mirror of
https://github.com/sockspls/badfish
synced 2025-06-28 00:19:50 +00:00
b60f9cc451
Happy New Year! closes https://github.com/official-stockfish/Stockfish/pull/4315 No functional change
180 lines
6.8 KiB
C++
180 lines
6.8 KiB
C++
/*
|
|
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
|
|
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
|
|
the Free Software Foundation, either version 3 of the License, or
|
|
(at your option) any later version.
|
|
|
|
Stockfish is distributed in the hope that it will be useful,
|
|
but WITHOUT ANY WARRANTY; without even the implied warranty of
|
|
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
|
GNU General Public License for more details.
|
|
|
|
You should have received a copy of the GNU General Public License
|
|
along with this program. If not, see <http://www.gnu.org/licenses/>.
|
|
*/
|
|
|
|
// Definition of layer ClippedReLU of NNUE evaluation function
|
|
|
|
#ifndef NNUE_LAYERS_CLIPPED_RELU_H_INCLUDED
|
|
#define NNUE_LAYERS_CLIPPED_RELU_H_INCLUDED
|
|
|
|
#include "../nnue_common.h"
|
|
|
|
namespace Stockfish::Eval::NNUE::Layers {
|
|
|
|
// Clipped ReLU
|
|
template <IndexType InDims>
|
|
class ClippedReLU {
|
|
public:
|
|
// Input/output type
|
|
using InputType = std::int32_t;
|
|
using OutputType = std::uint8_t;
|
|
|
|
// Number of input/output dimensions
|
|
static constexpr IndexType InputDimensions = InDims;
|
|
static constexpr IndexType OutputDimensions = InputDimensions;
|
|
static constexpr IndexType PaddedOutputDimensions =
|
|
ceil_to_multiple<IndexType>(OutputDimensions, 32);
|
|
|
|
using OutputBuffer = OutputType[PaddedOutputDimensions];
|
|
|
|
// Hash value embedded in the evaluation file
|
|
static constexpr std::uint32_t get_hash_value(std::uint32_t prevHash) {
|
|
std::uint32_t hashValue = 0x538D24C7u;
|
|
hashValue += prevHash;
|
|
return hashValue;
|
|
}
|
|
|
|
// Read network parameters
|
|
bool read_parameters(std::istream&) {
|
|
return true;
|
|
}
|
|
|
|
// Write network parameters
|
|
bool write_parameters(std::ostream&) const {
|
|
return true;
|
|
}
|
|
|
|
// Forward propagation
|
|
const OutputType* propagate(
|
|
const InputType* input, OutputType* output) const {
|
|
|
|
#if defined(USE_AVX2)
|
|
if constexpr (InputDimensions % SimdWidth == 0) {
|
|
constexpr IndexType NumChunks = InputDimensions / SimdWidth;
|
|
const __m256i Zero = _mm256_setzero_si256();
|
|
const __m256i Offsets = _mm256_set_epi32(7, 3, 6, 2, 5, 1, 4, 0);
|
|
const auto in = reinterpret_cast<const __m256i*>(input);
|
|
const auto out = reinterpret_cast<__m256i*>(output);
|
|
for (IndexType i = 0; i < NumChunks; ++i) {
|
|
const __m256i words0 = _mm256_srai_epi16(_mm256_packs_epi32(
|
|
_mm256_load_si256(&in[i * 4 + 0]),
|
|
_mm256_load_si256(&in[i * 4 + 1])), WeightScaleBits);
|
|
const __m256i words1 = _mm256_srai_epi16(_mm256_packs_epi32(
|
|
_mm256_load_si256(&in[i * 4 + 2]),
|
|
_mm256_load_si256(&in[i * 4 + 3])), WeightScaleBits);
|
|
_mm256_store_si256(&out[i], _mm256_permutevar8x32_epi32(_mm256_max_epi8(
|
|
_mm256_packs_epi16(words0, words1), Zero), Offsets));
|
|
}
|
|
} else {
|
|
constexpr IndexType NumChunks = InputDimensions / (SimdWidth / 2);
|
|
const __m128i Zero = _mm_setzero_si128();
|
|
const auto in = reinterpret_cast<const __m128i*>(input);
|
|
const auto out = reinterpret_cast<__m128i*>(output);
|
|
for (IndexType i = 0; i < NumChunks; ++i) {
|
|
const __m128i words0 = _mm_srai_epi16(_mm_packs_epi32(
|
|
_mm_load_si128(&in[i * 4 + 0]),
|
|
_mm_load_si128(&in[i * 4 + 1])), WeightScaleBits);
|
|
const __m128i words1 = _mm_srai_epi16(_mm_packs_epi32(
|
|
_mm_load_si128(&in[i * 4 + 2]),
|
|
_mm_load_si128(&in[i * 4 + 3])), WeightScaleBits);
|
|
const __m128i packedbytes = _mm_packs_epi16(words0, words1);
|
|
_mm_store_si128(&out[i], _mm_max_epi8(packedbytes, Zero));
|
|
}
|
|
}
|
|
constexpr IndexType Start =
|
|
InputDimensions % SimdWidth == 0
|
|
? InputDimensions / SimdWidth * SimdWidth
|
|
: InputDimensions / (SimdWidth / 2) * (SimdWidth / 2);
|
|
|
|
#elif defined(USE_SSE2)
|
|
constexpr IndexType NumChunks = InputDimensions / SimdWidth;
|
|
|
|
#ifdef USE_SSE41
|
|
const __m128i Zero = _mm_setzero_si128();
|
|
#else
|
|
const __m128i k0x80s = _mm_set1_epi8(-128);
|
|
#endif
|
|
|
|
const auto in = reinterpret_cast<const __m128i*>(input);
|
|
const auto out = reinterpret_cast<__m128i*>(output);
|
|
for (IndexType i = 0; i < NumChunks; ++i) {
|
|
const __m128i words0 = _mm_srai_epi16(_mm_packs_epi32(
|
|
_mm_load_si128(&in[i * 4 + 0]),
|
|
_mm_load_si128(&in[i * 4 + 1])), WeightScaleBits);
|
|
const __m128i words1 = _mm_srai_epi16(_mm_packs_epi32(
|
|
_mm_load_si128(&in[i * 4 + 2]),
|
|
_mm_load_si128(&in[i * 4 + 3])), WeightScaleBits);
|
|
const __m128i packedbytes = _mm_packs_epi16(words0, words1);
|
|
_mm_store_si128(&out[i],
|
|
|
|
#ifdef USE_SSE41
|
|
_mm_max_epi8(packedbytes, Zero)
|
|
#else
|
|
_mm_subs_epi8(_mm_adds_epi8(packedbytes, k0x80s), k0x80s)
|
|
#endif
|
|
|
|
);
|
|
}
|
|
constexpr IndexType Start = NumChunks * SimdWidth;
|
|
|
|
#elif defined(USE_MMX)
|
|
constexpr IndexType NumChunks = InputDimensions / SimdWidth;
|
|
const __m64 k0x80s = _mm_set1_pi8(-128);
|
|
const auto in = reinterpret_cast<const __m64*>(input);
|
|
const auto out = reinterpret_cast<__m64*>(output);
|
|
for (IndexType i = 0; i < NumChunks; ++i) {
|
|
const __m64 words0 = _mm_srai_pi16(
|
|
_mm_packs_pi32(in[i * 4 + 0], in[i * 4 + 1]),
|
|
WeightScaleBits);
|
|
const __m64 words1 = _mm_srai_pi16(
|
|
_mm_packs_pi32(in[i * 4 + 2], in[i * 4 + 3]),
|
|
WeightScaleBits);
|
|
const __m64 packedbytes = _mm_packs_pi16(words0, words1);
|
|
out[i] = _mm_subs_pi8(_mm_adds_pi8(packedbytes, k0x80s), k0x80s);
|
|
}
|
|
_mm_empty();
|
|
constexpr IndexType Start = NumChunks * SimdWidth;
|
|
|
|
#elif defined(USE_NEON)
|
|
constexpr IndexType NumChunks = InputDimensions / (SimdWidth / 2);
|
|
const int8x8_t Zero = {0};
|
|
const auto in = reinterpret_cast<const int32x4_t*>(input);
|
|
const auto out = reinterpret_cast<int8x8_t*>(output);
|
|
for (IndexType i = 0; i < NumChunks; ++i) {
|
|
int16x8_t shifted;
|
|
const auto pack = reinterpret_cast<int16x4_t*>(&shifted);
|
|
pack[0] = vqshrn_n_s32(in[i * 2 + 0], WeightScaleBits);
|
|
pack[1] = vqshrn_n_s32(in[i * 2 + 1], WeightScaleBits);
|
|
out[i] = vmax_s8(vqmovn_s16(shifted), Zero);
|
|
}
|
|
constexpr IndexType Start = NumChunks * (SimdWidth / 2);
|
|
#else
|
|
constexpr IndexType Start = 0;
|
|
#endif
|
|
|
|
for (IndexType i = Start; i < InputDimensions; ++i) {
|
|
output[i] = static_cast<OutputType>(
|
|
std::max(0, std::min(127, input[i] >> WeightScaleBits)));
|
|
}
|
|
|
|
return output;
|
|
}
|
|
};
|
|
|
|
} // namespace Stockfish::Eval::NNUE::Layers
|
|
|
|
#endif // NNUE_LAYERS_CLIPPED_RELU_H_INCLUDED
|