Update architecture to "SFNNv4". Update network to nn-6877cd24400e.nnue.

[stockfish] / src / nnue / layers / clipped_relu.h

diff --git a/src/nnue/layers/clipped_relu.h b/src/nnue/layers/clipped_relu.h
index 0da5e8210119a5a38e474dfeb5e2bfa2cc3e00e4..ffd2e3b76a94523eec1beb01d6b67bec122a89d2 100644 (file)
--- a/src/nnue/layers/clipped_relu.h
+++ b/src/nnue/layers/clipped_relu.h
@@ -26,51 +26,41 @@
 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) {
@@ -191,9 +181,6 @@ namespace Stockfish::Eval::NNUE::Layers {
 
       return output;
     }
-
-   private:
-    PreviousLayer previousLayer;
   };
 
 }  // namespace Stockfish::Eval::NNUE::Layers