X-Git-Url: https://git.sesse.net/?a=blobdiff_plain;ds=sidebyside;f=src%2Fnnue%2Fnnue_architecture.h;h=b4f65364c2e1c4bb727721ab3922550b75685e41;hb=5f781d366e0f4369ec12e36c9978ad63ffa32235;hp=8867fac72fc2f39f12ecf8bbc89673515f63998d;hpb=ad926d34c0105d523bfa5cb92cbcf9f337d54c08;p=stockfish

diff --git a/src/nnue/nnue_architecture.h b/src/nnue/nnue_architecture.h
index 8867fac7..b4f65364 100644
--- a/src/nnue/nnue_architecture.h
+++ b/src/nnue/nnue_architecture.h
@@ -25,35 +25,107 @@
 
 #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 + 1> 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;
+
+      Buffer()
+      {
+          std::memset(this, 0, sizeof(*this));
+      }
+    };
+
+#if defined(ALIGNAS_ON_STACK_VARIABLES_BROKEN)
+    static thread_local char bufferRaw[sizeof(Buffer) + alignment];
+    static thread_local char* bufferRawAligned = align_ptr_up<alignment>(&bufferRaw[0]);
+    static thread_local Buffer& buffer = *(new (bufferRawAligned) Buffer);
+#else
+    alignas(alignment) static thread_local 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;
+
+    return outputValue;
+  }
+};
 
 }  // namespace Stockfish::Eval::NNUE