/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
- Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
+ Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
#ifndef NNUE_LAYERS_AFFINE_TRANSFORM_H_INCLUDED
#define NNUE_LAYERS_AFFINE_TRANSFORM_H_INCLUDED
+#include <cstdint>
#include <iostream>
+
#include "../nnue_common.h"
+#include "simd.h"
+
+/*
+ This file contains the definition for a fully connected layer (aka affine transform).
+
+ - expected use-case is for when PaddedInputDimensions == 32 and InputDimensions <= 32.
+ - that's why AVX512 is hard to implement
+ - expected use-case is small layers
+ - inputs are processed in chunks of 4, weights are respectively transposed
+ - accumulation happens directly to int32s
+*/
namespace Stockfish::Eval::NNUE::Layers {
- // Affine transformation layer
- template <typename PreviousLayer, IndexType OutDims>
- class AffineTransform {
+// Fallback implementation for older/other architectures.
+// Requires the input to be padded to at least 16 values.
+#if !defined(USE_SSSE3)
+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_NEON_DOTPROD)
+ constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 16) / 16;
+ const auto inputVector = reinterpret_cast<const int8x16_t*>(input);
+
+ #elif defined(USE_NEON)
+ constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 16) / 16;
+ const auto inputVector = reinterpret_cast<const int8x8_t*>(input);
+ #endif
+
+ for (IndexType i = 0; i < OutputDimensions; ++i)
+ {
+ const IndexType offset = i * PaddedInputDimensions;
+
+ #if defined(USE_SSE2)
+ __m128i sumLo = _mm_cvtsi32_si128(biases[i]);
+ __m128i sumHi = Zeros;
+ const auto row = reinterpret_cast<const __m128i*>(&weights[offset]);
+ for (IndexType j = 0; j < NumChunks; ++j)
+ {
+ __m128i row_j = _mm_load_si128(&row[j]);
+ __m128i input_j = _mm_load_si128(&inputVector[j]);
+ __m128i extendedRowLo = _mm_srai_epi16(_mm_unpacklo_epi8(row_j, row_j), 8);
+ __m128i extendedRowHi = _mm_srai_epi16(_mm_unpackhi_epi8(row_j, row_j), 8);
+ __m128i extendedInputLo = _mm_unpacklo_epi8(input_j, Zeros);
+ __m128i extendedInputHi = _mm_unpackhi_epi8(input_j, Zeros);
+ __m128i productLo = _mm_madd_epi16(extendedRowLo, extendedInputLo);
+ __m128i productHi = _mm_madd_epi16(extendedRowHi, extendedInputHi);
+ sumLo = _mm_add_epi32(sumLo, productLo);
+ sumHi = _mm_add_epi32(sumHi, productHi);
+ }
+ __m128i sum = _mm_add_epi32(sumLo, sumHi);
+ __m128i sumHigh_64 = _mm_shuffle_epi32(sum, _MM_SHUFFLE(1, 0, 3, 2));
+ sum = _mm_add_epi32(sum, sumHigh_64);
+ __m128i sum_second_32 = _mm_shufflelo_epi16(sum, _MM_SHUFFLE(1, 0, 3, 2));
+ sum = _mm_add_epi32(sum, sum_second_32);
+ output[i] = _mm_cvtsi128_si32(sum);
+
+ #elif defined(USE_NEON_DOTPROD)
+ int32x4_t sum = {biases[i]};
+ const auto row = reinterpret_cast<const int8x16_t*>(&weights[offset]);
+ for (IndexType j = 0; j < NumChunks; ++j)
+ {
+ sum = vdotq_s32(sum, inputVector[j], row[j]);
+ }
+ output[i] = vaddvq_s32(sum);
+
+ #elif defined(USE_NEON)
+ int32x4_t sum = {biases[i]};
+ const auto row = reinterpret_cast<const int8x8_t*>(&weights[offset]);
+ for (IndexType j = 0; j < NumChunks; ++j)
+ {
+ int16x8_t product = vmull_s8(inputVector[j * 2], row[j * 2]);
+ product = vmlal_s8(product, inputVector[j * 2 + 1], row[j * 2 + 1]);
+ sum = vpadalq_s16(sum, product);
+ }
+ output[i] = sum[0] + sum[1] + sum[2] + sum[3];
+
+ #endif
+ }
+ #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
+
+template<IndexType InDims, IndexType OutDims>
+class AffineTransform {
public:
// Input/output type
- using InputType = typename PreviousLayer::OutputType;
+ using InputType = std::uint8_t;
using OutputType = std::int32_t;
- static_assert(std::is_same<InputType, std::uint8_t>::value, "");
// Number of input/output dimensions
- static constexpr IndexType InputDimensions =
- PreviousLayer::OutputDimensions;
+ static constexpr IndexType InputDimensions = InDims;
static constexpr IndexType OutputDimensions = OutDims;
- static constexpr IndexType PaddedInputDimensions =
- ceil_to_multiple<IndexType>(InputDimensions, MaxSimdWidth);
-#if defined (USE_AVX512)
- static constexpr const IndexType OutputSimdWidth = SimdWidth / 2;
-#elif defined (USE_SSSE3)
- static constexpr const IndexType OutputSimdWidth = SimdWidth / 4;
-#endif
- // Size of forward propagation buffer used in this layer
- static constexpr std::size_t SelfBufferSize =
- ceil_to_multiple(OutputDimensions * sizeof(OutputType), CacheLineSize);
+ static constexpr IndexType PaddedInputDimensions =
+ ceil_to_multiple<IndexType>(InputDimensions, MaxSimdWidth);
+ static constexpr IndexType PaddedOutputDimensions =
+ ceil_to_multiple<IndexType>(OutputDimensions, MaxSimdWidth);
- // Size of the forward propagation buffer used from the input layer to this layer
- static constexpr std::size_t BufferSize =
- PreviousLayer::BufferSize + SelfBufferSize;
+ using OutputBuffer = OutputType[PaddedOutputDimensions];
// Hash value embedded in the evaluation file
- static constexpr std::uint32_t get_hash_value() {
- std::uint32_t hashValue = 0xCC03DAE4u;
- hashValue += OutputDimensions;
- hashValue ^= PreviousLayer::get_hash_value() >> 1;
- hashValue ^= PreviousLayer::get_hash_value() << 31;
- return hashValue;
+ static constexpr std::uint32_t get_hash_value(std::uint32_t prevHash) {
+ std::uint32_t hashValue = 0xCC03DAE4u;
+ hashValue += OutputDimensions;
+ hashValue ^= prevHash >> 1;
+ hashValue ^= prevHash << 31;
+ return hashValue;
}
- // Read network parameters
- bool read_parameters(std::istream& stream) {
- if (!previousLayer.read_parameters(stream)) return false;
- for (std::size_t i = 0; i < OutputDimensions; ++i)
- biases[i] = read_little_endian<BiasType>(stream);
- for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
-#if !defined (USE_SSSE3)
- weights[i] = read_little_endian<WeightType>(stream);
-#else
- weights[
- (i / 4) % (PaddedInputDimensions / 4) * OutputDimensions * 4 +
- i / PaddedInputDimensions * 4 +
- i % 4
- ] = read_little_endian<WeightType>(stream);
-
- // 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];
- canSaturate16.add(j, i + maxK / 2 * 4 + maxK % 2 + x * 2, w[idx]);
- 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
- }
-#endif
-
- return !stream.fail();
+ static constexpr IndexType get_weight_index_scrambled(IndexType i) {
+ return (i / 4) % (PaddedInputDimensions / 4) * OutputDimensions * 4
+ + i / PaddedInputDimensions * 4 + i % 4;
}
- // Forward propagation
- const OutputType* propagate(
- const TransformedFeatureType* transformedFeatures, char* buffer) const {
- const auto input = previousLayer.propagate(
- transformedFeatures, buffer + SelfBufferSize);
-
-#if defined (USE_AVX512)
-
- [[maybe_unused]] const __m512i Ones512 = _mm512_set1_epi16(1);
-
- [[maybe_unused]] auto m512_hadd = [](__m512i sum, int bias) -> int {
- return _mm512_reduce_add_epi32(sum) + bias;
- };
-
- [[maybe_unused]] auto m512_add_dpbusd_epi32 = [=](__m512i& acc, __m512i a, __m512i b) {
-#if defined (USE_VNNI)
- acc = _mm512_dpbusd_epi32(acc, a, b);
+ static constexpr IndexType get_weight_index(IndexType i) {
+#if defined(USE_SSSE3)
+ return get_weight_index_scrambled(i);
#else
- __m512i product0 = _mm512_maddubs_epi16(a, b);
- product0 = _mm512_madd_epi16(product0, Ones512);
- acc = _mm512_add_epi32(acc, product0);
-#endif
- };
-
- [[maybe_unused]] auto m512_add_dpbusd_epi32x4 = [=](__m512i& acc, __m512i a0, __m512i b0, __m512i a1, __m512i b1,
- __m512i a2, __m512i b2, __m512i a3, __m512i b3) {
-#if defined (USE_VNNI)
- acc = _mm512_dpbusd_epi32(acc, a0, b0);
- acc = _mm512_dpbusd_epi32(acc, a1, b1);
- acc = _mm512_dpbusd_epi32(acc, a2, b2);
- acc = _mm512_dpbusd_epi32(acc, a3, b3);
-#else
- __m512i product0 = _mm512_maddubs_epi16(a0, b0);
- __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_madd_epi16(product0, Ones512);
- acc = _mm512_add_epi32(acc, product0);
-#endif
- };
-
+ return i;
#endif
-#if defined (USE_AVX2)
-
- [[maybe_unused]] const __m256i Ones256 = _mm256_set1_epi16(1);
-
- [[maybe_unused]] auto m256_hadd = [](__m256i sum, int bias) -> int {
- __m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(sum), _mm256_extracti128_si256(sum, 1));
- sum128 = _mm_add_epi32(sum128, _mm_shuffle_epi32(sum128, _MM_PERM_BADC));
- sum128 = _mm_add_epi32(sum128, _mm_shuffle_epi32(sum128, _MM_PERM_CDAB));
- return _mm_cvtsi128_si32(sum128) + bias;
- };
-
- [[maybe_unused]] auto m256_add_dpbusd_epi32 = [=](__m256i& acc, __m256i a, __m256i b) {
-#if defined (USE_VNNI)
- acc = _mm256_dpbusd_epi32(acc, a, b);
-#else
- __m256i product0 = _mm256_maddubs_epi16(a, b);
- product0 = _mm256_madd_epi16(product0, Ones256);
- acc = _mm256_add_epi32(acc, product0);
-#endif
- };
-
- [[maybe_unused]] auto m256_add_dpbusd_epi32x4 = [=](__m256i& acc, __m256i a0, __m256i b0, __m256i a1, __m256i b1,
- __m256i a2, __m256i b2, __m256i a3, __m256i b3) {
-#if defined (USE_VNNI)
- acc = _mm256_dpbusd_epi32(acc, a0, b0);
- acc = _mm256_dpbusd_epi32(acc, a1, b1);
- acc = _mm256_dpbusd_epi32(acc, a2, b2);
- acc = _mm256_dpbusd_epi32(acc, a3, b3);
-#else
- __m256i product0 = _mm256_maddubs_epi16(a0, b0);
- __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_madd_epi16(product0, Ones256);
- acc = _mm256_add_epi32(acc, product0);
-#endif
- };
-
-#endif
-#if defined (USE_SSSE3)
-
- [[maybe_unused]] const __m128i Ones128 = _mm_set1_epi16(1);
-
- [[maybe_unused]] auto m128_hadd = [](__m128i sum, int bias) -> int {
- sum = _mm_add_epi32(sum, _mm_shuffle_epi32(sum, 0x4E)); //_MM_PERM_BADC
- sum = _mm_add_epi32(sum, _mm_shuffle_epi32(sum, 0xB1)); //_MM_PERM_CDAB
- return _mm_cvtsi128_si32(sum) + bias;
- };
-
- [[maybe_unused]] auto m128_add_dpbusd_epi32 = [=](__m128i& acc, __m128i a, __m128i b) {
- __m128i product0 = _mm_maddubs_epi16(a, b);
- product0 = _mm_madd_epi16(product0, Ones128);
- acc = _mm_add_epi32(acc, product0);
- };
-
- [[maybe_unused]] auto m128_add_dpbusd_epi32x4 = [=](__m128i& acc, __m128i a0, __m128i b0, __m128i a1, __m128i b1,
- __m128i a2, __m128i b2, __m128i a3, __m128i b3) {
- __m128i product0 = _mm_maddubs_epi16(a0, b0);
- __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_madd_epi16(product0, Ones128);
- acc = _mm_add_epi32(acc, product0);
- };
-
-#endif
-
-#if defined (USE_AVX512)
- using vec_t = __m512i;
- #define vec_setzero _mm512_setzero_si512
- #define vec_set_32 _mm512_set1_epi32
- auto& vec_add_dpbusd_32 = m512_add_dpbusd_epi32;
- auto& vec_add_dpbusd_32x4 = m512_add_dpbusd_epi32x4;
- auto& vec_hadd = m512_hadd;
-#elif defined (USE_AVX2)
- using vec_t = __m256i;
- #define vec_setzero _mm256_setzero_si256
- #define vec_set_32 _mm256_set1_epi32
- auto& vec_add_dpbusd_32 = m256_add_dpbusd_epi32;
- auto& vec_add_dpbusd_32x4 = m256_add_dpbusd_epi32x4;
- auto& vec_hadd = m256_hadd;
-#elif defined (USE_SSSE3)
- using vec_t = __m128i;
- #define vec_setzero _mm_setzero_si128
- #define vec_set_32 _mm_set1_epi32
- auto& vec_add_dpbusd_32 = m128_add_dpbusd_epi32;
- auto& vec_add_dpbusd_32x4 = m128_add_dpbusd_epi32x4;
- auto& vec_hadd = m128_hadd;
-#endif
-
-#if defined (USE_SSSE3)
-
- const auto output = reinterpret_cast<OutputType*>(buffer);
- const auto inputVector = reinterpret_cast<const vec_t*>(input);
-
- static_assert(OutputDimensions % OutputSimdWidth == 0 || OutputDimensions == 1);
-
- // OutputDimensions is either 1 or a multiple of SimdWidth
- // because then it is also an input dimension.
- if constexpr (OutputDimensions % OutputSimdWidth == 0)
- {
- constexpr IndexType NumChunks = PaddedInputDimensions / 4;
-
- const auto input32 = reinterpret_cast<const std::int32_t*>(input);
- vec_t* outptr = reinterpret_cast<vec_t*>(output);
- std::memcpy(output, biases, OutputDimensions * sizeof(OutputType));
-
- for (int i = 0; i < (int)NumChunks - 3; i += 4)
- {
- const vec_t in0 = vec_set_32(input32[i + 0]);
- const vec_t in1 = vec_set_32(input32[i + 1]);
- const vec_t in2 = vec_set_32(input32[i + 2]);
- const vec_t in3 = vec_set_32(input32[i + 3]);
- const auto col0 = reinterpret_cast<const vec_t*>(&weights[(i + 0) * OutputDimensions * 4]);
- const auto col1 = reinterpret_cast<const vec_t*>(&weights[(i + 1) * OutputDimensions * 4]);
- const auto col2 = reinterpret_cast<const vec_t*>(&weights[(i + 2) * OutputDimensions * 4]);
- const auto col3 = reinterpret_cast<const vec_t*>(&weights[(i + 3) * OutputDimensions * 4]);
- 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)
- {
-#if defined (USE_AVX512)
- if constexpr (PaddedInputDimensions % (SimdWidth * 2) != 0)
- {
- constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
- const auto inputVector256 = reinterpret_cast<const __m256i*>(input);
-
- __m256i sum0 = _mm256_setzero_si256();
- const auto row0 = reinterpret_cast<const __m256i*>(&weights[0]);
-
- for (int j = 0; j < (int)NumChunks; ++j)
- {
- const __m256i in = inputVector256[j];
- m256_add_dpbusd_epi32(sum0, in, row0[j]);
- }
- output[0] = m256_hadd(sum0, biases[0]);
- }
- else
-#endif
- {
-#if defined (USE_AVX512)
- constexpr IndexType NumChunks = PaddedInputDimensions / (SimdWidth * 2);
-#else
- constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
-#endif
- vec_t sum0 = vec_setzero();
- const auto row0 = reinterpret_cast<const vec_t*>(&weights[0]);
-
- 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]);
- }
- }
-
-#else
-
-// Use old implementation for the other architectures.
-
- auto output = reinterpret_cast<OutputType*>(buffer);
+ }
-#if defined(USE_SSE2)
- constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
- const __m128i Zeros = _mm_setzero_si128();
- const auto inputVector = reinterpret_cast<const __m128i*>(input);
+ // Read network parameters
+ bool read_parameters(std::istream& stream) {
+ read_little_endian<BiasType>(stream, biases, OutputDimensions);
+ for (IndexType i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
+ weights[get_weight_index(i)] = read_little_endian<WeightType>(stream);
-#elif defined(USE_MMX)
- constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
- const __m64 Zeros = _mm_setzero_si64();
- const auto inputVector = reinterpret_cast<const __m64*>(input);
+ return !stream.fail();
+ }
-#elif defined(USE_NEON)
- constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
- const auto inputVector = reinterpret_cast<const int8x8_t*>(input);
-#endif
+ // Write network parameters
+ bool write_parameters(std::ostream& stream) const {
+ write_little_endian<BiasType>(stream, biases, OutputDimensions);
- for (IndexType i = 0; i < OutputDimensions; ++i) {
- const IndexType offset = i * PaddedInputDimensions;
+ for (IndexType i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
+ write_little_endian<WeightType>(stream, weights[get_weight_index(i)]);
-#if defined(USE_SSE2)
- __m128i sumLo = _mm_cvtsi32_si128(biases[i]);
- __m128i sumHi = Zeros;
- const auto row = reinterpret_cast<const __m128i*>(&weights[offset]);
- for (IndexType j = 0; j < NumChunks; ++j) {
- __m128i row_j = _mm_load_si128(&row[j]);
- __m128i input_j = _mm_load_si128(&inputVector[j]);
- __m128i extendedRowLo = _mm_srai_epi16(_mm_unpacklo_epi8(row_j, row_j), 8);
- __m128i extendedRowHi = _mm_srai_epi16(_mm_unpackhi_epi8(row_j, row_j), 8);
- __m128i extendedInputLo = _mm_unpacklo_epi8(input_j, Zeros);
- __m128i extendedInputHi = _mm_unpackhi_epi8(input_j, Zeros);
- __m128i productLo = _mm_madd_epi16(extendedRowLo, extendedInputLo);
- __m128i productHi = _mm_madd_epi16(extendedRowHi, extendedInputHi);
- sumLo = _mm_add_epi32(sumLo, productLo);
- sumHi = _mm_add_epi32(sumHi, productHi);
- }
- __m128i sum = _mm_add_epi32(sumLo, sumHi);
- __m128i sumHigh_64 = _mm_shuffle_epi32(sum, _MM_SHUFFLE(1, 0, 3, 2));
- sum = _mm_add_epi32(sum, sumHigh_64);
- __m128i sum_second_32 = _mm_shufflelo_epi16(sum, _MM_SHUFFLE(1, 0, 3, 2));
- 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);
+ return !stream.fail();
+ }
+ // Forward propagation
+ 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_set_32 _mm512_set1_epi32
+ #define vec_add_dpbusd_32 Simd::m512_add_dpbusd_epi32
+ #define vec_add_dpbusd_32x2 Simd::m512_add_dpbusd_epi32x2
+ #define vec_hadd Simd::m512_hadd
+ #elif 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
+
+ static constexpr IndexType OutputSimdWidth = sizeof(vec_t) / sizeof(OutputType);
+
+ static_assert(OutputDimensions % OutputSimdWidth == 0);
+
+ constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 8) / 4;
+ constexpr IndexType NumRegs = OutputDimensions / OutputSimdWidth;
+
+ const auto input32 = reinterpret_cast<const std::int32_t*>(input);
+ const vec_t* biasvec = reinterpret_cast<const vec_t*>(biases);
+ vec_t acc[NumRegs];
+ for (IndexType k = 0; k < NumRegs; ++k)
+ acc[k] = biasvec[k];
+
+ for (IndexType i = 0; i < NumChunks; i += 2)
+ {
+ const vec_t in0 = vec_set_32(input32[i + 0]);
+ const vec_t in1 = vec_set_32(input32[i + 1]);
+ const auto col0 =
+ reinterpret_cast<const vec_t*>(&weights[(i + 0) * OutputDimensions * 4]);
+ const auto col1 =
+ reinterpret_cast<const vec_t*>(&weights[(i + 1) * OutputDimensions * 4]);
+ for (IndexType k = 0; k < NumRegs; ++k)
+ vec_add_dpbusd_32x2(acc[k], in0, col0[k], in1, col1[k]);
+ }
+
+ 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
}
- __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)
- int32x4_t sum = {biases[i]};
- const auto row = reinterpret_cast<const int8x8_t*>(&weights[offset]);
- for (IndexType j = 0; j < NumChunks; ++j) {
- int16x8_t product = vmull_s8(inputVector[j * 2], row[j * 2]);
- product = vmlal_s8(product, inputVector[j * 2 + 1], row[j * 2 + 1]);
- sum = vpadalq_s16(sum, product);
+ else if constexpr (OutputDimensions == 1)
+ {
+
+ // We cannot use AVX512 for the last layer because there are 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)
+ {
+ 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
}
- output[i] = sum[0] + sum[1] + sum[2] + sum[3];
-
#else
- OutputType 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();
-#endif
-
+ // Use old implementation for the other architectures.
+ affine_transform_non_ssse3<InputDimensions, PaddedInputDimensions, OutputDimensions>(
+ output, weights, biases, input);
#endif
-
- return output;
}
private:
- using BiasType = OutputType;
+ using BiasType = OutputType;
using WeightType = std::int8_t;
- PreviousLayer previousLayer;
-
alignas(CacheLineSize) BiasType biases[OutputDimensions];
alignas(CacheLineSize) WeightType weights[OutputDimensions * PaddedInputDimensions];
-#if defined (USE_SSSE3)
- struct CanSaturate {
- int count;
- struct Entry {
- uint16_t out;
- uint16_t in;
- int8_t w;
- } ids[PaddedInputDimensions * OutputDimensions * 3 / 4];
-
- void add(int i, int j, int8_t w) {
- ids[count].out = i;
- ids[count].in = j;
- ids[count].w = w;
- ++count;
- }
- } canSaturate16;
-#endif
- };
+};
} // namespace Stockfish::Eval::NNUE::Layers
-#endif // #ifndef NNUE_LAYERS_AFFINE_TRANSFORM_H_INCLUDED
+#endif // #ifndef NNUE_LAYERS_AFFINE_TRANSFORM_H_INCLUDED
projects / stockfish / blobdiff