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Remove handcrafted MMX code

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too small a benefit to maintain this old target

closes https://github.com/official-stockfish/Stockfish/pull/4804

No functional change
This commit is contained in:
mstembera
2023-09-24 15:15:50 -07:00
committed by Joost VandeVondele
parent afe7f4d9b0
commit 8a912951de
7 changed files with 3 additions and 104 deletions
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src/nnue/layers/affine_transform.h
+1 -31
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@@ -45,18 +45,13 @@ 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) || 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;
const __m128i Zeros = _mm_setzero_si128();
const auto inputVector = reinterpret_cast<const __m128i*>(input);
# elif defined(USE_MMX)
constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 8) / 8;
const __m64 Zeros = _mm_setzero_si64();
const auto inputVector = reinterpret_cast<const __m64*>(input);
# elif defined(USE_NEON_DOTPROD)
constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 16) / 16;
const auto inputVector = reinterpret_cast<const int8x16_t*>(input);
@@ -92,26 +87,6 @@ namespace Stockfish::Eval::NNUE::Layers {
sum = _mm_add_epi32(sum, sum_second_32);
output[i] = _mm_cvtsi128_si32(sum);
# elif defined(USE_MMX)
__m64 sumLo = _mm_cvtsi32_si64(biases[i]);
__m64 sumHi = Zeros;
const auto row = reinterpret_cast<const __m64*>(&weights[offset]);
for (IndexType j = 0; j < NumChunks; ++j) {
__m64 row_j = row[j];
__m64 input_j = inputVector[j];
__m64 extendedRowLo = _mm_srai_pi16(_mm_unpacklo_pi8(row_j, row_j), 8);
__m64 extendedRowHi = _mm_srai_pi16(_mm_unpackhi_pi8(row_j, row_j), 8);
__m64 extendedInputLo = _mm_unpacklo_pi8(input_j, Zeros);
__m64 extendedInputHi = _mm_unpackhi_pi8(input_j, Zeros);
__m64 productLo = _mm_madd_pi16(extendedRowLo, extendedInputLo);
__m64 productHi = _mm_madd_pi16(extendedRowHi, extendedInputHi);
sumLo = _mm_add_pi32(sumLo, productLo);
sumHi = _mm_add_pi32(sumHi, productHi);
}
__m64 sum = _mm_add_pi32(sumLo, sumHi);
sum = _mm_add_pi32(sum, _mm_unpackhi_pi32(sum, sum));
output[i] = _mm_cvtsi64_si32(sum);
# elif defined(USE_NEON_DOTPROD)
int32x4_t sum = {biases[i]};
const auto row = reinterpret_cast<const int8x16_t*>(&weights[offset]);
@@ -132,11 +107,6 @@ namespace Stockfish::Eval::NNUE::Layers {
# endif
}
# if defined(USE_MMX)
_mm_empty();
# endif
# else
std::memcpy(output, biases, sizeof(std::int32_t) * OutputDimensions);
src/nnue/layers/clipped_relu.h
-18
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@@ -135,24 +135,6 @@ namespace Stockfish::Eval::NNUE::Layers {
}
constexpr IndexType Start = NumChunks * SimdWidth;
#elif defined(USE_MMX)
constexpr IndexType NumChunks = InputDimensions / SimdWidth;
const __m64 k0x80s = _mm_set1_pi8(-128);
const auto in = reinterpret_cast<const __m64*>(input);
const auto out = reinterpret_cast<__m64*>(output);
for (IndexType i = 0; i < NumChunks; ++i) {
const __m64 words0 = _mm_srai_pi16(
_mm_packs_pi32(in[i * 4 + 0], in[i * 4 + 1]),
WeightScaleBits);
const __m64 words1 = _mm_srai_pi16(
_mm_packs_pi32(in[i * 4 + 2], in[i * 4 + 3]),
WeightScaleBits);
const __m64 packedbytes = _mm_packs_pi16(words0, words1);
out[i] = _mm_subs_pi8(_mm_adds_pi8(packedbytes, k0x80s), k0x80s);
}
_mm_empty();
constexpr IndexType Start = NumChunks * SimdWidth;
#elif defined(USE_NEON)
constexpr IndexType NumChunks = InputDimensions / (SimdWidth / 2);
const int8x8_t Zero = {0};
src/nnue/layers/simd.h
-3
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@@ -31,9 +31,6 @@
#elif defined(USE_SSE2)
# include <emmintrin.h>
#elif defined(USE_MMX)
# include <mmintrin.h>
#elif defined(USE_NEON)
# include <arm_neon.h>
#endif