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Clean up and simplify some nnue code.

Browse source 
Remove some unnecessary code and it's execution during inference. Also the change on line 49 in nnue_architecture.h results in a more efficient SIMD code path through ClippedReLU::propagate().

passed STC:
https://tests.stockfishchess.org/tests/view/6217d3bfda649bba32ef25d5
LLR: 2.94 (-2.94,2.94) <-2.25,0.25>
Total: 12056 W: 3281 L: 3092 D: 5683
Ptnml(0-2): 55, 1213, 3312, 1384, 64

passed STC SMP:
https://tests.stockfishchess.org/tests/view/6217f344da649bba32ef295e
LLR: 2.94 (-2.94,2.94) <-2.25,0.25>
Total: 27376 W: 7295 L: 7137 D: 12944
Ptnml(0-2): 52, 2859, 7715, 3003, 59

closes https://github.com/official-stockfish/Stockfish/pull/3944

No functional change

bench: 6820724
This commit is contained in:
mstembera 2022-02-23 18:19:36 -08:00 • committed by Joost VandeVondele
parent 27139dedac
commit 5f781d366e
6 changed files with 27 additions and 32 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

6
src/nnue/evaluate_nnue.cpp
View file

@ -109,7 +109,7 @@ namespace Stockfish::Eval::NNUE {
{
write_little_endian<std::uint32_t>(stream, Version);
write_little_endian<std::uint32_t>(stream, hashValue);
write_little_endian<std::uint32_t>(stream, desc.size());
write_little_endian<std::uint32_t>(stream, (std::uint32_t)desc.size());
stream.write(&desc[0], desc.size());
return !stream.fail();
}
@ -157,7 +157,7 @@ namespace Stockfish::Eval::NNUE {
ASSERT_ALIGNED(transformedFeatures, alignment);
const std::size_t bucket = (pos.count<ALL_PIECES>() - 1) / 4;
const int bucket = (pos.count<ALL_PIECES>() - 1) / 4;
const auto psqt = featureTransformer->transform(pos, transformedFeatures, bucket);
const auto positional = network[bucket]->propagate(transformedFeatures);
@ -197,7 +197,7 @@ namespace Stockfish::Eval::NNUE {
NnueEvalTrace t{};
t.correctBucket = (pos.count<ALL_PIECES>() - 1) / 4;
for (std::size_t bucket = 0; bucket < LayerStacks; ++bucket) {
for (IndexType bucket = 0; bucket < LayerStacks; ++bucket) {
const auto materialist = featureTransformer->transform(pos, transformedFeatures, bucket);
const auto positional = network[bucket]->propagate(transformedFeatures);

16
src/nnue/layers/affine_transform.h
View file

@ -235,10 +235,10 @@ namespace Stockfish::Eval::NNUE::Layers {
// Read network parameters
bool read_parameters(std::istream& stream) {
for (std::size_t i = 0; i < OutputDimensions; ++i)
for (IndexType i = 0; i < OutputDimensions; ++i)
biases[i] = read_little_endian<BiasType>(stream);
for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
for (IndexType i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
weights[get_weight_index(i)] = read_little_endian<WeightType>(stream);
return !stream.fail();
@ -246,10 +246,10 @@ namespace Stockfish::Eval::NNUE::Layers {
// Write network parameters
bool write_parameters(std::ostream& stream) const {
for (std::size_t i = 0; i < OutputDimensions; ++i)
for (IndexType i = 0; i < OutputDimensions; ++i)
write_little_endian<BiasType>(stream, biases[i]);
for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
for (IndexType i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
write_little_endian<WeightType>(stream, weights[get_weight_index(i)]);
return !stream.fail();
@ -422,9 +422,9 @@ namespace Stockfish::Eval::NNUE::Layers {
// Read network parameters
bool read_parameters(std::istream& stream) {
for (std::size_t i = 0; i < OutputDimensions; ++i)
for (IndexType i = 0; i < OutputDimensions; ++i)
biases[i] = read_little_endian<BiasType>(stream);
for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
for (IndexType i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
weights[get_weight_index(i)] = read_little_endian<WeightType>(stream);
return !stream.fail();
@ -432,10 +432,10 @@ namespace Stockfish::Eval::NNUE::Layers {
// Write network parameters
bool write_parameters(std::ostream& stream) const {
for (std::size_t i = 0; i < OutputDimensions; ++i)
for (IndexType i = 0; i < OutputDimensions; ++i)
write_little_endian<BiasType>(stream, biases[i]);
for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
for (IndexType i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
write_little_endian<WeightType>(stream, weights[get_weight_index(i)]);
return !stream.fail();

8
src/nnue/layers/clipped_relu.h
View file

@ -171,14 +171,6 @@ namespace Stockfish::Eval::NNUE::Layers {
std::max(0, std::min(127, input[i] >> WeightScaleBits)));
}
// Affine transform layers expect that there is at least
// ceil_to_multiple(OutputDimensions, 32) initialized values.
// We cannot do this in the affine transform because it requires
// preallocating space here.
for (IndexType i = OutputDimensions; i < PaddedOutputDimensions; ++i) {
output[i] = 0;
}
return output;
}
};

19
src/nnue/nnue_architecture.h
View file

@ -46,7 +46,7 @@ struct Network
static constexpr int FC_1_OUTPUTS = 32;
Layers::AffineTransform<TransformedFeatureDimensions, FC_0_OUTPUTS + 1> fc_0;
Layers::ClippedReLU<FC_0_OUTPUTS> ac_0;
Layers::ClippedReLU<FC_0_OUTPUTS + 1> ac_0;
Layers::AffineTransform<FC_0_OUTPUTS, FC_1_OUTPUTS> fc_1;
Layers::ClippedReLU<FC_1_OUTPUTS> ac_1;
Layers::AffineTransform<FC_1_OUTPUTS, 1> fc_2;
@ -97,14 +97,19 @@ struct Network
alignas(CacheLineSize) decltype(fc_1)::OutputBuffer fc_1_out;
alignas(CacheLineSize) decltype(ac_1)::OutputBuffer ac_1_out;
alignas(CacheLineSize) decltype(fc_2)::OutputBuffer fc_2_out;
Buffer()
{
std::memset(this, 0, sizeof(*this));
}
};
#if defined(ALIGNAS_ON_STACK_VARIABLES_BROKEN)
char bufferRaw[sizeof(Buffer) + alignment];
char* bufferRawAligned = align_ptr_up<alignment>(&bufferRaw[0]);
Buffer& buffer = *(new (bufferRawAligned) Buffer);
static thread_local char bufferRaw[sizeof(Buffer) + alignment];
static thread_local char* bufferRawAligned = align_ptr_up<alignment>(&bufferRaw[0]);
static thread_local Buffer& buffer = *(new (bufferRawAligned) Buffer);
#else
alignas(alignment) Buffer buffer;
alignas(alignment) static thread_local Buffer buffer;
#endif
fc_0.propagate(transformedFeatures, buffer.fc_0_out);
@ -118,10 +123,6 @@ struct Network
std::int32_t fwdOut = int(buffer.fc_0_out[FC_0_OUTPUTS]) * (600*OutputScale) / (127*(1<<WeightScaleBits));
std::int32_t outputValue = buffer.fc_2_out[0] + fwdOut;
#if defined(ALIGNAS_ON_STACK_VARIABLES_BROKEN)
buffer.~Buffer();
#endif
return outputValue;
}
};

4
src/nnue/nnue_common.h
View file

@ -127,11 +127,11 @@ namespace Stockfish::Eval::NNUE {
{
for (; i + 1 < sizeof(IntType); ++i)
{
u[i] = v;
u[i] = (std::uint8_t)v;
v >>= 8;
}
}
u[i] = v;
u[i] = (std::uint8_t)v;
stream.write(reinterpret_cast<char*>(u), sizeof(IntType));
}

6
src/nnue/nnue_feature_transformer.h
View file

@ -123,8 +123,10 @@ namespace Stockfish::Eval::NNUE {
// We use __m* types as template arguments, which causes GCC to emit warnings
// about losing some attribute information. This is irrelevant to us as we
// only take their size, so the following pragma are harmless.
#if defined(__GNUC__)
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wignored-attributes"
#endif
template <typename SIMDRegisterType,
typename LaneType,
@ -156,9 +158,9 @@ namespace Stockfish::Eval::NNUE {
static constexpr int NumRegs = BestRegisterCount<vec_t, WeightType, TransformedFeatureDimensions, NumRegistersSIMD>();
static constexpr int NumPsqtRegs = BestRegisterCount<psqt_vec_t, PSQTWeightType, PSQTBuckets, NumRegistersSIMD>();
#if defined(__GNUC__)
#pragma GCC diagnostic pop
#endif
#endif