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split affine_transform on VNNI as well

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```
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 184544 W: 47990 L: 47474 D: 89080
Ptnml(0-2): 551, 20238, 50206, 20698, 579
```

Torom measured

```
sf_base =  2538696 +/-   2915 (95%)
sf_test =  2546510 +/-   3011 (95%)
diff    =     7814 +/-   4146 (95%)
speedup = 0.30782% +/- 0.163% (95%)
```

I get something similar. The benefit would be larger if we ever decide
to further increase the L2 size I think

closes https://github.com/official-stockfish/Stockfish/pull/6683

No functional change
This commit is contained in:
anematode
2026-04-02 20:30:33 +02:00
committed by Disservin
parent f614ff90e2
commit 8132dbcb5a
1 changed files with 36 additions and 3 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/nnue/layers/affine_transform.h
+36 -3
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@@ -201,10 +201,12 @@ class AffineTransform {
#if defined(USE_AVX512)
using vec_t = __m512i;
#define vec_set_32 _mm512_set1_epi32
#define vec_add_32 _mm512_add_epi32
#define vec_add_dpbusd_32 SIMD::m512_add_dpbusd_epi32
#elif defined(USE_AVX2)
using vec_t = __m256i;
#define vec_set_32 _mm256_set1_epi32
#define vec_add_32 _mm256_add_epi32
#define vec_add_dpbusd_32 SIMD::m256_add_dpbusd_epi32
#elif defined(USE_SSSE3)
using vec_t = __m128i;
@@ -223,14 +225,45 @@ class AffineTransform {
static_assert(OutputDimensions % OutputSimdWidth == 0);
constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 8) / 4;
constexpr IndexType NumRegs = OutputDimensions / OutputSimdWidth;
constexpr IndexType NumAccums = OutputDimensions / OutputSimdWidth;
#if defined(USE_VNNI)
constexpr IndexType NumRegs = 2 * NumAccums;
#else
constexpr IndexType NumRegs = NumAccums;
#endif
const vec_t* biasvec = reinterpret_cast<const vec_t*>(biases);
vec_t acc[NumRegs];
for (IndexType k = 0; k < NumRegs; ++k)
for (IndexType k = 0; k < NumAccums; ++k)
acc[k] = biasvec[k];
for (IndexType k = NumAccums; k < NumRegs; ++k)
acc[k] = vec_set_32(0);
for (IndexType i = 0; i < NumChunks; ++i)
IndexType i = 0;
#if defined(USE_VNNI)
for (; i < NumChunks; i += 2)
{
const vec_t in0 =
vec_set_32(load_as<std::int32_t>(input + i * sizeof(std::int32_t)));
const vec_t in1 =
vec_set_32(load_as<std::int32_t>(input + (i + 1) * sizeof(std::int32_t)));
const auto col0 =
reinterpret_cast<const vec_t*>(&weights[i * OutputDimensions * 4]);
const auto col1 =
reinterpret_cast<const vec_t*>(&weights[(i + 1) * OutputDimensions * 4]);
for (IndexType k = 0; k < NumAccums; ++k)
{
vec_add_dpbusd_32(acc[k], in0, col0[k]);
vec_add_dpbusd_32(acc[k + NumAccums], in1, col1[k]);
}
}
for (IndexType k = 0; k < NumAccums; ++k)
acc[k] = vec_add_32(acc[k], acc[k + NumAccums]);
#endif
for (; i < NumChunks; ++i)
{
const vec_t in0 =
vec_set_32(load_as<std::int32_t>(input + i * sizeof(std::int32_t)));