X-Git-Url: https://git.sesse.net/?p=stockfish;a=blobdiff_plain;f=src%2Fnnue%2Flayers%2Faffine_transform.h;h=0e0515f932a0773cc82f72c1620bc0a1afe5e5eb;hp=f0292e453c14237e59cd86717c06158103308bbe;hb=c7f0a768cb9d5972861baae0f215d69f9e86a626;hpb=75e06a1c89ebac9c9ec4247bc82ec728a2bffe1e diff --git a/src/nnue/layers/affine_transform.h b/src/nnue/layers/affine_transform.h index f0292e45..0e0515f9 100644 --- a/src/nnue/layers/affine_transform.h +++ b/src/nnue/layers/affine_transform.h @@ -83,7 +83,21 @@ namespace Eval::NNUE::Layers { return _mm512_reduce_add_epi32(sum) + bias; }; - [[maybe_unused]] auto m512_haddx4 = [](__m512i sum0, __m512i sum1, __m512i sum2, __m512i sum3, __m128i bias) -> __m128i { + // This function takes + // sum0 = [xmm0a, xmm0b, xmm0c, xmm0d] + // sum1 = [xmm1a, xmm1b, xmm1c, xmm1d] + // sum2 = [xmm2a, xmm2b, xmm2c, xmm2d] + // sum3 = [xmm3a, xmm3b, xmm3c, xmm3d] + // and returns + // ret = [ + // reduce_add_epi32(xmm0a), reduce_add_epi32(xmm1a), reduce_add_epi32(xmm2a), reduce_add_epi32(xmm3a), + // reduce_add_epi32(xmm0b), reduce_add_epi32(xmm1b), reduce_add_epi32(xmm2b), reduce_add_epi32(xmm3b), + // reduce_add_epi32(xmm0c), reduce_add_epi32(xmm1c), reduce_add_epi32(xmm2c), reduce_add_epi32(xmm3c), + // reduce_add_epi32(xmm0d), reduce_add_epi32(xmm1d), reduce_add_epi32(xmm2d), reduce_add_epi32(xmm3d) + // ] + [[maybe_unused]] auto m512_hadd128x16_interleave = []( + __m512i sum0, __m512i sum1, __m512i sum2, __m512i sum3) -> __m512i { + __m512i sum01a = _mm512_unpacklo_epi32(sum0, sum1); __m512i sum01b = _mm512_unpackhi_epi32(sum0, sum1); @@ -96,7 +110,13 @@ namespace Eval::NNUE::Layers { __m512i sum0123a = _mm512_unpacklo_epi64(sum01, sum23); __m512i sum0123b = _mm512_unpackhi_epi64(sum01, sum23); - __m512i sum = _mm512_add_epi32(sum0123a, sum0123b); + return _mm512_add_epi32(sum0123a, sum0123b); + }; + + [[maybe_unused]] auto m512_haddx4 = [m512_hadd128x16_interleave]( + __m512i sum0, __m512i sum1, __m512i sum2, __m512i sum3, __m128i bias) -> __m128i { + + __m512i sum = m512_hadd128x16_interleave(sum0, sum1, sum2, sum3); __m256i sum256lo = _mm512_castsi512_si256(sum); __m256i sum256hi = _mm512_extracti64x4_epi64(sum, 1); @@ -109,13 +129,65 @@ namespace Eval::NNUE::Layers { return _mm_add_epi32(_mm_add_epi32(sum128lo, sum128hi), bias); }; - [[maybe_unused]] auto m512_add_dpbusd_epi32 = [=](__m512i& acc, __m512i a, __m512i b) { + [[maybe_unused]] auto m512_haddx8 = [m512_hadd128x16_interleave]( + __m512i sum0, __m512i sum1, __m512i sum2, __m512i sum3, + __m512i sum4, __m512i sum5, __m512i sum6, __m512i sum7, __m256i bias) -> __m256i { + + __m512i suma = m512_hadd128x16_interleave(sum0, sum1, sum2, sum3); + __m512i sumb = m512_hadd128x16_interleave(sum4, sum5, sum6, sum7); + + __m512i indices0 = _mm512_setr_epi64(0, 1, 8, 9, 4, 5, 12, 13); + __m512i indices1 = _mm512_setr_epi64(2, 3, 10, 11, 6, 7, 14, 15); + __m512i x = _mm512_add_epi32( + _mm512_permutex2var_epi64(suma, indices0, sumb), + _mm512_permutex2var_epi64(suma, indices1, sumb)); + + __m256i sum256lo = _mm512_castsi512_si256(x); + __m256i sum256hi = _mm512_extracti64x4_epi64(x, 1); + + return _mm256_add_epi32(_mm256_add_epi32(sum256lo, sum256hi), bias); + }; + + [[maybe_unused]] auto m512_hadd256x8 =[m512_hadd128x16_interleave]( + __m512i sum0, __m512i sum1, __m512i sum2, __m512i sum3, __m256i bias) -> __m256i { + + __m512i sum = m512_hadd128x16_interleave(sum0, sum1, sum2, sum3); + + __m512i indices = _mm512_setr_epi32( + 0, 4, 8, 12, 2, 6, 10, 14, + 1, 5, 9, 13, 3, 7, 11, 15); + sum = _mm512_permutexvar_epi32(indices, sum); + + __m256i sum256lo = _mm512_castsi512_si256(sum); + __m256i sum256hi = _mm512_extracti64x4_epi64(sum, 1); + + return _mm256_add_epi32(_mm256_hadd_epi32(sum256lo, sum256hi), bias); + }; + + [[maybe_unused]] auto m512_hadd256x16 = [m512_hadd128x16_interleave]( + __m512i sum0, __m512i sum1, __m512i sum2, __m512i sum3, + __m512i sum4, __m512i sum5, __m512i sum6, __m512i sum7, __m512i bias) -> __m512i { + + __m512i suma = m512_hadd128x16_interleave(sum0, sum1, sum2, sum3); + __m512i sumb = m512_hadd128x16_interleave(sum4, sum5, sum6, sum7); + + __m512i indices0 = _mm512_setr_epi64(0, 1, 8, 9, 4, 5, 12, 13); + __m512i indices1 = _mm512_setr_epi64(2, 3, 10, 11, 6, 7, 14, 15); + __m512i x = _mm512_add_epi32( + _mm512_permutex2var_epi64(suma, indices0, sumb), + _mm512_permutex2var_epi64(suma, indices1, sumb)); + + __m512i indices = _mm512_setr_epi32(0, 8, 1, 9, 2, 10, 3, 11, 4, 12, 5, 13, 6, 14, 7, 15); + 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) { 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 }; @@ -142,14 +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 }; @@ -174,10 +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 @@ -205,7 +275,69 @@ namespace Eval::NNUE::Layers { // kOutputDimensions is either 1 or a multiple of kSimdWidth // because then it is also an input dimension. - if constexpr (kOutputDimensions % 4 == 0) + if constexpr (kOutputDimensions % 16 == 0 && kNumChunks256 == 1) + { + for (IndexType i = 0; i < kOutputDimensions; i += 16) + { + const IndexType offset01a = (i + 0) * kPaddedInputDimensions; + const IndexType offset23a = (i + 2) * kPaddedInputDimensions; + const IndexType offset45a = (i + 4) * kPaddedInputDimensions; + const IndexType offset67a = (i + 6) * kPaddedInputDimensions; + const IndexType offset01b = (i + 8) * kPaddedInputDimensions; + const IndexType offset23b = (i + 10) * kPaddedInputDimensions; + const IndexType offset45b = (i + 12) * kPaddedInputDimensions; + const IndexType offset67b = (i + 14) * kPaddedInputDimensions; + + const __m512i bias = *reinterpret_cast(&biases_[i]); + __m512i* outptr = reinterpret_cast<__m512i*>(&output[i]); + + const auto row01a = *reinterpret_cast(&weights_[offset01a]); + const auto row23a = *reinterpret_cast(&weights_[offset23a]); + const auto row45a = *reinterpret_cast(&weights_[offset45a]); + const auto row67a = *reinterpret_cast(&weights_[offset67a]); + const auto row01b = *reinterpret_cast(&weights_[offset01b]); + const auto row23b = *reinterpret_cast(&weights_[offset23b]); + const auto row45b = *reinterpret_cast(&weights_[offset45b]); + const auto row67b = *reinterpret_cast(&weights_[offset67b]); + + 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); + m512_add_dpbusd_epi32(sum67a, in, row67a); + m512_add_dpbusd_epi32(sum01b, in, row01b); + 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, + sum01b, sum23b, sum45b, sum67b, bias); + } + } + else if constexpr (kOutputDimensions % 4 == 0) { for (IndexType i = 0; i < kOutputDimensions; i += 4) { @@ -219,48 +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(&weights_[offset0]); const auto row1 = reinterpret_cast(&weights_[offset1]); const auto row2 = reinterpret_cast(&weights_[offset2]); const auto row3 = reinterpret_cast(&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(&weights_[offset0]); const auto row1 = reinterpret_cast(&weights_[offset1]); const auto row2 = reinterpret_cast(&weights_[offset2]); const auto row3 = reinterpret_cast(&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); @@ -271,30 +435,50 @@ namespace Eval::NNUE::Layers { { if constexpr (kPaddedInputDimensions % (kSimdWidth * 2) == 0) { - __m512i sum0 = _mm512_setzero_si512(); - const auto row0 = reinterpret_cast(&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(&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]); @@ -328,24 +512,40 @@ namespace Eval::NNUE::Layers { const __m128i bias = *reinterpret_cast(&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(&weights_[offset0]); const auto row1 = reinterpret_cast(&weights_[offset1]); const auto row2 = reinterpret_cast(&weights_[offset2]); const auto row3 = reinterpret_cast(&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); @@ -353,15 +553,25 @@ namespace Eval::NNUE::Layers { } else if constexpr (kOutputDimensions == 1) { - __m256i sum0 = _mm256_setzero_si256(); - const auto row0 = reinterpret_cast(&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]); @@ -394,24 +604,24 @@ namespace Eval::NNUE::Layers { const __m128i bias = *reinterpret_cast(&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(&weights_[offset0]); const auto row1 = reinterpret_cast(&weights_[offset1]); const auto row2 = reinterpret_cast(&weights_[offset2]); const auto row3 = reinterpret_cast(&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); @@ -419,16 +629,12 @@ namespace Eval::NNUE::Layers { } else if constexpr (kOutputDimensions == 1) { - __m128i sum0 = _mm_setzero_si128(); - const auto row0 = reinterpret_cast(&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]); } @@ -474,9 +680,8 @@ namespace Eval::NNUE::Layers { 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_row_lo = _mm_srai_epi16(_mm_unpacklo_epi8(row_j, row_j), 8); + __m128i extended_row_hi = _mm_srai_epi16(_mm_unpackhi_epi8(row_j, row_j), 8); __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); @@ -498,9 +703,8 @@ namespace Eval::NNUE::Layers { 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_row_lo = _mm_srai_pi16(_mm_unpacklo_pi8(row_j, row_j), 8); + __m64 extended_row_hi = _mm_srai_pi16(_mm_unpackhi_pi8(row_j, row_j), 8); __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);