Remove unused return type from propagate()

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

diff --git a/src/nnue/layers/clipped_relu.h b/src/nnue/layers/clipped_relu.h
index 00809c507b3d3cf1eaac5c0f22d4c67054fcf652..d5aa6fbfbd1a2d7640332e3e139aa373e57f4873 100644 (file)
--- a/src/nnue/layers/clipped_relu.h
+++ b/src/nnue/layers/clipped_relu.h
@@ -1,6 +1,6 @@
 /*
   Stockfish, a UCI chess playing engine derived from Glaurung 2.1
-  Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
+  Copyright (C) 2004-2023 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
@@ -26,63 +26,79 @@
 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&) 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);
+    void propagate(
+        const InputType* input, OutputType* output) const {
 
   #if defined(USE_AVX2)
-      constexpr IndexType NumChunks = InputDimensions / SimdWidth;
-      const __m256i Zero = _mm256_setzero_si256();
-      const __m256i Offsets = _mm256_set_epi32(7, 3, 6, 2, 5, 1, 4, 0);
-      const auto in = reinterpret_cast<const __m256i*>(input);
-      const auto out = reinterpret_cast<__m256i*>(output);
-      for (IndexType i = 0; i < NumChunks; ++i) {
-        const __m256i words0 = _mm256_srai_epi16(_mm256_packs_epi32(
-            _mm256_load_si256(&in[i * 4 + 0]),
-            _mm256_load_si256(&in[i * 4 + 1])), WeightScaleBits);
-        const __m256i words1 = _mm256_srai_epi16(_mm256_packs_epi32(
-            _mm256_load_si256(&in[i * 4 + 2]),
-            _mm256_load_si256(&in[i * 4 + 3])), WeightScaleBits);
-        _mm256_store_si256(&out[i], _mm256_permutevar8x32_epi32(_mm256_max_epi8(
-            _mm256_packs_epi16(words0, words1), Zero), Offsets));
+      if constexpr (InputDimensions % SimdWidth == 0) {
+        constexpr IndexType NumChunks = InputDimensions / SimdWidth;
+        const __m256i Zero = _mm256_setzero_si256();
+        const __m256i Offsets = _mm256_set_epi32(7, 3, 6, 2, 5, 1, 4, 0);
+        const auto in = reinterpret_cast<const __m256i*>(input);
+        const auto out = reinterpret_cast<__m256i*>(output);
+        for (IndexType i = 0; i < NumChunks; ++i) {
+          const __m256i words0 = _mm256_srai_epi16(_mm256_packs_epi32(
+              _mm256_load_si256(&in[i * 4 + 0]),
+              _mm256_load_si256(&in[i * 4 + 1])), WeightScaleBits);
+          const __m256i words1 = _mm256_srai_epi16(_mm256_packs_epi32(
+              _mm256_load_si256(&in[i * 4 + 2]),
+              _mm256_load_si256(&in[i * 4 + 3])), WeightScaleBits);
+          _mm256_store_si256(&out[i], _mm256_permutevar8x32_epi32(_mm256_max_epi8(
+              _mm256_packs_epi16(words0, words1), Zero), Offsets));
+        }
+      } else {
+        constexpr IndexType NumChunks = InputDimensions / (SimdWidth / 2);
+        const __m128i Zero = _mm_setzero_si128();
+        const auto in = reinterpret_cast<const __m128i*>(input);
+        const auto out = reinterpret_cast<__m128i*>(output);
+        for (IndexType i = 0; i < NumChunks; ++i) {
+          const __m128i words0 = _mm_srai_epi16(_mm_packs_epi32(
+              _mm_load_si128(&in[i * 4 + 0]),
+              _mm_load_si128(&in[i * 4 + 1])), WeightScaleBits);
+          const __m128i words1 = _mm_srai_epi16(_mm_packs_epi32(
+              _mm_load_si128(&in[i * 4 + 2]),
+              _mm_load_si128(&in[i * 4 + 3])), WeightScaleBits);
+          const __m128i packedbytes = _mm_packs_epi16(words0, words1);
+          _mm_store_si128(&out[i], _mm_max_epi8(packedbytes, Zero));
+        }
       }
-      constexpr IndexType Start = NumChunks * SimdWidth;
+      constexpr IndexType Start =
+        InputDimensions % SimdWidth == 0
+        ? InputDimensions / SimdWidth * SimdWidth
+        : InputDimensions / (SimdWidth / 2) * (SimdWidth / 2);
 
   #elif defined(USE_SSE2)
       constexpr IndexType NumChunks = InputDimensions / SimdWidth;
@@ -154,11 +170,7 @@ namespace Stockfish::Eval::NNUE::Layers {
         output[i] = static_cast<OutputType>(
             std::max(0, std::min(127, input[i] >> WeightScaleBits)));
       }
-      return output;
     }
-
-   private:
-    PreviousLayer previousLayer;
   };
 
 }  // namespace Stockfish::Eval::NNUE::Layers