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Add support for VNNI
Adds support for Vector Neural Network Instructions (avx512), as available on Intel Cascade Lake The _mm512_dpbusd_epi32() intrinsic (vpdpbusd instruction) is taylor made for NNUE. on a cascade lake CPU (AWS C5.24x.large, gcc 10) NNUE eval is at roughly 78% nps of classical (single core test) bench 1024 1 24 default depth: target classical NNUE ratio vnni 2207232 1725987 78.20 avx512 2216789 1671734 75.41 avx2 2194006 1611263 73.44 modern 2185001 1352469 61.90 closes https://github.com/official-stockfish/Stockfish/pull/2987 No functional change
This commit is contained in:
mstembera
Joost VandeVondele
parent
6bc0256292
commit
dd63b98fb0
3 changed files with 41 additions and 1 deletions
25
src/Makefile
25
src/Makefile
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@ -73,6 +73,7 @@ endif
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# avx2 = yes/no --- -mavx2 --- Use Intel Advanced Vector Extensions 2
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# pext = yes/no --- -DUSE_PEXT --- Use pext x86_64 asm-instruction
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# avx512 = yes/no --- -mavx512bw --- Use Intel Advanced Vector Extensions 512
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# vnni = yes/no --- -mavx512vnni --- Use Intel Vector Neural Network Instructions 512
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# neon = yes/no --- -DUSE_NEON --- Use ARM SIMD architecture
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#
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# Note that Makefile is space sensitive, so when adding new architectures
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@ -93,6 +94,7 @@ sse41 = no
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avx2 = no
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pext = no
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avx512 = no
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vnni = no
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neon = no
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ARCH = x86-64-modern
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@ -190,6 +192,19 @@ ifeq ($(ARCH),x86-64-avx512)
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avx512 = yes
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endif
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ifeq ($(ARCH),x86-64-vnni)
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arch = x86_64
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prefetch = yes
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popcnt = yes
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sse = yes
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ssse3 = yes
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sse41 = yes
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avx2 = yes
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pext = yes
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avx512 = yes
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vnni = yes
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endif
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ifeq ($(ARCH),armv7)
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arch = armv7
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prefetch = yes
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@ -420,6 +435,13 @@ ifeq ($(avx512),yes)
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endif
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endif
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ifeq ($(vnni),yes)
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CXXFLAGS += -DUSE_VNNI
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ifeq ($(comp),$(filter $(comp),gcc clang mingw))
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CXXFLAGS += -mavx512vnni -mavx512dq -mavx512vl
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endif
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endif
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ifeq ($(sse41),yes)
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CXXFLAGS += -DUSE_SSE41
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ifeq ($(comp),$(filter $(comp),gcc clang mingw))
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@ -522,6 +544,7 @@ help:
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@echo ""
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@echo "Supported archs:"
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@echo ""
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@echo "x86-64-vnni > x86 64-bit with vnni support"
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@echo "x86-64-avx512 > x86 64-bit with avx512 support"
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@echo "x86-64-bmi2 > x86 64-bit with bmi2 support"
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@echo "x86-64-avx2 > x86 64-bit with avx2 support"
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@ -640,6 +663,7 @@ config-sanity:
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@echo "avx2: '$(avx2)'"
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@echo "pext: '$(pext)'"
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@echo "avx512: '$(avx512)'"
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@echo "vnni: '$(vnni)'"
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@echo "neon: '$(neon)'"
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@echo ""
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@echo "Flags:"
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@ -664,6 +688,7 @@ config-sanity:
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@test "$(avx2)" = "yes" || test "$(avx2)" = "no"
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@test "$(pext)" = "yes" || test "$(pext)" = "no"
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@test "$(avx512)" = "yes" || test "$(avx512)" = "no"
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@test "$(vnni)" = "yes" || test "$(vnni)" = "no"
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@test "$(neon)" = "yes" || test "$(neon)" = "no"
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@test "$(comp)" = "gcc" || test "$(comp)" = "icc" || test "$(comp)" = "mingw" || test "$(comp)" = "clang"
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@ -219,6 +219,9 @@ const std::string compiler_info() {
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compiler += "\nCompilation settings include: ";
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compiler += (Is64Bit ? " 64bit" : " 32bit");
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#if defined(USE_VNNI)
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compiler += " VNNI";
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#endif
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#if defined(USE_AVX512)
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compiler += " AVX512";
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#endif
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@ -79,8 +79,10 @@ namespace Eval::NNUE::Layers {
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#if defined(USE_AVX512)
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constexpr IndexType kNumChunks = kPaddedInputDimensions / (kSimdWidth * 2);
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const __m512i kOnes = _mm512_set1_epi16(1);
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const auto input_vector = reinterpret_cast<const __m512i*>(input);
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#if !defined(USE_VNNI)
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const __m512i kOnes = _mm512_set1_epi16(1);
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#endif
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#elif defined(USE_AVX2)
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constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
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@ -113,9 +115,13 @@ namespace Eval::NNUE::Layers {
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__m512i sum = _mm512_setzero_si512();
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const auto row = reinterpret_cast<const __m512i*>(&weights_[offset]);
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for (IndexType j = 0; j < kNumChunks; ++j) {
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#if defined(USE_VNNI)
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sum = _mm512_dpbusd_epi32(sum, _mm512_loadA_si512(&input_vector[j]), _mm512_load_si512(&row[j]));
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#else
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__m512i product = _mm512_maddubs_epi16(_mm512_loadA_si512(&input_vector[j]), _mm512_load_si512(&row[j]));
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product = _mm512_madd_epi16(product, kOnes);
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sum = _mm512_add_epi32(sum, product);
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#endif
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}
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// Note: Changing kMaxSimdWidth from 32 to 64 breaks loading existing networks.
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@ -125,8 +131,14 @@ namespace Eval::NNUE::Layers {
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{
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const auto iv256 = reinterpret_cast<const __m256i*>(&input_vector[kNumChunks]);
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const auto row256 = reinterpret_cast<const __m256i*>(&row[kNumChunks]);
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#if defined(USE_VNNI)
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__m256i product256 = _mm256_dpbusd_epi32(
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_mm512_castsi512_si256(sum), _mm256_loadA_si256(&iv256[0]), _mm256_load_si256(&row256[0]));
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sum = _mm512_inserti32x8(sum, product256, 0);
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#else
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__m256i product256 = _mm256_maddubs_epi16(_mm256_loadA_si256(&iv256[0]), _mm256_load_si256(&row256[0]));
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sum = _mm512_add_epi32(sum, _mm512_cvtepi16_epi32(product256));
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#endif
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}
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output[i] = _mm512_reduce_add_epi32(sum) + biases_[i];
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