if (!previousLayer.read_parameters(stream)) return false;
for (std::size_t i = 0; i < OutputDimensions; ++i)
biases[i] = read_little_endian<BiasType>(stream);
-#if !defined (USE_SSSE3)
for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
+#if !defined (USE_SSSE3)
weights[i] = read_little_endian<WeightType>(stream);
#else
- std::unique_ptr<uint32_t[]> indexMap = std::make_unique<uint32_t[]>(OutputDimensions * PaddedInputDimensions);
- for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i) {
- const uint32_t scrambledIdx =
+ weights[
(i / 4) % (PaddedInputDimensions / 4) * OutputDimensions * 4 +
i / PaddedInputDimensions * 4 +
- i % 4;
- weights[scrambledIdx] = read_little_endian<WeightType>(stream);
- indexMap[scrambledIdx] = i;
- }
-
- // Determine if eights of weight and input products can be summed using 16bits
- // without saturation. We assume worst case combinations of 0 and 127 for all inputs.
- if (OutputDimensions > 1 && !stream.fail())
- {
- canSaturate16.count = 0;
-#if !defined(USE_VNNI)
- for (IndexType i = 0; i < PaddedInputDimensions; i += 16)
- for (IndexType j = 0; j < OutputDimensions; ++j)
- for (int x = 0; x < 2; ++x)
- {
- WeightType* w = &weights[i * OutputDimensions + j * 4 + x * 2];
- int sum[2] = {0, 0};
- for (int k = 0; k < 8; ++k)
- {
- IndexType idx = k / 2 * OutputDimensions * 4 + k % 2;
- sum[w[idx] < 0] += w[idx];
- }
- for (int sign : { -1, 1 })
- while (sign * sum[sign == -1] > 258)
- {
- int maxK = 0, maxW = 0;
- for (int k = 0; k < 8; ++k)
- {
- IndexType idx = k / 2 * OutputDimensions * 4 + k % 2;
- if (maxW < sign * w[idx])
- maxK = k, maxW = sign * w[idx];
- }
-
- IndexType idx = maxK / 2 * OutputDimensions * 4 + maxK % 2;
- sum[sign == -1] -= w[idx];
- const uint32_t scrambledIdx = idx + i * OutputDimensions + j * 4 + x * 2;
- canSaturate16.add(j, i + maxK / 2 * 4 + maxK % 2 + x * 2, w[idx], indexMap[scrambledIdx]);
- w[idx] = 0;
- }
- }
-
- // Non functional optimization for faster more linear access
- std::sort(canSaturate16.ids, canSaturate16.ids + canSaturate16.count,
- [](const typename CanSaturate::Entry& e1, const typename CanSaturate::Entry& e2)
- { return e1.in == e2.in ? e1.out < e2.out : e1.in < e2.in; });
-#endif
- }
+ i % 4
+ ] = read_little_endian<WeightType>(stream);
#endif
return !stream.fail();
i % 4
];
}
- for (int i = 0; i < canSaturate16.count; ++i)
- unscrambledWeights[canSaturate16.ids[i].wIdx] = canSaturate16.ids[i].w;
for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
write_little_endian<WeightType>(stream, unscrambledWeights[i]);
__m512i product1 = _mm512_maddubs_epi16(a1, b1);
__m512i product2 = _mm512_maddubs_epi16(a2, b2);
__m512i product3 = _mm512_maddubs_epi16(a3, b3);
- product0 = _mm512_add_epi16(product0, product1);
- product2 = _mm512_add_epi16(product2, product3);
- product0 = _mm512_add_epi16(product0, product2);
+ product0 = _mm512_adds_epi16(product0, product1);
product0 = _mm512_madd_epi16(product0, Ones512);
- acc = _mm512_add_epi32(acc, product0);
+ product2 = _mm512_adds_epi16(product2, product3);
+ product2 = _mm512_madd_epi16(product2, Ones512);
+ acc = _mm512_add_epi32(acc, _mm512_add_epi32(product0, product2));
#endif
};
__m256i product1 = _mm256_maddubs_epi16(a1, b1);
__m256i product2 = _mm256_maddubs_epi16(a2, b2);
__m256i product3 = _mm256_maddubs_epi16(a3, b3);
- product0 = _mm256_add_epi16(product0, product1);
- product2 = _mm256_add_epi16(product2, product3);
- product0 = _mm256_add_epi16(product0, product2);
+ product0 = _mm256_adds_epi16(product0, product1);
product0 = _mm256_madd_epi16(product0, Ones256);
- acc = _mm256_add_epi32(acc, product0);
+ product2 = _mm256_adds_epi16(product2, product3);
+ product2 = _mm256_madd_epi16(product2, Ones256);
+ acc = _mm256_add_epi32(acc, _mm256_add_epi32(product0, product2));
#endif
};
__m128i product1 = _mm_maddubs_epi16(a1, b1);
__m128i product2 = _mm_maddubs_epi16(a2, b2);
__m128i product3 = _mm_maddubs_epi16(a3, b3);
- product0 = _mm_add_epi16(product0, product1);
- product2 = _mm_add_epi16(product2, product3);
- product0 = _mm_add_epi16(product0, product2);
+ product0 = _mm_adds_epi16(product0, product1);
product0 = _mm_madd_epi16(product0, Ones128);
- acc = _mm_add_epi32(acc, product0);
+ product2 = _mm_adds_epi16(product2, product3);
+ product2 = _mm_madd_epi16(product2, Ones128);
+ acc = _mm_add_epi32(acc, _mm_add_epi32(product0, product2));
};
#endif
#endif
#if defined (USE_SSSE3)
+ // Different layout, we process 4 inputs at a time, always.
+ static_assert(InputDimensions % 4 == 0);
const auto output = reinterpret_cast<OutputType*>(buffer);
const auto inputVector = reinterpret_cast<const vec_t*>(input);
// because then it is also an input dimension.
if constexpr (OutputDimensions % OutputSimdWidth == 0)
{
- constexpr IndexType NumChunks = PaddedInputDimensions / 4;
+ constexpr IndexType NumChunks = InputDimensions / 4;
const auto input32 = reinterpret_cast<const std::int32_t*>(input);
vec_t* outptr = reinterpret_cast<vec_t*>(output);
for (int j = 0; j * OutputSimdWidth < OutputDimensions; ++j)
vec_add_dpbusd_32x4(outptr[j], in0, col0[j], in1, col1[j], in2, col2[j], in3, col3[j]);
}
- for (int i = 0; i < canSaturate16.count; ++i)
- output[canSaturate16.ids[i].out] += input[canSaturate16.ids[i].in] * canSaturate16.ids[i].w;
}
else if constexpr (OutputDimensions == 1)
{
auto output = reinterpret_cast<OutputType*>(buffer);
#if defined(USE_SSE2)
- constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
+ // At least a multiple of 16, with SSE2.
+ static_assert(InputDimensions % SimdWidth == 0);
+ constexpr IndexType NumChunks = InputDimensions / SimdWidth;
const __m128i Zeros = _mm_setzero_si128();
const auto inputVector = reinterpret_cast<const __m128i*>(input);
#elif defined(USE_MMX)
- constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
+ static_assert(InputDimensions % SimdWidth == 0);
+ constexpr IndexType NumChunks = InputDimensions / SimdWidth;
const __m64 Zeros = _mm_setzero_si64();
const auto inputVector = reinterpret_cast<const __m64*>(input);
#elif defined(USE_NEON)
- constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
+ static_assert(InputDimensions % SimdWidth == 0);
+ constexpr IndexType NumChunks = InputDimensions / SimdWidth;
const auto inputVector = reinterpret_cast<const int8x8_t*>(input);
#endif
alignas(CacheLineSize) BiasType biases[OutputDimensions];
alignas(CacheLineSize) WeightType weights[OutputDimensions * PaddedInputDimensions];
-#if defined (USE_SSSE3)
- struct CanSaturate {
- int count;
- struct Entry {
- uint32_t wIdx;
- uint16_t out;
- uint16_t in;
- int8_t w;
- } ids[PaddedInputDimensions * OutputDimensions * 3 / 4];
-
- void add(int i, int j, int8_t w, uint32_t wIdx) {
- ids[count].wIdx = wIdx;
- ids[count].out = i;
- ids[count].in = j;
- ids[count].w = w;
- ++count;
- }
- } canSaturate16;
-#endif
};
} // namespace Stockfish::Eval::NNUE::Layers
projects / stockfish / blobdiff