mirror of
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Merge branch 'feature/distil' of github.com:mmphys/Grid into feature/distil
This commit is contained in:
ferben
commit
97c6f770b4
@ -36,13 +36,19 @@ Author: Guido Cossu <guido.cossu@ed.ac.uk>
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#include <Grid/Eigen/unsupported/CXX11/Tensor>
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namespace Grid {
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// TODO Support Grid::complex from GPU port
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template<typename T> using Grid_complex = std::complex<T>;
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// Returns original type, except for Grid_complex, where it returns the underlying type
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template<typename T> struct RealType { using type = T; };
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template<typename T> struct RealType<Grid_complex<T>> { using type = T; };
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namespace EigenIO {
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template<typename T> struct is_complex : public std::false_type {};
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template<typename T> struct is_complex<std::complex<T>>
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template<typename T> struct is_complex<Grid_complex<T>>
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: std::integral_constant<bool, std::is_arithmetic<T>::value> {};
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// Eigen tensors can be composed of arithmetic scalar and complex types
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// TODO Support Grid::comples from GPU port
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template<typename T> struct is_scalar : std::integral_constant<bool,
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std::is_arithmetic<T>::value || is_complex<T>::value> {};
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@ -88,11 +94,11 @@ namespace Grid {
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template <typename T, typename C = void> struct Traits {}; // C needed for specialisation
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// This defines the bottom level - i.e. it's a description of the underlying scalar
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template <typename T> struct Traits<T, typename std::enable_if<is_scalar<T>::value, void>::type> {
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static constexpr unsigned int depth = 0; // How many levels of Grid Tensor there are (TensorLevel)
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using scalar_type = T; // Type of the underlying scalar
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using scalar_real = typename RealType<scalar_type>::type; // real type underlying scalar_type
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static constexpr unsigned int rank = 0; // The rank of the grid tensor (i.e. how many indices used)
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static constexpr unsigned int rank_non_trivial = 0; // As per rank, but excludes those of dimension 1
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static constexpr unsigned int count = 1; // total number of elements (i.e. product of dimensions)
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using scalar_type = T; // Type of the underlying scalar
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static constexpr std::size_t scalar_size = sizeof(T); // Size of the underlying scalar in bytes
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static constexpr std::size_t size = scalar_size * count; // total size of elements in bytes
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static constexpr std::size_t Dimension(unsigned int dim) { return 0; } // Dimension size
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@ -114,11 +120,11 @@ namespace Grid {
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// count = 48
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};
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template <typename T> struct Traits<iScalar<T>> {
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static constexpr unsigned int depth = 1 + Traits<T>::depth;
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static constexpr unsigned int rank = 0 + Traits<T>::rank;
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using scalar_type = typename Traits<T>::scalar_type;
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using scalar_real = typename RealType<scalar_type>::type;
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static constexpr unsigned int rank = 1 + Traits<T>::rank;
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static constexpr unsigned int rank_non_trivial = 0 + Traits<T>::rank_non_trivial;
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static constexpr unsigned int count = 1 * Traits<T>::count;
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using scalar_type = typename Traits<T>::scalar_type;
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static constexpr std::size_t scalar_size = Traits<T>::scalar_size;
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static constexpr std::size_t size = scalar_size * count;
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static constexpr std::size_t Dimension(unsigned int dim) {
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@ -127,11 +133,11 @@ namespace Grid {
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return Traits<T>::DimensionNT(dim); }
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};
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template <typename T, int N> struct Traits<iVector<T, N>> {
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static constexpr unsigned int depth = 1 + Traits<T>::depth;
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using scalar_type = typename Traits<T>::scalar_type;
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using scalar_real = typename RealType<scalar_type>::type;
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static constexpr unsigned int rank = 1 + Traits<T>::rank;
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static constexpr unsigned int rank_non_trivial = (N>1 ? 1 : 0) + Traits<T>::rank_non_trivial;
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static constexpr unsigned int count = N * Traits<T>::count;
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using scalar_type = typename Traits<T>::scalar_type;
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static constexpr std::size_t scalar_size = Traits<T>::scalar_size;
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static constexpr std::size_t size = scalar_size * count;
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static constexpr std::size_t Dimension(unsigned int dim) {
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@ -141,11 +147,11 @@ namespace Grid {
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}
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};
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template <typename T, int N> struct Traits<iMatrix<T, N>> {
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static constexpr unsigned int depth = 1 + Traits<T>::depth;
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using scalar_type = typename Traits<T>::scalar_type;
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using scalar_real = typename RealType<scalar_type>::type;
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static constexpr unsigned int rank = 2 + Traits<T>::rank;
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static constexpr unsigned int rank_non_trivial = (N>1 ? 2 : 0) + Traits<T>::rank_non_trivial;
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static constexpr unsigned int count = N * N * Traits<T>::count;
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using scalar_type = typename Traits<T>::scalar_type;
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static constexpr std::size_t scalar_size = Traits<T>::scalar_size;
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static constexpr std::size_t size = scalar_size * count;
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static constexpr std::size_t Dimension(unsigned int dim) {
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@ -230,16 +236,22 @@ namespace Grid {
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}
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}
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// Used for sequential initialisations
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template<typename T> constexpr T Flag = 1;
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template<typename T> constexpr std::complex<T> Flag<std::complex<T>> {1, -1};
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// Returns the type of the real part of an arithmetic type
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template<typename T> struct RealType { using type = T; };
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template<typename T> struct RealType<std::complex<T>> { using type = T; };
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// Sequential initialisation of tensors
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// Would have preferred to define template variables for this, but that's c++ 17
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template <typename ETensor>
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typename std::enable_if<EigenIO::is_tensor<ETensor>::value && !EigenIO::is_complex<typename EigenIO::Traits<typename ETensor::Scalar>::scalar_type>::value, void>::type
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SequentialInit( ETensor &ET, typename EigenIO::Traits<typename ETensor::Scalar>::scalar_type Inc = 1 )
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{
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using Traits = EigenIO::Traits<typename ETensor::Scalar>;
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using scalar_type = typename Traits::scalar_type;
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for_all( ET, [&](scalar_type &c, typename ETensor::Index n, const std::array<size_t, ETensor::NumIndices + Traits::rank_non_trivial> &Dims ) {
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c = Inc * static_cast<scalar_type>(n);
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} );
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}
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template <typename ETensor>
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typename std::enable_if<EigenIO::is_tensor<ETensor>::value, void>::type
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Sequential_Init( ETensor &ET, typename EigenIO::Traits<typename ETensor::Scalar>::scalar_type Inc = Flag<typename EigenIO::Traits<typename ETensor::Scalar>::scalar_type> )
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typename std::enable_if<EigenIO::is_tensor<ETensor>::value && EigenIO::is_complex<typename EigenIO::Traits<typename ETensor::Scalar>::scalar_type>::value, void>::type
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SequentialInit( ETensor &ET, typename EigenIO::Traits<typename ETensor::Scalar>::scalar_type Inc={1,-1})
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{
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using Traits = EigenIO::Traits<typename ETensor::Scalar>;
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using scalar_type = typename Traits::scalar_type;
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@ -361,10 +373,7 @@ namespace Grid {
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template <typename U, int N>
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void write(const std::string &s, const iMatrix<U, N> &output);
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template <typename ETensor>
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typename std::enable_if<EigenIO::is_tensor_of_scalar<ETensor>::value, void>::type
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write(const std::string &s, const ETensor &output);
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template <typename ETensor>
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typename std::enable_if<EigenIO::is_tensor_of_container<ETensor>::value, void>::type
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typename std::enable_if<EigenIO::is_tensor<ETensor>::value, void>::type
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write(const std::string &s, const ETensor &output);
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void scientificFormat(const bool set);
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@ -487,127 +496,63 @@ namespace Grid {
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upcast->writeDefault(s, tensorToVec(output));
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}
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// Eigen::Tensors of arithmetic/complex base type
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template <typename T>
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template <typename ETensor>
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typename std::enable_if<EigenIO::is_tensor_of_scalar<ETensor>::value, void>::type
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Writer<T>::write(const std::string &s, const ETensor &output)
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{
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const typename ETensor::Index NumElements{output.size()};
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assert( NumElements > 0 );
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if( NumElements == 1 )
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upcast->writeDefault(s, * output.data());
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else {
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// We're only interested in non-trivial dimensions (i.e. dimensions > 1)
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unsigned int TrivialDimCount{0};
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std::vector<size_t> NonTrivialDims;
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NonTrivialDims.reserve(output.NumDimensions); // Make sure we only do one malloc
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for(auto i = 0; i < output.NumDimensions; i++ ) {
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auto dim = output.dimension(i);
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if( dim <= 1 ) {
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TrivialDimCount++;
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assert( dim == 1 ); // Not expecting dimension to be <= 0
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} else {
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size_t s = static_cast<size_t>(dim);
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assert( s == dim ); // check we didn't lose anything in the conversion
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NonTrivialDims.push_back(s);
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}
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}
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// NB: NumElements > 1 implies this is not a scalar, so some dims should be left
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assert( output.NumDimensions > TrivialDimCount );
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// If the Tensor isn't in Row-Major order, then we'll need to copy it's data
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const bool CopyData{ETensor::Layout != Eigen::StorageOptions::RowMajor};
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using Scalar = typename ETensor::Scalar;
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const Scalar * pWriteBuffer;
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Scalar * pCopyBuffer = nullptr;
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if( !CopyData )
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pWriteBuffer = output.data();
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else {
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// Regardless of the Eigen::Tensor storage order, the copy will be Row Major
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pCopyBuffer = static_cast<Scalar *>(malloc(sizeof(Scalar) * NumElements));
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pWriteBuffer = pCopyBuffer;
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std::array<typename ETensor::Index, ETensor::NumIndices> MyIndex;
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for( auto &idx : MyIndex ) idx = 0;
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for( typename ETensor::Index n = 0; n < NumElements; n++ ) {
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pCopyBuffer[n] = output( MyIndex );
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// Now increment the index
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for( int i = output.NumDimensions - 1; i >= 0 && ++MyIndex[i] == output.dimension(i); i-- )
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MyIndex[i] = 0;
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}
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}
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upcast->template writeMultiDim<typename ETensor::Scalar>(s, NonTrivialDims, pWriteBuffer, NumElements);
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if( pCopyBuffer ) free( pCopyBuffer );
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}
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}
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// Eigen::Tensors of Grid tensors (iScalar, iVector, iMatrix)
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template <typename T>
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template <typename ETensor/*, typename U, int N*/>
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typename std::enable_if<EigenIO::is_tensor_of_container<ETensor>::value, void>::type
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typename std::enable_if<EigenIO::is_tensor<ETensor>::value, void>::type
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Writer<T>::write(const std::string &s, const ETensor &output)
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{
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const typename ETensor::Index NumElements{output.size()};
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using Index = typename ETensor::Index;
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using Container = typename ETensor::Scalar; // NB: could be same as Scalar
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using Traits = EigenIO::Traits<Container>;
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using Scalar = typename Traits::scalar_type; // type of the underlying scalar
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constexpr unsigned int TensorRank{ETensor::NumIndices};
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constexpr unsigned int ContainerRank{Traits::rank}; // Only non-zero for containers
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constexpr unsigned int TotalRank{TensorRank + ContainerRank};
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const Index NumElements{output.size()};
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assert( NumElements > 0 );
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if( NumElements == 1 )
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upcast->writeDefault(s, tensorToVec(* output.data()));
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else {
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// We're only interested in non-trivial dimensions (i.e. dimensions > 1)
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unsigned int TrivialDimCount{0};
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std::vector<size_t> NonTrivialDims;
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NonTrivialDims.reserve(output.NumDimensions + EigenIO::Traits<typename ETensor::Scalar>::rank_non_trivial); // Make sure we only do one malloc
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for(auto i = 0; i < output.NumDimensions; i++ ) {
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auto dim = output.dimension(i);
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if( dim <= 1 ) {
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TrivialDimCount++;
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assert( dim == 1 ); // Not expecting dimension to be <= 0
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} else {
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size_t s = static_cast<size_t>(dim);
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assert( s == dim ); // check we didn't lose anything in the conversion
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NonTrivialDims.push_back(s);
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}
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}
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// NB: NumElements > 1 implies this is not a scalar, so some dims should be left
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assert( output.NumDimensions > TrivialDimCount );
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// Now add the extra dimensions, based on object zero
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typename TensorToVec<typename ETensor::Scalar>::type ttv = tensorToVec(* output.data());
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Flatten<typename TensorToVec<typename ETensor::Scalar>::type> f(ttv);
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const std::vector<size_t> & ExtraDims{f.getDim()};
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assert(ExtraDims.size() == EigenIO::Traits<typename ETensor::Scalar>::rank_non_trivial);
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size_t ExtraCount{1};
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for( auto i : ExtraDims ) {
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assert( i > 0 );
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ExtraCount *= i;
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NonTrivialDims.push_back(i);
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}
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assert(EigenIO::Traits<typename ETensor::Scalar>::count == ExtraCount);
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assert(EigenIO::Traits<typename ETensor::Scalar>::size == sizeof( typename ETensor::Scalar ));
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// If the Tensor isn't in Row-Major order, then we'll need to copy it's data
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const bool CopyData{ETensor::Layout != Eigen::StorageOptions::RowMajor};
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using Scalar = typename ETensor::Scalar::scalar_type;
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const Scalar * pWriteBuffer;
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Scalar * pCopyBuffer = nullptr;
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const typename ETensor::Index TotalNumElements = NumElements * ExtraCount;
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if( !CopyData )
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pWriteBuffer = output.data()->begin();
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else {
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// Regardless of the Eigen::Tensor storage order, the copy will be Row Major
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pCopyBuffer = static_cast<Scalar *>(malloc(TotalNumElements * sizeof(Scalar)));
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pWriteBuffer = pCopyBuffer;
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Scalar * pCopy = pCopyBuffer;
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std::array<typename ETensor::Index, ETensor::NumIndices> MyIndex;
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for( auto &idx : MyIndex ) idx = 0;
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for( typename ETensor::Index n = 0; n < NumElements; n++ ) {
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// Copy the grid tensor
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for( const Scalar &Source : output( MyIndex ) )
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* pCopy ++ = Source;
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// Now increment the index
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for( int i = output.NumDimensions - 1; i >= 0 && ++MyIndex[i] == output.dimension(i); i-- )
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MyIndex[i] = 0;
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}
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}
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upcast->template writeMultiDim<Scalar>(s, NonTrivialDims, pWriteBuffer, TotalNumElements);
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if( pCopyBuffer ) free( pCopyBuffer );
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// Get the dimensionality of the tensor
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std::vector<std::size_t> TotalDims(TotalRank);
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for(auto i = 0; i < TensorRank; i++ ) {
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auto dim = output.dimension(i);
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TotalDims[i] = static_cast<size_t>(dim);
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assert( TotalDims[i] == dim ); // check we didn't lose anything in the conversion
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}
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for(auto i = 0; i < ContainerRank; i++ )
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TotalDims[TensorRank + i] = Traits::Dimension(i);
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// If the Tensor isn't in Row-Major order, then we'll need to copy it's data
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const bool CopyData{NumElements > 1 && ETensor::Layout != Eigen::StorageOptions::RowMajor};
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const Scalar * pWriteBuffer;
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Scalar * pCopyBuffer = nullptr;
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const Index TotalNumElements = NumElements * Traits::count;
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if( !CopyData ) {
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if constexpr ( ContainerRank == 0 )
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pWriteBuffer = output.data();
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else
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pWriteBuffer = output.data()->begin();
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} else {
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// Regardless of the Eigen::Tensor storage order, the copy will be Row Major
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pCopyBuffer = static_cast<Scalar *>(malloc(TotalNumElements * sizeof(Scalar)));
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pWriteBuffer = pCopyBuffer;
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Scalar * pCopy = pCopyBuffer;
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std::array<Index, TensorRank> MyIndex;
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for( auto &idx : MyIndex ) idx = 0;
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for( auto n = 0; n < NumElements; n++ ) {
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if constexpr ( ContainerRank == 0 )
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* pCopy ++ = output( MyIndex );
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else {
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for( const Scalar &Source : output( MyIndex ) )
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* pCopy ++ = Source;
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}
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// Now increment the index
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for( int i = output.NumDimensions - 1; i >= 0 && ++MyIndex[i] == output.dimension(i); i-- )
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MyIndex[i] = 0;
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}
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}
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upcast->template writeMultiDim<Scalar>(s, TotalDims, pWriteBuffer, TotalNumElements);
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if( pCopyBuffer ) free( pCopyBuffer );
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}
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template <typename T>
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@ -859,21 +804,21 @@ namespace Grid {
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return os;
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}
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template <typename T>
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static inline typename std::enable_if<!std::is_base_of<Eigen::TensorBase<T, Eigen::ReadOnlyAccessors>, T>::value, bool>::type
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CompareMember(const T &lhs, const T &rhs) {
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template <typename T1, typename T2>
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static inline typename std::enable_if<!EigenIO::is_tensor<T1>::value || !EigenIO::is_tensor<T2>::value, bool>::type
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CompareMember(const T1 &lhs, const T2 &rhs) {
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return lhs == rhs;
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}
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|
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template <typename T>
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static inline typename std::enable_if<std::is_base_of<Eigen::TensorBase<T, Eigen::ReadOnlyAccessors>, T>::value, bool>::type
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CompareMember(const T &lhs, const T &rhs) {
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template <typename T1, typename T2>
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static inline typename std::enable_if<EigenIO::is_tensor<T1>::value && EigenIO::is_tensor<T2>::value, bool>::type
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CompareMember(const T1 &lhs, const T2 &rhs) {
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// First check whether dimensions match (Eigen tensor library will assert if they don't match)
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bool bReturnValue = true;
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for( auto i = 0 ; bReturnValue && i < T::NumIndices ; i++ )
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bool bReturnValue = (T1::NumIndices == T2::NumIndices);
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for( auto i = 0 ; bReturnValue && i < T1::NumIndices ; i++ )
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bReturnValue = ( lhs.dimension(i)) == rhs.dimension(i);
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if( bReturnValue ) {
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Eigen::Tensor<bool, 0, T::Options> bResult = (lhs == rhs).all();
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Eigen::Tensor<bool, 0, T1::Options> bResult = (lhs == rhs).all();
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bReturnValue = bResult(0);
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}
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return bReturnValue;
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@ -80,161 +80,138 @@ double d = 2*M_PI;
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bool b = false;
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template <typename W, typename R, typename O>
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void ioTest(const std::string &filename, const O &object, const std::string &name)
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void ioTest(const std::string &filename, const O &object, const std::string &name, const char * tag = "testobject" )
|
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{
|
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std::cout << name << " IO test: writing ...";
|
||||
std::cout << "IO test: " << name << " -> " << filename << " ...";
|
||||
// writer needs to be destroyed so that writing physically happens
|
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{
|
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W writer(filename);
|
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|
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write(writer, "testobject", object);
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write(writer, tag , object);
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}
|
||||
|
||||
std::cout << " done. reading...";
|
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R reader(filename);
|
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O buf;
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||||
std::unique_ptr<O> buf( new O ); // In case object too big for stack
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||||
|
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read(reader, "testobject", buf);
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||||
bool good = Serializable::CompareMember(object, buf);
|
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read(reader, tag, *buf);
|
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bool good = Serializable::CompareMember(object, *buf);
|
||||
if (!good) {
|
||||
std::cout << " failure!" << std::endl;
|
||||
if constexpr (EigenIO::is_tensor<O>::value)
|
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dump_tensor(buf,"???");
|
||||
dump_tensor(*buf,"???");
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
std::cout << " done." << std::endl;
|
||||
}
|
||||
|
||||
#ifdef HAVE_HDF5
|
||||
typedef Eigen::Tensor<int, 5> ShortRank5Tensor;
|
||||
//typedef int TestScalar;
|
||||
typedef std::complex<double> TestScalar;
|
||||
typedef Eigen::Tensor<TestScalar, 3, Eigen::StorageOptions::RowMajor> TestTensor;
|
||||
typedef Eigen::TensorFixedSize<TestScalar, Eigen::Sizes<9,4,2>, Eigen::StorageOptions::RowMajor> TestTensorFixed;
|
||||
typedef std::vector<TestTensorFixed> aTestTensorFixed;
|
||||
typedef Eigen::TensorFixedSize<SpinColourVector, Eigen::Sizes<11,3,2>, Eigen::StorageOptions::RowMajor> LSCTensor;
|
||||
typedef Eigen::TensorFixedSize<LorentzColourMatrix, Eigen::Sizes<5,7,2>, Eigen::StorageOptions::RowMajor> LCMTensor;
|
||||
// From Test_serialisation.cc
|
||||
typedef Eigen::TensorFixedSize<unsigned short, Eigen::Sizes<5,4,3,2,1>> TensorRank5UShort;
|
||||
typedef Eigen::TensorFixedSize<unsigned short, Eigen::Sizes<5,4,3,2>, Eigen::StorageOptions::RowMajor> TensorRank5UShortAlt;
|
||||
typedef Eigen::Tensor<TestScalar, 3, Eigen::StorageOptions::RowMajor> TensorRank3;
|
||||
typedef Eigen::TensorFixedSize<TestScalar, Eigen::Sizes<9,4,2>, Eigen::StorageOptions::RowMajor> Tensor_9_4_2;
|
||||
typedef std::vector<Tensor_9_4_2> aTensor_9_4_2;
|
||||
typedef Eigen::TensorFixedSize<SpinColourVector, Eigen::Sizes<6,5>> LSCTensor;
|
||||
#ifdef DEBUG
|
||||
typedef Eigen::TensorFixedSize<iMatrix<iVector<iMatrix<iVector<LorentzColourMatrix,5>,2>,7>,3>, Eigen::Sizes<2,2,11,10,9>, Eigen::StorageOptions::RowMajor> LCMTensor;
|
||||
#endif
|
||||
|
||||
class PerambIOTestClass: Serializable {
|
||||
public:
|
||||
using PerambTensor = Eigen::Tensor<SpinColourVector, 6, Eigen::StorageOptions::RowMajor>;
|
||||
GRID_SERIALIZABLE_CLASS_MEMBERS(PerambIOTestClass
|
||||
, ShortRank5Tensor, shortRank5Tensor
|
||||
, PerambTensor, Perambulator
|
||||
, SpinColourVector, spinColourVector
|
||||
, SpinColourMatrix, spinColourMatrix
|
||||
, std::vector<std::string>, DistilParameterNames
|
||||
, std::vector<int>, DistilParameterValues
|
||||
, PerambTensor, Perambulator
|
||||
, PerambTensor, Perambulator2
|
||||
, SpinColourVector, scv
|
||||
, SpinColourMatrix, scm
|
||||
//, TestTensor, Critter
|
||||
//, TestTensorFixed, FixedCritter
|
||||
//, aTestTensorFixed, aFixedCritter
|
||||
//, LSCTensor, MyLSCTensor
|
||||
//, LCMTensor, MyLCMTensor
|
||||
, TensorRank5UShort, tensorRank5UShort
|
||||
, TensorRank3, tensorRank3
|
||||
, Tensor_9_4_2, tensor_9_4_2
|
||||
, aTensor_9_4_2, atensor_9_4_2
|
||||
, LSCTensor, MyLSCTensor
|
||||
#ifdef DEBUG
|
||||
, LCMTensor, MyLCMTensor
|
||||
#endif
|
||||
);
|
||||
PerambIOTestClass() : Perambulator(2,3,1,4,5,1), Perambulator2(7,1,6,1,5,1),
|
||||
DistilParameterNames {"alpha", "beta", "gamma", "delta", "epsilon", "what's f?"},
|
||||
DistilParameterValues{2,3,1,4,5,1}
|
||||
, shortRank5Tensor{5,4,3,2,1}
|
||||
//, Critter(7,3,2)//, aFixedCritter(3)
|
||||
PerambIOTestClass()
|
||||
: DistilParameterNames {"alpha", "beta", "gamma", "delta", "epsilon", "zeta"}
|
||||
, DistilParameterValues{2,3,1,4,5,1}
|
||||
, Perambulator(2,3,1,4,5,1)
|
||||
, Perambulator2(7,1,6,1,5,1)
|
||||
, tensorRank3(7,3,2)
|
||||
, atensor_9_4_2(3)
|
||||
{
|
||||
Sequential_Init(Perambulator);
|
||||
Sequential_Init(Perambulator2, {-3.1415927,7});
|
||||
Sequential_Init(shortRank5Tensor);
|
||||
Grid_complex<double> Flag{1,-3.1415927};
|
||||
SequentialInit(Perambulator, Flag);
|
||||
SequentialInit(Perambulator2, Flag);
|
||||
SequentialInit(tensorRank5UShort);
|
||||
SequentialInit(tensorRank3, Flag);
|
||||
SequentialInit(tensor_9_4_2, Flag);
|
||||
for( auto &t : atensor_9_4_2 )
|
||||
SequentialInit(t, Flag);
|
||||
SequentialInit( MyLSCTensor );
|
||||
#ifdef DEBUG
|
||||
SequentialInit( MyLCMTensor );
|
||||
#endif
|
||||
}
|
||||
};
|
||||
|
||||
void EigenHdf5IOTest(void) {
|
||||
SpinColourVector scv, scv2;
|
||||
scv2 = scv;
|
||||
ioTest<Hdf5Writer, Hdf5Reader, SpinColourVector>("iotest_vector.h5", scv, "SpinColourVector");
|
||||
SpinColourMatrix scm;
|
||||
ioTest<Hdf5Writer, Hdf5Reader, SpinColourMatrix>("iotest_matrix.h5", scm, "SpinColourMatrix");
|
||||
#define TensorWriteReadInnerNoInit( T ) \
|
||||
filename = "iotest_"s + std::to_string(++TestNum) + "_" #T ".h5"; \
|
||||
ioTest<Hdf5Writer, Hdf5Reader, T>(filename, t, #T, #T);
|
||||
#define TensorWriteReadInner( T ) SequentialInit( t ); TensorWriteReadInnerNoInit( T )
|
||||
#define TensorWriteRead( T ) { T t ; TensorWriteReadInner( T ) }
|
||||
#define TensorWriteReadV(T, ... ) { T t( __VA_ARGS__ ); TensorWriteReadInner( T ) }
|
||||
#define TensorWriteReadLarge( T ) { std::unique_ptr<T> p{new T}; T &t{*p}; TensorWriteReadInnerNoInit(T) }
|
||||
|
||||
constexpr TestScalar Inc{1,-1};
|
||||
void EigenHdf5IOTest(void)
|
||||
{
|
||||
using namespace std::string_literals;
|
||||
unsigned int TestNum = 0;
|
||||
std::string filename;
|
||||
using TensorSingle = Eigen::TensorFixedSize<int, Eigen::Sizes<1>>;
|
||||
TensorWriteRead( TensorSingle )
|
||||
using TensorSimple = Eigen::Tensor<iMatrix<TestScalar,1>, 6>;
|
||||
TensorWriteReadV( TensorSimple, 1, 1, 1, 1, 1, 1 )
|
||||
TensorWriteReadV( TensorRank3, 6, 3, 2 )
|
||||
TensorWriteRead ( Tensor_9_4_2 )
|
||||
{
|
||||
using TestTensorSingle = Eigen::TensorFixedSize<int, Eigen::Sizes<1>>;
|
||||
TestTensorSingle ts;
|
||||
ts(0) = 7; // lucky
|
||||
ioTest<Hdf5Writer, Hdf5Reader, TestTensorSingle>("iotest_single.h5", ts, "Tensor_single");
|
||||
TensorRank5UShort t;
|
||||
TensorWriteReadInner ( TensorRank5UShort );
|
||||
std::cout << " Testing alternate memory order read ... ";
|
||||
TensorRank5UShortAlt t2;
|
||||
Hdf5Reader reader(filename);
|
||||
read(reader, "TensorRank5UShort", t2);
|
||||
bool good = true;
|
||||
for_all( t2, [&](unsigned short c, unsigned short n,
|
||||
const std::array<size_t, TensorRank5UShortAlt::NumIndices> &Dims ) {
|
||||
good = good && ( c == n );
|
||||
} );
|
||||
if (!good) {
|
||||
std::cout << " failure!" << std::endl;
|
||||
dump_tensor(t2,"t2");
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
std::cout << " done." << std::endl;
|
||||
}
|
||||
|
||||
TensorWriteRead ( LSCTensor )
|
||||
TensorWriteReadLarge( PerambIOTestClass )
|
||||
#ifdef DEBUG
|
||||
std::cout << "sizeof( LCMTensor ) = " << sizeof( LCMTensor ) / 1024 / 1024 << " MB" << std::endl;
|
||||
TensorWriteReadLarge ( LCMTensor )
|
||||
// Also write > 4GB of complex numbers (I suspect this will fail inside Hdf5)
|
||||
{
|
||||
using TestTensorSimple = Eigen::Tensor<iMatrix<TestScalar,1>, 6>;
|
||||
TestTensorSimple ts(1,1,1,1,1,1);
|
||||
ts(0,0,0,0,0,0) = Inc * 3.1415927;
|
||||
ioTest<Hdf5Writer, Hdf5Reader, TestTensorSimple>("iotest_simple.h5", ts, "Tensor_simple");
|
||||
static constexpr size_t Num = 0x11000000;
|
||||
std::cout << "Stress test: " << Num * sizeof( Grid_complex<double> ) / 1024 / 1024
|
||||
<< " MB array of complex<double>" << std::endl;
|
||||
using Stress = std::vector<Grid_complex<double>>;
|
||||
Stress t (Num);
|
||||
TensorWriteReadInnerNoInit( Stress );
|
||||
}
|
||||
|
||||
TestTensor t(6,3,2);
|
||||
TestScalar Val{Inc};
|
||||
for( int i = 0 ; i < t.dimension(0) ; i++)
|
||||
for( int j = 0 ; j < t.dimension(1) ; j++)
|
||||
for( int k = 0 ; k < t.dimension(2) ; k++) {
|
||||
t(i,j,k) = Val;
|
||||
Val += Inc;
|
||||
}
|
||||
ioTest<Hdf5Writer, Hdf5Reader, TestTensor>("iotest_tensor.h5", t, "eigen_tensor_instance_name");
|
||||
//dump_tensor(t, "t");
|
||||
|
||||
// Now serialise a fixed size tensor
|
||||
using FixedTensor = Eigen::TensorFixedSize<TestScalar, Eigen::Sizes<8,4,3>>;
|
||||
FixedTensor tf;
|
||||
Val = Inc;
|
||||
for( int i = 0 ; i < tf.dimension(0) ; i++)
|
||||
for( int j = 0 ; j < tf.dimension(1) ; j++)
|
||||
for( int k = 0 ; k < tf.dimension(2) ; k++) {
|
||||
tf(i,j,k) = Val;
|
||||
Val += Inc;
|
||||
}
|
||||
ioTest<Hdf5Writer, Hdf5Reader, FixedTensor>("iotest_tensor_fixed.h5", tf, "eigen_tensor_fixed_name");
|
||||
//dump_tensor(tf, "tf");
|
||||
|
||||
PerambIOTestClass o;
|
||||
//dump_tensor(o.Perambulator, "Perambulator" );
|
||||
dump_tensor(o.shortRank5Tensor, "shortRank5Tensor");
|
||||
/*for_all( o.FixedCritter, [&](TestScalar &c, float f, const std::array<size_t,TestTensorFixed::NumIndices> &Dims ){
|
||||
c = TestScalar{f,-f};
|
||||
//std::cout << " a(" << Dims[0] << "," << Dims[1] << "," << Dims[2] << ")=" << c;
|
||||
} );
|
||||
for( auto &z : o.aFixedCritter )
|
||||
for_all( z, [&](TestScalar &c, float f, const std::array<size_t,TestTensorFixed::NumIndices> &Dims ){
|
||||
c = TestScalar{f,-f};
|
||||
//std::cout << " a(" << Dims[0] << "," << Dims[1] << "," << Dims[2] << ")=" << c;
|
||||
} );*/
|
||||
ioTest<Hdf5Writer, Hdf5Reader, PerambIOTestClass>("iotest_object.h5", o, "PerambIOTestClass_object_instance_name");
|
||||
//DumpMemoryOrder(o.Perambulator);
|
||||
|
||||
// Tensor of spin colour
|
||||
LSCTensor l;
|
||||
Val = 0;
|
||||
for( int i = 0 ; i < l.dimension(0) ; i++)
|
||||
for( int j = 0 ; j < l.dimension(1) ; j++)
|
||||
for( int k = 0 ; k < l.dimension(2) ; k++)
|
||||
for( int s = 0 ; s < Ns ; s++ )
|
||||
for( int c = 0 ; c < Nc ; c++ )
|
||||
{
|
||||
l(i,j,k)()(s)(c) = Val;
|
||||
Val += Inc;
|
||||
}
|
||||
ioTest<Hdf5Writer, Hdf5Reader, LSCTensor>("iotest_LSCTensor.h5", l, "LSCTensor_object_instance_name");
|
||||
//dump_tensor(l, "l");
|
||||
|
||||
// Tensor of spin colour
|
||||
LCMTensor l2;
|
||||
Val = 0;
|
||||
for( int i = 0 ; i < l2.dimension(0) ; i++)
|
||||
for( int j = 0 ; j < l2.dimension(1) ; j++)
|
||||
for( int k = 0 ; k < l2.dimension(2) ; k++)
|
||||
for( int l = 0 ; l < Ns ; l++ )
|
||||
for( int c = 0 ; c < Nc ; c++ )
|
||||
for( int c2 = 0 ; c2 < Nc ; c2++ )
|
||||
{
|
||||
l2(i,j,k)(l)()(c,c2) = Val;
|
||||
Val += Inc;
|
||||
}
|
||||
ioTest<Hdf5Writer, Hdf5Reader, LCMTensor>("iotest_LCMTensor.h5", l2, "LCMTensor_object_instance_name");
|
||||
//dump_tensor(l2, "l2");
|
||||
#endif
|
||||
}
|
||||
#endif
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user