#include "features/half_ka_v2_hm.h"
-#include "layers/input_slice.h"
#include "layers/affine_transform.h"
#include "layers/clipped_relu.h"
-namespace Stockfish::Eval::NNUE {
-
- // Input features used in evaluation function
- using FeatureSet = Features::HalfKAv2_hm;
-
- // Number of input feature dimensions after conversion
- constexpr IndexType TransformedFeatureDimensions = 1024;
- constexpr IndexType PSQTBuckets = 8;
- constexpr IndexType LayerStacks = 8;
-
- namespace Layers {
+#include "../misc.h"
- // Define network structure
- using InputLayer = InputSlice<TransformedFeatureDimensions * 2>;
- using HiddenLayer1 = ClippedReLU<AffineTransform<InputLayer, 8>>;
- using HiddenLayer2 = ClippedReLU<AffineTransform<HiddenLayer1, 32>>;
- using OutputLayer = AffineTransform<HiddenLayer2, 1>;
-
- } // namespace Layers
-
- using Network = Layers::OutputLayer;
+namespace Stockfish::Eval::NNUE {
- static_assert(TransformedFeatureDimensions % MaxSimdWidth == 0, "");
- static_assert(Network::OutputDimensions == 1, "");
- static_assert(std::is_same<Network::OutputType, std::int32_t>::value, "");
+// Input features used in evaluation function
+using FeatureSet = Features::HalfKAv2_hm;
+
+// Number of input feature dimensions after conversion
+constexpr IndexType TransformedFeatureDimensions = 1024;
+constexpr IndexType PSQTBuckets = 8;
+constexpr IndexType LayerStacks = 8;
+
+struct Network
+{
+ static constexpr int FC_0_OUTPUTS = 15;
+ static constexpr int FC_1_OUTPUTS = 32;
+
+ Layers::AffineTransform<TransformedFeatureDimensions, FC_0_OUTPUTS + 1> fc_0;
+ Layers::ClippedReLU<FC_0_OUTPUTS> ac_0;
+ Layers::AffineTransform<FC_0_OUTPUTS, FC_1_OUTPUTS> fc_1;
+ Layers::ClippedReLU<FC_1_OUTPUTS> ac_1;
+ Layers::AffineTransform<FC_1_OUTPUTS, 1> fc_2;
+
+ // Hash value embedded in the evaluation file
+ static constexpr std::uint32_t get_hash_value() {
+ // input slice hash
+ std::uint32_t hashValue = 0xEC42E90Du;
+ hashValue ^= TransformedFeatureDimensions * 2;
+
+ hashValue = decltype(fc_0)::get_hash_value(hashValue);
+ hashValue = decltype(ac_0)::get_hash_value(hashValue);
+ hashValue = decltype(fc_1)::get_hash_value(hashValue);
+ hashValue = decltype(ac_1)::get_hash_value(hashValue);
+ hashValue = decltype(fc_2)::get_hash_value(hashValue);
+
+ return hashValue;
+ }
+
+ // Read network parameters
+ bool read_parameters(std::istream& stream) {
+ if (!fc_0.read_parameters(stream)) return false;
+ if (!ac_0.read_parameters(stream)) return false;
+ if (!fc_1.read_parameters(stream)) return false;
+ if (!ac_1.read_parameters(stream)) return false;
+ if (!fc_2.read_parameters(stream)) return false;
+ return true;
+ }
+
+ // Read network parameters
+ bool write_parameters(std::ostream& stream) const {
+ if (!fc_0.write_parameters(stream)) return false;
+ if (!ac_0.write_parameters(stream)) return false;
+ if (!fc_1.write_parameters(stream)) return false;
+ if (!ac_1.write_parameters(stream)) return false;
+ if (!fc_2.write_parameters(stream)) return false;
+ return true;
+ }
+
+ std::int32_t propagate(const TransformedFeatureType* transformedFeatures)
+ {
+ constexpr uint64_t alignment = CacheLineSize;
+
+ struct Buffer
+ {
+ alignas(CacheLineSize) decltype(fc_0)::OutputBuffer fc_0_out;
+ alignas(CacheLineSize) decltype(ac_0)::OutputBuffer ac_0_out;
+ alignas(CacheLineSize) decltype(fc_1)::OutputBuffer fc_1_out;
+ alignas(CacheLineSize) decltype(ac_1)::OutputBuffer ac_1_out;
+ alignas(CacheLineSize) decltype(fc_2)::OutputBuffer fc_2_out;
+ };
+
+#if defined(ALIGNAS_ON_STACK_VARIABLES_BROKEN)
+ char bufferRaw[sizeof(Buffer) + alignment];
+ char* bufferRawAligned = align_ptr_up<alignment>(&bufferRaw[0]);
+ Buffer& buffer = *(new (bufferRawAligned) Buffer);
+#else
+ alignas(alignment) Buffer buffer;
+#endif
+
+ fc_0.propagate(transformedFeatures, buffer.fc_0_out);
+ ac_0.propagate(buffer.fc_0_out, buffer.ac_0_out);
+ fc_1.propagate(buffer.ac_0_out, buffer.fc_1_out);
+ ac_1.propagate(buffer.fc_1_out, buffer.ac_1_out);
+ fc_2.propagate(buffer.ac_1_out, buffer.fc_2_out);
+
+ // buffer.fc_0_out[FC_0_OUTPUTS] is such that 1.0 is equal to 127*(1<<WeightScaleBits) in quantized form
+ // but we want 1.0 to be equal to 600*OutputScale
+ std::int32_t fwdOut = int(buffer.fc_0_out[FC_0_OUTPUTS]) * (600*OutputScale) / (127*(1<<WeightScaleBits));
+ std::int32_t outputValue = buffer.fc_2_out[0] + fwdOut;
+
+#if defined(ALIGNAS_ON_STACK_VARIABLES_BROKEN)
+ buffer.~Buffer();
+#endif
+
+ return outputValue;
+ }
+};
} // namespace Stockfish::Eval::NNUE