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Optimise pairwise multiplication

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This speedup was first inspired by a comment by @AndyGrant on my recent
PR "If mullo_epi16 would preserve the signedness, then this could be
used to remove 50% of the max operations during the halfkp-pairwise
mat-mul relu deal."

That got me thinking, because although mullo_epi16 did not preserve the
signedness, mulhi_epi16 did, and so we could shift left and then use
mulhi_epi16, instead of shifting right after the mullo.

However, due to some issues with shifting into the sign bit, the FT
weights and biases had to be multiplied by 2 for the optimisation to
work.

Speedup on "Arch=x86-64-bmi2 COMP=clang", courtesy of @Torom
Result of 50 runs
base (...es/stockfish) =     962946  +/- 1202
test (...ise-max-less) =     979696  +/- 1084
diff                   =     +16750  +/- 1794

speedup        = +0.0174
P(speedup > 0) =  1.0000

CPU: 4 x Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz
Hyperthreading: on

Also a speedup on "COMP=gcc", courtesy of Torom once again
Result of 50 runs
base (...tockfish_gcc) =     966033  +/- 1574
test (...max-less_gcc) =     983319  +/- 1513
diff                   =     +17286  +/- 2515

speedup        = +0.0179
P(speedup > 0) =  1.0000

CPU: 4 x Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz
Hyperthreading: on

Passed STC:
LLR: 2.96 (-2.94,2.94) <0.00,2.00>
Total: 67712 W: 17715 L: 17358 D: 32639
Ptnml(0-2): 225, 7472, 18140, 7759, 260
https://tests.stockfishchess.org/tests/view/664c1d75830eb9f886616906

closes https://github.com/official-stockfish/Stockfish/pull/5282

No functional change
This commit is contained in:
cj5716
2024-05-19 13:15:42 +08:00
committed by Disservin
parent c39b98b9e3
commit c6a1e7fd42
1 changed files with 54 additions and 26 deletions
Download Patch File Download Diff File Expand all files Collapse all files

80
src/nnue/nnue_feature_transformer.h
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@@ -55,14 +55,14 @@ using psqt_vec_t = __m256i;
#define vec_store(a, b) _mm512_store_si512(a, b)
#define vec_add_16(a, b) _mm512_add_epi16(a, b)
#define vec_sub_16(a, b) _mm512_sub_epi16(a, b)
#define vec_mul_16(a, b) _mm512_mullo_epi16(a, b)
#define vec_mulhi_16(a, b) _mm512_mulhi_epi16(a, b)
#define vec_zero() _mm512_setzero_epi32()
#define vec_set_16(a) _mm512_set1_epi16(a)
#define vec_max_16(a, b) _mm512_max_epi16(a, b)
#define vec_min_16(a, b) _mm512_min_epi16(a, b)
#define vec_slli_16(a, b) _mm512_slli_epi16(a, b)
// Inverse permuted at load time
#define vec_msb_pack_16(a, b) \
_mm512_packs_epi16(_mm512_srli_epi16(a, 7), _mm512_srli_epi16(b, 7))
#define vec_packus_16(a, b) _mm512_packus_epi16(a, b)
#define vec_load_psqt(a) _mm256_load_si256(a)
#define vec_store_psqt(a, b) _mm256_store_si256(a, b)
#define vec_add_psqt_32(a, b) _mm256_add_epi32(a, b)
@@ -78,14 +78,14 @@ using psqt_vec_t = __m256i;
#define vec_store(a, b) _mm256_store_si256(a, b)
#define vec_add_16(a, b) _mm256_add_epi16(a, b)
#define vec_sub_16(a, b) _mm256_sub_epi16(a, b)
#define vec_mul_16(a, b) _mm256_mullo_epi16(a, b)
#define vec_mulhi_16(a, b) _mm256_mulhi_epi16(a, b)
#define vec_zero() _mm256_setzero_si256()
#define vec_set_16(a) _mm256_set1_epi16(a)
#define vec_max_16(a, b) _mm256_max_epi16(a, b)
#define vec_min_16(a, b) _mm256_min_epi16(a, b)
#define vec_slli_16(a, b) _mm256_slli_epi16(a, b)
// Inverse permuted at load time
#define vec_msb_pack_16(a, b) \
_mm256_packs_epi16(_mm256_srli_epi16(a, 7), _mm256_srli_epi16(b, 7))
#define vec_packus_16(a, b) _mm256_packus_epi16(a, b)
#define vec_load_psqt(a) _mm256_load_si256(a)
#define vec_store_psqt(a, b) _mm256_store_si256(a, b)
#define vec_add_psqt_32(a, b) _mm256_add_epi32(a, b)
@@ -101,12 +101,13 @@ using psqt_vec_t = __m128i;
#define vec_store(a, b) *(a) = (b)
#define vec_add_16(a, b) _mm_add_epi16(a, b)
#define vec_sub_16(a, b) _mm_sub_epi16(a, b)
#define vec_mul_16(a, b) _mm_mullo_epi16(a, b)
#define vec_mulhi_16(a, b) _mm_mulhi_epi16(a, b)
#define vec_zero() _mm_setzero_si128()
#define vec_set_16(a) _mm_set1_epi16(a)
#define vec_max_16(a, b) _mm_max_epi16(a, b)
#define vec_min_16(a, b) _mm_min_epi16(a, b)
#define vec_msb_pack_16(a, b) _mm_packs_epi16(_mm_srli_epi16(a, 7), _mm_srli_epi16(b, 7))
#define vec_slli_16(a, b) _mm_slli_epi16(a, b)
#define vec_packus_16(a, b) _mm_packus_epi16(a, b)
#define vec_load_psqt(a) (*(a))
#define vec_store_psqt(a, b) *(a) = (b)
#define vec_add_psqt_32(a, b) _mm_add_epi32(a, b)
@@ -122,18 +123,14 @@ using psqt_vec_t = int32x4_t;
#define vec_store(a, b) *(a) = (b)
#define vec_add_16(a, b) vaddq_s16(a, b)
#define vec_sub_16(a, b) vsubq_s16(a, b)
#define vec_mul_16(a, b) vmulq_s16(a, b)
#define vec_mulhi_16(a, b) vqdmulhq_s16(a, b)
#define vec_zero() \
vec_t { 0 }
#define vec_set_16(a) vdupq_n_s16(a)
#define vec_max_16(a, b) vmaxq_s16(a, b)
#define vec_min_16(a, b) vminq_s16(a, b)
inline vec_t vec_msb_pack_16(vec_t a, vec_t b) {
const int8x8_t shifta = vshrn_n_s16(a, 7);
const int8x8_t shiftb = vshrn_n_s16(b, 7);
const int8x16_t compacted = vcombine_s8(shifta, shiftb);
return *reinterpret_cast<const vec_t*>(&compacted);
}
#define vec_slli_16(a, b) vshlq_s16(a, vec_set_16(b))
#define vec_packus_16(a, b) reinterpret_cast<vec_t>(vcombine_u8(vqmovun_s16(a), vqmovun_s16(b)))
#define vec_load_psqt(a) (*(a))
#define vec_store_psqt(a, b) *(a) = (b)
#define vec_add_psqt_32(a, b) vaddq_s32(a, b)
@@ -281,6 +278,19 @@ class FeatureTransformer {
#endif
}
inline void scale_weights(bool read) const {
for (IndexType j = 0; j < InputDimensions; ++j)
{
WeightType* w = const_cast<WeightType*>(&weights[j * HalfDimensions]);
for (IndexType i = 0; i < HalfDimensions; ++i)
w[i] = read ? w[i] * 2 : w[i] / 2;
}
BiasType* b = const_cast<BiasType*>(biases);
for (IndexType i = 0; i < HalfDimensions; ++i)
b[i] = read ? b[i] * 2 : b[i] / 2;
}
// Read network parameters
bool read_parameters(std::istream& stream) {
@@ -289,6 +299,7 @@ class FeatureTransformer {
read_leb_128<PSQTWeightType>(stream, psqtWeights, PSQTBuckets * InputDimensions);
permute_weights(inverse_order_packs);
scale_weights(true);
return !stream.fail();
}
@@ -296,12 +307,14 @@ class FeatureTransformer {
bool write_parameters(std::ostream& stream) const {
permute_weights(order_packs);
scale_weights(false);
write_leb_128<BiasType>(stream, biases, HalfDimensions);
write_leb_128<WeightType>(stream, weights, HalfDimensions * InputDimensions);
write_leb_128<PSQTWeightType>(stream, psqtWeights, PSQTBuckets * InputDimensions);
permute_weights(inverse_order_packs);
scale_weights(true);
return !stream.fail();
}
@@ -332,7 +345,7 @@ class FeatureTransformer {
constexpr IndexType NumOutputChunks = HalfDimensions / 2 / OutputChunkSize;
const vec_t Zero = vec_zero();
const vec_t One = vec_set_16(127);
const vec_t One = vec_set_16(127 * 2);
const vec_t* in0 = reinterpret_cast<const vec_t*>(&(accumulation[perspectives[p]][0]));
const vec_t* in1 =
@@ -341,15 +354,30 @@ class FeatureTransformer {
for (IndexType j = 0; j < NumOutputChunks; ++j)
{
const vec_t sum0a = vec_max_16(vec_min_16(in0[j * 2 + 0], One), Zero);
const vec_t sum0b = vec_max_16(vec_min_16(in0[j * 2 + 1], One), Zero);
const vec_t sum1a = vec_max_16(vec_min_16(in1[j * 2 + 0], One), Zero);
const vec_t sum1b = vec_max_16(vec_min_16(in1[j * 2 + 1], One), Zero);
// What we want to do is multiply inputs in a pairwise manner (after clipping), and then shift right by 9.
// Instead, we shift left by 7, and use mulhi, stripping the bottom 16 bits, effectively shifting right by 16,
// resulting in a net shift of 9 bits. We use mulhi because it maintains the sign of the multiplication (unlike mullo),
// allowing us to make use of packus to clip 2 of the inputs, resulting in a save of 2 "vec_max_16" calls.
// A special case is when we use NEON, where we shift left by 6 instead, because the instruction "vqdmulhq_s16"
// also doubles the return value after the multiplication, adding an extra shift to the left by 1, so we
// compensate by shifting less before the multiplication.
const vec_t pa = vec_mul_16(sum0a, sum1a);
const vec_t pb = vec_mul_16(sum0b, sum1b);
#if defined(USE_SSE2)
constexpr int shift = 7;
#else
constexpr int shift = 6;
#endif
const vec_t sum0a =
vec_slli_16(vec_max_16(vec_min_16(in0[j * 2 + 0], One), Zero), shift);
const vec_t sum0b =
vec_slli_16(vec_max_16(vec_min_16(in0[j * 2 + 1], One), Zero), shift);
const vec_t sum1a = vec_min_16(in1[j * 2 + 0], One);
const vec_t sum1b = vec_min_16(in1[j * 2 + 1], One);
out[j] = vec_msb_pack_16(pa, pb);
const vec_t pa = vec_mulhi_16(sum0a, sum1a);
const vec_t pb = vec_mulhi_16(sum0b, sum1b);
out[j] = vec_packus_16(pa, pb);
}
#else
@@ -359,9 +387,9 @@ class FeatureTransformer {
BiasType sum0 = accumulation[static_cast<int>(perspectives[p])][j + 0];
BiasType sum1 =
accumulation[static_cast<int>(perspectives[p])][j + HalfDimensions / 2];
sum0 = std::clamp<BiasType>(sum0, 0, 127);
sum1 = std::clamp<BiasType>(sum1, 0, 127);
output[offset + j] = static_cast<OutputType>(unsigned(sum0 * sum1) / 128);
sum0 = std::clamp<BiasType>(sum0, 0, 127 * 2);
sum1 = std::clamp<BiasType>(sum1, 0, 127 * 2);
output[offset + j] = static_cast<OutputType>(unsigned(sum0 * sum1) / 512);
}
#endif