Merge remote-tracking branch 'upstream/master' into HEAD

[stockfish] / src / nnue / nnue_architecture.h

diff --git a/src/nnue/nnue_architecture.h b/src/nnue/nnue_architecture.h
index 91cdc4bda23158b9a418d535cb14063a3b3c2b90..879a39cdbe68cfb4a4a6e3b64a457909520623c9 100644 (file)
--- a/src/nnue/nnue_architecture.h
+++ b/src/nnue/nnue_architecture.h
@@ -1,6 +1,6 @@
 /*
   Stockfish, a UCI chess playing engine derived from Glaurung 2.1
-  Copyright (C) 2004-2020 The Stockfish developers (see AUTHORS file)
+  Copyright (C) 2004-2021 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
@@ -21,18 +21,40 @@
 #ifndef NNUE_ARCHITECTURE_H_INCLUDED
 #define NNUE_ARCHITECTURE_H_INCLUDED
 
-// Defines the network structure
-#include "architectures/halfkp_256x2-32-32.h"
+#include "nnue_common.h"
 
-namespace Eval::NNUE {
+#include "features/half_ka_v2.h"
 
-  static_assert(kTransformedFeatureDimensions % kMaxSimdWidth == 0, "");
-  static_assert(Network::kOutputDimensions == 1, "");
-  static_assert(std::is_same<Network::OutputType, std::int32_t>::value, "");
+#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;
+
+  // Number of input feature dimensions after conversion
+  constexpr IndexType TransformedFeatureDimensions = 512;
+  constexpr IndexType PSQTBuckets = 8;
+  constexpr IndexType LayerStacks = 8;
+
+  namespace Layers {
 
-  // Trigger for full calculation instead of difference calculation
-  constexpr auto kRefreshTriggers = RawFeatures::kRefreshTriggers;
+    // Define network structure
+    using InputLayer = InputSlice<TransformedFeatureDimensions * 2>;
+    using HiddenLayer1 = ClippedReLU<AffineTransform<InputLayer, 16>>;
+    using HiddenLayer2 = ClippedReLU<AffineTransform<HiddenLayer1, 32>>;
+    using OutputLayer = AffineTransform<HiddenLayer2, 1>;
+
+  }  // namespace Layers
+
+  using Network = Layers::OutputLayer;
+
+  static_assert(TransformedFeatureDimensions % MaxSimdWidth == 0, "");
+  static_assert(Network::OutputDimensions == 1, "");
+  static_assert(std::is_same<Network::OutputType, std::int32_t>::value, "");
 
-}  // namespace Eval::NNUE
+}  // namespace Stockfish::Eval::NNUE
 
 #endif // #ifndef NNUE_ARCHITECTURE_H_INCLUDED