Workflow for compiling SFMnps (NNUEv5 net)

.github/workflows/SFMnps_ArmWinBinariesUpload.yml

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+name: SFMnpsArmWinBinariesUpload
+on:
+  workflow_dispatch:
+jobs:
+  SFnpsArmWinBuilds:
+    name: ${{ matrix.config.name }}
+    runs-on: ${{ matrix.config.os }}
+    env:
+      COMPILER: ${{ matrix.config.compiler }}
+      COMP: ${{ matrix.config.comp }} 
+    strategy:
+      matrix:
+        config:
+          - name: Ubuntu 22.04 NDK armv8
+            os: ubuntu-22.04
+            compiler: aarch64-linux-android21-clang++
+            comp: ndk
+            run_armv8_build: true
+            shell: bash {0}
+
+          - name: Windows 2022 Mingw-w64 GCC x86_64
+            os: windows-2022
+            compiler: g++
+            comp: mingw
+            run_win11_build: true
+            msys_sys: mingw64
+            msys_env: x86_64-gcc
+            shell: msys2 {0}   
+
+    defaults:
+      run:
+        working-directory: src
+        shell: ${{ matrix.config.shell }}
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+
+      - name: Setup msys and install required packages
+        if: runner.os == 'Windows'
+        uses: msys2/setup-msys2@v2
+        with:
+          msystem: ${{ matrix.config.msys_sys }}
+          install: mingw-w64-${{ matrix.config.msys_env }} make git
+
+      - name: Download the MEDIUM network from the fishtest framework
+        run: |
+            cp evaluateM.h evaluate.h
+            cd nnue
+            cp nnue_architectureM.h nnue_architecture.h
+            cd ..
+            make net
+
+      - name: armv8 build
+        if: ${{ matrix.config.run_armv8_build }}
+        run: |
+            export PATH=$ANDROID_NDK_HOME:$PATH
+            export PATH=$ANDROID_NDK_HOME/toolchains/llvm/prebuilt/linux-x86_64/bin:$PATH
+
+            cp nn-*.nnue ../jni
+            cd ../jni
+            cp Application_v8.mk Application.mk
+            ndk-build
+            cd ../libs/arm64-v8a
+            cp Stockfish ../../SFMnps_armv8
+           
+      - uses: xresloader/upload-to-github-release@v1
+        if: ${{ matrix.config.run_armv8_build }}
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        with:
+          overwrite: true
+          file: "SFMnps_armv8" 
+          update_latest_release: true
+
+      - uses: actions/upload-artifact@v3
+        if: ${{ matrix.config.run_armv8_build }}
+        with:
+          name: SFMnps-armv8
+          path: SFMnps_armv8
+
+      - name: win11 build
+        if: ${{ matrix.config.run_win11_build }}
+        run: |
+          make clean
+          make -j3 profile-build ARCH=x86-64-modern COMP=$COMP
+          make strip ARCH=x86-64-modern COMP=$COMP
+          cp stockfish.exe ../SFMnps_modern.exe
+        
+      - uses: xresloader/upload-to-github-release@v1
+        if: ${{ matrix.config.run_win11_build }}
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        with:
+          overwrite: true
+          file: "SFMnps_modern.exe"
+          update_latest_release: true
+
+      - uses: actions/upload-artifact@v3
+        if: ${{ matrix.config.run_win11_build }}
+        with:
+          name: SFMnps-modern
+          path: SFMnps_modern.exe
+
+

src/evaluateM.h

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+/*
+  Stockfish, a UCI chess playing engine derived from Glaurung 2.1
+  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
+  the Free Software Foundation, either version 3 of the License, or
+  (at your option) any later version.
+
+  Stockfish is distributed in the hope that it will be useful,
+  but WITHOUT ANY WARRANTY; without even the implied warranty of
+  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+  GNU General Public License for more details.
+
+  You should have received a copy of the GNU General Public License
+  along with this program.  If not, see <http://www.gnu.org/licenses/>.
+*/
+
+#ifndef EVALUATE_H_INCLUDED
+#define EVALUATE_H_INCLUDED
+
+#include <string>
+#include <optional>
+
+#include "types.h"
+
+namespace Stockfish {
+
+class Position;
+
+namespace Eval {
+
+  std::string trace(Position& pos);
+  Value evaluate(const Position& pos);
+
+  extern bool useNNUE;
+  extern std::string currentEvalFileName;
+
+  // The default net name MUST follow the format nn-[SHA256 first 12 digits].nnue
+  // for the build process (profile-build and fishtest) to work. Do not change the
+  // name of the macro, as it is used in the Makefile.
+  #define EvalFileDefaultName   "nn-e1fb1ade4432.nnue"
+
+  namespace NNUE {
+
+    extern int RandomEvalPerturb;
+    extern int waitms;
+
+    void init();
+    void verify();
+
+  } // namespace NNUE
+
+} // namespace Eval
+
+} // namespace Stockfish
+
+#endif // #ifndef EVALUATE_H_INCLUDED

src/nnue/nnue_architectureM.h

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+/*
+  Stockfish, a UCI chess playing engine derived from Glaurung 2.1
+  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
+  the Free Software Foundation, either version 3 of the License, or
+  (at your option) any later version.
+
+  Stockfish is distributed in the hope that it will be useful,
+  but WITHOUT ANY WARRANTY; without even the implied warranty of
+  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+  GNU General Public License for more details.
+
+  You should have received a copy of the GNU General Public License
+  along with this program.  If not, see <http://www.gnu.org/licenses/>.
+*/
+
+// Input features and network structure used in NNUE evaluation function
+
+#ifndef NNUE_ARCHITECTURE_H_INCLUDED
+#define NNUE_ARCHITECTURE_H_INCLUDED
+
+#include <memory>
+
+#include "nnue_common.h"
+
+#include "features/half_ka_v2_hm.h"
+
+#include "layers/affine_transform.h"
+#include "layers/clipped_relu.h"
+#include "layers/sqr_clipped_relu.h"
+
+#include "../misc.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;
+
+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::SqrClippedReLU<FC_0_OUTPUTS + 1> ac_sqr_0;
+  Layers::ClippedReLU<FC_0_OUTPUTS + 1> ac_0;
+  Layers::AffineTransform<FC_0_OUTPUTS * 2, 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)
+  {
+    struct alignas(CacheLineSize) Buffer
+    {
+      alignas(CacheLineSize) decltype(fc_0)::OutputBuffer fc_0_out;
+      alignas(CacheLineSize) decltype(ac_sqr_0)::OutputType ac_sqr_0_out[ceil_to_multiple<IndexType>(FC_0_OUTPUTS * 2, 32)];
+      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(__clang__) && (__APPLE__)
+    // workaround for a bug reported with xcode 12
+    static thread_local auto tlsBuffer = std::make_unique<Buffer>();
+    // Access TLS only once, cache result.
+    Buffer& buffer = *tlsBuffer;
+#else
+    alignas(CacheLineSize) static thread_local Buffer buffer;
+#endif
+
+    fc_0.propagate(transformedFeatures, buffer.fc_0_out);
+    ac_sqr_0.propagate(buffer.fc_0_out, buffer.ac_sqr_0_out);
+    ac_0.propagate(buffer.fc_0_out, buffer.ac_0_out);
+    std::memcpy(buffer.ac_sqr_0_out + FC_0_OUTPUTS, buffer.ac_0_out, FC_0_OUTPUTS * sizeof(decltype(ac_0)::OutputType));
+    fc_1.propagate(buffer.ac_sqr_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
+
+#endif // #ifndef NNUE_ARCHITECTURE_H_INCLUDED