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Provide vectorized NNUE code for SSE2 and MMX targets

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This patch allows old x86 CPUs, from AMD K8 (which the x86-64 baseline
targets) all the way down to the Pentium MMX, to benefit from NNUE with
comparable performance hit versus hand-written eval as on more modern
processors.

NPS of the bench with NNUE enabled on a Pentium III 1.13 GHz (using the
MMX code):
  master: 38951
  this patch: 80586

NPS of the bench with NNUE enabled using baseline x86-64 arch, which is
how linux distros are likely to package stockfish, on a modern CPU
(using the SSE2 code):
  master: 882584
  this patch: 1203945

closes https://github.com/official-stockfish/Stockfish/pull/2956

No functional change.
This commit is contained in:
Fanael Linithien 2020-08-09 16:20:45 +02:00 • committed by Joost VandeVondele
parent f948cd008d
commit 21df37d7fd
7 changed files with 150 additions and 6 deletions
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1
AUTHORS
View file

@ -53,6 +53,7 @@ Ernesto Gatti
Linmiao Xu (linrock)
Fabian Beuke (madnight)
Fabian Fichter (ianfab)
Fanael Linithien (Fanael)
fanon
Fauzi Akram Dabat (FauziAkram)
Felix Wittmann

13
src/Makefile
View file

@ -86,6 +86,7 @@ sanitize = no
bits = 64
prefetch = no
popcnt = no
mmx = no
sse = no
ssse3 = no
sse41 = no
@ -110,6 +111,7 @@ ifeq ($(ARCH),x86-32)
arch = i386
bits = 32
prefetch = yes
mmx = yes
sse = yes
endif
@ -250,7 +252,7 @@ ifeq ($(COMP),gcc)
ifneq ($(KERNEL),Darwin)
LDFLAGS += -Wl,--no-as-needed
endif
gccversion = $(shell $(CXX) --version)
gccisclang = $(findstring clang,$(gccversion))
endif
@ -432,6 +434,13 @@ ifeq ($(ssse3),yes)
endif
endif
ifeq ($(mmx),yes)
CXXFLAGS += -DUSE_MMX
ifeq ($(comp),$(filter $(comp),gcc clang mingw))
CXXFLAGS += -mmmx
endif
endif
ifeq ($(neon),yes)
CXXFLAGS += -DUSE_NEON
endif
@ -516,7 +525,7 @@ help:
@echo "x86-64-ssse3 > x86 64-bit with ssse3 support"
@echo "x86-64-sse3-popcnt > x86 64-bit with sse3 and popcnt support"
@echo "x86-64 > x86 64-bit generic"
@echo "x86-32 > x86 32-bit (also enables SSE)"
@echo "x86-32 > x86 32-bit (also enables MMX and SSE)"
@echo "x86-32-old > x86 32-bit fall back for old hardware"
@echo "ppc-64 > PPC 64-bit"
@echo "ppc-32 > PPC 32-bit"

3
src/misc.cpp
View file

@ -228,6 +228,9 @@ const std::string compiler_info() {
#endif
compiler += (HasPext ? " BMI2" : "");
compiler += (HasPopCnt ? " POPCNT" : "");
#if defined(USE_MMX)
compiler += " MMX";
#endif
#if !defined(NDEBUG)
compiler += " DEBUG";
#endif

59
src/nnue/layers/affine_transform.h
View file

@ -87,11 +87,20 @@ namespace Eval::NNUE::Layers {
const __m256i kOnes = _mm256_set1_epi16(1);
const auto input_vector = reinterpret_cast<const __m256i*>(input);
#elif defined(USE_SSSE3)
#elif defined(USE_SSE2)
constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
#ifndef USE_SSSE3
const __m128i kZeros = _mm_setzero_si128();
#else
const __m128i kOnes = _mm_set1_epi16(1);
#endif
const auto input_vector = reinterpret_cast<const __m128i*>(input);
#elif defined(USE_MMX)
constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
const __m64 kZeros = _mm_setzero_si64();
const auto input_vector = reinterpret_cast<const __m64*>(input);
#elif defined(USE_NEON)
constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
const auto input_vector = reinterpret_cast<const int8x8_t*>(input);
@ -155,6 +164,51 @@ namespace Eval::NNUE::Layers {
sum = _mm_add_epi32(sum, _mm_shuffle_epi32(sum, 0xB1)); //_MM_PERM_CDAB
output[i] = _mm_cvtsi128_si32(sum) + biases_[i];
#elif defined(USE_SSE2)
__m128i sum_lo = _mm_cvtsi32_si128(biases_[i]);
__m128i sum_hi = kZeros;
const auto row = reinterpret_cast<const __m128i*>(&weights_[offset]);
for (IndexType j = 0; j < kNumChunks; ++j) {
__m128i row_j = _mm_load_si128(&row[j]);
__m128i input_j = _mm_load_si128(&input_vector[j]);
__m128i row_signs = _mm_cmpgt_epi8(kZeros, row_j);
__m128i extended_row_lo = _mm_unpacklo_epi8(row_j, row_signs);
__m128i extended_row_hi = _mm_unpackhi_epi8(row_j, row_signs);
__m128i extended_input_lo = _mm_unpacklo_epi8(input_j, kZeros);
__m128i extended_input_hi = _mm_unpackhi_epi8(input_j, kZeros);
__m128i product_lo = _mm_madd_epi16(extended_row_lo, extended_input_lo);
__m128i product_hi = _mm_madd_epi16(extended_row_hi, extended_input_hi);
sum_lo = _mm_add_epi32(sum_lo, product_lo);
sum_hi = _mm_add_epi32(sum_hi, product_hi);
}
__m128i sum = _mm_add_epi32(sum_lo, sum_hi);
__m128i sum_high_64 = _mm_shuffle_epi32(sum, _MM_SHUFFLE(1, 0, 3, 2));
sum = _mm_add_epi32(sum, sum_high_64);
__m128i sum_second_32 = _mm_shufflelo_epi16(sum, _MM_SHUFFLE(1, 0, 3, 2));
sum = _mm_add_epi32(sum, sum_second_32);
output[i] = _mm_cvtsi128_si32(sum);
#elif defined(USE_MMX)
__m64 sum_lo = _mm_cvtsi32_si64(biases_[i]);
__m64 sum_hi = kZeros;
const auto row = reinterpret_cast<const __m64*>(&weights_[offset]);
for (IndexType j = 0; j < kNumChunks; ++j) {
__m64 row_j = row[j];
__m64 input_j = input_vector[j];
__m64 row_signs = _mm_cmpgt_pi8(kZeros, row_j);
__m64 extended_row_lo = _mm_unpacklo_pi8(row_j, row_signs);
__m64 extended_row_hi = _mm_unpackhi_pi8(row_j, row_signs);
__m64 extended_input_lo = _mm_unpacklo_pi8(input_j, kZeros);
__m64 extended_input_hi = _mm_unpackhi_pi8(input_j, kZeros);
__m64 product_lo = _mm_madd_pi16(extended_row_lo, extended_input_lo);
__m64 product_hi = _mm_madd_pi16(extended_row_hi, extended_input_hi);
sum_lo = _mm_add_pi32(sum_lo, product_lo);
sum_hi = _mm_add_pi32(sum_hi, product_hi);
}
__m64 sum = _mm_add_pi32(sum_lo, sum_hi);
sum = _mm_add_pi32(sum, _mm_unpackhi_pi32(sum, sum));
output[i] = _mm_cvtsi64_si32(sum);
#elif defined(USE_NEON)
int32x4_t sum = {biases_[i]};
const auto row = reinterpret_cast<const int8x8_t*>(&weights_[offset]);
@ -174,6 +228,9 @@ namespace Eval::NNUE::Layers {
#endif
}
#if defined(USE_MMX)
_mm_empty();
#endif
return output;
}

20
src/nnue/layers/clipped_relu.h
View file

@ -84,7 +84,7 @@ namespace Eval::NNUE::Layers {
}
constexpr IndexType kStart = kNumChunks * kSimdWidth;
#elif defined(USE_SSSE3)
#elif defined(USE_SSE2)
constexpr IndexType kNumChunks = kInputDimensions / kSimdWidth;
#ifdef USE_SSE41
@ -115,6 +115,24 @@ namespace Eval::NNUE::Layers {
}
constexpr IndexType kStart = kNumChunks * kSimdWidth;
#elif defined(USE_MMX)
constexpr IndexType kNumChunks = kInputDimensions / kSimdWidth;
const __m64 k0x80s = _mm_set1_pi8(-128);
const auto in = reinterpret_cast<const __m64*>(input);
const auto out = reinterpret_cast<__m64*>(output);
for (IndexType i = 0; i < kNumChunks; ++i) {
const __m64 words0 = _mm_srai_pi16(
_mm_packs_pi32(in[i * 4 + 0], in[i * 4 + 1]),
kWeightScaleBits);
const __m64 words1 = _mm_srai_pi16(
_mm_packs_pi32(in[i * 4 + 2], in[i * 4 + 3]),
kWeightScaleBits);
const __m64 packedbytes = _mm_packs_pi16(words0, words1);
out[i] = _mm_subs_pi8(_mm_adds_pi8(packedbytes, k0x80s), k0x80s);
}
_mm_empty();
constexpr IndexType kStart = kNumChunks * kSimdWidth;
#elif defined(USE_NEON)
constexpr IndexType kNumChunks = kInputDimensions / (kSimdWidth / 2);
const int8x8_t kZero = {0};

6
src/nnue/nnue_common.h
View file

@ -33,6 +33,9 @@
#elif defined(USE_SSE2)
#include <emmintrin.h>
#elif defined(USE_MMX)
#include <mmintrin.h>
#elif defined(USE_NEON)
#include <arm_neon.h>
#endif
@ -79,6 +82,9 @@ namespace Eval::NNUE {
#elif defined(USE_SSE2)
constexpr std::size_t kSimdWidth = 16;
#elif defined(USE_MMX)
constexpr std::size_t kSimdWidth = 8;
#elif defined(USE_NEON)
constexpr std::size_t kSimdWidth = 16;
#endif

54
src/nnue/nnue_feature_transformer.h
View file

@ -88,7 +88,7 @@ namespace Eval::NNUE {
constexpr int kControl = 0b11011000;
const __m256i kZero = _mm256_setzero_si256();
#elif defined(USE_SSSE3)
#elif defined(USE_SSE2)
constexpr IndexType kNumChunks = kHalfDimensions / kSimdWidth;
#ifdef USE_SSE41
@ -97,6 +97,10 @@ namespace Eval::NNUE {
const __m128i k0x80s = _mm_set1_epi8(-128);
#endif
#elif defined(USE_MMX)
constexpr IndexType kNumChunks = kHalfDimensions / kSimdWidth;
const __m64 k0x80s = _mm_set1_pi8(-128);
#elif defined(USE_NEON)
constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
const int8x8_t kZero = {0};
@ -117,7 +121,7 @@ namespace Eval::NNUE {
_mm256_packs_epi16(sum0, sum1), kZero), kControl));
}
#elif defined(USE_SSSE3)
#elif defined(USE_SSE2)
auto out = reinterpret_cast<__m128i*>(&output[offset]);
for (IndexType j = 0; j < kNumChunks; ++j) {
__m128i sum0 = _mm_load_si128(&reinterpret_cast<const __m128i*>(
@ -137,6 +141,17 @@ namespace Eval::NNUE {
);
}
#elif defined(USE_MMX)
auto out = reinterpret_cast<__m64*>(&output[offset]);
for (IndexType j = 0; j < kNumChunks; ++j) {
__m64 sum0 = *(&reinterpret_cast<const __m64*>(
accumulation[perspectives[p]][0])[j * 2 + 0]);
__m64 sum1 = *(&reinterpret_cast<const __m64*>(
accumulation[perspectives[p]][0])[j * 2 + 1]);
const __m64 packedbytes = _mm_packs_pi16(sum0, sum1);
out[j] = _mm_subs_pi8(_mm_adds_pi8(packedbytes, k0x80s), k0x80s);
}
#elif defined(USE_NEON)
const auto out = reinterpret_cast<int8x8_t*>(&output[offset]);
for (IndexType j = 0; j < kNumChunks; ++j) {
@ -154,6 +169,9 @@ namespace Eval::NNUE {
#endif
}
#if defined(USE_MMX)
_mm_empty();
#endif
}
private:
@ -193,6 +211,15 @@ namespace Eval::NNUE {
for (IndexType j = 0; j < kNumChunks; ++j)
accumulation[j] = _mm_add_epi16(accumulation[j], column[j]);
#elif defined(USE_MMX)
auto accumulation = reinterpret_cast<__m64*>(
&accumulator.accumulation[perspective][i][0]);
auto column = reinterpret_cast<const __m64*>(&weights_[offset]);
constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
for (IndexType j = 0; j < kNumChunks; ++j) {
accumulation[j] = _mm_add_pi16(accumulation[j], column[j]);
}
#elif defined(USE_NEON)
auto accumulation = reinterpret_cast<int16x8_t*>(
&accumulator.accumulation[perspective][i][0]);
@ -208,6 +235,9 @@ namespace Eval::NNUE {
}
}
#if defined(USE_MMX)
_mm_empty();
#endif
accumulator.computed_accumulation = true;
accumulator.computed_score = false;
@ -234,6 +264,11 @@ namespace Eval::NNUE {
auto accumulation = reinterpret_cast<__m128i*>(
&accumulator.accumulation[perspective][i][0]);
#elif defined(USE_MMX)
constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
auto accumulation = reinterpret_cast<__m64*>(
&accumulator.accumulation[perspective][i][0]);
#elif defined(USE_NEON)
constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
auto accumulation = reinterpret_cast<int16x8_t*>(
@ -263,6 +298,12 @@ namespace Eval::NNUE {
accumulation[j] = _mm_sub_epi16(accumulation[j], column[j]);
}
#elif defined(USE_MMX)
auto column = reinterpret_cast<const __m64*>(&weights_[offset]);
for (IndexType j = 0; j < kNumChunks; ++j) {
accumulation[j] = _mm_sub_pi16(accumulation[j], column[j]);
}
#elif defined(USE_NEON)
auto column = reinterpret_cast<const int16x8_t*>(&weights_[offset]);
for (IndexType j = 0; j < kNumChunks; ++j) {
@ -294,6 +335,12 @@ namespace Eval::NNUE {
accumulation[j] = _mm_add_epi16(accumulation[j], column[j]);
}
#elif defined(USE_MMX)
auto column = reinterpret_cast<const __m64*>(&weights_[offset]);
for (IndexType j = 0; j < kNumChunks; ++j) {
accumulation[j] = _mm_add_pi16(accumulation[j], column[j]);
}
#elif defined(USE_NEON)
auto column = reinterpret_cast<const int16x8_t*>(&weights_[offset]);
for (IndexType j = 0; j < kNumChunks; ++j) {
@ -310,6 +357,9 @@ namespace Eval::NNUE {
}
}
}
#if defined(USE_MMX)
_mm_empty();
#endif
accumulator.computed_accumulation = true;
accumulator.computed_score = false;