/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
- Copyright (C) 2004-2020 The Stockfish developers (see AUTHORS file)
+ Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
static constexpr IndexType kOutputDimensions = OutputDimensions;
static constexpr IndexType kPaddedInputDimensions =
CeilToMultiple<IndexType>(kInputDimensions, kMaxSimdWidth);
+#if defined (USE_AVX512)
+ static constexpr const IndexType kOutputSimdWidth = kSimdWidth / 2;
+#elif defined (USE_SSSE3)
+ static constexpr const IndexType kOutputSimdWidth = kSimdWidth / 4;
+#endif
// Size of forward propagation buffer used in this layer
static constexpr std::size_t kSelfBufferSize =
// Read network parameters
bool ReadParameters(std::istream& stream) {
if (!previous_layer_.ReadParameters(stream)) return false;
- stream.read(reinterpret_cast<char*>(biases_),
- kOutputDimensions * sizeof(BiasType));
- stream.read(reinterpret_cast<char*>(weights_),
- kOutputDimensions * kPaddedInputDimensions *
- sizeof(WeightType));
+ for (std::size_t i = 0; i < kOutputDimensions; ++i)
+ biases_[i] = read_little_endian<BiasType>(stream);
+ for (std::size_t i = 0; i < kOutputDimensions * kPaddedInputDimensions; ++i)
+#if !defined (USE_SSSE3)
+ weights_[i] = read_little_endian<WeightType>(stream);
+#else
+ weights_[
+ (i / 4) % (kPaddedInputDimensions / 4) * kOutputDimensions * 4 +
+ i / kPaddedInputDimensions * 4 +
+ i % 4
+ ] = read_little_endian<WeightType>(stream);
+
+ // Determine if eights 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 (kOutputDimensions > 1 && !stream.fail())
+ {
+ canSaturate16.count = 0;
+#if !defined(USE_VNNI)
+ for (IndexType i = 0; i < kPaddedInputDimensions; i += 16)
+ for (IndexType j = 0; j < kOutputDimensions; ++j)
+ for (int x = 0; x < 2; ++x)
+ {
+ WeightType* w = &weights_[i * kOutputDimensions + j * 4 + x * 2];
+ int sum[2] = {0, 0};
+ for (int k = 0; k < 8; ++k)
+ {
+ IndexType idx = k / 2 * kOutputDimensions * 4 + k % 2;
+ sum[w[idx] < 0] += w[idx];
+ }
+ for (int sign : {-1, 1})
+ while (sign * sum[sign == -1] > 258)
+ {
+ int maxK = 0, maxW = 0;
+ for (int k = 0; k < 8; ++k)
+ {
+ IndexType idx = k / 2 * kOutputDimensions * 4 + k % 2;
+ if (maxW < sign * w[idx])
+ maxK = k, maxW = sign * w[idx];
+ }
+
+ IndexType idx = maxK / 2 * kOutputDimensions * 4 + maxK % 2;
+ sum[sign == -1] -= w[idx];
+ canSaturate16.add(j, i + maxK / 2 * 4 + maxK % 2 + x * 2, w[idx]);
+ w[idx] = 0;
+ }
+ }
+
+ // Non functional optimization for faster more linear access
+ std::sort(canSaturate16.ids, canSaturate16.ids + canSaturate16.count,
+ [](const typename CanSaturate::Entry& e1, const typename CanSaturate::Entry& e2)
+ { return e1.in == e2.in ? e1.out < e2.out : e1.in < e2.in; });
+#endif
+ }
+#endif
+
return !stream.fail();
}
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;
+ };
+
+ [[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
+ __m512i product0 = _mm512_maddubs_epi16(a, b);
+ product0 = _mm512_madd_epi16(product0, kOnes512);
+ acc = _mm512_add_epi32(acc, product0);
+#endif
+ };
+
+ [[maybe_unused]] auto m512_add_dpbusd_epi32x4 = [=](__m512i& acc, __m512i a0, __m512i b0, __m512i a1, __m512i b1,
+ __m512i a2, __m512i b2, __m512i a3, __m512i b3) {
+#if defined (USE_VNNI)
+ acc = _mm512_dpbusd_epi32(acc, a0, b0);
+ acc = _mm512_dpbusd_epi32(acc, a1, b1);
+ acc = _mm512_dpbusd_epi32(acc, a2, b2);
+ acc = _mm512_dpbusd_epi32(acc, a3, b3);
+#else
+ __m512i product0 = _mm512_maddubs_epi16(a0, b0);
+ __m512i product1 = _mm512_maddubs_epi16(a1, b1);
+ __m512i product2 = _mm512_maddubs_epi16(a2, b2);
+ __m512i product3 = _mm512_maddubs_epi16(a3, b3);
+ product0 = _mm512_add_epi16(product0, product1);
+ product2 = _mm512_add_epi16(product2, product3);
+ product0 = _mm512_add_epi16(product0, product2);
+ product0 = _mm512_madd_epi16(product0, kOnes512);
+ acc = _mm512_add_epi32(acc, product0);
+#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_add_dpbusd_epi32 = [=](__m256i& acc, __m256i a, __m256i b) {
+#if defined (USE_VNNI)
+ acc = _mm256_dpbusd_epi32(acc, a, b);
+#else
+ __m256i product0 = _mm256_maddubs_epi16(a, b);
+ product0 = _mm256_madd_epi16(product0, kOnes256);
+ acc = _mm256_add_epi32(acc, product0);
+#endif
+ };
+
+ [[maybe_unused]] auto m256_add_dpbusd_epi32x4 = [=](__m256i& acc, __m256i a0, __m256i b0, __m256i a1, __m256i b1,
+ __m256i a2, __m256i b2, __m256i a3, __m256i b3) {
+#if defined (USE_VNNI)
+ acc = _mm256_dpbusd_epi32(acc, a0, b0);
+ acc = _mm256_dpbusd_epi32(acc, a1, b1);
+ acc = _mm256_dpbusd_epi32(acc, a2, b2);
+ acc = _mm256_dpbusd_epi32(acc, a3, b3);
+#else
+ __m256i product0 = _mm256_maddubs_epi16(a0, b0);
+ __m256i product1 = _mm256_maddubs_epi16(a1, b1);
+ __m256i product2 = _mm256_maddubs_epi16(a2, b2);
+ __m256i product3 = _mm256_maddubs_epi16(a3, b3);
+ product0 = _mm256_add_epi16(product0, product1);
+ product2 = _mm256_add_epi16(product2, product3);
+ product0 = _mm256_add_epi16(product0, product2);
+ product0 = _mm256_madd_epi16(product0, kOnes256);
+ acc = _mm256_add_epi32(acc, product0);
+#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_add_dpbusd_epi32 = [=](__m128i& acc, __m128i a, __m128i b) {
+ __m128i product0 = _mm_maddubs_epi16(a, b);
+ product0 = _mm_madd_epi16(product0, kOnes128);
+ acc = _mm_add_epi32(acc, product0);
+ };
+
+ [[maybe_unused]] auto m128_add_dpbusd_epi32x4 = [=](__m128i& acc, __m128i a0, __m128i b0, __m128i a1, __m128i b1,
+ __m128i a2, __m128i b2, __m128i a3, __m128i b3) {
+ __m128i product0 = _mm_maddubs_epi16(a0, b0);
+ __m128i product1 = _mm_maddubs_epi16(a1, b1);
+ __m128i product2 = _mm_maddubs_epi16(a2, b2);
+ __m128i product3 = _mm_maddubs_epi16(a3, b3);
+ product0 = _mm_adds_epi16(product0, product1);
+ product2 = _mm_adds_epi16(product2, product3);
+ product0 = _mm_adds_epi16(product0, product2);
+ product0 = _mm_madd_epi16(product0, kOnes128);
+ acc = _mm_add_epi32(acc, product0);
+ };
+
+#endif
+
+#if defined (USE_AVX512)
+ using vec_t = __m512i;
+ #define vec_setzero _mm512_setzero_si512
+ #define vec_set_32 _mm512_set1_epi32
+ auto& vec_add_dpbusd_32 = m512_add_dpbusd_epi32;
+ auto& vec_add_dpbusd_32x4 = m512_add_dpbusd_epi32x4;
+ auto& vec_hadd = m512_hadd;
+#elif defined (USE_AVX2)
+ using vec_t = __m256i;
+ #define vec_setzero _mm256_setzero_si256
+ #define vec_set_32 _mm256_set1_epi32
+ auto& vec_add_dpbusd_32 = m256_add_dpbusd_epi32;
+ auto& vec_add_dpbusd_32x4 = m256_add_dpbusd_epi32x4;
+ auto& vec_hadd = m256_hadd;
+#elif defined (USE_SSSE3)
+ using vec_t = __m128i;
+ #define vec_setzero _mm_setzero_si128
+ #define vec_set_32 _mm_set1_epi32
+ auto& vec_add_dpbusd_32 = m128_add_dpbusd_epi32;
+ auto& vec_add_dpbusd_32x4 = m128_add_dpbusd_epi32x4;
+ auto& vec_hadd = m128_hadd;
+#endif
+
+#if defined (USE_SSSE3)
+
const auto output = reinterpret_cast<OutputType*>(buffer);
+ const auto input_vector = reinterpret_cast<const vec_t*>(input);
+
+ static_assert(kOutputDimensions % kOutputSimdWidth == 0 || kOutputDimensions == 1);
+
+ // kOutputDimensions is either 1 or a multiple of kSimdWidth
+ // because then it is also an input dimension.
+ if constexpr (kOutputDimensions % kOutputSimdWidth == 0)
+ {
+ constexpr IndexType kNumChunks = kPaddedInputDimensions / 4;
+
+ const auto input32 = reinterpret_cast<const std::int32_t*>(input);
+ vec_t* outptr = reinterpret_cast<vec_t*>(output);
+ std::memcpy(output, biases_, kOutputDimensions * sizeof(OutputType));
+
+ for (int i = 0; i < (int)kNumChunks - 3; i += 4)
+ {
+ const vec_t in0 = vec_set_32(input32[i + 0]);
+ const vec_t in1 = vec_set_32(input32[i + 1]);
+ const vec_t in2 = vec_set_32(input32[i + 2]);
+ const vec_t in3 = vec_set_32(input32[i + 3]);
+ const auto col0 = reinterpret_cast<const vec_t*>(&weights_[(i + 0) * kOutputDimensions * 4]);
+ const auto col1 = reinterpret_cast<const vec_t*>(&weights_[(i + 1) * kOutputDimensions * 4]);
+ const auto col2 = reinterpret_cast<const vec_t*>(&weights_[(i + 2) * kOutputDimensions * 4]);
+ const auto col3 = reinterpret_cast<const vec_t*>(&weights_[(i + 3) * kOutputDimensions * 4]);
+ for (int j = 0; j * kOutputSimdWidth < kOutputDimensions; ++j)
+ vec_add_dpbusd_32x4(outptr[j], in0, col0[j], in1, col1[j], in2, col2[j], in3, col3[j]);
+ }
+ for (int i = 0; i < canSaturate16.count; ++i)
+ output[canSaturate16.ids[i].out] += input[canSaturate16.ids[i].in] * canSaturate16.ids[i].w;
+ }
+ else if constexpr (kOutputDimensions == 1)
+ {
+#if defined (USE_AVX512)
+ if constexpr (kPaddedInputDimensions % (kSimdWidth * 2) != 0)
+ {
+ constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
+ const auto input_vector256 = reinterpret_cast<const __m256i*>(input);
+
+ __m256i sum0 = _mm256_setzero_si256();
+ const auto row0 = reinterpret_cast<const __m256i*>(&weights_[0]);
+
+ for (int j = 0; j < (int)kNumChunks; ++j)
+ {
+ const __m256i in = input_vector256[j];
+ m256_add_dpbusd_epi32(sum0, in, row0[j]);
+ }
+ output[0] = m256_hadd(sum0, biases_[0]);
+ }
+ else
+#endif
+ {
+#if defined (USE_AVX512)
+ constexpr IndexType kNumChunks = kPaddedInputDimensions / (kSimdWidth * 2);
+#else
+ constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
+#endif
+ vec_t sum0 = vec_setzero();
+ const auto row0 = reinterpret_cast<const vec_t*>(&weights_[0]);
+
+ for (int j = 0; j < (int)kNumChunks; ++j)
+ {
+ const vec_t in = input_vector[j];
+ vec_add_dpbusd_32(sum0, in, row0[j]);
+ }
+ output[0] = vec_hadd(sum0, biases_[0]);
+ }
+ }
- #if defined(USE_AVX512)
- constexpr IndexType kNumChunks = kPaddedInputDimensions / (kSimdWidth * 2);
- const __m512i kOnes = _mm512_set1_epi16(1);
- const auto input_vector = reinterpret_cast<const __m512i*>(input);
+#else
- #elif defined(USE_AVX2)
- constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
- const __m256i kOnes = _mm256_set1_epi16(1);
- const auto input_vector = reinterpret_cast<const __m256i*>(input);
+// Use old implementation for the other architectures.
+
+ auto output = reinterpret_cast<OutputType*>(buffer);
- #elif defined(USE_SSE2)
+#if 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)
+#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)
+#elif defined(USE_NEON)
constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
const auto input_vector = reinterpret_cast<const int8x8_t*>(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<const __m512i*>(&weights_[offset]);
- for (IndexType j = 0; j < kNumChunks; ++j) {
- __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);
- }
-
- // 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<const __m256i*>(&input_vector[kNumChunks]);
- const auto row256 = reinterpret_cast<const __m256i*>(&row[kNumChunks]);
- __m256i product256 = _mm256_maddubs_epi16(_mm256_loadA_si256(&iv256[0]), _mm256_load_si256(&row256[0]));
- product256 = _mm256_madd_epi16(product256, _mm256_set1_epi16(1));
- sum = _mm512_add_epi32(sum, _mm512_zextsi256_si512(product256));
- }
- output[i] = _mm512_reduce_add_epi32(sum) + biases_[i];
-
- #elif defined(USE_AVX2)
- __m256i sum = _mm256_setzero_si256();
- const auto row = reinterpret_cast<const __m256i*>(&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<const __m128i*>(&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<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_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);
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<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_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);
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<const int8x8_t*>(&weights_[offset]);
for (IndexType j = 0; j < kNumChunks; ++j) {
}
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;
}
PreviousLayer previous_layer_;
alignas(kCacheLineSize) BiasType biases_[kOutputDimensions];
- alignas(kCacheLineSize)
- WeightType weights_[kOutputDimensions * kPaddedInputDimensions];
+ alignas(kCacheLineSize) WeightType weights_[kOutputDimensions * kPaddedInputDimensions];
+#if defined (USE_SSSE3)
+ struct CanSaturate {
+ int count;
+ struct Entry {
+ uint16_t out;
+ uint16_t in;
+ int8_t w;
+ } ids[kPaddedInputDimensions * kOutputDimensions * 3 / 4];
+
+ void add(int i, int j, int8_t w) {
+ ids[count].out = i;
+ ids[count].in = j;
+ ids[count].w = w;
+ ++count;
+ }
+ } canSaturate16;
+#endif
};
} // namespace Eval::NNUE::Layers
projects / stockfish / blobdiff