Merge pull request #58 from ewanwm/feature_more_relaxed_linter

Chill the linter out a bit

.github/workflows/cpp-linter.yaml

@@ -26,7 +26,7 @@ jobs:
 
       - name: Configure CMake
         # Configure CMake so we can get a compile_commands.json so that clang-tidy won't be angry about missing headers any more
-        run: cmake -DCMAKE_EXPORT_COMPILE_COMMANDS=ON -DCMAKE_PREFIX_PATH=`python3 -c 'import torch;print(torch.utils.cmake_prefix_path)'` -B ${{github.workspace}} -DCMAKE_BUILD_TYPE=${{env.BUILD_TYPE}}
+        run: cmake -DCMAKE_EXPORT_COMPILE_COMMANDS=ON -DNT_ENABLE_BENCHMARKING=ON -DCMAKE_PREFIX_PATH=`python3 -c 'import torch;print(torch.utils.cmake_prefix_path)'` -B ${{github.workspace}} -DCMAKE_BUILD_TYPE=${{env.BUILD_TYPE}}
 
       ## horrible little hack to put spdlog include files somewhere that clang-tidy can find them
       ## it doesn't seem to like headers included by cpm
@@ -43,10 +43,12 @@ jobs:
         with:
           style: "Microsoft"
           ignore: '.github|_deps|CMakeFiles|spdlog*'
-          format-review: true
           tidy-review: true
+          step-summary: true
           files-changed-only: true
-          thread-comments: true
+
+          thread-comments: false
+          file-annotations: false
           tidy-checks: 'boost-*,bugprone-*,performance-*,readability-*,portability-*,modernize-*,clang-analyzer-*,cppcoreguidelines-*,-modernize-use-trailing-return-type,-modernize-use-emplace,-readability-redundant-access-specifiers'
 
       - name: Fail fast?!

benchmarks/benchmarks.cpp

@@ -4,6 +4,23 @@
 #include <nuTens/propagator/propagator.hpp>
 #include <nuTens/tensors/tensor.hpp>
 
+// The random seed to use for the RNG
+// want this to be fixed for reproducibility
+const int randSeed = 123;
+
+// set the PMNS parameters to use
+// Will very likely change the benchmark so that energies are fixed
+// and these get randomised but for now just set them here
+const float m1 = 0.1;
+const float m2 = 0.2;
+const float m3 = 0.3;
+
+const float th12 = 0.12;
+const float th23 = 0.23;
+const float th13 = 0.13;
+
+const float dcp = 0.5;
+
 Tensor buildPMNS(const Tensor &theta12, const Tensor &theta13, const Tensor &theta23, const Tensor &deltaCP)
 {
     // set up the three matrices to build the PMNS matrix
@@ -20,8 +37,9 @@ Tensor buildPMNS(const Tensor &theta12, const Tensor &theta13, const Tensor &the
 
     M2.setValue({0, 1, 1}, 1.0);
     M2.setValue({0, 0, 0}, Tensor::cos(theta13));
-    M2.setValue({0, 0, 2}, Tensor::mul(Tensor::sin(theta13), Tensor::exp(Tensor::scale(deltaCP, -1.0J))));
-    M2.setValue({0, 2, 0}, -Tensor::mul(Tensor::sin(theta13), Tensor::exp(Tensor::scale(deltaCP, 1.0J))));
+    std::complex<float> i(0.0, 1.0);
+    M2.setValue({0, 0, 2}, Tensor::mul(Tensor::sin(theta13), Tensor::exp(Tensor::scale(deltaCP, -i))));
+    M2.setValue({0, 2, 0}, -Tensor::mul(Tensor::sin(theta13), Tensor::exp(Tensor::scale(deltaCP, i))));
     M2.setValue({0, 2, 2}, Tensor::cos(theta13));
     M2.requiresGrad(true);
 
@@ -39,7 +57,7 @@ Tensor buildPMNS(const Tensor &theta12, const Tensor &theta13, const Tensor &the
     return PMNS;
 }
 
-static void batchedOscProbs(const Propagator &prop, int batchSize, int nBatches)
+static void batchedOscProbs(const Propagator &prop, long batchSize, long nBatches)
 {
     for (int _ = 0; _ < nBatches; _++)
     {
@@ -58,12 +76,12 @@ static void BM_vacuumOscillations(benchmark::State &state)
 {
 
     // set up the inputs
-    Tensor masses = Tensor({0.1, 0.2, 0.3}, NTdtypes::kFloat).requiresGrad(false).addBatchDim();
+    Tensor masses = Tensor({m1, m2, m3}, NTdtypes::kFloat).requiresGrad(false).addBatchDim();
 
-    Tensor theta23 = Tensor({0.23}).dType(NTdtypes::kComplexFloat).requiresGrad(false);
-    Tensor theta13 = Tensor({0.13}).dType(NTdtypes::kComplexFloat).requiresGrad(false);
-    Tensor theta12 = Tensor({0.12}).dType(NTdtypes::kComplexFloat).requiresGrad(false);
-    Tensor deltaCP = Tensor({0.5}).dType(NTdtypes::kComplexFloat).requiresGrad(false);
+    Tensor theta23 = Tensor({th23}).dType(NTdtypes::kComplexFloat).requiresGrad(false);
+    Tensor theta13 = Tensor({th13}).dType(NTdtypes::kComplexFloat).requiresGrad(false);
+    Tensor theta12 = Tensor({th12}).dType(NTdtypes::kComplexFloat).requiresGrad(false);
+    Tensor deltaCP = Tensor({dcp}).dType(NTdtypes::kComplexFloat).requiresGrad(false);
 
     Tensor PMNS = buildPMNS(theta12, theta13, theta23, deltaCP);
 
@@ -73,8 +91,10 @@ static void BM_vacuumOscillations(benchmark::State &state)
     vacuumProp.setMasses(masses);
 
     // seed the random number generator for the energies
-    std::srand(123);
+    std::srand(randSeed);
 
+    // linter gets angry about this as _ is never used :)))
+    // NOLINTNEXTLINE
     for (auto _ : state)
     {
         // This code gets timed
@@ -86,12 +106,12 @@ static void BM_constMatterOscillations(benchmark::State &state)
 {
 
     // set up the inputs
-    Tensor masses = Tensor({0.1, 0.2, 0.3}, NTdtypes::kFloat).requiresGrad(false).addBatchDim();
+    Tensor masses = Tensor({m1, m2, m3}, NTdtypes::kFloat).requiresGrad(false).addBatchDim();
 
-    Tensor theta23 = Tensor({0.23}).dType(NTdtypes::kComplexFloat).requiresGrad(false);
-    Tensor theta13 = Tensor({0.13}).dType(NTdtypes::kComplexFloat).requiresGrad(false);
-    Tensor theta12 = Tensor({0.12}).dType(NTdtypes::kComplexFloat).requiresGrad(false);
-    Tensor deltaCP = Tensor({0.5}).dType(NTdtypes::kComplexFloat).requiresGrad(false);
+    Tensor theta23 = Tensor({th23}).dType(NTdtypes::kComplexFloat).requiresGrad(false);
+    Tensor theta13 = Tensor({th13}).dType(NTdtypes::kComplexFloat).requiresGrad(false);
+    Tensor theta12 = Tensor({th12}).dType(NTdtypes::kComplexFloat).requiresGrad(false);
+    Tensor deltaCP = Tensor({dcp}).dType(NTdtypes::kComplexFloat).requiresGrad(false);
 
     Tensor PMNS = buildPMNS(theta12, theta13, theta23, deltaCP);
 
@@ -103,8 +123,10 @@ static void BM_constMatterOscillations(benchmark::State &state)
     matterProp.setMatterSolver(matterSolver);
 
     // seed the random number generator for the energies
-    std::srand(123);
+    std::srand(randSeed);
 
+    // linter gets angry about this as _ is never used :)))
+    // NOLINTNEXTLINE
     for (auto _ : state)
     {
         // This code gets timed
@@ -113,10 +135,13 @@ static void BM_constMatterOscillations(benchmark::State &state)
 }
 
 // Register the function as a benchmark
+// NOLINTNEXTLINE
 BENCHMARK(BM_vacuumOscillations)->Name("Vacuum Oscillations")->Args({1 << 10, 1 << 10});
 
 // Register the function as a benchmark
+// NOLINTNEXTLINE
 BENCHMARK(BM_constMatterOscillations)->Name("Const Density Oscillations")->Args({1 << 10, 1 << 10});
 
 // Run the benchmark
+// NOLINTNEXTLINE
 BENCHMARK_MAIN();

nuTens/propagator/const-density-solver.hpp

@@ -80,7 +80,7 @@ class ConstDensityMatterSolver : public BaseMatterSolver
     /// shape should look like {Nbatches, 1, 1}.
     /// @param[out] eigenvectors The returned eigenvectors
     /// @param[out] eigenvalues The corresponding eigenvalues
-    void calculateEigenvalues(const Tensor &energies, Tensor &eigenvectors, Tensor &eigenvalues);
+    void calculateEigenvalues(const Tensor &energies, Tensor &eigenvectors, Tensor &eigenvalues) override;
 
   private:
     Tensor PMNS;

nuTens/tensors/tensor.hpp

@@ -63,7 +63,7 @@ class Tensor
 
     /// @brief Construct a 1-d array with specified values
     /// @arg values The values to include in the tensor
-    Tensor(std::vector<float> values, NTdtypes::scalarType type = NTdtypes::kFloat,
+    Tensor(const std::vector<float> &values, NTdtypes::scalarType type = NTdtypes::kFloat,
            NTdtypes::deviceType device = NTdtypes::kCPU, bool requiresGrad = true);
 
     /// @brief Construct an identity tensor (has to be a 2d square tensor)
@@ -164,10 +164,18 @@ class Tensor
     /// @arg s The scalar
     /// @arg t The tensor
     static Tensor scale(const Tensor &t, float s);
+    /// @brief Scale a matrix by some scalar
+    /// @arg s The scalar
+    /// @arg t The tensor
+    static Tensor scale(const Tensor &t, double s);
     /// @brief Scale a matrix by some complex scalar
     /// @arg s The scalar
     /// @arg t The tensor
     static Tensor scale(const Tensor &t, std::complex<float> s);
+    /// @brief Scale a matrix by some complex scalar
+    /// @arg s The scalar
+    /// @arg t The tensor
+    static Tensor scale(const Tensor &t, std::complex<double> s);
 
     // ############################################
     // ################ Inlines ###################

nuTens/tensors/torch-tensor.cpp

@@ -6,7 +6,8 @@ std::string Tensor::getTensorLibrary()
     return "PyTorch";
 }
 
-Tensor::Tensor(std::vector<float> values, NTdtypes::scalarType type, NTdtypes::deviceType device, bool requiresGrad)
+Tensor::Tensor(const std::vector<float> &values, NTdtypes::scalarType type, NTdtypes::deviceType device,
+               bool requiresGrad)
 {
     NT_PROFILE();
 
@@ -298,6 +299,15 @@ Tensor Tensor::scale(const Tensor &t, float s)
     return ret;
 }
 
+Tensor Tensor::scale(const Tensor &t, double s)
+{
+    NT_PROFILE();
+
+    Tensor ret;
+    ret.setTensor(torch::multiply(t._tensor, s));
+    return ret;
+}
+
 Tensor Tensor::scale(const Tensor &t, std::complex<float> s)
 {
     NT_PROFILE();
@@ -307,6 +317,15 @@ Tensor Tensor::scale(const Tensor &t, std::complex<float> s)
     return ret;
 }
 
+Tensor Tensor::scale(const Tensor &t, std::complex<double> s)
+{
+    NT_PROFILE();
+
+    Tensor ret;
+    ret.setTensor(torch::multiply(t._tensor, c10::complex<double>(s.real(), s.imag())));
+    return ret;
+}
+
 void Tensor::matmul_(const Tensor &t2)
 {
     NT_PROFILE();