mirror of
https://github.com/HChaZZY/Stockfish.git
synced 2025-12-26 20:16:14 +08:00
Unify naming convention of the NNUE code
matches the rest of the stockfish code base closes https://github.com/official-stockfish/Stockfish/pull/3437 No functional change
This commit is contained in:
Tomasz Sobczyk
committed by
Joost VandeVondele
Joost VandeVondele
parent
a7ab92ec25
commit
fbbd4adc3c
@@ -27,7 +27,7 @@
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namespace Stockfish::Eval::NNUE::Layers {
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// Affine transformation layer
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template <typename PreviousLayer, IndexType OutputDimensions>
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template <typename PreviousLayer, IndexType OutDims>
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class AffineTransform {
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public:
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// Input/output type
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@@ -36,64 +36,64 @@ namespace Stockfish::Eval::NNUE::Layers {
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static_assert(std::is_same<InputType, std::uint8_t>::value, "");
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// Number of input/output dimensions
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static constexpr IndexType kInputDimensions =
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PreviousLayer::kOutputDimensions;
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static constexpr IndexType kOutputDimensions = OutputDimensions;
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static constexpr IndexType kPaddedInputDimensions =
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CeilToMultiple<IndexType>(kInputDimensions, kMaxSimdWidth);
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static constexpr IndexType InputDimensions =
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PreviousLayer::OutputDimensions;
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static constexpr IndexType OutputDimensions = OutDims;
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static constexpr IndexType PaddedInputDimensions =
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ceil_to_multiple<IndexType>(InputDimensions, MaxSimdWidth);
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#if defined (USE_AVX512)
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static constexpr const IndexType kOutputSimdWidth = kSimdWidth / 2;
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static constexpr const IndexType OutputSimdWidth = SimdWidth / 2;
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#elif defined (USE_SSSE3)
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static constexpr const IndexType kOutputSimdWidth = kSimdWidth / 4;
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static constexpr const IndexType OutputSimdWidth = SimdWidth / 4;
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#endif
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// Size of forward propagation buffer used in this layer
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static constexpr std::size_t kSelfBufferSize =
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CeilToMultiple(kOutputDimensions * sizeof(OutputType), kCacheLineSize);
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static constexpr std::size_t SelfBufferSize =
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ceil_to_multiple(OutputDimensions * sizeof(OutputType), CacheLineSize);
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// Size of the forward propagation buffer used from the input layer to this layer
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static constexpr std::size_t kBufferSize =
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PreviousLayer::kBufferSize + kSelfBufferSize;
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static constexpr std::size_t BufferSize =
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PreviousLayer::BufferSize + SelfBufferSize;
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// Hash value embedded in the evaluation file
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static constexpr std::uint32_t GetHashValue() {
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std::uint32_t hash_value = 0xCC03DAE4u;
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hash_value += kOutputDimensions;
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hash_value ^= PreviousLayer::GetHashValue() >> 1;
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hash_value ^= PreviousLayer::GetHashValue() << 31;
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return hash_value;
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static constexpr std::uint32_t get_hash_value() {
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std::uint32_t hashValue = 0xCC03DAE4u;
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hashValue += OutputDimensions;
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hashValue ^= PreviousLayer::get_hash_value() >> 1;
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hashValue ^= PreviousLayer::get_hash_value() << 31;
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return hashValue;
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}
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// Read network parameters
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bool ReadParameters(std::istream& stream) {
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if (!previous_layer_.ReadParameters(stream)) return false;
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for (std::size_t i = 0; i < kOutputDimensions; ++i)
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biases_[i] = read_little_endian<BiasType>(stream);
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for (std::size_t i = 0; i < kOutputDimensions * kPaddedInputDimensions; ++i)
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// Read network parameters
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bool read_parameters(std::istream& stream) {
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if (!previousLayer.read_parameters(stream)) return false;
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for (std::size_t i = 0; i < OutputDimensions; ++i)
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biases[i] = read_little_endian<BiasType>(stream);
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for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
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#if !defined (USE_SSSE3)
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weights_[i] = read_little_endian<WeightType>(stream);
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weights[i] = read_little_endian<WeightType>(stream);
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#else
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weights_[
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(i / 4) % (kPaddedInputDimensions / 4) * kOutputDimensions * 4 +
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i / kPaddedInputDimensions * 4 +
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weights[
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(i / 4) % (PaddedInputDimensions / 4) * OutputDimensions * 4 +
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i / PaddedInputDimensions * 4 +
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i % 4
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] = read_little_endian<WeightType>(stream);
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// Determine if eights of weight and input products can be summed using 16bits
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// without saturation. We assume worst case combinations of 0 and 127 for all inputs.
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if (kOutputDimensions > 1 && !stream.fail())
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if (OutputDimensions > 1 && !stream.fail())
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{
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canSaturate16.count = 0;
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#if !defined(USE_VNNI)
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for (IndexType i = 0; i < kPaddedInputDimensions; i += 16)
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for (IndexType j = 0; j < kOutputDimensions; ++j)
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for (IndexType i = 0; i < PaddedInputDimensions; i += 16)
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for (IndexType j = 0; j < OutputDimensions; ++j)
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for (int x = 0; x < 2; ++x)
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{
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WeightType* w = &weights_[i * kOutputDimensions + j * 4 + x * 2];
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WeightType* w = &weights[i * OutputDimensions + j * 4 + x * 2];
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int sum[2] = {0, 0};
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for (int k = 0; k < 8; ++k)
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{
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IndexType idx = k / 2 * kOutputDimensions * 4 + k % 2;
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IndexType idx = k / 2 * OutputDimensions * 4 + k % 2;
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sum[w[idx] < 0] += w[idx];
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}
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for (int sign : { -1, 1 })
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@@ -102,12 +102,12 @@ namespace Stockfish::Eval::NNUE::Layers {
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int maxK = 0, maxW = 0;
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for (int k = 0; k < 8; ++k)
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{
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IndexType idx = k / 2 * kOutputDimensions * 4 + k % 2;
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IndexType idx = k / 2 * OutputDimensions * 4 + k % 2;
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if (maxW < sign * w[idx])
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maxK = k, maxW = sign * w[idx];
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}
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IndexType idx = maxK / 2 * kOutputDimensions * 4 + maxK % 2;
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IndexType idx = maxK / 2 * OutputDimensions * 4 + maxK % 2;
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sum[sign == -1] -= w[idx];
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canSaturate16.add(j, i + maxK / 2 * 4 + maxK % 2 + x * 2, w[idx]);
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w[idx] = 0;
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@@ -126,14 +126,14 @@ namespace Stockfish::Eval::NNUE::Layers {
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}
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// Forward propagation
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const OutputType* Propagate(
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const TransformedFeatureType* transformed_features, char* buffer) const {
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const auto input = previous_layer_.Propagate(
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transformed_features, buffer + kSelfBufferSize);
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const OutputType* propagate(
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const TransformedFeatureType* transformedFeatures, char* buffer) const {
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const auto input = previousLayer.propagate(
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transformedFeatures, buffer + SelfBufferSize);
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#if defined (USE_AVX512)
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[[maybe_unused]] const __m512i kOnes512 = _mm512_set1_epi16(1);
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[[maybe_unused]] const __m512i Ones512 = _mm512_set1_epi16(1);
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[[maybe_unused]] auto m512_hadd = [](__m512i sum, int bias) -> int {
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return _mm512_reduce_add_epi32(sum) + bias;
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@@ -144,7 +144,7 @@ namespace Stockfish::Eval::NNUE::Layers {
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acc = _mm512_dpbusd_epi32(acc, a, b);
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#else
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__m512i product0 = _mm512_maddubs_epi16(a, b);
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product0 = _mm512_madd_epi16(product0, kOnes512);
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product0 = _mm512_madd_epi16(product0, Ones512);
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acc = _mm512_add_epi32(acc, product0);
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#endif
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};
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@@ -164,7 +164,7 @@ namespace Stockfish::Eval::NNUE::Layers {
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product0 = _mm512_add_epi16(product0, product1);
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product2 = _mm512_add_epi16(product2, product3);
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product0 = _mm512_add_epi16(product0, product2);
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product0 = _mm512_madd_epi16(product0, kOnes512);
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product0 = _mm512_madd_epi16(product0, Ones512);
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acc = _mm512_add_epi32(acc, product0);
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#endif
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};
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@@ -172,7 +172,7 @@ namespace Stockfish::Eval::NNUE::Layers {
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#endif
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#if defined (USE_AVX2)
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[[maybe_unused]] const __m256i kOnes256 = _mm256_set1_epi16(1);
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[[maybe_unused]] const __m256i Ones256 = _mm256_set1_epi16(1);
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[[maybe_unused]] auto m256_hadd = [](__m256i sum, int bias) -> int {
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__m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(sum), _mm256_extracti128_si256(sum, 1));
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@@ -186,7 +186,7 @@ namespace Stockfish::Eval::NNUE::Layers {
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acc = _mm256_dpbusd_epi32(acc, a, b);
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#else
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__m256i product0 = _mm256_maddubs_epi16(a, b);
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product0 = _mm256_madd_epi16(product0, kOnes256);
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product0 = _mm256_madd_epi16(product0, Ones256);
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acc = _mm256_add_epi32(acc, product0);
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#endif
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};
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@@ -206,7 +206,7 @@ namespace Stockfish::Eval::NNUE::Layers {
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product0 = _mm256_add_epi16(product0, product1);
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product2 = _mm256_add_epi16(product2, product3);
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product0 = _mm256_add_epi16(product0, product2);
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product0 = _mm256_madd_epi16(product0, kOnes256);
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product0 = _mm256_madd_epi16(product0, Ones256);
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acc = _mm256_add_epi32(acc, product0);
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#endif
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};
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@@ -214,7 +214,7 @@ namespace Stockfish::Eval::NNUE::Layers {
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#endif
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#if defined (USE_SSSE3)
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[[maybe_unused]] const __m128i kOnes128 = _mm_set1_epi16(1);
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[[maybe_unused]] const __m128i Ones128 = _mm_set1_epi16(1);
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[[maybe_unused]] auto m128_hadd = [](__m128i sum, int bias) -> int {
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sum = _mm_add_epi32(sum, _mm_shuffle_epi32(sum, 0x4E)); //_MM_PERM_BADC
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@@ -224,7 +224,7 @@ namespace Stockfish::Eval::NNUE::Layers {
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[[maybe_unused]] auto m128_add_dpbusd_epi32 = [=](__m128i& acc, __m128i a, __m128i b) {
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__m128i product0 = _mm_maddubs_epi16(a, b);
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product0 = _mm_madd_epi16(product0, kOnes128);
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product0 = _mm_madd_epi16(product0, Ones128);
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acc = _mm_add_epi32(acc, product0);
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};
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@@ -237,7 +237,7 @@ namespace Stockfish::Eval::NNUE::Layers {
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product0 = _mm_add_epi16(product0, product1);
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product2 = _mm_add_epi16(product2, product3);
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product0 = _mm_add_epi16(product0, product2);
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product0 = _mm_madd_epi16(product0, kOnes128);
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product0 = _mm_madd_epi16(product0, Ones128);
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acc = _mm_add_epi32(acc, product0);
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};
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@@ -269,71 +269,71 @@ namespace Stockfish::Eval::NNUE::Layers {
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#if defined (USE_SSSE3)
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const auto output = reinterpret_cast<OutputType*>(buffer);
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const auto input_vector = reinterpret_cast<const vec_t*>(input);
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const auto inputVector = reinterpret_cast<const vec_t*>(input);
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static_assert(kOutputDimensions % kOutputSimdWidth == 0 || kOutputDimensions == 1);
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static_assert(OutputDimensions % OutputSimdWidth == 0 || OutputDimensions == 1);
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// kOutputDimensions is either 1 or a multiple of kSimdWidth
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// OutputDimensions is either 1 or a multiple of SimdWidth
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// because then it is also an input dimension.
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if constexpr (kOutputDimensions % kOutputSimdWidth == 0)
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if constexpr (OutputDimensions % OutputSimdWidth == 0)
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{
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constexpr IndexType kNumChunks = kPaddedInputDimensions / 4;
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constexpr IndexType NumChunks = PaddedInputDimensions / 4;
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const auto input32 = reinterpret_cast<const std::int32_t*>(input);
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vec_t* outptr = reinterpret_cast<vec_t*>(output);
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std::memcpy(output, biases_, kOutputDimensions * sizeof(OutputType));
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std::memcpy(output, biases, OutputDimensions * sizeof(OutputType));
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for (int i = 0; i < (int)kNumChunks - 3; i += 4)
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for (int i = 0; i < (int)NumChunks - 3; i += 4)
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{
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const vec_t in0 = vec_set_32(input32[i + 0]);
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const vec_t in1 = vec_set_32(input32[i + 1]);
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const vec_t in2 = vec_set_32(input32[i + 2]);
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const vec_t in3 = vec_set_32(input32[i + 3]);
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const auto col0 = reinterpret_cast<const vec_t*>(&weights_[(i + 0) * kOutputDimensions * 4]);
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const auto col1 = reinterpret_cast<const vec_t*>(&weights_[(i + 1) * kOutputDimensions * 4]);
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const auto col2 = reinterpret_cast<const vec_t*>(&weights_[(i + 2) * kOutputDimensions * 4]);
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const auto col3 = reinterpret_cast<const vec_t*>(&weights_[(i + 3) * kOutputDimensions * 4]);
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for (int j = 0; j * kOutputSimdWidth < kOutputDimensions; ++j)
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const auto col0 = reinterpret_cast<const vec_t*>(&weights[(i + 0) * OutputDimensions * 4]);
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const auto col1 = reinterpret_cast<const vec_t*>(&weights[(i + 1) * OutputDimensions * 4]);
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const auto col2 = reinterpret_cast<const vec_t*>(&weights[(i + 2) * OutputDimensions * 4]);
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const auto col3 = reinterpret_cast<const vec_t*>(&weights[(i + 3) * OutputDimensions * 4]);
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for (int j = 0; j * OutputSimdWidth < OutputDimensions; ++j)
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vec_add_dpbusd_32x4(outptr[j], in0, col0[j], in1, col1[j], in2, col2[j], in3, col3[j]);
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}
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for (int i = 0; i < canSaturate16.count; ++i)
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output[canSaturate16.ids[i].out] += input[canSaturate16.ids[i].in] * canSaturate16.ids[i].w;
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}
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else if constexpr (kOutputDimensions == 1)
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else if constexpr (OutputDimensions == 1)
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{
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#if defined (USE_AVX512)
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if constexpr (kPaddedInputDimensions % (kSimdWidth * 2) != 0)
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if constexpr (PaddedInputDimensions % (SimdWidth * 2) != 0)
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{
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constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
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const auto input_vector256 = reinterpret_cast<const __m256i*>(input);
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constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
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const auto inputVector256 = reinterpret_cast<const __m256i*>(input);
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__m256i sum0 = _mm256_setzero_si256();
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const auto row0 = reinterpret_cast<const __m256i*>(&weights_[0]);
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const auto row0 = reinterpret_cast<const __m256i*>(&weights[0]);
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for (int j = 0; j < (int)kNumChunks; ++j)
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for (int j = 0; j < (int)NumChunks; ++j)
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{
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const __m256i in = input_vector256[j];
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const __m256i in = inputVector256[j];
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m256_add_dpbusd_epi32(sum0, in, row0[j]);
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}
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output[0] = m256_hadd(sum0, biases_[0]);
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output[0] = m256_hadd(sum0, biases[0]);
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}
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else
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#endif
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{
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#if defined (USE_AVX512)
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constexpr IndexType kNumChunks = kPaddedInputDimensions / (kSimdWidth * 2);
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constexpr IndexType NumChunks = PaddedInputDimensions / (SimdWidth * 2);
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#else
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constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
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constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
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#endif
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vec_t sum0 = vec_setzero();
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const auto row0 = reinterpret_cast<const vec_t*>(&weights_[0]);
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const auto row0 = reinterpret_cast<const vec_t*>(&weights[0]);
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for (int j = 0; j < (int)kNumChunks; ++j)
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for (int j = 0; j < (int)NumChunks; ++j)
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{
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const vec_t in = input_vector[j];
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const vec_t in = inputVector[j];
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vec_add_dpbusd_32(sum0, in, row0[j]);
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}
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output[0] = vec_hadd(sum0, biases_[0]);
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output[0] = vec_hadd(sum0, biases[0]);
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}
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}
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@@ -344,80 +344,80 @@ namespace Stockfish::Eval::NNUE::Layers {
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auto output = reinterpret_cast<OutputType*>(buffer);
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#if defined(USE_SSE2)
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constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
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const __m128i kZeros = _mm_setzero_si128();
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const auto input_vector = reinterpret_cast<const __m128i*>(input);
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constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
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const __m128i Zeros = _mm_setzero_si128();
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const auto inputVector = reinterpret_cast<const __m128i*>(input);
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#elif defined(USE_MMX)
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constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
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const __m64 kZeros = _mm_setzero_si64();
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const auto input_vector = reinterpret_cast<const __m64*>(input);
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constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
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const __m64 Zeros = _mm_setzero_si64();
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const auto inputVector = reinterpret_cast<const __m64*>(input);
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#elif defined(USE_NEON)
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constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
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const auto input_vector = reinterpret_cast<const int8x8_t*>(input);
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constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
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const auto inputVector = reinterpret_cast<const int8x8_t*>(input);
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#endif
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for (IndexType i = 0; i < kOutputDimensions; ++i) {
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const IndexType offset = i * kPaddedInputDimensions;
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for (IndexType i = 0; i < OutputDimensions; ++i) {
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const IndexType offset = i * PaddedInputDimensions;
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#if defined(USE_SSE2)
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__m128i sum_lo = _mm_cvtsi32_si128(biases_[i]);
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__m128i sum_hi = kZeros;
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const auto row = reinterpret_cast<const __m128i*>(&weights_[offset]);
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for (IndexType j = 0; j < kNumChunks; ++j) {
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__m128i sumLo = _mm_cvtsi32_si128(biases[i]);
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__m128i sumHi = Zeros;
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const auto row = reinterpret_cast<const __m128i*>(&weights[offset]);
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for (IndexType j = 0; j < NumChunks; ++j) {
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__m128i row_j = _mm_load_si128(&row[j]);
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__m128i input_j = _mm_load_si128(&input_vector[j]);
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__m128i extended_row_lo = _mm_srai_epi16(_mm_unpacklo_epi8(row_j, row_j), 8);
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__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);
|
||||
__m128i product_hi = _mm_madd_epi16(extended_row_hi, extended_input_hi);
|
||||
sum_lo = _mm_add_epi32(sum_lo, product_lo);
|
||||
sum_hi = _mm_add_epi32(sum_hi, product_hi);
|
||||
__m128i input_j = _mm_load_si128(&inputVector[j]);
|
||||
__m128i extendedRowLo = _mm_srai_epi16(_mm_unpacklo_epi8(row_j, row_j), 8);
|
||||
__m128i extendedRowHi = _mm_srai_epi16(_mm_unpackhi_epi8(row_j, row_j), 8);
|
||||
__m128i extendedInputLo = _mm_unpacklo_epi8(input_j, Zeros);
|
||||
__m128i extendedInputHi = _mm_unpackhi_epi8(input_j, Zeros);
|
||||
__m128i productLo = _mm_madd_epi16(extendedRowLo, extendedInputLo);
|
||||
__m128i productHi = _mm_madd_epi16(extendedRowHi, extendedInputHi);
|
||||
sumLo = _mm_add_epi32(sumLo, productLo);
|
||||
sumHi = _mm_add_epi32(sumHi, productHi);
|
||||
}
|
||||
__m128i sum = _mm_add_epi32(sum_lo, sum_hi);
|
||||
__m128i sum_high_64 = _mm_shuffle_epi32(sum, _MM_SHUFFLE(1, 0, 3, 2));
|
||||
sum = _mm_add_epi32(sum, sum_high_64);
|
||||
__m128i sum = _mm_add_epi32(sumLo, sumHi);
|
||||
__m128i sumHigh_64 = _mm_shuffle_epi32(sum, _MM_SHUFFLE(1, 0, 3, 2));
|
||||
sum = _mm_add_epi32(sum, sumHigh_64);
|
||||
__m128i sum_second_32 = _mm_shufflelo_epi16(sum, _MM_SHUFFLE(1, 0, 3, 2));
|
||||
sum = _mm_add_epi32(sum, sum_second_32);
|
||||
output[i] = _mm_cvtsi128_si32(sum);
|
||||
|
||||
#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 sumLo = _mm_cvtsi32_si64(biases[i]);
|
||||
__m64 sumHi = Zeros;
|
||||
const auto row = reinterpret_cast<const __m64*>(&weights[offset]);
|
||||
for (IndexType j = 0; j < NumChunks; ++j) {
|
||||
__m64 row_j = row[j];
|
||||
__m64 input_j = input_vector[j];
|
||||
__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);
|
||||
__m64 product_hi = _mm_madd_pi16(extended_row_hi, extended_input_hi);
|
||||
sum_lo = _mm_add_pi32(sum_lo, product_lo);
|
||||
sum_hi = _mm_add_pi32(sum_hi, product_hi);
|
||||
__m64 input_j = inputVector[j];
|
||||
__m64 extendedRowLo = _mm_srai_pi16(_mm_unpacklo_pi8(row_j, row_j), 8);
|
||||
__m64 extendedRowHi = _mm_srai_pi16(_mm_unpackhi_pi8(row_j, row_j), 8);
|
||||
__m64 extendedInputLo = _mm_unpacklo_pi8(input_j, Zeros);
|
||||
__m64 extendedInputHi = _mm_unpackhi_pi8(input_j, Zeros);
|
||||
__m64 productLo = _mm_madd_pi16(extendedRowLo, extendedInputLo);
|
||||
__m64 productHi = _mm_madd_pi16(extendedRowHi, extendedInputHi);
|
||||
sumLo = _mm_add_pi32(sumLo, productLo);
|
||||
sumHi = _mm_add_pi32(sumHi, productHi);
|
||||
}
|
||||
__m64 sum = _mm_add_pi32(sum_lo, sum_hi);
|
||||
__m64 sum = _mm_add_pi32(sumLo, sumHi);
|
||||
sum = _mm_add_pi32(sum, _mm_unpackhi_pi32(sum, sum));
|
||||
output[i] = _mm_cvtsi64_si32(sum);
|
||||
|
||||
#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) {
|
||||
int16x8_t product = vmull_s8(input_vector[j * 2], row[j * 2]);
|
||||
product = vmlal_s8(product, input_vector[j * 2 + 1], row[j * 2 + 1]);
|
||||
int32x4_t sum = {biases[i]};
|
||||
const auto row = reinterpret_cast<const int8x8_t*>(&weights[offset]);
|
||||
for (IndexType j = 0; j < NumChunks; ++j) {
|
||||
int16x8_t product = vmull_s8(inputVector[j * 2], row[j * 2]);
|
||||
product = vmlal_s8(product, inputVector[j * 2 + 1], row[j * 2 + 1]);
|
||||
sum = vpadalq_s16(sum, product);
|
||||
}
|
||||
output[i] = sum[0] + sum[1] + sum[2] + sum[3];
|
||||
|
||||
#else
|
||||
OutputType sum = biases_[i];
|
||||
for (IndexType j = 0; j < kInputDimensions; ++j) {
|
||||
sum += weights_[offset + j] * input[j];
|
||||
OutputType sum = biases[i];
|
||||
for (IndexType j = 0; j < InputDimensions; ++j) {
|
||||
sum += weights[offset + j] * input[j];
|
||||
}
|
||||
output[i] = sum;
|
||||
#endif
|
||||
@@ -436,10 +436,10 @@ namespace Stockfish::Eval::NNUE::Layers {
|
||||
using BiasType = OutputType;
|
||||
using WeightType = std::int8_t;
|
||||
|
||||
PreviousLayer previous_layer_;
|
||||
PreviousLayer previousLayer;
|
||||
|
||||
alignas(kCacheLineSize) BiasType biases_[kOutputDimensions];
|
||||
alignas(kCacheLineSize) WeightType weights_[kOutputDimensions * kPaddedInputDimensions];
|
||||
alignas(CacheLineSize) BiasType biases[OutputDimensions];
|
||||
alignas(CacheLineSize) WeightType weights[OutputDimensions * PaddedInputDimensions];
|
||||
#if defined (USE_SSSE3)
|
||||
struct CanSaturate {
|
||||
int count;
|
||||
@@ -447,7 +447,7 @@ namespace Stockfish::Eval::NNUE::Layers {
|
||||
uint16_t out;
|
||||
uint16_t in;
|
||||
int8_t w;
|
||||
} ids[kPaddedInputDimensions * kOutputDimensions * 3 / 4];
|
||||
} ids[PaddedInputDimensions * OutputDimensions * 3 / 4];
|
||||
|
||||
void add(int i, int j, int8_t w) {
|
||||
ids[count].out = i;
|
||||
|
||||
@@ -35,130 +35,130 @@ namespace Stockfish::Eval::NNUE::Layers {
|
||||
static_assert(std::is_same<InputType, std::int32_t>::value, "");
|
||||
|
||||
// Number of input/output dimensions
|
||||
static constexpr IndexType kInputDimensions =
|
||||
PreviousLayer::kOutputDimensions;
|
||||
static constexpr IndexType kOutputDimensions = kInputDimensions;
|
||||
static constexpr IndexType InputDimensions =
|
||||
PreviousLayer::OutputDimensions;
|
||||
static constexpr IndexType OutputDimensions = InputDimensions;
|
||||
|
||||
// Size of forward propagation buffer used in this layer
|
||||
static constexpr std::size_t kSelfBufferSize =
|
||||
CeilToMultiple(kOutputDimensions * sizeof(OutputType), kCacheLineSize);
|
||||
static constexpr std::size_t SelfBufferSize =
|
||||
ceil_to_multiple(OutputDimensions * sizeof(OutputType), CacheLineSize);
|
||||
|
||||
// Size of the forward propagation buffer used from the input layer to this layer
|
||||
static constexpr std::size_t kBufferSize =
|
||||
PreviousLayer::kBufferSize + kSelfBufferSize;
|
||||
static constexpr std::size_t BufferSize =
|
||||
PreviousLayer::BufferSize + SelfBufferSize;
|
||||
|
||||
// Hash value embedded in the evaluation file
|
||||
static constexpr std::uint32_t GetHashValue() {
|
||||
std::uint32_t hash_value = 0x538D24C7u;
|
||||
hash_value += PreviousLayer::GetHashValue();
|
||||
return hash_value;
|
||||
static constexpr std::uint32_t get_hash_value() {
|
||||
std::uint32_t hashValue = 0x538D24C7u;
|
||||
hashValue += PreviousLayer::get_hash_value();
|
||||
return hashValue;
|
||||
}
|
||||
|
||||
// Read network parameters
|
||||
bool ReadParameters(std::istream& stream) {
|
||||
return previous_layer_.ReadParameters(stream);
|
||||
bool read_parameters(std::istream& stream) {
|
||||
return previousLayer.read_parameters(stream);
|
||||
}
|
||||
|
||||
// Forward propagation
|
||||
const OutputType* Propagate(
|
||||
const TransformedFeatureType* transformed_features, char* buffer) const {
|
||||
const auto input = previous_layer_.Propagate(
|
||||
transformed_features, buffer + kSelfBufferSize);
|
||||
const OutputType* propagate(
|
||||
const TransformedFeatureType* transformedFeatures, char* buffer) const {
|
||||
const auto input = previousLayer.propagate(
|
||||
transformedFeatures, buffer + SelfBufferSize);
|
||||
const auto output = reinterpret_cast<OutputType*>(buffer);
|
||||
|
||||
#if defined(USE_AVX2)
|
||||
constexpr IndexType kNumChunks = kInputDimensions / kSimdWidth;
|
||||
const __m256i kZero = _mm256_setzero_si256();
|
||||
const __m256i kOffsets = _mm256_set_epi32(7, 3, 6, 2, 5, 1, 4, 0);
|
||||
constexpr IndexType NumChunks = InputDimensions / SimdWidth;
|
||||
const __m256i Zero = _mm256_setzero_si256();
|
||||
const __m256i Offsets = _mm256_set_epi32(7, 3, 6, 2, 5, 1, 4, 0);
|
||||
const auto in = reinterpret_cast<const __m256i*>(input);
|
||||
const auto out = reinterpret_cast<__m256i*>(output);
|
||||
for (IndexType i = 0; i < kNumChunks; ++i) {
|
||||
for (IndexType i = 0; i < NumChunks; ++i) {
|
||||
const __m256i words0 = _mm256_srai_epi16(_mm256_packs_epi32(
|
||||
_mm256_load_si256(&in[i * 4 + 0]),
|
||||
_mm256_load_si256(&in[i * 4 + 1])), kWeightScaleBits);
|
||||
_mm256_load_si256(&in[i * 4 + 1])), WeightScaleBits);
|
||||
const __m256i words1 = _mm256_srai_epi16(_mm256_packs_epi32(
|
||||
_mm256_load_si256(&in[i * 4 + 2]),
|
||||
_mm256_load_si256(&in[i * 4 + 3])), kWeightScaleBits);
|
||||
_mm256_load_si256(&in[i * 4 + 3])), WeightScaleBits);
|
||||
_mm256_store_si256(&out[i], _mm256_permutevar8x32_epi32(_mm256_max_epi8(
|
||||
_mm256_packs_epi16(words0, words1), kZero), kOffsets));
|
||||
_mm256_packs_epi16(words0, words1), Zero), Offsets));
|
||||
}
|
||||
constexpr IndexType kStart = kNumChunks * kSimdWidth;
|
||||
constexpr IndexType Start = NumChunks * SimdWidth;
|
||||
|
||||
#elif defined(USE_SSE2)
|
||||
constexpr IndexType kNumChunks = kInputDimensions / kSimdWidth;
|
||||
constexpr IndexType NumChunks = InputDimensions / SimdWidth;
|
||||
|
||||
#ifdef USE_SSE41
|
||||
const __m128i kZero = _mm_setzero_si128();
|
||||
const __m128i Zero = _mm_setzero_si128();
|
||||
#else
|
||||
const __m128i k0x80s = _mm_set1_epi8(-128);
|
||||
#endif
|
||||
|
||||
const auto in = reinterpret_cast<const __m128i*>(input);
|
||||
const auto out = reinterpret_cast<__m128i*>(output);
|
||||
for (IndexType i = 0; i < kNumChunks; ++i) {
|
||||
for (IndexType i = 0; i < NumChunks; ++i) {
|
||||
const __m128i words0 = _mm_srai_epi16(_mm_packs_epi32(
|
||||
_mm_load_si128(&in[i * 4 + 0]),
|
||||
_mm_load_si128(&in[i * 4 + 1])), kWeightScaleBits);
|
||||
_mm_load_si128(&in[i * 4 + 1])), WeightScaleBits);
|
||||
const __m128i words1 = _mm_srai_epi16(_mm_packs_epi32(
|
||||
_mm_load_si128(&in[i * 4 + 2]),
|
||||
_mm_load_si128(&in[i * 4 + 3])), kWeightScaleBits);
|
||||
_mm_load_si128(&in[i * 4 + 3])), WeightScaleBits);
|
||||
const __m128i packedbytes = _mm_packs_epi16(words0, words1);
|
||||
_mm_store_si128(&out[i],
|
||||
|
||||
#ifdef USE_SSE41
|
||||
_mm_max_epi8(packedbytes, kZero)
|
||||
_mm_max_epi8(packedbytes, Zero)
|
||||
#else
|
||||
_mm_subs_epi8(_mm_adds_epi8(packedbytes, k0x80s), k0x80s)
|
||||
#endif
|
||||
|
||||
);
|
||||
}
|
||||
constexpr IndexType kStart = kNumChunks * kSimdWidth;
|
||||
constexpr IndexType Start = NumChunks * SimdWidth;
|
||||
|
||||
#elif defined(USE_MMX)
|
||||
constexpr IndexType kNumChunks = kInputDimensions / kSimdWidth;
|
||||
constexpr IndexType NumChunks = InputDimensions / SimdWidth;
|
||||
const __m64 k0x80s = _mm_set1_pi8(-128);
|
||||
const auto in = reinterpret_cast<const __m64*>(input);
|
||||
const auto out = reinterpret_cast<__m64*>(output);
|
||||
for (IndexType i = 0; i < kNumChunks; ++i) {
|
||||
for (IndexType i = 0; i < NumChunks; ++i) {
|
||||
const __m64 words0 = _mm_srai_pi16(
|
||||
_mm_packs_pi32(in[i * 4 + 0], in[i * 4 + 1]),
|
||||
kWeightScaleBits);
|
||||
WeightScaleBits);
|
||||
const __m64 words1 = _mm_srai_pi16(
|
||||
_mm_packs_pi32(in[i * 4 + 2], in[i * 4 + 3]),
|
||||
kWeightScaleBits);
|
||||
WeightScaleBits);
|
||||
const __m64 packedbytes = _mm_packs_pi16(words0, words1);
|
||||
out[i] = _mm_subs_pi8(_mm_adds_pi8(packedbytes, k0x80s), k0x80s);
|
||||
}
|
||||
_mm_empty();
|
||||
constexpr IndexType kStart = kNumChunks * kSimdWidth;
|
||||
constexpr IndexType Start = NumChunks * SimdWidth;
|
||||
|
||||
#elif defined(USE_NEON)
|
||||
constexpr IndexType kNumChunks = kInputDimensions / (kSimdWidth / 2);
|
||||
const int8x8_t kZero = {0};
|
||||
constexpr IndexType NumChunks = InputDimensions / (SimdWidth / 2);
|
||||
const int8x8_t Zero = {0};
|
||||
const auto in = reinterpret_cast<const int32x4_t*>(input);
|
||||
const auto out = reinterpret_cast<int8x8_t*>(output);
|
||||
for (IndexType i = 0; i < kNumChunks; ++i) {
|
||||
for (IndexType i = 0; i < NumChunks; ++i) {
|
||||
int16x8_t shifted;
|
||||
const auto pack = reinterpret_cast<int16x4_t*>(&shifted);
|
||||
pack[0] = vqshrn_n_s32(in[i * 2 + 0], kWeightScaleBits);
|
||||
pack[1] = vqshrn_n_s32(in[i * 2 + 1], kWeightScaleBits);
|
||||
out[i] = vmax_s8(vqmovn_s16(shifted), kZero);
|
||||
pack[0] = vqshrn_n_s32(in[i * 2 + 0], WeightScaleBits);
|
||||
pack[1] = vqshrn_n_s32(in[i * 2 + 1], WeightScaleBits);
|
||||
out[i] = vmax_s8(vqmovn_s16(shifted), Zero);
|
||||
}
|
||||
constexpr IndexType kStart = kNumChunks * (kSimdWidth / 2);
|
||||
constexpr IndexType Start = NumChunks * (SimdWidth / 2);
|
||||
#else
|
||||
constexpr IndexType kStart = 0;
|
||||
constexpr IndexType Start = 0;
|
||||
#endif
|
||||
|
||||
for (IndexType i = kStart; i < kInputDimensions; ++i) {
|
||||
for (IndexType i = Start; i < InputDimensions; ++i) {
|
||||
output[i] = static_cast<OutputType>(
|
||||
std::max(0, std::min(127, input[i] >> kWeightScaleBits)));
|
||||
std::max(0, std::min(127, input[i] >> WeightScaleBits)));
|
||||
}
|
||||
return output;
|
||||
}
|
||||
|
||||
private:
|
||||
PreviousLayer previous_layer_;
|
||||
PreviousLayer previousLayer;
|
||||
};
|
||||
|
||||
} // namespace Stockfish::Eval::NNUE::Layers
|
||||
|
||||
@@ -26,38 +26,38 @@
|
||||
namespace Stockfish::Eval::NNUE::Layers {
|
||||
|
||||
// Input layer
|
||||
template <IndexType OutputDimensions, IndexType Offset = 0>
|
||||
template <IndexType OutDims, IndexType Offset = 0>
|
||||
class InputSlice {
|
||||
public:
|
||||
// Need to maintain alignment
|
||||
static_assert(Offset % kMaxSimdWidth == 0, "");
|
||||
static_assert(Offset % MaxSimdWidth == 0, "");
|
||||
|
||||
// Output type
|
||||
using OutputType = TransformedFeatureType;
|
||||
|
||||
// Output dimensionality
|
||||
static constexpr IndexType kOutputDimensions = OutputDimensions;
|
||||
static constexpr IndexType OutputDimensions = OutDims;
|
||||
|
||||
// Size of forward propagation buffer used from the input layer to this layer
|
||||
static constexpr std::size_t kBufferSize = 0;
|
||||
static constexpr std::size_t BufferSize = 0;
|
||||
|
||||
// Hash value embedded in the evaluation file
|
||||
static constexpr std::uint32_t GetHashValue() {
|
||||
std::uint32_t hash_value = 0xEC42E90Du;
|
||||
hash_value ^= kOutputDimensions ^ (Offset << 10);
|
||||
return hash_value;
|
||||
static constexpr std::uint32_t get_hash_value() {
|
||||
std::uint32_t hashValue = 0xEC42E90Du;
|
||||
hashValue ^= OutputDimensions ^ (Offset << 10);
|
||||
return hashValue;
|
||||
}
|
||||
|
||||
// Read network parameters
|
||||
bool ReadParameters(std::istream& /*stream*/) {
|
||||
bool read_parameters(std::istream& /*stream*/) {
|
||||
return true;
|
||||
}
|
||||
|
||||
// Forward propagation
|
||||
const OutputType* Propagate(
|
||||
const TransformedFeatureType* transformed_features,
|
||||
const OutputType* propagate(
|
||||
const TransformedFeatureType* transformedFeatures,
|
||||
char* /*buffer*/) const {
|
||||
return transformed_features + Offset;
|
||||
return transformedFeatures + Offset;
|
||||
}
|
||||
|
||||
private:
|
||||
|
||||
Reference in New Issue
Block a user