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);
+ const InputType* input, OutputType* output) const {
#if defined(USE_AVX2)
if constexpr (InputDimensions % SimdWidth == 0) {
return output;
}
-
- private:
- PreviousLayer previousLayer;
};
} // namespace Stockfish::Eval::NNUE::Layers