrq_n_u16(x[i], 8)), 8);
}
return vcombine_u8(result[0], result[1]);
}
typedef SideMap<const std::uint8_t, SideMapOrder::WidthMajor>
WidthMajorUint8SideMap;
template <int Cells>
using DepthMajorSideFormatNCells4x2 = KernelSideFormat<CellFormat<4, 2>, Cells>;
template <typename QuantizationParams, int Cells>
class PackingRegisterBlock<
QuantizationParams, WidthMajorUint8SideMap,
PackedSideBlock<DepthMajorSideFormatNCells4x2<Cells> > >
: public PackingRegisterBlockBase<
QuantizationParams, WidthMajorUint8SideMap,
PackedSideBlock<DepthMajorSideFormatNCells4x2<Cells> > > {
public:
typedef DepthMajorSideFormatNCells4x2<Cells> KernelSideFormat;
typedef typename KernelSideFormat::Cell CellFormat;
static const int kCells = KernelSideFormat::kCells;
static const int kCellWidth = CellFormat::kWidth;
static const int kKernelWidth = CellFormat::kWidth * kCells;
static const int kCellDepth = CellFormat::kDepth;
static const int kCellSize = CellFormat::kSize;
typedef NEONRoundingOffsetGenerator<QuantizationParams::kRoundingMode>
RoundingOffsetGenerator;
void Pack(PackedSideBlock<KernelSideFormat>* dst, int start_width,
RoundingOffsetGenerator* rounding_offset_generator) {
std::uint8_t* dst_ptr = dst->current_data();
const std::uint8_t* const src_ptr = this->complete_src_.data();
const int stride = this->complete_src_.stride();
// Load and requantize source WidthMajor data
uint8x16_t src_lines[4 * kCells];
for (int i = 0; i < 4 * kCells; i++) {
src_lines[i] = Requantize<QuantizationParams>(
vld1q_u8(src_ptr + i * stride), rounding_offset_generator);
}
// Reorder the data within registers to make DepthMajor 4x2 cells
uint8x16x2_t src_lines_intertwined_2x[2 * kCells];
for (int i = 0; i < kCells; i++) {
src_lines_intertwined_2x[2 * i] =
vzipq_u8(src_lines[4 * i], src_lines[4 * i + 2]);
src_lines_intertwined_2x[2 * i + 1] =
vzipq_u8(src_lines[4 * i + 1], src_lines[4 * i + 3]);
}
uint8x16x2_t src_lines_intertwined_4x[2 * kCells];
for (int i = 0; i < kCells; i++) {
src_lines_intertwined_4x[2 * i] =
vzipq_u8(src_lines_intertwined_2x[2 * i].val[0],
src_lines_intertwined_2x[2 * i + 1].val[0]);
src_lines_intertwined_4x[2 * i + 1] =
vzipq_u8(src_lines_intertwined_2x[2 * i].val[1],
src_lines_intertwined_2x[2 * i + 1].val[1]);
}
// Store the resulting DepthMajor 4x2 cells in the destination packed block
for (int outer = 0; outer < 2; outer++) {
for (int inner = 0; inner < 2; inner++) {
for (int cell = 0; cell < kCells; cell++) {
uint8x8_t value = vget_low_u8(
src_lines_intertwined_4x[2 * cell + outer].val[inner]);
vst1_u8(dst_ptr, value);
dst_ptr += 8;
}
for (int cell = 0; cell < kCells; cell++) {
uint8x8_t value = vget_high_u8(
src_lines_intertwined_4x[2 * cell + outer].val[inner]);
vst1_u8(dst_ptr, value);
dst_ptr += 8;
}
}
}
// Compute sums across the depth dimension
uint16x8_t sums_of_2_cells[kCells][4];
for (int outer = 0; outer < 2; outer++) {
for (int inner = 0; inner < 2; inner++) {
int i = 2 * outer + inner;
for (int cell = 0; cell < kCells; cell++) {
sums_of_2_cells[cell][i] = vaddl_u8(
vget_low_u8(
src_lines_intertwined_4x[2 * cell + outer].val[inner]),
vget_high_u8(
src_lines_intertwined_4x[2 * cell + outer].val[inner]));
}
}
}
int32x4_t sums_of_4_cells[kCells][4];
for (int i = 0; i < 4; i++) {
for (int cell = 0; cell < kCells; cell++) {
sums_of_4_cells[cell][i] = vreinterpretq_s32_u32(
vaddl_u16(vget_low_u16(sums_of_2_cells[cell][i]),
vget_high_u16(sums_of_2_cells[cell][i])));
}
}
// Update the sums_of_each_slice vector
for (int cell = 0; cell < kCells; cell++) {
int32x4_t s01 =
vaddq_s32(sums_of_4_cells[cell][0], sums_of_4_cells[cell][1]);
int32x4_t s23 =
vaddq_s32(sums_of_4_cells[cell][2], sums_of_4_cells[cell][3]);
int32x4_t s = vaddq_s32(s01, s23);
std::int32_t* sums_of_each_slice_ptr =
dst->sums_of_each_slice() + start_width + 4 * cell;
vst1q_s32(sums_of_each_slice_ptr,
vaddq_s32(s, vld1q_s32(sums_of_each_slice_ptr)));
}
dst->seek_forward_n_cells(kCells * kRegisterSize / kCellDepth);
}
};
template <int Cells>
using WidthMajorSideFormatNCells4x2 =
KernelSideFormat<CellFormat<4, 2, CellOrder::WidthMajor>, Cells>;
template <typename QuantizationParams, int Cells>
class PackingRegisterBlock<
QuantizationParams, WidthMajorUint8SideMap,
PackedSideBlock<WidthMajorSideFormatNCells4x2<Cells> > >
: public PackingRegisterBlockBase<
QuantizationParams, WidthMajorUint8SideMap,
PackedSideBlock<WidthMajorSideFormatNCells4x2<Cells> > > {
public:
typedef WidthMajorSideFormatNCells4x2<Cells> KernelSideFormat;
typedef typename KernelSideFormat::Cell CellFormat;
static const int kCells = KernelSideFormat::kCells;
static const int kCellWidth = CellFormat::kWidth;
static const int kKernelWidth = CellFormat::kWidth * kCells;
static const int kCellDepth = CellFormat::kDepth;
static const int kCellSize = CellFormat::kSize;
typedef NEONRoundingOffsetGenerator<QuantizationParams::kRoundingMode>
RoundingOffsetGenerator;
void Pack(PackedSideBlock<KernelSideFormat>* dst, int start_width,
RoundingOffsetGenerator* rounding_offset_generator) {
std::uint8_t* dst_ptr = dst->current_data();
const std::uint8_t* src_ptr = this->complete_src_.data();
const int stride = this->complete_src_.stride();
// Load and requantize source WidthMajor data
uint16x8_t src_lines[kCells * 4];
for (int i = 0; i < kCells; i++) {
// This packing path is used with our current
// less-than-8-bit kernel, and the partial unrolling of this loop
// results in substantially faster code (thanks to better
// register allocation) on Nexus 5.
#define GEMMLOWP_UNROLLED_LOOP_ITER(k) \
src_lines[4 * i + k] = vreinterpretq_u16_u8(Requantize<QuantizationParams>( \
vld1q_u8(src_ptr), rounding_offset_generator)); \
src_ptr += stride;
GEMMLOWP_UNROLLED_LOOP_ITER(0)
GEMMLOWP_UNROLLED_LOOP_ITER(1)
GEMMLOWP_UNROLLED_LOOP_ITER(2)
GEMMLOWP_UNROLLED_LOOP_ITER(3)
#undef GEMMLOWP_UNROLLED_LOOP_ITER
}
// Reorder the data within registers to make WidthMajor 4x2 cells
uint16x8x2_t src_lines_intertwined_2x[2 * kCells];
for (int i = 0; i < kCells; i++) {
src_lines_intertwined_2x[2 * i] =
vzipq_u16(src_lines[4 * i], src_lines[4 * i + 2]);
src_lines_intertwined_2x[2 * i + 1] =
vzipq_u16(src_lines[4 * i + 1], src_lines[4 * i + 3]);
}
uint16x8x2_t src_lines_intertwined_4x[2 * kCells];
for (int i = 0; i < kCells; i++) {
src_lines_intertwined_4x[2 * i] =
vzipq_u16(src_lines_intertwined_2x[2 * i].val[0],
src_lines_intertwined_2x[2 * i + 1].val[0]);
src_lines_intertwined_4x[2 * i + 1] =
vzipq_u16(src_lines_intertwined_2x[2 * i].val[1],
src_lines_intertwined_2x[2 * i + 1].val[1]);
}
// Store the resulting WidthMajor 4x2 cells in the destination packed block
for (int outer = 0; outer < 2; outer++) {
for (int inner = 0; inner < 2; inner++) {
for (int cell = 0; cell < kCells; cell++) {
uint8x8_t value = vreinterpret_u8_u16(vget_low_u16(
src_lines_intertwined_4x[2 * cell + outer].val[inner]));
vst1_u8(dst_ptr, value);
dst_ptr += 8;
}
for (int cell = 0; cell < kCells; cell++) {
uint8x8_t value = vreinterpret_u8_u16(vget_high_u16(
src_lines_intertwined_4x[2 * cell + outer].val[inner]));
vst1_u8(dst_ptr, value);
dst_ptr += 8;
}
}
}
// Compute sums across the depth dimension
uint16x8_t sums_of_2[kCells][4];
for (int outer = 0; outer < 2; outer++) {
for (int inner = 0; inner < 2; inner++) {
int i = 2 * outer + inner;
for (int cell = 0; cell < kCells; cell++) {
sums_of_2[cell][i] = vpaddlq_u8(vreinterpretq_u8_u16(
src_lines_intertwined_4x[2 * cell + outer].val[inner]));
}
}
}
uint16x8_t sums_of_4[kCells][2];
for (int i = 0; i < 2; i++) {
for (int cell = 0; cell < kCells; cell++) {
sums_of_4[cell][i] =
vaddq_u16(sums_of_2[cell][2 * i], sums_of_2[cell][2 * i + 1]);
}
}
uint16x8_t sums_of_8[kCells];
for (int cell = 0; cell < kCells; cell++) {
sums_of_8[cell] = vaddq_u16(sums_of_4[cell][0], sums_of_4[cell][1]);
}
uint16x4_t sums_of_16[kCells];
for (int cell = 0; cell < kCells; cell++) {
sums_of_16[cell] = vadd_u16(vget_low_u16(sums_of_8[cell]),
vget_high_u16(sums_of_8[cell]));
}
// Update the sums_of_each_slice vector
for (int cell = 0; cell < kCells; cell++) {
int32x4_t s = vreinterpretq_s32_u32(vmovl_u16(sums_of_16[cell]));
std::int32_t* sums_of_each_slice_ptr =
dst->sums_of_each_slice() + start_width + 4 * cell;
vst1q_s32(sums_of_each_slice_ptr,
vaddq_s32(s, vld1q_s32(sums_of_each_slice_ptr)));
}
dst->seek_forward_n_cells(kCells * kRegisterSize / kCellDepth);
}
};
} // namespace gemmlowp
#endif // GEMMLOWP_INTERNAL_PACK_NEON_H_