/*
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_CLIPPED_RELU_H_INCLUDED
#define NNUE_LAYERS_CLIPPED_RELU_H_INCLUDED
+#include <algorithm>
+#include <cstdint>
+#include <iosfwd>
+
#include "../nnue_common.h"
namespace Stockfish::Eval::NNUE::Layers {
// Clipped ReLU
- template <typename PreviousLayer>
+ template <IndexType InDims>
class ClippedReLU {
public:
// Input/output type
- using InputType = typename PreviousLayer::OutputType;
+ using InputType = std::int32_t;
using OutputType = std::uint8_t;
- static_assert(std::is_same<InputType, std::int32_t>::value, "");
// Number of input/output dimensions
- static constexpr IndexType InputDimensions =
- PreviousLayer::OutputDimensions;
+ static constexpr IndexType InputDimensions = InDims;
static constexpr IndexType OutputDimensions = InputDimensions;
+ static constexpr IndexType PaddedOutputDimensions =
+ ceil_to_multiple<IndexType>(OutputDimensions, 32);
- // Size of forward propagation buffer used in this layer
- static constexpr std::size_t SelfBufferSize =
- ceil_to_multiple(OutputDimensions * sizeof(OutputType), CacheLineSize);
-
- // 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() {
+ static constexpr std::uint32_t get_hash_value(std::uint32_t prevHash) {
std::uint32_t hashValue = 0x538D24C7u;
- hashValue += PreviousLayer::get_hash_value();
+ hashValue += prevHash;
return hashValue;
}
// Read network parameters
- bool read_parameters(std::istream& stream) {
- return previousLayer.read_parameters(stream);
+ bool read_parameters(std::istream&) {
+ return true;
}
// Write network parameters
- bool write_parameters(std::ostream& stream) const {
- return previousLayer.write_parameters(stream);
+ bool write_parameters(std::ostream&) const {
+ return true;
}
// Forward propagation
- const OutputType* propagate(
- const TransformedFeatureType* transformedFeatures, char* buffer) const {
- const auto input = previousLayer.propagate(
- transformedFeatures, buffer + SelfBufferSize);
- const auto output = reinterpret_cast<OutputType*>(buffer);
+ void propagate(
+ const InputType* input, OutputType* output) const {
#if defined(USE_AVX2)
if constexpr (InputDimensions % SimdWidth == 0) {
}
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};
for (IndexType i = Start; i < InputDimensions; ++i) {
output[i] = static_cast<OutputType>(
- std::max(0, std::min(127, input[i] >> WeightScaleBits)));
+ std::clamp(input[i] >> WeightScaleBits, 0, 127));
}
- return output;
}
-
- private:
- PreviousLayer previousLayer;
};
} // namespace Stockfish::Eval::NNUE::Layers