diff --git a/AUTHORS b/AUTHORS index d01d23cd..63b862ce 100644 --- a/AUTHORS +++ b/AUTHORS @@ -159,6 +159,7 @@ Norman Schmidt (FireFather) notruck Ofek Shochat (OfekShochat, ghostway) Ondrej Mosnáček (WOnder93) +Ondřej Mišina (AndrovT) Oskar Werkelin Ahlin Pablo Vazquez Panthee diff --git a/src/evaluate.h b/src/evaluate.h index fc852c8d..94cd42cc 100644 --- a/src/evaluate.h +++ b/src/evaluate.h @@ -39,7 +39,7 @@ namespace Eval { // The default net name MUST follow the format nn-[SHA256 first 12 digits].nnue // for the build process (profile-build and fishtest) to work. Do not change the // name of the macro, as it is used in the Makefile. - #define EvalFileDefaultName "nn-ea57bea57e32.nnue" + #define EvalFileDefaultName "nn-fdc1d0fe6455.nnue" namespace NNUE { diff --git a/src/nnue/layers/affine_transform_sparse_input.h b/src/nnue/layers/affine_transform_sparse_input.h new file mode 100644 index 00000000..00b17c19 --- /dev/null +++ b/src/nnue/layers/affine_transform_sparse_input.h @@ -0,0 +1,286 @@ +/* + Stockfish, a UCI chess playing engine derived from Glaurung 2.1 + Copyright (C) 2004-2023 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 + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + Stockfish is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +*/ + +// Definition of layer AffineTransformSparseInput of NNUE evaluation function + +#ifndef NNUE_LAYERS_AFFINE_TRANSFORM_SPARSE_INPUT_H_INCLUDED +#define NNUE_LAYERS_AFFINE_TRANSFORM_SPARSE_INPUT_H_INCLUDED + +#include +#include +#include +#include +#include "../nnue_common.h" +#include "affine_transform.h" +#include "simd.h" + +/* + This file contains the definition for a fully connected layer (aka affine transform) with block sparse input. +*/ + +namespace Stockfish::Eval::NNUE::Layers { +#if defined(__GNUC__) // GCC, Clang, ICC + + static inline IndexType lsb_(std::uint32_t b) { + assert(b); + return IndexType(__builtin_ctzl(b)); + } + +#elif defined(_MSC_VER) // MSVC + + static inline IndexType lsb_(std::uint32_t b) { + assert(b); + unsigned long idx; + _BitScanForward(&idx, b); + return (IndexType) idx; + } + +#else // Compiler is neither GCC nor MSVC compatible + +#error "Compiler not supported." + +#endif + + +#if defined(USE_SSSE3) + alignas(CacheLineSize) static inline const std::array, 256> lookup_indices = [](){ + std::array, 256> v{}; + for (int i = 0; i < 256; ++i) + { + int j = i; + int k = 0; + while(j) + { + const IndexType lsbIndex = lsb_(std::uint32_t(j)); + j &= j - 1; + v[i][k] = lsbIndex; + ++k; + } + } + return v; + }(); + alignas(CacheLineSize) static inline const std::array lookup_count = [](){ + std::array v; + for (int i = 0; i < 256; ++i) + { + int j = i; + int k = 0; + while(j) + { + j &= j - 1; + ++k; + } + v[i] = k; + } + return v; + }(); + + // Find indices of nonzero numbers in an int32_t array + template + void find_nnz(const std::int32_t* input, std::uint16_t* out, IndexType& count_out) { +#if defined (USE_AVX512) + using vec_t = __m512i; + #define vec_nnz(a) _mm512_cmpgt_epi32_mask(a, _mm512_setzero_si512()) +#elif defined (USE_AVX2) + using vec_t = __m256i; + #define vec_nnz(a) _mm256_movemask_ps((__m256)_mm256_cmpgt_epi32(a, _mm256_setzero_si256())) +#elif defined (USE_SSSE3) + using vec_t = __m128i; + #define vec_nnz(a) _mm_movemask_ps((__m128)_mm_cmpgt_epi32(a, _mm_setzero_si128())) +#endif + constexpr IndexType InputSimdWidth = sizeof(vec_t) / sizeof(std::int32_t); + // Inputs are processed InputSimdWidth at a time and outputs are processed 8 at a time so we process in chunks of max(InputSimdWidth, 8) + constexpr IndexType ChunkSize = std::max(InputSimdWidth, 8); + constexpr IndexType NumChunks = InputDimensions / ChunkSize; + constexpr IndexType InputsPerChunk = ChunkSize / InputSimdWidth; + constexpr IndexType OutputsPerChunk = ChunkSize / 8; + + const auto inputVector = reinterpret_cast(input); + IndexType count = 0; + __m128i base = _mm_set1_epi16(0); + __m128i increment = _mm_set1_epi16(8); + for (IndexType i = 0; i < NumChunks; ++i) + { + // bitmask of nonzero values in this chunk + unsigned nnz = 0; + for (IndexType j = 0; j < InputsPerChunk; ++j) + { + const vec_t inputChunk = inputVector[i * InputsPerChunk + j]; + nnz |= (unsigned)vec_nnz(inputChunk) << (j * InputSimdWidth); + } + for (IndexType j = 0; j < OutputsPerChunk; ++j) + { + const auto lookup = (nnz >> (j * 8)) & 0xFF; + const auto offsets = _mm_loadu_si128(reinterpret_cast(&lookup_indices[lookup])); + _mm_storeu_si128(reinterpret_cast<__m128i*>(out + count), _mm_add_epi16(base, offsets)); + count += lookup_count[lookup]; + base = _mm_add_epi16(base, increment); + } + } + count_out = count; + } +# undef vec_nnz +#endif + + // Sparse input implementation + template + class AffineTransformSparseInput { + public: + // Input/output type + // Input/output type + using InputType = std::uint8_t; + using OutputType = std::int32_t; + + // Number of input/output dimensions + static constexpr IndexType InputDimensions = InDims; + static constexpr IndexType OutputDimensions = OutDims; + + static_assert(OutputDimensions % 16 == 0, "Only implemented for OutputDimensions divisible by 16."); + + static constexpr IndexType PaddedInputDimensions = + ceil_to_multiple(InputDimensions, MaxSimdWidth); + static constexpr IndexType PaddedOutputDimensions = + ceil_to_multiple(OutputDimensions, MaxSimdWidth); + +#if defined (USE_SSSE3) + static constexpr IndexType ChunkSize = 4; +#else + static constexpr IndexType ChunkSize = 1; +#endif + + using OutputBuffer = OutputType[PaddedOutputDimensions]; + + // Hash value embedded in the evaluation file + static constexpr std::uint32_t get_hash_value(std::uint32_t prevHash) { + std::uint32_t hashValue = 0xCC03DAE4u; + hashValue += OutputDimensions; + hashValue ^= prevHash >> 1; + hashValue ^= prevHash << 31; + return hashValue; + } + + static IndexType get_weight_index_scrambled(IndexType i) + { + return + (i / ChunkSize) % (PaddedInputDimensions / ChunkSize) * OutputDimensions * ChunkSize + + i / PaddedInputDimensions * ChunkSize + + i % ChunkSize; + } + + static IndexType get_weight_index(IndexType i) + { +#if defined (USE_SSSE3) + return get_weight_index_scrambled(i); +#else + return i; +#endif + } + + // Read network parameters + bool read_parameters(std::istream& stream) { + read_little_endian(stream, biases, OutputDimensions); + for (IndexType i = 0; i < OutputDimensions * PaddedInputDimensions; ++i) + weights[get_weight_index(i)] = read_little_endian(stream); + + return !stream.fail(); + } + + // Write network parameters + bool write_parameters(std::ostream& stream) const { + write_little_endian(stream, biases, OutputDimensions); + + for (IndexType i = 0; i < OutputDimensions * PaddedInputDimensions; ++i) + write_little_endian(stream, weights[get_weight_index(i)]); + + return !stream.fail(); + } + // Forward propagation + const OutputType* propagate( + const InputType* input, OutputType* output) const { + +#if defined (USE_SSSE3) +#if defined (USE_AVX512) + using vec_t = __m512i; + #define vec_setzero _mm512_setzero_si512 + #define vec_set_32 _mm512_set1_epi32 + #define vec_add_dpbusd_32 Simd::m512_add_dpbusd_epi32 +#elif defined (USE_AVX2) + using vec_t = __m256i; + #define vec_setzero _mm256_setzero_si256 + #define vec_set_32 _mm256_set1_epi32 + #define vec_add_dpbusd_32 Simd::m256_add_dpbusd_epi32 +#elif defined (USE_SSSE3) + using vec_t = __m128i; + #define vec_setzero _mm_setzero_si128 + #define vec_set_32 _mm_set1_epi32 + #define vec_add_dpbusd_32 Simd::m128_add_dpbusd_epi32 +#endif + static constexpr IndexType OutputSimdWidth = sizeof(vec_t) / sizeof(OutputType); + + constexpr IndexType NumChunks = ceil_to_multiple(InputDimensions, 8) / ChunkSize; + constexpr IndexType NumRegs = OutputDimensions / OutputSimdWidth; + std::uint16_t nnz[NumChunks]; + IndexType count; + + const auto input32 = reinterpret_cast(input); + + // Find indices of nonzero 32bit blocks + find_nnz(input32, nnz, count); + + const vec_t* biasvec = reinterpret_cast(biases); + vec_t acc[NumRegs]; + for (IndexType k = 0; k < NumRegs; ++k) + acc[k] = biasvec[k]; + + for (IndexType j = 0; j < count; ++j) + { + const auto i = nnz[j]; + const vec_t in = vec_set_32(input32[i]); + const auto col = reinterpret_cast(&weights[i * OutputDimensions * ChunkSize]); + for (IndexType k = 0; k < NumRegs; ++k) + vec_add_dpbusd_32(acc[k], in, col[k]); + } + + vec_t* outptr = reinterpret_cast(output); + for (IndexType k = 0; k < NumRegs; ++k) + outptr[k] = acc[k]; +# undef vec_setzero +# undef vec_set_32 +# undef vec_add_dpbusd_32 +#else + // Use dense implementation for the other architectures. + affine_transform_non_ssse3< + InputDimensions, + PaddedInputDimensions, + OutputDimensions>(output, weights, biases, input); +#endif + + return output; + } + + private: + using BiasType = OutputType; + using WeightType = std::int8_t; + + alignas(CacheLineSize) BiasType biases[OutputDimensions]; + alignas(CacheLineSize) WeightType weights[OutputDimensions * PaddedInputDimensions]; + }; + +} // namespace Stockfish::Eval::NNUE::Layers + +#endif // #ifndef NNUE_LAYERS_AFFINE_TRANSFORM_SPARSE_INPUT_H_INCLUDED diff --git a/src/nnue/nnue_architecture.h b/src/nnue/nnue_architecture.h index d10434f3..413dbb3d 100644 --- a/src/nnue/nnue_architecture.h +++ b/src/nnue/nnue_architecture.h @@ -27,6 +27,7 @@ #include "features/half_ka_v2_hm.h" +#include "layers/affine_transform_sparse_input.h" #include "layers/affine_transform.h" #include "layers/clipped_relu.h" #include "layers/sqr_clipped_relu.h" @@ -48,7 +49,7 @@ struct Network static constexpr int FC_0_OUTPUTS = 15; static constexpr int FC_1_OUTPUTS = 32; - Layers::AffineTransform fc_0; + Layers::AffineTransformSparseInput fc_0; Layers::SqrClippedReLU ac_sqr_0; Layers::ClippedReLU ac_0; Layers::AffineTransform fc_1;