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Bug fix in do_null_move() and NNUE simplification.

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This fixes #3108 and removes some NNUE code that is currently not used.

At the moment, do_null_move() copies the accumulator from the previous
state into the new state, which is correct. It then clears the "computed_score"
flag because the side to move has changed, and with the other side to move
NNUE will return a completely different evaluation (normally with changed
sign but also with different NNUE-internal tempo bonus).

The problem is that do_null_move() clears the wrong flag. It clears the
computed_score flag of the old state, not of the new state. It turns out
that this almost never affects the search. For example, fixing it does not
change the current bench (but it does change the previous bench). This is
because the search code usually avoids calling evaluate() after a null move.

This PR corrects do_null_move() by removing the computed_score flag altogether.
The flag is not needed because nnue_evaluate() is never called twice on a position.

This PR also removes some unnecessary {}s and inserts a few blank lines
in the modified NNUE files in line with SF coding style.

Resulf ot STC non-regression test:
LLR: 2.95 (-2.94,2.94) {-1.25,0.25}
Total: 26328 W: 3118 L: 3012 D: 20198
Ptnml(0-2): 126, 2208, 8397, 2300, 133
https://tests.stockfishchess.org/tests/view/5f553ccc2d02727c56b36db1

closes https://github.com/official-stockfish/Stockfish/pull/3109

bench: 4109324
This commit is contained in:
syzygy1 2020-09-06 17:29:12 +02:00 • committed by Joost VandeVondele
parent d539da19d2
commit fc27d158c0
4 changed files with 29 additions and 71 deletions
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38
src/nnue/evaluate_nnue.cpp
View file

@ -115,31 +115,16 @@ namespace Eval::NNUE {
return stream && stream.peek() == std::ios::traits_type::eof();
}
// Proceed with the difference calculation if possible
static void UpdateAccumulatorIfPossible(const Position& pos) {
feature_transformer->UpdateAccumulatorIfPossible(pos);
}
// Calculate the evaluation value
static Value ComputeScore(const Position& pos, bool refresh) {
auto& accumulator = pos.state()->accumulator;
if (!refresh && accumulator.computed_score) {
return accumulator.score;
}
// Evaluation function. Perform differential calculation.
Value evaluate(const Position& pos) {
alignas(kCacheLineSize) TransformedFeatureType
transformed_features[FeatureTransformer::kBufferSize];
feature_transformer->Transform(pos, transformed_features, refresh);
feature_transformer->Transform(pos, transformed_features);
alignas(kCacheLineSize) char buffer[Network::kBufferSize];
const auto output = network->Propagate(transformed_features, buffer);
auto score = static_cast<Value>(output[0] / FV_SCALE);
accumulator.score = score;
accumulator.computed_score = true;
return accumulator.score;
return static_cast<Value>(output[0] / FV_SCALE);
}
// Load eval, from a file stream or a memory stream
@ -150,19 +135,4 @@ namespace Eval::NNUE {
return ReadParameters(stream);
}
// Evaluation function. Perform differential calculation.
Value evaluate(const Position& pos) {
return ComputeScore(pos, false);
}
// Evaluation function. Perform full calculation.
Value compute_eval(const Position& pos) {
return ComputeScore(pos, true);
}
// Proceed with the difference calculation if possible
void update_eval(const Position& pos) {
UpdateAccumulatorIfPossible(pos);
}
} // namespace Eval::NNUE

2
src/nnue/nnue_accumulator.h
View file

@ -29,9 +29,7 @@ namespace Eval::NNUE {
struct alignas(kCacheLineSize) Accumulator {
std::int16_t
accumulation[2][kRefreshTriggers.size()][kTransformedFeatureDimensions];
Value score;
bool computed_accumulation;
bool computed_score;
};
} // namespace Eval::NNUE

58
src/nnue/nnue_feature_transformer.h
View file

@ -50,11 +50,13 @@ namespace Eval::NNUE {
// Hash value embedded in the evaluation file
static constexpr std::uint32_t GetHashValue() {
return RawFeatures::kHashValue ^ kOutputDimensions;
}
// Read network parameters
bool ReadParameters(std::istream& stream) {
for (std::size_t i = 0; i < kHalfDimensions; ++i)
biases_[i] = read_little_endian<BiasType>(stream);
for (std::size_t i = 0; i < kHalfDimensions * kInputDimensions; ++i)
@ -64,23 +66,26 @@ namespace Eval::NNUE {
// Proceed with the difference calculation if possible
bool UpdateAccumulatorIfPossible(const Position& pos) const {
const auto now = pos.state();
if (now->accumulator.computed_accumulation) {
if (now->accumulator.computed_accumulation)
return true;
}
const auto prev = now->previous;
if (prev && prev->accumulator.computed_accumulation) {
UpdateAccumulator(pos);
return true;
}
return false;
}
// Convert input features
void Transform(const Position& pos, OutputType* output, bool refresh) const {
if (refresh || !UpdateAccumulatorIfPossible(pos)) {
void Transform(const Position& pos, OutputType* output) const {
if (!UpdateAccumulatorIfPossible(pos))
RefreshAccumulator(pos);
}
const auto& accumulation = pos.state()->accumulator.accumulation;
#if defined(USE_AVX2)
@ -177,6 +182,7 @@ namespace Eval::NNUE {
private:
// Calculate cumulative value without using difference calculation
void RefreshAccumulator(const Position& pos) const {
auto& accumulator = pos.state()->accumulator;
IndexType i = 0;
Features::IndexList active_indices[2];
@ -216,9 +222,8 @@ namespace Eval::NNUE {
&accumulator.accumulation[perspective][i][0]);
auto column = reinterpret_cast<const __m64*>(&weights_[offset]);
constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
for (IndexType j = 0; j < kNumChunks; ++j) {
for (IndexType j = 0; j < kNumChunks; ++j)
accumulation[j] = _mm_add_pi16(accumulation[j], column[j]);
}
#elif defined(USE_NEON)
auto accumulation = reinterpret_cast<int16x8_t*>(
@ -240,11 +245,11 @@ namespace Eval::NNUE {
#endif
accumulator.computed_accumulation = true;
accumulator.computed_score = false;
}
// Calculate cumulative value using difference calculation
void UpdateAccumulator(const Position& pos) const {
const auto prev_accumulator = pos.state()->previous->accumulator;
auto& accumulator = pos.state()->accumulator;
IndexType i = 0;
@ -288,33 +293,27 @@ namespace Eval::NNUE {
#if defined(USE_AVX2)
auto column = reinterpret_cast<const __m256i*>(&weights_[offset]);
for (IndexType j = 0; j < kNumChunks; ++j) {
for (IndexType j = 0; j < kNumChunks; ++j)
accumulation[j] = _mm256_sub_epi16(accumulation[j], column[j]);
}
#elif defined(USE_SSE2)
auto column = reinterpret_cast<const __m128i*>(&weights_[offset]);
for (IndexType j = 0; j < kNumChunks; ++j) {
for (IndexType j = 0; j < kNumChunks; ++j)
accumulation[j] = _mm_sub_epi16(accumulation[j], column[j]);
}
#elif defined(USE_MMX)
auto column = reinterpret_cast<const __m64*>(&weights_[offset]);
for (IndexType j = 0; j < kNumChunks; ++j) {
for (IndexType j = 0; j < kNumChunks; ++j)
accumulation[j] = _mm_sub_pi16(accumulation[j], column[j]);
}
#elif defined(USE_NEON)
auto column = reinterpret_cast<const int16x8_t*>(&weights_[offset]);
for (IndexType j = 0; j < kNumChunks; ++j) {
for (IndexType j = 0; j < kNumChunks; ++j)
accumulation[j] = vsubq_s16(accumulation[j], column[j]);
}
#else
for (IndexType j = 0; j < kHalfDimensions; ++j) {
accumulator.accumulation[perspective][i][j] -=
weights_[offset + j];
}
for (IndexType j = 0; j < kHalfDimensions; ++j)
accumulator.accumulation[perspective][i][j] -= weights_[offset + j];
#endif
}
@ -325,33 +324,27 @@ namespace Eval::NNUE {
#if defined(USE_AVX2)
auto column = reinterpret_cast<const __m256i*>(&weights_[offset]);
for (IndexType j = 0; j < kNumChunks; ++j) {
for (IndexType j = 0; j < kNumChunks; ++j)
accumulation[j] = _mm256_add_epi16(accumulation[j], column[j]);
}
#elif defined(USE_SSE2)
auto column = reinterpret_cast<const __m128i*>(&weights_[offset]);
for (IndexType j = 0; j < kNumChunks; ++j) {
for (IndexType j = 0; j < kNumChunks; ++j)
accumulation[j] = _mm_add_epi16(accumulation[j], column[j]);
}
#elif defined(USE_MMX)
auto column = reinterpret_cast<const __m64*>(&weights_[offset]);
for (IndexType j = 0; j < kNumChunks; ++j) {
for (IndexType j = 0; j < kNumChunks; ++j)
accumulation[j] = _mm_add_pi16(accumulation[j], column[j]);
}
#elif defined(USE_NEON)
auto column = reinterpret_cast<const int16x8_t*>(&weights_[offset]);
for (IndexType j = 0; j < kNumChunks; ++j) {
for (IndexType j = 0; j < kNumChunks; ++j)
accumulation[j] = vaddq_s16(accumulation[j], column[j]);
}
#else
for (IndexType j = 0; j < kHalfDimensions; ++j) {
accumulator.accumulation[perspective][i][j] +=
weights_[offset + j];
}
for (IndexType j = 0; j < kHalfDimensions; ++j)
accumulator.accumulation[perspective][i][j] += weights_[offset + j];
#endif
}
@ -362,7 +355,6 @@ namespace Eval::NNUE {
#endif
accumulator.computed_accumulation = true;
accumulator.computed_score = false;
}
using BiasType = std::int16_t;

2
src/position.cpp
View file

@ -704,7 +704,6 @@ void Position::do_move(Move m, StateInfo& newSt, bool givesCheck) {
// Used by NNUE
st->accumulator.computed_accumulation = false;
st->accumulator.computed_score = false;
auto& dp = st->dirtyPiece;
dp.dirty_num = 1;
@ -1000,7 +999,6 @@ void Position::do_null_move(StateInfo& newSt) {
if (Eval::useNNUE)
{
std::memcpy(&newSt, st, sizeof(StateInfo));
st->accumulator.computed_score = false;
}
else
std::memcpy(&newSt, st, offsetof(StateInfo, accumulator));