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Calculate sum from first elements

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in affine transform for AVX512/AVX2/SSSE3

The idea is to initialize sum with the first element instead of zero.
Reduce one add_epi32 and one set_zero SIMD instructions for each output dimension.

sum = 0; for i = 1 to n sum += a[i] ->
sum = a[1]; for i = 2 to n sum += a[i]

STC:
LLR: 2.95 (-2.94,2.94) {-0.25,1.25}
Total: 69048 W: 7024 L: 6799 D: 55225
Ptnml(0-2): 260, 5175, 23458, 5342, 289
https://tests.stockfishchess.org/tests/view/5faf2cf467cbf42301d6aa06

closes https://github.com/official-stockfish/Stockfish/pull/3227

No functional change.
This commit is contained in:
MaximMolchanov 2020-11-14 02:55:29 +02:00 • committed by Joost VandeVondele
parent 9fb6383ed8
commit 7615e3485e
2 changed files with 148 additions and 64 deletions
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1
AUTHORS
View file

@ -112,6 +112,7 @@ Mark Tenzer (31m059)
marotear
Matthew Lai (matthewlai)
Matthew Sullivan (Matt14916)
Maxim Molchanov (Maxim)
Michael An (man)
Michael Byrne (MichaelB7)
Michael Chaly (Vizvezdenec)

211
src/nnue/layers/affine_transform.h
View file

@ -181,13 +181,13 @@ namespace Eval::NNUE::Layers {
return _mm512_add_epi32(_mm512_permutexvar_epi32(indices, x), bias);
};
[[maybe_unused]] auto m512_add_dpbusd_epi32 = [=](__m512i& acc, __m512i a, __m512i b) {
#if defined (USE_VNNI)
[[maybe_unused]] auto m512_add_dpbusd_epi32 = [=](__m512i& acc, __m512i a, __m512i b) {
acc = _mm512_dpbusd_epi32(acc, a, b);
#else
[[maybe_unused]] auto m512_dpbusd_epi32 = [=](__m512i a, __m512i b) -> __m512i {
__m512i product0 = _mm512_maddubs_epi16(a, b);
product0 = _mm512_madd_epi16(product0, kOnes512);
acc = _mm512_add_epi32(acc, product0);
return _mm512_madd_epi16(product0, kOnes512);
#endif
};
@ -214,14 +214,13 @@ namespace Eval::NNUE::Layers {
return _mm_add_epi32(_mm_add_epi32(sum128lo, sum128hi), bias);
};
[[maybe_unused]] auto m256_add_dpbusd_epi32 = [=](__m256i& acc, __m256i a, __m256i b) {
#if defined (USE_VNNI)
[[maybe_unused]] auto m256_add_dpbusd_epi32 = [=](__m256i& acc, __m256i a, __m256i b) {
acc = _mm256_dpbusd_epi32(acc, a, b);
#else
[[maybe_unused]] auto m256_dpbusd_epi32 = [=](__m256i a, __m256i b) -> __m256i {
__m256i product0 = _mm256_maddubs_epi16(a, b);
product0 = _mm256_madd_epi16(product0, kOnes256);
acc = _mm256_add_epi32(acc, product0);
return _mm256_madd_epi16(product0, kOnes256);
#endif
};
@ -246,10 +245,9 @@ namespace Eval::NNUE::Layers {
return _mm_add_epi32(sum0, bias);
};
[[maybe_unused]] auto m128_add_dpbusd_epi32 = [=](__m128i& acc, __m128i a, __m128i b) {
[[maybe_unused]] auto m128_dpbusd_epi32 = [=](__m128i a, __m128i b) -> __m128i {
__m128i product0 = _mm_maddubs_epi16(a, b);
product0 = _mm_madd_epi16(product0, kOnes128);
acc = _mm_add_epi32(acc, product0);
return _mm_madd_epi16(product0, kOnes128);
};
#endif
@ -293,15 +291,6 @@ namespace Eval::NNUE::Layers {
const __m512i bias = *reinterpret_cast<const __m512i*>(&biases_[i]);
__m512i* outptr = reinterpret_cast<__m512i*>(&output[i]);
__m512i sum01a = _mm512_setzero_si512();
__m512i sum23a = _mm512_setzero_si512();
__m512i sum45a = _mm512_setzero_si512();
__m512i sum67a = _mm512_setzero_si512();
__m512i sum01b = _mm512_setzero_si512();
__m512i sum23b = _mm512_setzero_si512();
__m512i sum45b = _mm512_setzero_si512();
__m512i sum67b = _mm512_setzero_si512();
const auto row01a = *reinterpret_cast<const __m512i*>(&weights_[offset01a]);
const auto row23a = *reinterpret_cast<const __m512i*>(&weights_[offset23a]);
const auto row45a = *reinterpret_cast<const __m512i*>(&weights_[offset45a]);
@ -314,6 +303,16 @@ namespace Eval::NNUE::Layers {
const __m256i in256 = input_vector256[0];
const __m512i in = _mm512_inserti64x4(_mm512_castsi256_si512(in256), in256, 1);
#if defined (USE_VNNI)
__m512i sum01a = _mm512_setzero_si512();
__m512i sum23a = _mm512_setzero_si512();
__m512i sum45a = _mm512_setzero_si512();
__m512i sum67a = _mm512_setzero_si512();
__m512i sum01b = _mm512_setzero_si512();
__m512i sum23b = _mm512_setzero_si512();
__m512i sum45b = _mm512_setzero_si512();
__m512i sum67b = _mm512_setzero_si512();
m512_add_dpbusd_epi32(sum01a, in, row01a);
m512_add_dpbusd_epi32(sum23a, in, row23a);
m512_add_dpbusd_epi32(sum45a, in, row45a);
@ -322,6 +321,16 @@ namespace Eval::NNUE::Layers {
m512_add_dpbusd_epi32(sum23b, in, row23b);
m512_add_dpbusd_epi32(sum45b, in, row45b);
m512_add_dpbusd_epi32(sum67b, in, row67b);
#else
__m512i sum01a = m512_dpbusd_epi32(in, row01a);
__m512i sum23a = m512_dpbusd_epi32(in, row23a);
__m512i sum45a = m512_dpbusd_epi32(in, row45a);
__m512i sum67a = m512_dpbusd_epi32(in, row67a);
__m512i sum01b = m512_dpbusd_epi32(in, row01b);
__m512i sum23b = m512_dpbusd_epi32(in, row23b);
__m512i sum45b = m512_dpbusd_epi32(in, row45b);
__m512i sum67b = m512_dpbusd_epi32(in, row67b);
#endif
*outptr = m512_hadd256x16(
sum01a, sum23a, sum45a, sum67a,
@ -342,48 +351,80 @@ namespace Eval::NNUE::Layers {
if constexpr (kPaddedInputDimensions % (kSimdWidth * 2) == 0)
{
__m512i sum0 = _mm512_setzero_si512();
__m512i sum1 = _mm512_setzero_si512();
__m512i sum2 = _mm512_setzero_si512();
__m512i sum3 = _mm512_setzero_si512();
const auto row0 = reinterpret_cast<const __m512i*>(&weights_[offset0]);
const auto row1 = reinterpret_cast<const __m512i*>(&weights_[offset1]);
const auto row2 = reinterpret_cast<const __m512i*>(&weights_[offset2]);
const auto row3 = reinterpret_cast<const __m512i*>(&weights_[offset3]);
for (IndexType j = 0; j < kNumChunks512; ++j)
#if defined (USE_VNNI)
__m512i sum0 = _mm512_setzero_si512();
__m512i sum1 = _mm512_setzero_si512();
__m512i sum2 = _mm512_setzero_si512();
__m512i sum3 = _mm512_setzero_si512();
const IndexType kStart = 0;
#else
__m512i sum0 = m512_dpbusd_epi32(input_vector512[0], row0[0]);
__m512i sum1 = m512_dpbusd_epi32(input_vector512[0], row1[0]);
__m512i sum2 = m512_dpbusd_epi32(input_vector512[0], row2[0]);
__m512i sum3 = m512_dpbusd_epi32(input_vector512[0], row3[0]);
const IndexType kStart = 1;
#endif
for (IndexType j = kStart; j < kNumChunks512; ++j)
{
const __m512i in = input_vector512[j];
#if defined (USE_VNNI)
m512_add_dpbusd_epi32(sum0, in, row0[j]);
m512_add_dpbusd_epi32(sum1, in, row1[j]);
m512_add_dpbusd_epi32(sum2, in, row2[j]);
m512_add_dpbusd_epi32(sum3, in, row3[j]);
#else
sum0 = _mm512_add_epi32(sum0, m512_dpbusd_epi32(in, row0[j]));
sum1 = _mm512_add_epi32(sum1, m512_dpbusd_epi32(in, row1[j]));
sum2 = _mm512_add_epi32(sum2, m512_dpbusd_epi32(in, row2[j]));
sum3 = _mm512_add_epi32(sum3, m512_dpbusd_epi32(in, row3[j]));
#endif
}
*outptr = m512_haddx4(sum0, sum1, sum2, sum3, bias);
}
else
{
__m256i sum0 = _mm256_setzero_si256();
__m256i sum1 = _mm256_setzero_si256();
__m256i sum2 = _mm256_setzero_si256();
__m256i sum3 = _mm256_setzero_si256();
const auto row0 = reinterpret_cast<const __m256i*>(&weights_[offset0]);
const auto row1 = reinterpret_cast<const __m256i*>(&weights_[offset1]);
const auto row2 = reinterpret_cast<const __m256i*>(&weights_[offset2]);
const auto row3 = reinterpret_cast<const __m256i*>(&weights_[offset3]);
for (IndexType j = 0; j < kNumChunks256; ++j)
#if defined (USE_VNNI)
__m256i sum0 = _mm256_setzero_si256();
__m256i sum1 = _mm256_setzero_si256();
__m256i sum2 = _mm256_setzero_si256();
__m256i sum3 = _mm256_setzero_si256();
const IndexType kStart = 0;
#else
__m256i sum0 = m256_dpbusd_epi32(input_vector256[0], row0[0]);
__m256i sum1 = m256_dpbusd_epi32(input_vector256[0], row1[0]);
__m256i sum2 = m256_dpbusd_epi32(input_vector256[0], row2[0]);
__m256i sum3 = m256_dpbusd_epi32(input_vector256[0], row3[0]);
const IndexType kStart = 1;
#endif
for (IndexType j = kStart; j < kNumChunks256; ++j)
{
const __m256i in = input_vector256[j];
#if defined (USE_VNNI)
m256_add_dpbusd_epi32(sum0, in, row0[j]);
m256_add_dpbusd_epi32(sum1, in, row1[j]);
m256_add_dpbusd_epi32(sum2, in, row2[j]);
m256_add_dpbusd_epi32(sum3, in, row3[j]);
#else
sum0 = _mm256_add_epi32(sum0, m256_dpbusd_epi32(in, row0[j]));
sum1 = _mm256_add_epi32(sum1, m256_dpbusd_epi32(in, row1[j]));
sum2 = _mm256_add_epi32(sum2, m256_dpbusd_epi32(in, row2[j]));
sum3 = _mm256_add_epi32(sum3, m256_dpbusd_epi32(in, row3[j]));
#endif
}
*outptr = m256_haddx4(sum0, sum1, sum2, sum3, bias);
@ -394,30 +435,50 @@ namespace Eval::NNUE::Layers {
{
if constexpr (kPaddedInputDimensions % (kSimdWidth * 2) == 0)
{
__m512i sum0 = _mm512_setzero_si512();
const auto row0 = reinterpret_cast<const __m512i*>(&weights_[0]);
for (IndexType j = 0; j < kNumChunks512; ++j)
#if defined (USE_VNNI)
__m512i sum0 = _mm512_setzero_si512();
const IndexType kStart = 0;
#else
__m512i sum0 = m512_dpbusd_epi32(input_vector512[0], row0[0]);
const IndexType kStart = 1;
#endif
for (IndexType j = kStart; j < kNumChunks512; ++j)
{
const __m512i in = input_vector512[j];
#if defined (USE_VNNI)
m512_add_dpbusd_epi32(sum0, in, row0[j]);
#else
sum0 = _mm512_add_epi32(sum0, m512_dpbusd_epi32(in, row0[j]));
#endif
}
output[0] = m512_hadd(sum0, biases_[0]);
}
else
{
__m256i sum0 = _mm256_setzero_si256();
const auto row0 = reinterpret_cast<const __m256i*>(&weights_[0]);
for (IndexType j = 0; j < kNumChunks256; ++j)
#if defined (USE_VNNI)
__m256i sum0 = _mm256_setzero_si256();
const IndexType kStart = 0;
#else
__m256i sum0 = m256_dpbusd_epi32(input_vector256[0], row0[0]);
const IndexType kStart = 1;
#endif
for (IndexType j = kStart; j < kNumChunks256; ++j)
{
const __m256i in = input_vector256[j];
#if defined (USE_VNNI)
m256_add_dpbusd_epi32(sum0, in, row0[j]);
#else
sum0 = _mm256_add_epi32(sum0, m256_dpbusd_epi32(in, row0[j]));
#endif
}
output[0] = m256_hadd(sum0, biases_[0]);
@ -451,24 +512,40 @@ namespace Eval::NNUE::Layers {
const __m128i bias = *reinterpret_cast<const __m128i*>(&biases_[i]);
__m128i* outptr = reinterpret_cast<__m128i*>(&output[i]);
__m256i sum0 = _mm256_setzero_si256();
__m256i sum1 = _mm256_setzero_si256();
__m256i sum2 = _mm256_setzero_si256();
__m256i sum3 = _mm256_setzero_si256();
const auto row0 = reinterpret_cast<const __m256i*>(&weights_[offset0]);
const auto row1 = reinterpret_cast<const __m256i*>(&weights_[offset1]);
const auto row2 = reinterpret_cast<const __m256i*>(&weights_[offset2]);
const auto row3 = reinterpret_cast<const __m256i*>(&weights_[offset3]);
for (IndexType j = 0; j < kNumChunks; ++j)
#if defined (USE_VNNI)
__m256i sum0 = _mm256_setzero_si256();
__m256i sum1 = _mm256_setzero_si256();
__m256i sum2 = _mm256_setzero_si256();
__m256i sum3 = _mm256_setzero_si256();
const IndexType kStart = 0;
#else
__m256i sum0 = m256_dpbusd_epi32(input_vector[0], row0[0]);
__m256i sum1 = m256_dpbusd_epi32(input_vector[0], row1[0]);
__m256i sum2 = m256_dpbusd_epi32(input_vector[0], row2[0]);
__m256i sum3 = m256_dpbusd_epi32(input_vector[0], row3[0]);
const IndexType kStart = 1;
#endif
for (IndexType j = kStart; j < kNumChunks; ++j)
{
const __m256i in = input_vector[j];
#if defined (USE_VNNI)
m256_add_dpbusd_epi32(sum0, in, row0[j]);
m256_add_dpbusd_epi32(sum1, in, row1[j]);
m256_add_dpbusd_epi32(sum2, in, row2[j]);
m256_add_dpbusd_epi32(sum3, in, row3[j]);
#else
sum0 = _mm256_add_epi32(sum0, m256_dpbusd_epi32(in, row0[j]));
sum1 = _mm256_add_epi32(sum1, m256_dpbusd_epi32(in, row1[j]));
sum2 = _mm256_add_epi32(sum2, m256_dpbusd_epi32(in, row2[j]));
sum3 = _mm256_add_epi32(sum3, m256_dpbusd_epi32(in, row3[j]));
#endif
}
*outptr = m256_haddx4(sum0, sum1, sum2, sum3, bias);
@ -476,15 +553,25 @@ namespace Eval::NNUE::Layers {
}
else if constexpr (kOutputDimensions == 1)
{
__m256i sum0 = _mm256_setzero_si256();
const auto row0 = reinterpret_cast<const __m256i*>(&weights_[0]);
for (IndexType j = 0; j < kNumChunks; ++j)
#if defined (USE_VNNI)
__m256i sum0 = _mm256_setzero_si256();
const IndexType kStart = 0;
#else
__m256i sum0 = m256_dpbusd_epi32(input_vector[0], row0[0]);
const IndexType kStart = 1;
#endif
for (IndexType j = kStart; j < kNumChunks; ++j)
{
const __m256i in = input_vector[j];
m256_add_dpbusd_epi32(sum0, in, row0[j]);
#if defined (USE_VNNI)
m256_add_dpbusd_epi32(sum0, in, row0[j]);
#else
sum0 = _mm256_add_epi32(sum0, m256_dpbusd_epi32(in, row0[j]));
#endif
}
output[0] = m256_hadd(sum0, biases_[0]);
@ -517,24 +604,24 @@ namespace Eval::NNUE::Layers {
const __m128i bias = *reinterpret_cast<const __m128i*>(&biases_[i]);
__m128i* outptr = reinterpret_cast<__m128i*>(&output[i]);
__m128i sum0 = _mm_setzero_si128();
__m128i sum1 = _mm_setzero_si128();
__m128i sum2 = _mm_setzero_si128();
__m128i sum3 = _mm_setzero_si128();
const auto row0 = reinterpret_cast<const __m128i*>(&weights_[offset0]);
const auto row1 = reinterpret_cast<const __m128i*>(&weights_[offset1]);
const auto row2 = reinterpret_cast<const __m128i*>(&weights_[offset2]);
const auto row3 = reinterpret_cast<const __m128i*>(&weights_[offset3]);
for (int j = 0; j < (int)kNumChunks; j += 1)
__m128i sum0 = m128_dpbusd_epi32(input_vector[0], row0[0]);
__m128i sum1 = m128_dpbusd_epi32(input_vector[0], row1[0]);
__m128i sum2 = m128_dpbusd_epi32(input_vector[0], row2[0]);
__m128i sum3 = m128_dpbusd_epi32(input_vector[0], row3[0]);
for (int j = 1; j < (int)kNumChunks; ++j)
{
const __m128i in = input_vector[j];
m128_add_dpbusd_epi32(sum0, in, row0[j]);
m128_add_dpbusd_epi32(sum1, in, row1[j]);
m128_add_dpbusd_epi32(sum2, in, row2[j]);
m128_add_dpbusd_epi32(sum3, in, row3[j]);
sum0 = _mm_add_epi32(sum0, m128_dpbusd_epi32(in, row0[j]));
sum1 = _mm_add_epi32(sum1, m128_dpbusd_epi32(in, row1[j]));
sum2 = _mm_add_epi32(sum2, m128_dpbusd_epi32(in, row2[j]));
sum3 = _mm_add_epi32(sum3, m128_dpbusd_epi32(in, row3[j]));
}
*outptr = m128_haddx4(sum0, sum1, sum2, sum3, bias);
@ -542,16 +629,12 @@ namespace Eval::NNUE::Layers {
}
else if constexpr (kOutputDimensions == 1)
{
__m128i sum0 = _mm_setzero_si128();
const auto row0 = reinterpret_cast<const __m128i*>(&weights_[0]);
for (int j = 0; j < (int)kNumChunks; j += 1)
{
const __m128i in = input_vector[j];
__m128i sum0 = m128_dpbusd_epi32(input_vector[0], row0[0]);
m128_add_dpbusd_epi32(sum0, in, row0[j]);
}
for (int j = 1; j < (int)kNumChunks; ++j)
sum0 = _mm_add_epi32(sum0, m128_dpbusd_epi32(input_vector[j], row0[j]));
output[0] = m128_hadd(sum0, biases_[0]);
}