#ifndef NNUE_LAYERS_AFFINE_TRANSFORM_H_INCLUDED
#define NNUE_LAYERS_AFFINE_TRANSFORM_H_INCLUDED
+#include <cstdint>
#include <iostream>
-#include <algorithm>
-#include <type_traits>
+
#include "../nnue_common.h"
#include "simd.h"
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_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);
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]);
}
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();
+# 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
return hashValue;
}
- static IndexType get_weight_index_scrambled(IndexType i)
+ static constexpr IndexType get_weight_index_scrambled(IndexType i)
{
return
(i / 4) % (PaddedInputDimensions / 4) * OutputDimensions * 4 +
i % 4;
}
- static IndexType get_weight_index(IndexType i)
+ static constexpr IndexType get_weight_index(IndexType i)
{
#if defined (USE_SSSE3)
return get_weight_index_scrambled(i);
return !stream.fail();
}
// Forward propagation
- const OutputType* propagate(
+ void propagate(
const InputType* input, OutputType* output) const {
+#if defined (USE_SSSE3)
+
+ if constexpr (OutputDimensions > 1)
+ {
+
#if defined (USE_AVX512)
using vec_t = __m512i;
#define vec_setzero _mm512_setzero_si512
#define vec_hadd Simd::m128_hadd
#endif
-#if defined (USE_SSSE3)
- const auto inputVector = reinterpret_cast<const vec_t*>(input);
-
- static constexpr IndexType OutputSimdWidth = sizeof(vec_t) / sizeof(OutputType);
+ static constexpr IndexType OutputSimdWidth = sizeof(vec_t) / sizeof(OutputType);
- static_assert(OutputDimensions % OutputSimdWidth == 0 || OutputDimensions == 1);
+ static_assert(OutputDimensions % OutputSimdWidth == 0);
- if constexpr (OutputDimensions % OutputSimdWidth == 0)
- {
constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 8) / 4;
constexpr IndexType NumRegs = OutputDimensions / OutputSimdWidth;
vec_t* outptr = reinterpret_cast<vec_t*>(output);
for (IndexType k = 0; k < NumRegs; ++k)
outptr[k] = acc[k];
+
+# undef vec_setzero
+# undef vec_set_32
+# undef vec_add_dpbusd_32
+# undef vec_add_dpbusd_32x2
+# undef vec_hadd
+
}
else if constexpr (OutputDimensions == 1)
{
- constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
+
+// We cannot use AVX512 for the last layer because there's only 32 inputs and the buffer is not padded to 64 elements.
+#if defined (USE_AVX2)
+ using vec_t = __m256i;
+ #define vec_setzero _mm256_setzero_si256
+ #define vec_set_32 _mm256_set1_epi32
+ #define vec_add_dpbusd_32 Simd::m256_add_dpbusd_epi32
+ #define vec_add_dpbusd_32x2 Simd::m256_add_dpbusd_epi32x2
+ #define vec_hadd Simd::m256_hadd
+#elif defined (USE_SSSE3)
+ using vec_t = __m128i;
+ #define vec_setzero _mm_setzero_si128
+ #define vec_set_32 _mm_set1_epi32
+ #define vec_add_dpbusd_32 Simd::m128_add_dpbusd_epi32
+ #define vec_add_dpbusd_32x2 Simd::m128_add_dpbusd_epi32x2
+ #define vec_hadd Simd::m128_hadd
+#endif
+
+ const auto inputVector = reinterpret_cast<const vec_t*>(input);
+
+ static constexpr IndexType InputSimdWidth = sizeof(vec_t) / sizeof(InputType);
+
+ static_assert(PaddedInputDimensions % InputSimdWidth == 0);
+
+ constexpr IndexType NumChunks = PaddedInputDimensions / InputSimdWidth;
vec_t sum0 = vec_setzero();
const auto row0 = reinterpret_cast<const vec_t*>(&weights[0]);
- for (int j = 0; j < (int)NumChunks; ++j)
+ for (int j = 0; j < int(NumChunks); ++j)
{
const vec_t in = inputVector[j];
vec_add_dpbusd_32(sum0, in, row0[j]);
}
output[0] = vec_hadd(sum0, biases[0]);
- }
# undef vec_setzero
# undef vec_set_32
# undef vec_add_dpbusd_32
# undef vec_add_dpbusd_32x2
# undef vec_hadd
+
+ }
#else
// Use old implementation for the other architectures.
affine_transform_non_ssse3<
PaddedInputDimensions,
OutputDimensions>(output, weights, biases, input);
#endif
-
- return output;
}
private:
projects / stockfish / blobdiff