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AVX512, AVX2 and SSSE3 speedups 

Improves throughput by summing 2 intermediate dot products using 16 bit addition before upconverting to 32 bit.

Potential saturation is detected and the code-path is avoided in this case.
The saturation can't happen with the current nets,
but nets can be constructed that trigger this check.

STC https://tests.stockfishchess.org/tests/view/5fd40a861ac1691201888479
LLR: 2.94 (-2.94,2.94) {-0.25,1.25}
Total: 25544 W: 2451 L: 2296 D: 20797
Ptnml(0-2): 92, 1761, 8925, 1888, 106

about 5% speedup

closes https://github.com/official-stockfish/Stockfish/pull/3261

No functional change
pull/3265/head
mstembera 2020-12-12 14:18:38 -08:00 committed by Joost VandeVondele
parent d706ae62d7
commit d862ba4069
1 changed files with 198 additions and 155 deletions
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353
src/nnue/layers/affine_transform.h
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@ -66,6 +66,53 @@ namespace Eval::NNUE::Layers {
biases_[i] = read_little_endian<BiasType>(stream);
for (std::size_t i = 0; i < kOutputDimensions * kPaddedInputDimensions; ++i)
weights_[i] = read_little_endian<WeightType>(stream);
#if defined (USE_SSSE3)
// Determine if quadruplets of weight and input products can be summed using 16bits
// without saturation. We assume worst case combinations of 0 and 127 for all inputs.
if (!stream.fail())
{
auto can_saturate = [](const WeightType* w, int idx[4]) {
int pSum = 0, nSum = 0;
for (int p = 0; p < 4; ++p)
if (w[idx[p]] > 0)
pSum += w[idx[p]];
else
nSum += w[idx[p]];
return pSum > 258 || nSum < -258;
};
for (IndexType i = 0; i < kOutputDimensions; ++i)
{
canSaturate16[i] = false;
const WeightType* w = &weights_[i * kPaddedInputDimensions];
#if defined (USE_AVX512)
for (IndexType j = 0; j < (kPaddedInputDimensions & ~127) && !canSaturate16[i]; j += 128)
for (int k = 0; k < 64 && !canSaturate16[i]; k += 2)
{
int spacing[4] = { 0, 1, 64, 65 };
canSaturate16[i] = can_saturate(&w[j + k], spacing);
}
#elif defined (USE_AVX2)
for (IndexType j = 0; j < (kPaddedInputDimensions & ~63) && !canSaturate16[i]; j += 64)
for (int k = 0; k < 32 && !canSaturate16[i]; k += 2)
{
int spacing[4] = { 0, 1, 32, 33 };
canSaturate16[i] = can_saturate(&w[j + k], spacing);
}
#elif defined (USE_SSSE3)
for (IndexType j = 0; j < (kPaddedInputDimensions & ~31) && !canSaturate16[i]; j += 32)
for (int k = 0; k < 16 && !canSaturate16[i]; k += 2)
{
int spacing[4] = { 0, 1, 16, 17 };
canSaturate16[i] = can_saturate(&w[j + k], spacing);
}
#endif
}
}
#endif
return !stream.fail();
}
@ -181,13 +228,26 @@ namespace Eval::NNUE::Layers {
return _mm512_add_epi32(_mm512_permutexvar_epi32(indices, x), bias);
};
#if defined (USE_VNNI)
[[maybe_unused]] auto m512_add_dpbusd_epi32 = [=](__m512i& acc, __m512i a, __m512i b) {
#if defined (USE_VNNI)
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);
return _mm512_madd_epi16(product0, kOnes512);
product0 = _mm512_madd_epi16(product0, kOnes512);
acc = _mm512_add_epi32(acc, product0);
#endif
};
[[maybe_unused]] auto m512_add_dpbusd_epi32x2 = [=](__m512i& acc, __m512i a0, __m512i b0, __m512i a1, __m512i b1) {
#if defined (USE_VNNI)
acc = _mm512_dpbusd_epi32(acc, a0, b0);
acc = _mm512_dpbusd_epi32(acc, a1, b1);
#else
__m512i product0 = _mm512_maddubs_epi16(a0, b0);
__m512i product1 = _mm512_maddubs_epi16(a1, b1);
product0 = _mm512_adds_epi16(product0, product1);
product0 = _mm512_madd_epi16(product0, kOnes512);
acc = _mm512_add_epi32(acc, product0);
#endif
};
@ -214,13 +274,27 @@ namespace Eval::NNUE::Layers {
return _mm_add_epi32(_mm_add_epi32(sum128lo, sum128hi), bias);
};
#if defined (USE_VNNI)
[[maybe_unused]] auto m256_add_dpbusd_epi32 = [=](__m256i& acc, __m256i a, __m256i b) {
#if defined (USE_VNNI)
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);
return _mm256_madd_epi16(product0, kOnes256);
product0 = _mm256_madd_epi16(product0, kOnes256);
acc = _mm256_add_epi32(acc, product0);
#endif
};
[[maybe_unused]] auto m256_add_dpbusd_epi32x2 = [=](__m256i& acc, __m256i a0, __m256i b0, __m256i a1, __m256i b1) {
#if defined (USE_VNNI)
acc = _mm256_dpbusd_epi32(acc, a0, b0);
acc = _mm256_dpbusd_epi32(acc, a1, b1);
#else
__m256i product0 = _mm256_maddubs_epi16(a0, b0);
__m256i product1 = _mm256_maddubs_epi16(a1, b1);
product0 = _mm256_adds_epi16(product0, product1);
product0 = _mm256_madd_epi16(product0, kOnes256);
acc = _mm256_add_epi32(acc, product0);
#endif
};
@ -245,9 +319,18 @@ namespace Eval::NNUE::Layers {
return _mm_add_epi32(sum0, bias);
};
[[maybe_unused]] auto m128_dpbusd_epi32 = [=](__m128i a, __m128i b) -> __m128i {
[[maybe_unused]] auto m128_add_dpbusd_epi32 = [=](__m128i& acc, __m128i a, __m128i b) {
__m128i product0 = _mm_maddubs_epi16(a, b);
return _mm_madd_epi16(product0, kOnes128);
product0 = _mm_madd_epi16(product0, kOnes128);
acc = _mm_add_epi32(acc, product0);
};
[[maybe_unused]] auto m128_add_dpbusd_epi32x2 = [=](__m128i& acc, __m128i a0, __m128i b0, __m128i a1, __m128i b1) {
__m128i product0 = _mm_maddubs_epi16(a0, b0);
__m128i product1 = _mm_maddubs_epi16(a1, b1);
product0 = _mm_adds_epi16(product0, product1);
product0 = _mm_madd_epi16(product0, kOnes128);
acc = _mm_add_epi32(acc, product0);
};
#endif
@ -291,6 +374,15 @@ 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]);
@ -303,16 +395,6 @@ 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);
@ -321,16 +403,6 @@ 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,
@ -351,80 +423,62 @@ 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]);
#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
int j = 0;
if (!canSaturate16x4[i / 4])
{
for (; j < (int)kNumChunks512 - 1; j += 2)
{
const __m512i in0 = input_vector512[j];
const __m512i in1 = input_vector512[j + 1];
for (IndexType j = kStart; j < kNumChunks512; ++j)
m512_add_dpbusd_epi32x2(sum0, in0, row0[j], in1, row0[j + 1]);
m512_add_dpbusd_epi32x2(sum1, in0, row1[j], in1, row1[j + 1]);
m512_add_dpbusd_epi32x2(sum2, in0, row2[j], in1, row2[j + 1]);
m512_add_dpbusd_epi32x2(sum3, in0, row3[j], in1, row3[j + 1]);
}
}
for (; j < (int)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]);
#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)
for (IndexType j = 0; 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);
@ -435,50 +489,30 @@ namespace Eval::NNUE::Layers {
{
if constexpr (kPaddedInputDimensions % (kSimdWidth * 2) == 0)
{
__m512i sum0 = _mm512_setzero_si512();
const auto row0 = reinterpret_cast<const __m512i*>(&weights_[0]);
#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)
for (IndexType j = 0; 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]);
#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)
for (IndexType j = 0; 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]);
@ -512,40 +546,38 @@ 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]);
#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)
int j = 0;
if (!canSaturate16x4[i / 4])
{
const __m256i in = input_vector[j];
for (; j < (int)kNumChunks - 1; j += 2)
{
const __m256i in0 = input_vector[j];
const __m256i in1 = input_vector[j + 1];
#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
m256_add_dpbusd_epi32x2(sum0, in0, row0[j], in1, row0[j + 1]);
m256_add_dpbusd_epi32x2(sum1, in0, row1[j], in1, row1[j + 1]);
m256_add_dpbusd_epi32x2(sum2, in0, row2[j], in1, row2[j + 1]);
m256_add_dpbusd_epi32x2(sum3, in0, row3[j], in1, row3[j + 1]);
}
}
for (; j < (int)kNumChunks; ++j)
{
const __m256i in = input_vector[j];
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]);
}
*outptr = m256_haddx4(sum0, sum1, sum2, sum3, bias);
@ -553,25 +585,15 @@ namespace Eval::NNUE::Layers {
}
else if constexpr (kOutputDimensions == 1)
{
__m256i sum0 = _mm256_setzero_si256();
const auto row0 = reinterpret_cast<const __m256i*>(&weights_[0]);
#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)
for (IndexType j = 0; j < kNumChunks; ++j)
{
const __m256i in = input_vector[j];
const __m256i in = input_vector[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
m256_add_dpbusd_epi32(sum0, in, row0[j]);
}
output[0] = m256_hadd(sum0, biases_[0]);
@ -604,24 +626,38 @@ 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]);
__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)
int j = 0;
if (!canSaturate16x4[i / 4])
{
const __m128i in = input_vector[j];
for (; j < (int)kNumChunks - 1; j += 2)
{
const __m128i in0 = input_vector[j];
const __m128i in1 = input_vector[j + 1];
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]));
m128_add_dpbusd_epi32x2(sum0, in0, row0[j], in1, row0[j + 1]);
m128_add_dpbusd_epi32x2(sum1, in0, row1[j], in1, row1[j + 1]);
m128_add_dpbusd_epi32x2(sum2, in0, row2[j], in1, row2[j + 1]);
m128_add_dpbusd_epi32x2(sum3, in0, row3[j], in1, row3[j + 1]);
}
}
for (; 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]);
}
*outptr = m128_haddx4(sum0, sum1, sum2, sum3, bias);
@ -629,12 +665,16 @@ namespace Eval::NNUE::Layers {
}
else if constexpr (kOutputDimensions == 1)
{
__m128i sum0 = _mm_setzero_si128();
const auto row0 = reinterpret_cast<const __m128i*>(&weights_[0]);
__m128i sum0 = m128_dpbusd_epi32(input_vector[0], row0[0]);
for (int j = 0; j < (int)kNumChunks; ++j)
{
const __m128i in = input_vector[j];
for (int j = 1; j < (int)kNumChunks; ++j)
sum0 = _mm_add_epi32(sum0, m128_dpbusd_epi32(input_vector[j], row0[j]));
m128_add_dpbusd_epi32(sum0, in, row0[j]);
}
output[0] = m128_hadd(sum0, biases_[0]);
}
@ -751,8 +791,11 @@ namespace Eval::NNUE::Layers {
PreviousLayer previous_layer_;
alignas(kCacheLineSize) BiasType biases_[kOutputDimensions];
alignas(kCacheLineSize)
WeightType weights_[kOutputDimensions * kPaddedInputDimensions];
alignas(kCacheLineSize) WeightType weights_[kOutputDimensions * kPaddedInputDimensions];
union {
uint32_t canSaturate16x4[(kOutputDimensions + 3) / 4];
bool canSaturate16[kOutputDimensions];
};
};
} // namespace Eval::NNUE::Layers