X-Git-Url: https://git.sesse.net/?p=stockfish;a=blobdiff_plain;f=src%2Fnnue%2Flayers%2Faffine_transform.h;h=0e0515f932a0773cc82f72c1620bc0a1afe5e5eb;hp=322e32402500029033caa5ddbb7123965adc45d7;hb=c7f0a768cb9d5972861baae0f215d69f9e86a626;hpb=dd63b98fb06e050aa961fbad6fd1f9316f2b17df diff --git a/src/nnue/layers/affine_transform.h b/src/nnue/layers/affine_transform.h index 322e3240..0e0515f9 100644 --- a/src/nnue/layers/affine_transform.h +++ b/src/nnue/layers/affine_transform.h @@ -62,11 +62,10 @@ namespace Eval::NNUE::Layers { // Read network parameters bool ReadParameters(std::istream& stream) { if (!previous_layer_.ReadParameters(stream)) return false; - stream.read(reinterpret_cast(biases_), - kOutputDimensions * sizeof(BiasType)); - stream.read(reinterpret_cast(weights_), - kOutputDimensions * kPaddedInputDimensions * - sizeof(WeightType)); + for (std::size_t i = 0; i < kOutputDimensions; ++i) + biases_[i] = read_little_endian(stream); + for (std::size_t i = 0; i < kOutputDimensions * kPaddedInputDimensions; ++i) + weights_[i] = read_little_endian(stream); return !stream.fail(); } @@ -75,116 +74,614 @@ namespace Eval::NNUE::Layers { const TransformedFeatureType* transformed_features, char* buffer) const { const auto input = previous_layer_.Propagate( transformed_features, buffer + kSelfBufferSize); + +#if defined (USE_AVX512) + + [[maybe_unused]] const __m512i kOnes512 = _mm512_set1_epi16(1); + + [[maybe_unused]] auto m512_hadd = [](__m512i sum, int bias) -> int { + return _mm512_reduce_add_epi32(sum) + bias; + }; + + // 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); + + __m512i sum23a = _mm512_unpacklo_epi32(sum2, sum3); + __m512i sum23b = _mm512_unpackhi_epi32(sum2, sum3); + + __m512i sum01 = _mm512_add_epi32(sum01a, sum01b); + __m512i sum23 = _mm512_add_epi32(sum23a, sum23b); + + __m512i sum0123a = _mm512_unpacklo_epi64(sum01, sum23); + __m512i sum0123b = _mm512_unpackhi_epi64(sum01, sum23); + + 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); + + sum256lo = _mm256_add_epi32(sum256lo, sum256hi); + + __m128i sum128lo = _mm256_castsi256_si128(sum256lo); + __m128i sum128hi = _mm256_extracti128_si256(sum256lo, 1); + + return _mm_add_epi32(_mm_add_epi32(sum128lo, sum128hi), bias); + }; + + [[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); + return _mm512_madd_epi16(product0, kOnes512); +#endif + }; + +#endif +#if defined (USE_AVX2) + + [[maybe_unused]] const __m256i kOnes256 = _mm256_set1_epi16(1); + + [[maybe_unused]] auto m256_hadd = [](__m256i sum, int bias) -> int { + __m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(sum), _mm256_extracti128_si256(sum, 1)); + sum128 = _mm_add_epi32(sum128, _mm_shuffle_epi32(sum128, _MM_PERM_BADC)); + sum128 = _mm_add_epi32(sum128, _mm_shuffle_epi32(sum128, _MM_PERM_CDAB)); + return _mm_cvtsi128_si32(sum128) + bias; + }; + + [[maybe_unused]] auto m256_haddx4 = [](__m256i sum0, __m256i sum1, __m256i sum2, __m256i sum3, __m128i bias) -> __m128i { + sum0 = _mm256_hadd_epi32(sum0, sum1); + sum2 = _mm256_hadd_epi32(sum2, sum3); + + sum0 = _mm256_hadd_epi32(sum0, sum2); + + __m128i sum128lo = _mm256_castsi256_si128(sum0); + __m128i sum128hi = _mm256_extracti128_si256(sum0, 1); + + 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) { + 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); +#endif + }; + +#endif + +#if defined (USE_SSSE3) + + [[maybe_unused]] const __m128i kOnes128 = _mm_set1_epi16(1); + + [[maybe_unused]] auto m128_hadd = [](__m128i sum, int bias) -> int { + sum = _mm_add_epi32(sum, _mm_shuffle_epi32(sum, 0x4E)); //_MM_PERM_BADC + sum = _mm_add_epi32(sum, _mm_shuffle_epi32(sum, 0xB1)); //_MM_PERM_CDAB + return _mm_cvtsi128_si32(sum) + bias; + }; + + [[maybe_unused]] auto m128_haddx4 = [](__m128i sum0, __m128i sum1, __m128i sum2, __m128i sum3, __m128i bias) -> __m128i { + sum0 = _mm_hadd_epi32(sum0, sum1); + sum2 = _mm_hadd_epi32(sum2, sum3); + + sum0 = _mm_hadd_epi32(sum0, sum2); + + return _mm_add_epi32(sum0, bias); + }; + + [[maybe_unused]] auto m128_dpbusd_epi32 = [=](__m128i a, __m128i b) -> __m128i { + __m128i product0 = _mm_maddubs_epi16(a, b); + return _mm_madd_epi16(product0, kOnes128); + }; + +#endif + +#if defined (USE_AVX512) + + constexpr IndexType kNumChunks512 = kPaddedInputDimensions / (kSimdWidth * 2); + constexpr IndexType kNumChunks256 = kPaddedInputDimensions / kSimdWidth; + const auto output = reinterpret_cast(buffer); - #if defined(USE_AVX512) - constexpr IndexType kNumChunks = kPaddedInputDimensions / (kSimdWidth * 2); - const auto input_vector = reinterpret_cast(input); - #if !defined(USE_VNNI) - const __m512i kOnes = _mm512_set1_epi16(1); - #endif + // Since to saturate a zmm register it takes 64 bytes we + // cannot use AVX512 for the smaller affine transforms. + // Instead we fallback to a AVX2 implementation if the + // kInputDimensions isn't a multiple of 64. + // Note that this means that for example for + // kInputDimensions of 96 we fallback to AVX2 even though + // the first 64 elements could be processed with AVX512. + // This is caused by mixing the __m256 and __m512 variables + // required to better handle that case and it would + // require handling more cases statically not to lose performance. + // This should be revisited if such input dimensions are to be considered. + [[maybe_unused]] const auto input_vector512 = reinterpret_cast(input); + [[maybe_unused]] const auto input_vector256 = reinterpret_cast(input); + + // kOutputDimensions is either 1 or a multiple of kSimdWidth + // because then it is also an input dimension. + 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) + { + const IndexType offset0 = (i + 0) * kPaddedInputDimensions; + const IndexType offset1 = (i + 1) * kPaddedInputDimensions; + const IndexType offset2 = (i + 2) * kPaddedInputDimensions; + const IndexType offset3 = (i + 3) * kPaddedInputDimensions; + + const __m128i bias = *reinterpret_cast(&biases_[i]); + __m128i* outptr = reinterpret_cast<__m128i*>(&output[i]); + + if constexpr (kPaddedInputDimensions % (kSimdWidth * 2) == 0) + { + 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]); + +#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 + { + 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]); + +#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); + } + } + } + else if constexpr (kOutputDimensions == 1) + { + if constexpr (kPaddedInputDimensions % (kSimdWidth * 2) == 0) + { + const auto row0 = reinterpret_cast(&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) + { + 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 + { + const auto row0 = reinterpret_cast(&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) + { + 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]); + } + } + else + { + // This case can never happen because kOutputDimensions + // is always 1 or a multiple of kSimdWidth. + assert(false); + } + +#elif defined (USE_AVX2) - #elif defined(USE_AVX2) constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth; - const __m256i kOnes = _mm256_set1_epi16(1); + + const auto output = reinterpret_cast(buffer); const auto input_vector = reinterpret_cast(input); - #elif defined(USE_SSE2) + // kOutputDimensions is either 1 or a multiple of kSimdWidth + // because then it is also an input dimension. + if constexpr (kOutputDimensions % 4 == 0) + { + for (IndexType i = 0; i < kOutputDimensions; i += 4) + { + const IndexType offset0 = (i + 0) * kPaddedInputDimensions; + const IndexType offset1 = (i + 1) * kPaddedInputDimensions; + const IndexType offset2 = (i + 2) * kPaddedInputDimensions; + const IndexType offset3 = (i + 3) * kPaddedInputDimensions; + + const __m128i bias = *reinterpret_cast(&biases_[i]); + __m128i* outptr = reinterpret_cast<__m128i*>(&output[i]); + + 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]); + +#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); + } + } + else if constexpr (kOutputDimensions == 1) + { + const auto row0 = reinterpret_cast(&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) + { + 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 + } + + output[0] = m256_hadd(sum0, biases_[0]); + } + else + { + // This case can never happen because kOutputDimensions + // is always 1 or a multiple of kSimdWidth. + assert(false); + } + +#elif defined (USE_SSSE3) + + constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth; + + auto output = reinterpret_cast(buffer); + const auto input_vector = reinterpret_cast(input); + + // kOutputDimensions is either 1 or a multiple of kSimdWidth + // because then it is also an input dimension. + if constexpr (kOutputDimensions % 4 == 0) + { + for (IndexType i = 0; i < kOutputDimensions; i += 4) + { + const IndexType offset0 = (i + 0) * kPaddedInputDimensions; + const IndexType offset1 = (i + 1) * kPaddedInputDimensions; + const IndexType offset2 = (i + 2) * kPaddedInputDimensions; + const IndexType offset3 = (i + 3) * kPaddedInputDimensions; + + const __m128i bias = *reinterpret_cast(&biases_[i]); + __m128i* outptr = reinterpret_cast<__m128i*>(&output[i]); + + 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]); + + __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]; + + 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); + } + } + else if constexpr (kOutputDimensions == 1) + { + const auto row0 = reinterpret_cast(&weights_[0]); + + __m128i sum0 = m128_dpbusd_epi32(input_vector[0], row0[0]); + + 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]); + } + else + { + // This case can never happen because kOutputDimensions + // is always 1 or a multiple of kSimdWidth. + assert(false); + } + +#else + +// Use old implementation for the other architectures. + + auto output = reinterpret_cast(buffer); + +#if defined(USE_SSE2) constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth; - #ifndef USE_SSSE3 +#ifndef USE_SSSE3 const __m128i kZeros = _mm_setzero_si128(); - #else +#else const __m128i kOnes = _mm_set1_epi16(1); - #endif +#endif const auto input_vector = reinterpret_cast(input); - #elif defined(USE_MMX) +#elif defined(USE_MMX) constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth; const __m64 kZeros = _mm_setzero_si64(); const auto input_vector = reinterpret_cast(input); - #elif defined(USE_NEON) +#elif defined(USE_NEON) constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth; const auto input_vector = reinterpret_cast(input); - #endif +#endif for (IndexType i = 0; i < kOutputDimensions; ++i) { const IndexType offset = i * kPaddedInputDimensions; - #if defined(USE_AVX512) - __m512i sum = _mm512_setzero_si512(); - const auto row = reinterpret_cast(&weights_[offset]); - for (IndexType j = 0; j < kNumChunks; ++j) { - #if defined(USE_VNNI) - sum = _mm512_dpbusd_epi32(sum, _mm512_loadA_si512(&input_vector[j]), _mm512_load_si512(&row[j])); - #else - __m512i product = _mm512_maddubs_epi16(_mm512_loadA_si512(&input_vector[j]), _mm512_load_si512(&row[j])); - product = _mm512_madd_epi16(product, kOnes); - sum = _mm512_add_epi32(sum, product); - #endif - } - - // Note: Changing kMaxSimdWidth from 32 to 64 breaks loading existing networks. - // As a result kPaddedInputDimensions may not be an even multiple of 64(512bit) - // and we have to do one more 256bit chunk. - if (kPaddedInputDimensions != kNumChunks * kSimdWidth * 2) - { - const auto iv256 = reinterpret_cast(&input_vector[kNumChunks]); - const auto row256 = reinterpret_cast(&row[kNumChunks]); - #if defined(USE_VNNI) - __m256i product256 = _mm256_dpbusd_epi32( - _mm512_castsi512_si256(sum), _mm256_loadA_si256(&iv256[0]), _mm256_load_si256(&row256[0])); - sum = _mm512_inserti32x8(sum, product256, 0); - #else - __m256i product256 = _mm256_maddubs_epi16(_mm256_loadA_si256(&iv256[0]), _mm256_load_si256(&row256[0])); - sum = _mm512_add_epi32(sum, _mm512_cvtepi16_epi32(product256)); - #endif - } - output[i] = _mm512_reduce_add_epi32(sum) + biases_[i]; - - #elif defined(USE_AVX2) - __m256i sum = _mm256_setzero_si256(); - const auto row = reinterpret_cast(&weights_[offset]); - for (IndexType j = 0; j < kNumChunks; ++j) { - __m256i product = _mm256_maddubs_epi16(_mm256_loadA_si256(&input_vector[j]), _mm256_load_si256(&row[j])); - product = _mm256_madd_epi16(product, kOnes); - sum = _mm256_add_epi32(sum, product); - } - __m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(sum), _mm256_extracti128_si256(sum, 1)); - sum128 = _mm_add_epi32(sum128, _mm_shuffle_epi32(sum128, _MM_PERM_BADC)); - sum128 = _mm_add_epi32(sum128, _mm_shuffle_epi32(sum128, _MM_PERM_CDAB)); - output[i] = _mm_cvtsi128_si32(sum128) + biases_[i]; - - #elif defined(USE_SSSE3) - __m128i sum = _mm_setzero_si128(); - const auto row = reinterpret_cast(&weights_[offset]); - for (int j = 0; j < (int)kNumChunks - 1; j += 2) { - __m128i product0 = _mm_maddubs_epi16(_mm_load_si128(&input_vector[j]), _mm_load_si128(&row[j])); - product0 = _mm_madd_epi16(product0, kOnes); - sum = _mm_add_epi32(sum, product0); - __m128i product1 = _mm_maddubs_epi16(_mm_load_si128(&input_vector[j+1]), _mm_load_si128(&row[j+1])); - product1 = _mm_madd_epi16(product1, kOnes); - sum = _mm_add_epi32(sum, product1); - } - if (kNumChunks & 0x1) { - __m128i product = _mm_maddubs_epi16(_mm_load_si128(&input_vector[kNumChunks-1]), _mm_load_si128(&row[kNumChunks-1])); - product = _mm_madd_epi16(product, kOnes); - sum = _mm_add_epi32(sum, product); - } - sum = _mm_add_epi32(sum, _mm_shuffle_epi32(sum, 0x4E)); //_MM_PERM_BADC - 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) +#if defined(USE_SSE2) __m128i sum_lo = _mm_cvtsi32_si128(biases_[i]); __m128i sum_hi = kZeros; const auto row = reinterpret_cast(&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_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); @@ -199,16 +696,15 @@ namespace Eval::NNUE::Layers { sum = _mm_add_epi32(sum, sum_second_32); output[i] = _mm_cvtsi128_si32(sum); - #elif defined(USE_MMX) +#elif defined(USE_MMX) __m64 sum_lo = _mm_cvtsi32_si64(biases_[i]); __m64 sum_hi = kZeros; const auto row = reinterpret_cast(&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_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); @@ -220,7 +716,7 @@ namespace Eval::NNUE::Layers { sum = _mm_add_pi32(sum, _mm_unpackhi_pi32(sum, sum)); output[i] = _mm_cvtsi64_si32(sum); - #elif defined(USE_NEON) +#elif defined(USE_NEON) int32x4_t sum = {biases_[i]}; const auto row = reinterpret_cast(&weights_[offset]); for (IndexType j = 0; j < kNumChunks; ++j) { @@ -230,18 +726,21 @@ namespace Eval::NNUE::Layers { } output[i] = sum[0] + sum[1] + sum[2] + sum[3]; - #else +#else OutputType sum = biases_[i]; for (IndexType j = 0; j < kInputDimensions; ++j) { sum += weights_[offset + j] * input[j]; } output[i] = sum; - #endif +#endif } - #if defined(USE_MMX) +#if defined(USE_MMX) _mm_empty(); - #endif +#endif + +#endif + return output; }