mirror of
https://github.com/paboyle/Grid.git
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Merge branch 'feature/distil' of github.com:mmphys/Grid into feature/distil
This commit is contained in:
ferben
commit
73cdca3973
@ -37,16 +37,16 @@ Author: Guido Cossu <guido.cossu@ed.ac.uk>
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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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//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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//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<Grid_complex<T>>
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: std::integral_constant<bool, std::is_arithmetic<T>::value> {};
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//template<typename T> struct is_complex : public std::false_type {};
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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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template<typename T> struct is_scalar : std::integral_constant<bool,
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@ -54,11 +54,11 @@ namespace Grid {
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// Eigen tensors can also be composed of a limited number of containers
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// NB: grid tensors (iScalar, iVector, iMatrix) have stricter limits on complex types
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template <typename T> struct is_container : public std::false_type {};
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template <typename T> struct is_container<iScalar<T>> : public std::true_type {};
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template <typename T, int N> struct is_container<iVector<T, N>> : public std::true_type {};
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template <typename T, int N> struct is_container<iMatrix<T, N>> : public std::true_type {};
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template <typename T, std::size_t N> struct is_container<std::array<T, N>> : public std::true_type {};
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//template <typename T> struct is_container : public std::false_type {};
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//template <typename T> struct is_container<iScalar<T>> : public std::true_type {};
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//template <typename T, int N> struct is_container<iVector<T, N>> : public std::true_type {};
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//template <typename T, int N> struct is_container<iMatrix<T, N>> : public std::true_type {};
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//template <typename T, std::size_t N> struct is_container<std::array<T, N>> : public std::true_type {};
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// Is this an Eigen tensor
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template<typename T> struct is_tensor : std::integral_constant<bool,
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@ -70,7 +70,7 @@ namespace Grid {
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// Is this an Eigen tensor of a supported container
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template<typename T, typename C = void> struct is_tensor_of_container : public std::false_type {};
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template<typename T> struct is_tensor_of_container<T, typename std::enable_if<is_tensor<T>::value && is_container<typename T::Scalar>::value, void>::type> : public std::true_type {};
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template<typename T> struct is_tensor_of_container<T, typename std::enable_if<is_tensor<T>::value && isGridTensor<typename T::Scalar>::value, void>::type> : public std::true_type {};
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// Is this a fixed-size Eigen tensor
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template<typename T> struct is_tensor_fixed : public std::false_type {};
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@ -94,11 +94,11 @@ namespace Grid {
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// EigenIO::Traits are not defined for Eigen tensors, but rather their top-level scalar
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// This is because Eigen tensors have a dynamic size flavour, but the scalars are all fixed size
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// This allows the traits to all be defined as constexpr
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template <typename T, typename C = void> struct Traits {}; // C needed for specialisation
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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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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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using scalar_real = typename RealPart<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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@ -121,7 +121,7 @@ namespace Grid {
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};
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template <typename T> struct Traits<iScalar<T>> {
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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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using scalar_real = typename RealPart<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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@ -134,7 +134,7 @@ namespace Grid {
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};
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template <typename T, int N> struct Traits<iVector<T, N>> {
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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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using scalar_real = typename RealPart<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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@ -148,7 +148,7 @@ namespace Grid {
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};
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template <typename T, int N> struct Traits<iMatrix<T, N>> {
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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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using scalar_real = typename RealPart<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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@ -160,224 +160,9 @@ namespace Grid {
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//return ( N == 1 ) ? Traits<T>::DimensionNT(dim) : ( dim == 0 || dim == 1 ) ? N : Traits<T>::DimensionNT(dim - 2);
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//}
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};
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template <typename T, int N> struct Traits<std::array<T, N>> : Traits<iVector<T, N>> {};
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template <typename T, int N> struct Traits<std::array<T, N>> : Traits<iVector<T, N>> {};*/
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}
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// for_all helper function to call the lambda for scalar
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template <typename ETensor, typename Lambda>
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typename std::enable_if<EigenIO::is_tensor_of_scalar<ETensor>::value, void>::type
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for_all_do_lambda( Lambda lambda, typename ETensor::Scalar &scalar, typename ETensor::Index &Seq,
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std::array<std::size_t, ETensor::NumIndices + EigenIO::Traits<typename ETensor::Scalar>::rank> &MyIndex)
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{
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lambda( scalar, Seq++, MyIndex );
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}
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// for_all helper function to call the lambda for container
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template <typename ETensor, typename Lambda>
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typename std::enable_if<EigenIO::is_tensor_of_container<ETensor>::value, void>::type
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for_all_do_lambda( Lambda lambda, typename ETensor::Scalar &container, typename ETensor::Index &Seq,
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std::array<std::size_t, ETensor::NumIndices + EigenIO::Traits<typename ETensor::Scalar>::rank> &MyIndex)
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{
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using Traits = EigenIO::Traits<typename ETensor::Scalar>;
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const auto rank{ETensor::NumIndices};
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const auto InnerRank = Traits::rank;
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for( typename Traits::scalar_type &Source : container ) {
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lambda(Source, Seq++, MyIndex );
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// Now increment SubIndex
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for( auto i = InnerRank - 1; i != -1 && ++MyIndex[rank + i] == Traits::Dimension(i); i-- )
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MyIndex[rank + i] = 0;
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}
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}
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// Calls a lamda (passing index and sequence number) for every member of an Eigen::Tensor
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// For efficiency, iteration proceeds in memory order,
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// ... but parameters guaranteed to be the same regardless of memory order
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template <typename ETensor, typename Lambda>
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typename std::enable_if<EigenIO::is_tensor<ETensor>::value, void>::type
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for_all( ETensor &ET, Lambda lambda )
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{
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using Scalar = typename ETensor::Scalar; // This could be a Container - we'll check later
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const std::size_t NumScalars = ET.size();
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assert( NumScalars > 0 );
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using Index = typename ETensor::Index;
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Index ScalarElementCount{1};
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const auto InnerRank = EigenIO::Traits<Scalar>::rank;
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const auto rank{ETensor::NumIndices};
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std::array<std::size_t, rank + InnerRank> Dims;
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for(auto i = 0; i < rank; i++ ) {
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auto dim = ET.dimension(i);
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assert( dim > 0 );
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Dims[i] = static_cast<std::size_t>(dim);
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assert( Dims[i] == dim ); // check we didn't lose anything in the conversion
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ScalarElementCount *= Dims[i];
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}
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// Check that the number of containers is correct ... and we didn't lose anything in conversions
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assert( NumScalars == ScalarElementCount );
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// If the Scalar is actually a container, add the inner Scalar's dimensions
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size_t InnerScalarCount{1};
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for(auto i = 0; i < InnerRank; i++ ) {
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auto dim = EigenIO::Traits<Scalar>::Dimension(i);
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assert( dim > 0 );
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Dims[rank + i] = static_cast<std::size_t>(dim);
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assert( Dims[rank + i] == dim ); // check we didn't lose anything in the conversion
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InnerScalarCount *= dim;
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}
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assert(EigenIO::Traits<Scalar>::count == InnerScalarCount);
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assert(EigenIO::Traits<Scalar>::size == sizeof( Scalar ));
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std::array<std::size_t, rank + InnerRank> MyIndex;
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for( auto &idx : MyIndex ) idx = 0;
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Index Seq = 0;
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Scalar * pScalar = ET.data();
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for( std::size_t j = 0; j < NumScalars; j++ ) {
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for_all_do_lambda<ETensor, Lambda>( lambda, * pScalar, Seq, MyIndex );
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// Now increment the index to pass to the lambda (bearing in mind we're walking in memory order)
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if( ETensor::Options & Eigen::RowMajor ) {
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for( auto i = rank - 1; i != -1 && ++MyIndex[i] == Dims[i]; i-- )
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MyIndex[i] = 0;
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} else {
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for( auto i = 0; i < rank && ++MyIndex[i] == Dims[i]; i++ )
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MyIndex[i] = 0;
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size_t NewSeq = 0;
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for( auto i = 0; i < rank + InnerRank ; i++ ) {
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NewSeq *= Dims[i];
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NewSeq += MyIndex[i];
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}
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Seq = static_cast<Index>( NewSeq );
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}
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pScalar++;
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}
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}
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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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unsigned short Precision = 0 )
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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> &Dims ) {
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scalar_type x = Inc * static_cast<scalar_type>(n);
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if( Precision ) {
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std::stringstream s;
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s << std::scientific << std::setprecision(Precision) << x;
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s >> x;
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}
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c = x;
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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 && 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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unsigned short Precision = 0 )
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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> &Dims ) {
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auto re = Inc.real();
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auto im = Inc.imag();
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re *= n;
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im *= n;
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if( Precision ) {
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std::stringstream s;
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s << std::setprecision(Precision) << re;
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s >> re;
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s.clear();
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s << im;
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s >> im;
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}
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c = scalar_type(re,im);
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} );
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}
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// Helper to dump a tensor
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#ifdef DEBUG
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#define dump_tensor(args...) dump_tensor_func(args)
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template <typename T>
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typename std::enable_if<EigenIO::is_tensor<T>::value, void>::type
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dump_tensor_func(T &t, const char * pName = nullptr)
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{
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using Traits = typename EigenIO::Traits<typename T::Scalar>;
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const auto rank{T::NumIndices};
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const auto &dims = t.dimensions();
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std::cout << "Dumping rank " << rank << ((T::Options & Eigen::RowMajor) ? ", row" : ", column") << "-major tensor ";
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if( pName )
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std::cout << pName;
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for( auto i = 0 ; i < rank; i++ ) std::cout << "[" << dims[i] << "]";
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std::cout << " in memory order:" << std::endl;
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for_all( t, [&](typename Traits::scalar_type &c, typename T::Index index, const std::array<size_t, T::NumIndices + Traits::rank> &Dims ){
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std::cout << " ";
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for( auto dim : Dims )
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std::cout << "[" << dim << "]";
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std::cout << " = " << c << std::endl;
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} );
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std::cout << "========================================" << std::endl;
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}
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template <typename T>
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typename std::enable_if<!EigenIO::is_tensor<T>::value, void>::type
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dump_tensor_func(T &t, const char * pName = nullptr)
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{
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std::cout << "Dumping non-tensor object ";
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if( pName )
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std::cout << pName;
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std::cout << "=" << t;
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}
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// Helper to dump a tensor in memory order
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// Kind of superfluous given the above ... just keeping in case I need to fall back to this
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#define DumpMemoryOrder(args...) DumpMemoryOrder_func(args)
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template <typename T>
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typename std::enable_if<EigenIO::is_tensor_of_scalar<T>::value, void>::type
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DumpMemoryOrder_func(T &t, const char * pName = nullptr)
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{
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const auto rank = t.rank();
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const auto &dims = t.dimensions();
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std::cout << "Dumping rank " << rank << ((T::Options & Eigen::RowMajor) ? ", row" : ", column") << "-major tensor ";
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if( pName )
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std::cout << pName;
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for( auto d : dims ) std::cout << "[" << d << "]";
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std::cout << " in memory order:" << std::endl;
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const typename T::Scalar * p = t.data();
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const auto size = t.size();
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const typename T::Scalar * pEnd = p + size;
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if( rank <= 2 ) {
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for( unsigned int i = 0 ; i < t.size() ; i++ )
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std::cout << "[" << i << "]=" << *p++ << " ";
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std::cout << std::endl;
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} else {
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const auto innersize = dims[rank-2] * dims[rank-1];
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using Index = typename T::Index;
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std::vector<Index> idx(rank - 2);
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for( auto &i : idx ) i = 0;
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Index idxCounter = 0;
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while( p < pEnd ) {
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if( T::Options & Eigen::RowMajor ) {
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if( pName )
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std::cout << pName;
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idxCounter = 0;
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for(auto i = 0 ; i < rank - 2 ; i++)
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std::cout << "[" << idx[i] << "]:";
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}
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for( unsigned int i = 0 ; i < innersize ; i++ )
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std::cout << " [" << idxCounter++ << "]=" << *p++;
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if( T::Options & Eigen::RowMajor )
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std::cout << std::endl;
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// Now increment MyIndex
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for( auto i = rank - 3; i != -1 && ++idx[i] == dims[i]; i-- )
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idx[i] = 0;
|
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}
|
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if( ! ( T::Options & Eigen::RowMajor ) )
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std::cout << std::endl;
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}
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}
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#else
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#define dump_tensor(args...)
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#define DumpMemoryOrder(args...)
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#endif
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// Abstract writer/reader classes ////////////////////////////////////////////
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// static polymorphism implemented using CRTP idiom
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class Serializable;
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@ -410,14 +195,14 @@ namespace Grid {
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// Helper functions for Scalar vs Container specialisations
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template <typename ETensor>
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inline typename std::enable_if<EigenIO::is_tensor_of_scalar<ETensor>::value, const typename EigenIO::Traits<typename ETensor::Scalar>::scalar_type *>::type
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inline typename std::enable_if<EigenIO::is_tensor_of_scalar<ETensor>::value, const typename GridTypeMapper<typename ETensor::Scalar>::scalar_type *>::type
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getFirstScalar(const ETensor &output)
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{
|
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return output.data();
|
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}
|
||||
|
||||
template <typename ETensor>
|
||||
inline typename std::enable_if<EigenIO::is_tensor_of_container<ETensor>::value, const typename EigenIO::Traits<typename ETensor::Scalar>::scalar_type *>::type
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||||
inline typename std::enable_if<EigenIO::is_tensor_of_container<ETensor>::value, const typename GridTypeMapper<typename ETensor::Scalar>::scalar_type *>::type
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getFirstScalar(const ETensor &output)
|
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{
|
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return output.data()->begin();
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@ -425,16 +210,16 @@ namespace Grid {
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||||
|
||||
template <typename S>
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inline typename std::enable_if<EigenIO::is_scalar<S>::value, void>::type
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copyScalars(typename EigenIO::Traits<S>::scalar_type * &pCopy, const S &Source)
|
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copyScalars(typename GridTypeMapper<S>::scalar_type * &pCopy, const S &Source)
|
||||
{
|
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* pCopy ++ = Source;
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}
|
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|
||||
template <typename S>
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||||
inline typename std::enable_if<EigenIO::is_container<S>::value, void>::type
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copyScalars(typename EigenIO::Traits<S>::scalar_type * &pCopy, const S &Source)
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inline typename std::enable_if<isGridTensor<S>::value, void>::type
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copyScalars(typename GridTypeMapper<S>::scalar_type * &pCopy, const S &Source)
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{
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for( const typename EigenIO::Traits<S>::scalar_type &item : Source )
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for( const typename GridTypeMapper<S>::scalar_type &item : Source )
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* pCopy ++ = item;
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}
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||||
|
||||
@ -483,16 +268,16 @@ namespace Grid {
|
||||
// Helper functions for Scalar vs Container specialisations
|
||||
template <typename S>
|
||||
inline typename std::enable_if<EigenIO::is_scalar<S>::value, void>::type
|
||||
copyScalars(S &Dest, const typename EigenIO::Traits<S>::scalar_type * &pSource)
|
||||
copyScalars(S &Dest, const typename GridTypeMapper<S>::scalar_type * &pSource)
|
||||
{
|
||||
Dest = * pSource ++;
|
||||
}
|
||||
|
||||
template <typename S>
|
||||
inline typename std::enable_if<EigenIO::is_container<S>::value, void>::type
|
||||
copyScalars(S &Dest, const typename EigenIO::Traits<S>::scalar_type * &pSource)
|
||||
inline typename std::enable_if<isGridTensor<S>::value, void>::type
|
||||
copyScalars(S &Dest, const typename GridTypeMapper<S>::scalar_type * &pSource)
|
||||
{
|
||||
for( typename EigenIO::Traits<S>::scalar_type &item : Dest )
|
||||
for( typename GridTypeMapper<S>::scalar_type &item : Dest )
|
||||
item = * pSource ++;
|
||||
}
|
||||
|
||||
@ -577,10 +362,10 @@ namespace Grid {
|
||||
{
|
||||
using Index = typename ETensor::Index;
|
||||
using Container = typename ETensor::Scalar; // NB: could be same as scalar
|
||||
using Traits = EigenIO::Traits<Container>;
|
||||
using Traits = GridTypeMapper<Container>;
|
||||
using Scalar = typename Traits::scalar_type; // type of the underlying scalar
|
||||
constexpr unsigned int TensorRank{ETensor::NumIndices};
|
||||
constexpr unsigned int ContainerRank{Traits::rank}; // Only non-zero for containers
|
||||
constexpr unsigned int ContainerRank{Traits::Rank}; // Only non-zero for containers
|
||||
constexpr unsigned int TotalRank{TensorRank + ContainerRank};
|
||||
const Index NumElements{output.size()};
|
||||
assert( NumElements > 0 );
|
||||
@ -728,10 +513,10 @@ namespace Grid {
|
||||
{
|
||||
using Index = typename ETensor::Index;
|
||||
using Container = typename ETensor::Scalar; // NB: could be same as scalar
|
||||
using Traits = EigenIO::Traits<Container>;
|
||||
using Traits = GridTypeMapper<Container>;
|
||||
using Scalar = typename Traits::scalar_type; // type of the underlying scalar
|
||||
constexpr unsigned int TensorRank{ETensor::NumIndices};
|
||||
constexpr unsigned int ContainerRank{Traits::rank}; // Only non-zero for containers
|
||||
constexpr unsigned int ContainerRank{Traits::Rank}; // Only non-zero for containers
|
||||
constexpr unsigned int TotalRank{TensorRank + ContainerRank};
|
||||
using ETDims = std::array<Index, TensorRank>; // Dimensions of the tensor
|
||||
|
||||
|
||||
249
Grid/util/Eigen.h
Normal file
249
Grid/util/Eigen.h
Normal file
@ -0,0 +1,249 @@
|
||||
/*************************************************************************************
|
||||
|
||||
Grid physics library, www.github.com/paboyle/Grid
|
||||
|
||||
Source file: Grid/util/Eigen.h
|
||||
|
||||
Copyright (C) 2019
|
||||
|
||||
Author: Michael Marshall <michael.marshall@ed.ac.uk>
|
||||
|
||||
This program 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 2 of the License, or
|
||||
(at your option) any later version.
|
||||
|
||||
This program 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, write to the Free Software Foundation, Inc.,
|
||||
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
|
||||
|
||||
See the full license in the file "LICENSE" in the top level distribution directory
|
||||
*************************************************************************************/
|
||||
/* END LEGAL */
|
||||
#ifndef GRID_UTIL_EIGEN_H
|
||||
#define GRID_UTIL_EIGEN_H
|
||||
#include <Grid/tensors/Tensor_traits.h>
|
||||
#include <Grid/Eigen/unsupported/CXX11/Tensor>
|
||||
|
||||
namespace Grid {
|
||||
// for_all helper function to call the lambda for scalar
|
||||
template <typename ETensor, typename Lambda>
|
||||
typename std::enable_if<EigenIO::is_tensor_of_scalar<ETensor>::value, void>::type
|
||||
for_all_do_lambda( Lambda lambda, typename ETensor::Scalar &scalar, typename ETensor::Index &Seq,
|
||||
std::array<std::size_t, ETensor::NumIndices + GridTypeMapper<typename ETensor::Scalar>::Rank> &MyIndex)
|
||||
{
|
||||
lambda( scalar, Seq++, MyIndex );
|
||||
}
|
||||
|
||||
// for_all helper function to call the lambda for container
|
||||
template <typename ETensor, typename Lambda>
|
||||
typename std::enable_if<EigenIO::is_tensor_of_container<ETensor>::value, void>::type
|
||||
for_all_do_lambda( Lambda lambda, typename ETensor::Scalar &container, typename ETensor::Index &Seq,
|
||||
std::array<std::size_t, ETensor::NumIndices + GridTypeMapper<typename ETensor::Scalar>::Rank> &MyIndex)
|
||||
{
|
||||
using Traits = GridTypeMapper<typename ETensor::Scalar>;
|
||||
const auto rank{ETensor::NumIndices};
|
||||
const auto InnerRank = Traits::Rank;
|
||||
for( typename Traits::scalar_type &Source : container ) {
|
||||
lambda(Source, Seq++, MyIndex );
|
||||
// Now increment SubIndex
|
||||
for( auto i = InnerRank - 1; i != -1 && ++MyIndex[rank + i] == Traits::Dimension(i); i-- )
|
||||
MyIndex[rank + i] = 0;
|
||||
}
|
||||
}
|
||||
|
||||
// Calls a lamda (passing index and sequence number) for every member of an Eigen::Tensor
|
||||
// For efficiency, iteration proceeds in memory order,
|
||||
// ... but parameters guaranteed to be the same regardless of memory order
|
||||
template <typename ETensor, typename Lambda>
|
||||
typename std::enable_if<EigenIO::is_tensor<ETensor>::value, void>::type
|
||||
for_all( ETensor &ET, Lambda lambda )
|
||||
{
|
||||
using Scalar = typename ETensor::Scalar; // This could be a Container - we'll check later
|
||||
const std::size_t NumScalars = ET.size();
|
||||
assert( NumScalars > 0 );
|
||||
using Index = typename ETensor::Index;
|
||||
Index ScalarElementCount{1};
|
||||
const auto InnerRank = GridTypeMapper<Scalar>::Rank;
|
||||
const auto rank{ETensor::NumIndices};
|
||||
std::array<std::size_t, rank + InnerRank> Dims;
|
||||
for(auto i = 0; i < rank; i++ ) {
|
||||
auto dim = ET.dimension(i);
|
||||
assert( dim > 0 );
|
||||
Dims[i] = static_cast<std::size_t>(dim);
|
||||
assert( Dims[i] == dim ); // check we didn't lose anything in the conversion
|
||||
ScalarElementCount *= Dims[i];
|
||||
}
|
||||
// Check that the number of containers is correct ... and we didn't lose anything in conversions
|
||||
assert( NumScalars == ScalarElementCount );
|
||||
// If the Scalar is actually a container, add the inner Scalar's dimensions
|
||||
size_t InnerScalarCount{1};
|
||||
for(auto i = 0; i < InnerRank; i++ ) {
|
||||
auto dim = GridTypeMapper<Scalar>::Dimension(i);
|
||||
assert( dim > 0 );
|
||||
Dims[rank + i] = static_cast<std::size_t>(dim);
|
||||
assert( Dims[rank + i] == dim ); // check we didn't lose anything in the conversion
|
||||
InnerScalarCount *= dim;
|
||||
}
|
||||
assert(GridTypeMapper<Scalar>::count == InnerScalarCount);
|
||||
assert(GridTypeMapper<Scalar>::size == sizeof( Scalar ));
|
||||
std::array<std::size_t, rank + InnerRank> MyIndex;
|
||||
for( auto &idx : MyIndex ) idx = 0;
|
||||
Index Seq = 0;
|
||||
Scalar * pScalar = ET.data();
|
||||
for( std::size_t j = 0; j < NumScalars; j++ ) {
|
||||
for_all_do_lambda<ETensor, Lambda>( lambda, * pScalar, Seq, MyIndex );
|
||||
// Now increment the index to pass to the lambda (bearing in mind we're walking in memory order)
|
||||
if( ETensor::Options & Eigen::RowMajor ) {
|
||||
for( auto i = rank - 1; i != -1 && ++MyIndex[i] == Dims[i]; i-- )
|
||||
MyIndex[i] = 0;
|
||||
} else {
|
||||
for( auto i = 0; i < rank && ++MyIndex[i] == Dims[i]; i++ )
|
||||
MyIndex[i] = 0;
|
||||
size_t NewSeq = 0;
|
||||
for( auto i = 0; i < rank + InnerRank ; i++ ) {
|
||||
NewSeq *= Dims[i];
|
||||
NewSeq += MyIndex[i];
|
||||
}
|
||||
Seq = static_cast<Index>( NewSeq );
|
||||
}
|
||||
pScalar++;
|
||||
}
|
||||
}
|
||||
|
||||
// Sequential initialisation of tensors
|
||||
// Would have preferred to define template variables for this, but that's c++ 17
|
||||
template <typename ETensor>
|
||||
typename std::enable_if<EigenIO::is_tensor<ETensor>::value && !is_complex<typename GridTypeMapper<typename ETensor::Scalar>::scalar_type>::value, void>::type
|
||||
SequentialInit( ETensor &ET, typename GridTypeMapper<typename ETensor::Scalar>::scalar_type Inc = 1,
|
||||
unsigned short Precision = 0 )
|
||||
{
|
||||
using Traits = GridTypeMapper<typename ETensor::Scalar>;
|
||||
using scalar_type = typename Traits::scalar_type;
|
||||
for_all( ET, [&](scalar_type &c, typename ETensor::Index n, const std::array<size_t, ETensor::NumIndices + Traits::Rank> &Dims ) {
|
||||
scalar_type x = Inc * static_cast<scalar_type>(n);
|
||||
if( Precision ) {
|
||||
std::stringstream s;
|
||||
s << std::scientific << std::setprecision(Precision) << x;
|
||||
s >> x;
|
||||
}
|
||||
c = x;
|
||||
} );
|
||||
}
|
||||
|
||||
template <typename ETensor>
|
||||
typename std::enable_if<EigenIO::is_tensor<ETensor>::value && is_complex<typename GridTypeMapper<typename ETensor::Scalar>::scalar_type>::value, void>::type
|
||||
SequentialInit( ETensor &ET, typename GridTypeMapper<typename ETensor::Scalar>::scalar_type Inc={1,-1},
|
||||
unsigned short Precision = 0 )
|
||||
{
|
||||
using Traits = GridTypeMapper<typename ETensor::Scalar>;
|
||||
using scalar_type = typename Traits::scalar_type;
|
||||
for_all( ET, [&](scalar_type &c, typename ETensor::Index n, const std::array<size_t, ETensor::NumIndices + Traits::Rank> &Dims ) {
|
||||
auto re = Inc.real();
|
||||
auto im = Inc.imag();
|
||||
re *= n;
|
||||
im *= n;
|
||||
if( Precision ) {
|
||||
std::stringstream s;
|
||||
s << std::setprecision(Precision) << re;
|
||||
s >> re;
|
||||
s.clear();
|
||||
s << im;
|
||||
s >> im;
|
||||
}
|
||||
c = scalar_type(re,im);
|
||||
} );
|
||||
}
|
||||
|
||||
// Helper to dump a tensor
|
||||
#ifdef DEBUG
|
||||
#define dump_tensor(args...) dump_tensor_func(args)
|
||||
template <typename T>
|
||||
typename std::enable_if<EigenIO::is_tensor<T>::value, void>::type
|
||||
dump_tensor_func(T &t, const char * pName = nullptr)
|
||||
{
|
||||
using Traits = GridTypeMapper<typename T::Scalar>;
|
||||
const auto rank{T::NumIndices};
|
||||
const auto &dims = t.dimensions();
|
||||
std::cout << "Dumping rank " << rank << ((T::Options & Eigen::RowMajor) ? ", row" : ", column") << "-major tensor ";
|
||||
if( pName )
|
||||
std::cout << pName;
|
||||
for( auto i = 0 ; i < rank; i++ ) std::cout << "[" << dims[i] << "]";
|
||||
std::cout << " in memory order:" << std::endl;
|
||||
for_all( t, [&](typename Traits::scalar_type &c, typename T::Index index, const std::array<size_t, T::NumIndices + Traits::Rank> &Dims ){
|
||||
std::cout << " ";
|
||||
for( auto dim : Dims )
|
||||
std::cout << "[" << dim << "]";
|
||||
std::cout << " = " << c << std::endl;
|
||||
} );
|
||||
std::cout << "========================================" << std::endl;
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
typename std::enable_if<!EigenIO::is_tensor<T>::value, void>::type
|
||||
dump_tensor_func(T &t, const char * pName = nullptr)
|
||||
{
|
||||
std::cout << "Dumping non-tensor object ";
|
||||
if( pName )
|
||||
std::cout << pName;
|
||||
std::cout << "=" << t;
|
||||
}
|
||||
|
||||
// Helper to dump a tensor in memory order
|
||||
// Kind of superfluous given the above ... just keeping in case I need to fall back to this
|
||||
#define DumpMemoryOrder(args...) DumpMemoryOrder_func(args)
|
||||
template <typename T>
|
||||
typename std::enable_if<EigenIO::is_tensor_of_scalar<T>::value, void>::type
|
||||
DumpMemoryOrder_func(T &t, const char * pName = nullptr)
|
||||
{
|
||||
const auto rank = t.rank();
|
||||
const auto &dims = t.dimensions();
|
||||
std::cout << "Dumping rank " << rank << ((T::Options & Eigen::RowMajor) ? ", row" : ", column") << "-major tensor ";
|
||||
if( pName )
|
||||
std::cout << pName;
|
||||
for( auto d : dims ) std::cout << "[" << d << "]";
|
||||
std::cout << " in memory order:" << std::endl;
|
||||
const typename T::Scalar * p = t.data();
|
||||
const auto size = t.size();
|
||||
const typename T::Scalar * pEnd = p + size;
|
||||
if( rank <= 2 ) {
|
||||
for( unsigned int i = 0 ; i < t.size() ; i++ )
|
||||
std::cout << "[" << i << "]=" << *p++ << " ";
|
||||
std::cout << std::endl;
|
||||
} else {
|
||||
const auto innersize = dims[rank-2] * dims[rank-1];
|
||||
using Index = typename T::Index;
|
||||
std::vector<Index> idx(rank - 2);
|
||||
for( auto &i : idx ) i = 0;
|
||||
Index idxCounter = 0;
|
||||
while( p < pEnd ) {
|
||||
if( T::Options & Eigen::RowMajor ) {
|
||||
if( pName )
|
||||
std::cout << pName;
|
||||
idxCounter = 0;
|
||||
for(auto i = 0 ; i < rank - 2 ; i++)
|
||||
std::cout << "[" << idx[i] << "]:";
|
||||
}
|
||||
for( unsigned int i = 0 ; i < innersize ; i++ )
|
||||
std::cout << " [" << idxCounter++ << "]=" << *p++;
|
||||
if( T::Options & Eigen::RowMajor )
|
||||
std::cout << std::endl;
|
||||
// Now increment MyIndex
|
||||
for( auto i = rank - 3; i != -1 && ++idx[i] == dims[i]; i-- )
|
||||
idx[i] = 0;
|
||||
}
|
||||
if( ! ( T::Options & Eigen::RowMajor ) )
|
||||
std::cout << std::endl;
|
||||
}
|
||||
}
|
||||
#else
|
||||
#define dump_tensor(args...)
|
||||
#define DumpMemoryOrder(args...)
|
||||
#endif
|
||||
}
|
||||
#endif
|
||||
@ -29,6 +29,7 @@ Author: Michael Marshall <michael.marshall@ed.ac.uk>
|
||||
*************************************************************************************/
|
||||
/* END LEGAL */
|
||||
#include <Grid/Grid.h>
|
||||
#include <Grid/util/Eigen.h>
|
||||
|
||||
using namespace Grid;
|
||||
using namespace Grid::QCD;
|
||||
@ -108,16 +109,16 @@ void ioTest(const std::string &filename, const O &object, const std::string &nam
|
||||
std::cout << " done." << std::endl;
|
||||
}
|
||||
|
||||
typedef std::complex<double> TestScalar;
|
||||
typedef Eigen::TensorFixedSize<unsigned short, Eigen::Sizes<5,4,3,2,1>> TensorRank5UShort;
|
||||
typedef Eigen::TensorFixedSize<unsigned short, Eigen::Sizes<5,4,3,2,1>, Eigen::StorageOptions::RowMajor> TensorRank5UShortAlt;
|
||||
typedef ComplexD TestScalar;
|
||||
typedef Eigen::TensorFixedSize<Integer, Eigen::Sizes<5,4,3,2,1>> TensorRank5UShort;
|
||||
typedef Eigen::TensorFixedSize<Integer, Eigen::Sizes<5,4,3,2,1>, 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;
|
||||
|
||||
class PerambIOTestClass: Serializable {
|
||||
Grid_complex<double> Flag;
|
||||
ComplexD Flag;
|
||||
public:
|
||||
using PerambTensor = Eigen::Tensor<SpinColourVector, 6, Eigen::StorageOptions::RowMajor>;
|
||||
GRID_SERIALIZABLE_CLASS_MEMBERS(PerambIOTestClass
|
||||
@ -156,11 +157,11 @@ public:
|
||||
#define TEST_PARAMS( T ) #T, Flag, Precision, filename, pszExtension, TestNum
|
||||
|
||||
template <typename WTR_, typename RDR_, typename T, typename... IndexTypes>
|
||||
void EigenTensorTestSingle(const char * MyTypeName, typename EigenIO::Traits<typename T::Scalar>::scalar_type Flag,
|
||||
void EigenTensorTestSingle(const char * MyTypeName, typename GridTypeMapper<typename T::Scalar>::scalar_type Flag,
|
||||
unsigned short Precision, std::string &filename, const char * pszExtension, unsigned int &TestNum,
|
||||
IndexTypes... otherDims)
|
||||
{
|
||||
using Traits = EigenIO::Traits<typename T::Scalar>;
|
||||
using Traits = GridTypeMapper<typename T::Scalar>;
|
||||
using scalar_type = typename Traits::scalar_type;
|
||||
std::unique_ptr<T> pTensor{new T(otherDims...)};
|
||||
SequentialInit( * pTensor, Flag, Precision );
|
||||
@ -175,7 +176,7 @@ void EigenTensorTest(const char * pszExtension, unsigned short Precision = 0)
|
||||
std::string filename;
|
||||
{
|
||||
int Flag = 7;
|
||||
using TensorSingle = Eigen::TensorFixedSize<int, Eigen::Sizes<1>>;
|
||||
using TensorSingle = Eigen::TensorFixedSize<Integer, Eigen::Sizes<1>>;
|
||||
EigenTensorTestSingle<WTR_, RDR_, TensorSingle>(TEST_PARAMS( TensorSingle ));
|
||||
}
|
||||
TestScalar Flag{1,-3.1415927};
|
||||
@ -239,6 +240,20 @@ void tensorConvTestFn(GridSerialRNG &rng, const std::string label)
|
||||
|
||||
int main(int argc,char **argv)
|
||||
{
|
||||
{
|
||||
LSCTensor Bingo;
|
||||
constexpr Complex Flag{1,-3.1415927};
|
||||
Complex z{0};
|
||||
SpinColourVector * pV = Bingo.data();
|
||||
for( std::size_t i = Bingo.size(); i--; ) {
|
||||
for( typename GridTypeMapper<SpinColourVector>::scalar_type &s : *pV++ ) {
|
||||
s = z;
|
||||
z += Flag;
|
||||
}
|
||||
}
|
||||
dump_tensor( Bingo );
|
||||
}
|
||||
|
||||
Grid_init(&argc,&argv);
|
||||
std::cout << std::boolalpha << "==== basic IO" << std::endl; // display true / false for boolean
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user