diff --git a/src/Makefile b/src/Makefile
index b021ba12..c1dab86b 100644
--- a/src/Makefile
+++ b/src/Makefile
@@ -41,7 +41,7 @@ endif
SRCS = benchmark.cpp bitbase.cpp bitboard.cpp endgame.cpp evaluate.cpp main.cpp \
material.cpp misc.cpp movegen.cpp movepick.cpp pawns.cpp position.cpp psqt.cpp \
search.cpp thread.cpp timeman.cpp tt.cpp uci.cpp ucioption.cpp tune.cpp syzygy/tbprobe.cpp \
- nnue/evaluate_nnue.cpp nnue/features/half_ka_v2.cpp
+ nnue/evaluate_nnue.cpp nnue/features/half_ka_v2_hm.cpp
OBJS = $(notdir $(SRCS:.cpp=.o))
diff --git a/src/evaluate.h b/src/evaluate.h
index 1ac224b6..4430a28c 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-46832cfbead3.nnue"
+ #define EvalFileDefaultName "nn-e8321e467bf6.nnue"
namespace NNUE {
diff --git a/src/nnue/features/half_ka_v2.cpp b/src/nnue/features/half_ka_v2_hm.cpp
similarity index 68%
rename from src/nnue/features/half_ka_v2.cpp
rename to src/nnue/features/half_ka_v2_hm.cpp
index 57f43e50..098a6d60 100644
--- a/src/nnue/features/half_ka_v2.cpp
+++ b/src/nnue/features/half_ka_v2_hm.cpp
@@ -16,31 +16,32 @@
along with this program. If not, see .
*/
-//Definition of input features HalfKAv2 of NNUE evaluation function
+//Definition of input features HalfKAv2_hm of NNUE evaluation function
-#include "half_ka_v2.h"
+#include "half_ka_v2_hm.h"
#include "../../position.h"
namespace Stockfish::Eval::NNUE::Features {
// Orient a square according to perspective (rotates by 180 for black)
- inline Square HalfKAv2::orient(Color perspective, Square s) {
- return Square(int(s) ^ (bool(perspective) * 56));
+ inline Square HalfKAv2_hm::orient(Color perspective, Square s, Square ksq) {
+ return Square(int(s) ^ (bool(perspective) * SQ_A8) ^ ((file_of(ksq) < FILE_E) * SQ_H1));
}
// Index of a feature for a given king position and another piece on some square
- inline IndexType HalfKAv2::make_index(Color perspective, Square s, Piece pc, Square ksq) {
- return IndexType(orient(perspective, s) + PieceSquareIndex[perspective][pc] + PS_NB * ksq);
+ inline IndexType HalfKAv2_hm::make_index(Color perspective, Square s, Piece pc, Square ksq) {
+ Square o_ksq = orient(perspective, ksq, ksq);
+ return IndexType(orient(perspective, s, ksq) + PieceSquareIndex[perspective][pc] + PS_NB * KingBuckets[o_ksq]);
}
// Get a list of indices for active features
- void HalfKAv2::append_active_indices(
+ void HalfKAv2_hm::append_active_indices(
const Position& pos,
Color perspective,
ValueListInserter active
) {
- Square ksq = orient(perspective, pos.square(perspective));
+ Square ksq = pos.square(perspective);
Bitboard bb = pos.pieces();
while (bb)
{
@@ -52,7 +53,7 @@ namespace Stockfish::Eval::NNUE::Features {
// append_changed_indices() : get a list of indices for recently changed features
- void HalfKAv2::append_changed_indices(
+ void HalfKAv2_hm::append_changed_indices(
Square ksq,
StateInfo* st,
Color perspective,
@@ -60,25 +61,24 @@ namespace Stockfish::Eval::NNUE::Features {
ValueListInserter added
) {
const auto& dp = st->dirtyPiece;
- Square oriented_ksq = orient(perspective, ksq);
for (int i = 0; i < dp.dirty_num; ++i) {
Piece pc = dp.piece[i];
if (dp.from[i] != SQ_NONE)
- removed.push_back(make_index(perspective, dp.from[i], pc, oriented_ksq));
+ removed.push_back(make_index(perspective, dp.from[i], pc, ksq));
if (dp.to[i] != SQ_NONE)
- added.push_back(make_index(perspective, dp.to[i], pc, oriented_ksq));
+ added.push_back(make_index(perspective, dp.to[i], pc, ksq));
}
}
- int HalfKAv2::update_cost(StateInfo* st) {
+ int HalfKAv2_hm::update_cost(StateInfo* st) {
return st->dirtyPiece.dirty_num;
}
- int HalfKAv2::refresh_cost(const Position& pos) {
+ int HalfKAv2_hm::refresh_cost(const Position& pos) {
return pos.count();
}
- bool HalfKAv2::requires_refresh(StateInfo* st, Color perspective) {
+ bool HalfKAv2_hm::requires_refresh(StateInfo* st, Color perspective) {
return st->dirtyPiece.piece[0] == make_piece(perspective, KING);
}
diff --git a/src/nnue/features/half_ka_v2.h b/src/nnue/features/half_ka_v2_hm.h
similarity index 80%
rename from src/nnue/features/half_ka_v2.h
rename to src/nnue/features/half_ka_v2_hm.h
index e4b2edd9..2c1144f6 100644
--- a/src/nnue/features/half_ka_v2.h
+++ b/src/nnue/features/half_ka_v2_hm.h
@@ -18,8 +18,8 @@
//Definition of input features HalfKP of NNUE evaluation function
-#ifndef NNUE_FEATURES_HALF_KA_V2_H_INCLUDED
-#define NNUE_FEATURES_HALF_KA_V2_H_INCLUDED
+#ifndef NNUE_FEATURES_HALF_KA_V2_HM_H_INCLUDED
+#define NNUE_FEATURES_HALF_KA_V2_HM_H_INCLUDED
#include "../nnue_common.h"
@@ -32,9 +32,9 @@ namespace Stockfish {
namespace Stockfish::Eval::NNUE::Features {
- // Feature HalfKAv2: Combination of the position of own king
- // and the position of pieces
- class HalfKAv2 {
+ // Feature HalfKAv2_hm: Combination of the position of own king
+ // and the position of pieces. Position mirrored such that king always on e..h files.
+ class HalfKAv2_hm {
// unique number for each piece type on each square
enum {
@@ -63,21 +63,32 @@ namespace Stockfish::Eval::NNUE::Features {
};
// Orient a square according to perspective (rotates by 180 for black)
- static Square orient(Color perspective, Square s);
+ static Square orient(Color perspective, Square s, Square ksq);
// Index of a feature for a given king position and another piece on some square
static IndexType make_index(Color perspective, Square s, Piece pc, Square ksq);
public:
// Feature name
- static constexpr const char* Name = "HalfKAv2(Friend)";
+ static constexpr const char* Name = "HalfKAv2_hm(Friend)";
// Hash value embedded in the evaluation file
- static constexpr std::uint32_t HashValue = 0x5f234cb8u;
+ static constexpr std::uint32_t HashValue = 0x7f234cb8u;
// Number of feature dimensions
static constexpr IndexType Dimensions =
- static_cast(SQUARE_NB) * static_cast(PS_NB);
+ static_cast(SQUARE_NB) * static_cast(PS_NB) / 2;
+
+ static constexpr int KingBuckets[64] = {
+ -1, -1, -1, -1, 31, 30, 29, 28,
+ -1, -1, -1, -1, 27, 26, 25, 24,
+ -1, -1, -1, -1, 23, 22, 21, 20,
+ -1, -1, -1, -1, 19, 18, 17, 16,
+ -1, -1, -1, -1, 15, 14, 13, 12,
+ -1, -1, -1, -1, 11, 10, 9, 8,
+ -1, -1, -1, -1, 7, 6, 5, 4,
+ -1, -1, -1, -1, 3, 2, 1, 0
+ };
// Maximum number of simultaneously active features.
static constexpr IndexType MaxActiveDimensions = 32;
@@ -108,4 +119,4 @@ namespace Stockfish::Eval::NNUE::Features {
} // namespace Stockfish::Eval::NNUE::Features
-#endif // #ifndef NNUE_FEATURES_HALF_KA_V2_H_INCLUDED
+#endif // #ifndef NNUE_FEATURES_HALF_KA_V2_HM_H_INCLUDED
diff --git a/src/nnue/layers/affine_transform.h b/src/nnue/layers/affine_transform.h
index 9a5f62c0..d1318368 100644
--- a/src/nnue/layers/affine_transform.h
+++ b/src/nnue/layers/affine_transform.h
@@ -46,6 +46,11 @@ namespace Stockfish::Eval::NNUE::Layers {
#elif defined (USE_SSSE3)
static constexpr const IndexType OutputSimdWidth = SimdWidth / 4;
#endif
+#if defined (USE_AVX512)
+ static constexpr const IndexType InputSimdWidth = SimdWidth * 2;
+#elif defined (USE_SSSE3)
+ static constexpr const IndexType InputSimdWidth = SimdWidth;
+#endif
// Size of forward propagation buffer used in this layer
static constexpr std::size_t SelfBufferSize =
@@ -72,6 +77,15 @@ namespace Stockfish::Eval::NNUE::Layers {
for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
#if !defined (USE_SSSE3)
weights[i] = read_little_endian(stream);
+#elif defined (USE_VNNI) || defined (USE_AVX512)
+ if constexpr (OutputDimensions <= 8 && OutputDimensions != 1)
+ weights[i] = read_little_endian(stream);
+ else
+ weights[
+ (i / 4) % (PaddedInputDimensions / 4) * OutputDimensions * 4 +
+ i / PaddedInputDimensions * 4 +
+ i % 4
+ ] = read_little_endian(stream);
#else
weights[
(i / 4) % (PaddedInputDimensions / 4) * OutputDimensions * 4 +
@@ -108,7 +122,6 @@ namespace Stockfish::Eval::NNUE::Layers {
return !stream.fail();
}
-
// Forward propagation
const OutputType* propagate(
const TransformedFeatureType* transformedFeatures, char* buffer) const {
@@ -123,6 +136,40 @@ namespace Stockfish::Eval::NNUE::Layers {
return _mm512_reduce_add_epi32(sum) + bias;
};
+ [[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_add_dpbusd_epi32 = [=](__m512i& acc, __m512i a, __m512i b) {
#if defined (USE_VNNI)
acc = _mm512_dpbusd_epi32(acc, a, b);
@@ -133,6 +180,19 @@ namespace Stockfish::Eval::NNUE::Layers {
#endif
};
+ [[maybe_unused]] auto m512_add_dpbusd_epi32x2 = [=](__m512i& acc, __m512i a0, __m512i b0, __m512i a1, __m512i b1) {
+#if defined (USE_VNNI)
+ acc = _mm512_dpbusd_epi32(acc, a0, b0);
+ acc = _mm512_dpbusd_epi32(acc, a1, b1);
+#else
+ __m512i product0 = _mm512_maddubs_epi16(a0, b0);
+ __m512i product1 = _mm512_maddubs_epi16(a1, b1);
+ product0 = _mm512_adds_epi16(product0, product1);
+ product0 = _mm512_madd_epi16(product0, Ones512);
+ 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)
@@ -165,6 +225,18 @@ namespace Stockfish::Eval::NNUE::Layers {
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);
+ };
+
[[maybe_unused]] auto m256_add_dpbusd_epi32 = [=](__m256i& acc, __m256i a, __m256i b) {
#if defined (USE_VNNI)
acc = _mm256_dpbusd_epi32(acc, a, b);
@@ -175,6 +247,19 @@ namespace Stockfish::Eval::NNUE::Layers {
#endif
};
+ [[maybe_unused]] auto m256_add_dpbusd_epi32x2 = [=](__m256i& acc, __m256i a0, __m256i b0, __m256i a1, __m256i b1) {
+#if defined (USE_VNNI)
+ acc = _mm256_dpbusd_epi32(acc, a0, b0);
+ acc = _mm256_dpbusd_epi32(acc, a1, b1);
+#else
+ __m256i product0 = _mm256_maddubs_epi16(a0, b0);
+ __m256i product1 = _mm256_maddubs_epi16(a1, b1);
+ product0 = _mm256_adds_epi16(product0, product1);
+ product0 = _mm256_madd_epi16(product0, Ones256);
+ 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)
@@ -206,12 +291,27 @@ namespace Stockfish::Eval::NNUE::Layers {
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_add_dpbusd_epi32 = [=](__m128i& acc, __m128i a, __m128i b) {
__m128i product0 = _mm_maddubs_epi16(a, b);
product0 = _mm_madd_epi16(product0, Ones128);
acc = _mm_add_epi32(acc, product0);
};
+ [[maybe_unused]] auto m128_add_dpbusd_epi32x2 = [=](__m128i& acc, __m128i a0, __m128i b0, __m128i a1, __m128i b1) {
+ __m128i product0 = _mm_maddubs_epi16(a0, b0);
+ __m128i product1 = _mm_maddubs_epi16(a1, b1);
+ product0 = _mm_adds_epi16(product0, product1);
+ product0 = _mm_madd_epi16(product0, Ones128);
+ 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);
@@ -231,33 +331,116 @@ namespace Stockfish::Eval::NNUE::Layers {
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;
+ [[maybe_unused]] auto& vec_add_dpbusd_32 = m512_add_dpbusd_epi32;
+ [[maybe_unused]] auto& vec_add_dpbusd_32x2 = m512_add_dpbusd_epi32x2;
+ [[maybe_unused]] auto& vec_add_dpbusd_32x4 = m512_add_dpbusd_epi32x4;
+ [[maybe_unused]] auto& vec_hadd = m512_hadd;
+ [[maybe_unused]] auto& vec_haddx4 = m512_haddx4;
#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;
+ [[maybe_unused]] auto& vec_add_dpbusd_32 = m256_add_dpbusd_epi32;
+ [[maybe_unused]] auto& vec_add_dpbusd_32x2 = m256_add_dpbusd_epi32x2;
+ [[maybe_unused]] auto& vec_add_dpbusd_32x4 = m256_add_dpbusd_epi32x4;
+ [[maybe_unused]] auto& vec_hadd = m256_hadd;
+ [[maybe_unused]] auto& vec_haddx4 = m256_haddx4;
#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;
+ [[maybe_unused]] auto& vec_add_dpbusd_32 = m128_add_dpbusd_epi32;
+ [[maybe_unused]] auto& vec_add_dpbusd_32x2 = m128_add_dpbusd_epi32x2;
+ [[maybe_unused]] auto& vec_add_dpbusd_32x4 = m128_add_dpbusd_epi32x4;
+ [[maybe_unused]] auto& vec_hadd = m128_hadd;
+ [[maybe_unused]] auto& vec_haddx4 = m128_haddx4;
#endif
#if defined (USE_SSSE3)
const auto output = reinterpret_cast(buffer);
const auto inputVector = reinterpret_cast(input);
+#endif
- static_assert(OutputDimensions % OutputSimdWidth == 0 || OutputDimensions == 1);
+#if defined (USE_VNNI) || defined (USE_AVX512)
+
+ static_assert(OutputDimensions == 1 || OutputDimensions % 4 == 0);
// OutputDimensions is either 1 or a multiple of SimdWidth
// because then it is also an input dimension.
+ if constexpr (OutputDimensions <= 8 && OutputDimensions != 1)
+ {
+ constexpr IndexType NumChunks = PaddedInputDimensions / InputSimdWidth;
+
+ static_assert(NumChunks % 2 == 0);
+
+ const auto input_vec = reinterpret_cast(input);
+ const auto bias_vec = reinterpret_cast(biases);
+ auto out_vec = reinterpret_cast<__m128i*>(output);
+
+ vec_t regs[OutputDimensions];
+ for (IndexType k = 0; k < OutputDimensions; ++k)
+ regs[k] = vec_setzero();
+
+ for (IndexType i = 0; i < NumChunks / 2; ++i)
+ {
+ const vec_t in0 = input_vec[i * 2 + 0];
+ const vec_t in1 = input_vec[i * 2 + 1];
+ for (IndexType k = 0; k < OutputDimensions; ++k)
+ {
+ const vec_t w0 = reinterpret_cast(&weights[k * PaddedInputDimensions])[i * 2 + 0];
+ const vec_t w1 = reinterpret_cast(&weights[k * PaddedInputDimensions])[i * 2 + 1];
+ vec_add_dpbusd_32(regs[k], in0, w0);
+ vec_add_dpbusd_32(regs[k], in1, w1);
+ }
+ }
+
+ for (IndexType k = 0; k < OutputDimensions / 4; ++k)
+ {
+ out_vec[k] = vec_haddx4(
+ regs[k * 4 + 0],
+ regs[k * 4 + 1],
+ regs[k * 4 + 2],
+ regs[k * 4 + 3],
+ bias_vec[k]
+ );
+ }
+ }
+ else if constexpr (InputDimensions == 8)
+ {
+ const auto input32 = reinterpret_cast(input);
+ __m256i* outptr = reinterpret_cast<__m256i*>(output);
+ std::memcpy(output, biases, OutputDimensions * sizeof(OutputType));
+
+ const __m256i in0 = _mm256_set1_epi32(input32[0]);
+ const __m256i in1 = _mm256_set1_epi32(input32[1]);
+ const auto col0 = reinterpret_cast(&weights[0]);
+ const auto col1 = reinterpret_cast(&weights[OutputDimensions * 4]);
+ for (IndexType j = 0; j * 8 < OutputDimensions; ++j)
+ m256_add_dpbusd_epi32x2(outptr[j], in0, col0[j], in1, col1[j]);
+ }
+ else
+
+#elif defined (USE_SSSE3)
+
+ if constexpr (OutputDimensions % OutputSimdWidth == 0 && InputDimensions == 8)
+ {
+ const auto input32 = reinterpret_cast(input);
+ vec_t* outptr = reinterpret_cast(output);
+ std::memcpy(output, biases, OutputDimensions * sizeof(OutputType));
+
+ const vec_t in0 = vec_set_32(input32[0]);
+ const vec_t in1 = vec_set_32(input32[1]);
+ const auto col0 = reinterpret_cast(&weights[0]);
+ const auto col1 = reinterpret_cast(&weights[OutputDimensions * 4]);
+ for (IndexType j = 0; j * OutputSimdWidth < OutputDimensions; ++j)
+ vec_add_dpbusd_32x2(outptr[j], in0, col0[j], in1, col1[j]);
+ }
+ else
+
+#endif
+
+#if defined (USE_SSSE3)
+
if constexpr (OutputDimensions % OutputSimdWidth == 0)
{
static_assert(InputDimensions % 16 == 0);
@@ -337,8 +520,8 @@ namespace Stockfish::Eval::NNUE::Layers {
#if defined(USE_SSE2)
// At least a multiple of 16, with SSE2.
- static_assert(InputDimensions % SimdWidth == 0);
- constexpr IndexType NumChunks = InputDimensions / SimdWidth;
+ static_assert(PaddedInputDimensions % SimdWidth == 0);
+ constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
const __m128i Zeros = _mm_setzero_si128();
const auto inputVector = reinterpret_cast(input);
@@ -349,8 +532,8 @@ namespace Stockfish::Eval::NNUE::Layers {
const auto inputVector = reinterpret_cast(input);
#elif defined(USE_NEON)
- static_assert(InputDimensions % SimdWidth == 0);
- constexpr IndexType NumChunks = InputDimensions / SimdWidth;
+ static_assert(PaddedInputDimensions % SimdWidth == 0);
+ constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
const auto inputVector = reinterpret_cast(input);
#endif
@@ -423,6 +606,13 @@ namespace Stockfish::Eval::NNUE::Layers {
_mm_empty();
#endif
+#endif
+
+#if (!defined (USE_SSSE3) && defined (USE_SSE2)) || defined (USE_NEON)
+ static_assert(SimdWidth <= 16, "Otherwise we run outside of the padding for the output.");
+ if constexpr (SimdWidth > OutputDimensions && OutputDimensions != 1)
+ for (IndexType i = OutputDimensions; i < SimdWidth; ++i)
+ output[i] = 0;
#endif
return output;
diff --git a/src/nnue/nnue_architecture.h b/src/nnue/nnue_architecture.h
index 879a39cd..193a197d 100644
--- a/src/nnue/nnue_architecture.h
+++ b/src/nnue/nnue_architecture.h
@@ -23,7 +23,7 @@
#include "nnue_common.h"
-#include "features/half_ka_v2.h"
+#include "features/half_ka_v2_hm.h"
#include "layers/input_slice.h"
#include "layers/affine_transform.h"
@@ -32,10 +32,10 @@
namespace Stockfish::Eval::NNUE {
// Input features used in evaluation function
- using FeatureSet = Features::HalfKAv2;
+ using FeatureSet = Features::HalfKAv2_hm;
// Number of input feature dimensions after conversion
- constexpr IndexType TransformedFeatureDimensions = 512;
+ constexpr IndexType TransformedFeatureDimensions = 1024;
constexpr IndexType PSQTBuckets = 8;
constexpr IndexType LayerStacks = 8;
@@ -43,7 +43,7 @@ namespace Stockfish::Eval::NNUE {
// Define network structure
using InputLayer = InputSlice;
- using HiddenLayer1 = ClippedReLU>;
+ using HiddenLayer1 = ClippedReLU>;
using HiddenLayer2 = ClippedReLU>;
using OutputLayer = AffineTransform;