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Sparse impl of affine_transform_non_ssse3()

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deal with the general case

About a 8.6% speedup (for general arch)

Results for 200 tests for each version:

            Base      Test      Diff
    Mean    141741    153998    -12257
    StDev   2990      3042      3742

p-value: 0.999
speedup: 0.086

closes https://github.com/official-stockfish/Stockfish/pull/4786

No functional change
This commit is contained in:
mstembera 2023-09-12 12:23:24 -07:00 • committed by Joost VandeVondele
parent 0e32287af4
commit 97f706ecc1
4 changed files with 21 additions and 13 deletions
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20
src/nnue/layers/affine_transform.h
View file

@ -45,6 +45,7 @@ namespace Stockfish::Eval::NNUE::Layers {
template <IndexType InputDimensions, IndexType PaddedInputDimensions, IndexType OutputDimensions>
static void affine_transform_non_ssse3(std::int32_t* output, const std::int8_t* weights, const std::int32_t* biases, const std::uint8_t* input)
{
# if defined(USE_SSE2) || defined(USE_MMX) || defined(USE_NEON_DOTPROD) || defined(USE_NEON)
# if defined(USE_SSE2)
// At least a multiple of 16, with SSE2.
constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 16) / 16;
@ -129,18 +130,25 @@ namespace Stockfish::Eval::NNUE::Layers {
}
output[i] = sum[0] + sum[1] + sum[2] + sum[3];
# else
std::int32_t sum = biases[i];
for (IndexType j = 0; j < InputDimensions; ++j) {
sum += weights[offset + j] * input[j];
}
output[i] = sum;
# endif
}
# if defined(USE_MMX)
_mm_empty();
# endif
# else
std::memcpy(output, biases, sizeof(std::int32_t) * OutputDimensions);
// Traverse weights in transpose order to take advantage of input sparsity
for (IndexType i = 0; i < InputDimensions; ++i)
if (input[i]) {
const std::int8_t* w = &weights[i];
const int in = input[i];
for (IndexType j = 0; j < OutputDimensions; ++j)
output[j] += w[j * PaddedInputDimensions] * in;
}
# endif
}
#endif

2
src/nnue/layers/clipped_relu.h
View file

@ -172,7 +172,7 @@ namespace Stockfish::Eval::NNUE::Layers {
for (IndexType i = Start; i < InputDimensions; ++i) {
output[i] = static_cast<OutputType>(
std::max(0, std::min(127, input[i] >> WeightScaleBits)));
std::clamp(input[i] >> WeightScaleBits, 0, 127));
}
}
};

6
src/nnue/layers/sqr_clipped_relu.h
View file

@ -96,9 +96,9 @@ namespace Stockfish::Eval::NNUE::Layers {
for (IndexType i = Start; i < InputDimensions; ++i) {
output[i] = static_cast<OutputType>(
// really should be /127 but we need to make it fast
// needs to be accounted for in the trainer
std::min(127ll, (((long long)input[i] * input[i]) >> (2 * WeightScaleBits)) / 128));
// Really should be /127 but we need to make it fast so we right shift
// by an extra 7 bits instead. Needs to be accounted for in the trainer.
std::min(127ll, ((long long)input[i] * input[i]) >> (2 * WeightScaleBits + 7)));
}
}
};

6
src/nnue/nnue_feature_transformer.h
View file

@ -327,9 +327,9 @@ namespace Stockfish::Eval::NNUE {
for (IndexType j = 0; j < HalfDimensions / 2; ++j) {
BiasType sum0 = accumulation[static_cast<int>(perspectives[p])][j + 0];
BiasType sum1 = accumulation[static_cast<int>(perspectives[p])][j + HalfDimensions / 2];
sum0 = std::max<int>(0, std::min<int>(127, sum0));
sum1 = std::max<int>(0, std::min<int>(127, sum1));
output[offset + j] = static_cast<OutputType>(sum0 * sum1 / 128);
sum0 = std::clamp<BiasType>(sum0, 0, 127);
sum1 = std::clamp<BiasType>(sum1, 0, 127);
output[offset + j] = static_cast<OutputType>(unsigned(sum0 * sum1) / 128);
}
#endif