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Eigen::Tensor serialisation. Tested on single and double precision builds
This commit is contained in:
Michael Marshall
1
.gitignore
vendored
1
.gitignore
vendored
@@ -114,3 +114,4 @@ gh-pages/
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#####################
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Grid/qcd/spin/gamma-gen/*.h
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Grid/qcd/spin/gamma-gen/*.cc
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Grid/util/Version.h
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@@ -33,8 +33,62 @@ Author: Guido Cossu <guido.cossu@ed.ac.uk>
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#include <type_traits>
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#include <Grid/tensors/Tensors.h>
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#include <Grid/serialisation/VectorUtils.h>
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#include <Grid/Eigen/unsupported/CXX11/Tensor>
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namespace Grid {
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namespace EigenIO {
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// EigenIO works for scalars that are not just Grid supported scalars
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template<typename T, typename V = void> struct is_complex : public std::false_type {};
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// Support all complex types (not just Grid complex types) - even if the definitions overlap (!)
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template<typename T> struct is_complex< T , typename
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std::enable_if< ::Grid::is_complex< T >::value>::type> : public std::true_type {};
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template<typename T> struct is_complex<std::complex<T>, typename
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std::enable_if<!::Grid::is_complex<std::complex<T>>::value>::type> : public std::true_type {};
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// Helpers to support I/O for Eigen tensors of arithmetic scalars, complex types, or Grid tensors
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template<typename T, typename V = void> struct is_scalar : public std::false_type {};
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template<typename T> struct is_scalar<T, typename std::enable_if<std::is_arithmetic<T>::value || is_complex<T>::value>::type> : 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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std::is_base_of<Eigen::TensorBase<T, Eigen::ReadOnlyAccessors>, T>::value> {};
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// Is this an Eigen tensor of a supported scalar
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template<typename T, typename V = void> struct is_tensor_of_scalar : public std::false_type {};
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template<typename T> struct is_tensor_of_scalar<T, typename std::enable_if<is_tensor<T>::value && is_scalar<typename T::Scalar>::value>::type> : public std::true_type {};
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// Is this an Eigen tensor of a supported container
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template<typename T, typename V = 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 && isGridTensor<typename T::Scalar>::value>::type> : public std::true_type {};
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// Traits are the default for scalars, or come from GridTypeMapper for GridTensors
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template<typename T, typename V = void> struct Traits {};
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template<typename T> struct Traits<T, typename std::enable_if<is_tensor_of_scalar<T>::value>::type> : public GridTypeMapper_Base {
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using scalar_type = typename T::Scalar;
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static constexpr bool is_complex = ::Grid::EigenIO::is_complex<scalar_type>::value;
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};
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template<typename T> struct Traits<T, typename std::enable_if<is_tensor_of_container<T>::value>::type> : public GridTypeMapper<typename T::Scalar> {
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using scalar_type = typename GridTypeMapper<typename T::Scalar>::scalar_type;
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static constexpr bool is_complex = ::Grid::EigenIO::is_complex<scalar_type>::value;
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};
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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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template<typename Scalar_, typename Dimensions_, int Options_, typename IndexType>
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struct is_tensor_fixed<Eigen::TensorFixedSize<Scalar_, Dimensions_, Options_, IndexType>>
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: public std::true_type {};
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template<typename Scalar_, typename Dimensions_, int Options_, typename IndexType,
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int MapOptions_, template <class> class MapPointer_>
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struct is_tensor_fixed<Eigen::TensorMap<Eigen::TensorFixedSize<Scalar_, Dimensions_,
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Options_, IndexType>, MapOptions_, MapPointer_>>
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: public std::true_type {};
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// Is this a variable-size Eigen tensor
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template<typename T, typename V = void> struct is_tensor_variable : public std::false_type {};
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template<typename T> struct is_tensor_variable<T, typename std::enable_if<is_tensor<T>::value
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&& !is_tensor_fixed<T>::value>::type> : public std::true_type {};
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}
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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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@@ -49,10 +103,10 @@ namespace Grid {
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void push(const std::string &s);
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void pop(void);
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template <typename U>
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typename std::enable_if<std::is_base_of<Serializable, U>::value, void>::type
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typename std::enable_if<std::is_base_of<Serializable, U>::value>::type
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write(const std::string& s, const U &output);
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template <typename U>
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typename std::enable_if<!std::is_base_of<Serializable, U>::value, void>::type
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typename std::enable_if<!std::is_base_of<Serializable, U>::value && !EigenIO::is_tensor<U>::value>::type
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write(const std::string& s, const U &output);
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template <typename U>
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void write(const std::string &s, const iScalar<U> &output);
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@@ -60,6 +114,42 @@ namespace Grid {
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void write(const std::string &s, const iVector<U, N> &output);
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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<ETensor>::value>::type
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write(const std::string &s, const ETensor &output);
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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,
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const typename ETensor::Scalar *>::type
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getFirstScalar(const ETensor &output)
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{
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return output.data();
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}
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template <typename ETensor>
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inline typename std::enable_if<EigenIO::is_tensor_of_container<ETensor>::value,
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const typename EigenIO::Traits<ETensor>::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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}
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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(S * &pCopy, const S &Source)
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{
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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<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 GridTypeMapper<S>::scalar_type &item : Source )
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* pCopy ++ = item;
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}
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void scientificFormat(const bool set);
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bool isScientific(void);
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void setPrecision(const unsigned int prec);
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@@ -83,7 +173,8 @@ namespace Grid {
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typename std::enable_if<std::is_base_of<Serializable, U>::value, void>::type
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read(const std::string& s, U &output);
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template <typename U>
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typename std::enable_if<!std::is_base_of<Serializable, U>::value, void>::type
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typename std::enable_if<!std::is_base_of<Serializable, U>::value
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&& !EigenIO::is_tensor<U>::value, void>::type
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read(const std::string& s, U &output);
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template <typename U>
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void read(const std::string &s, iScalar<U> &output);
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@@ -91,6 +182,32 @@ namespace Grid {
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void read(const std::string &s, iVector<U, N> &output);
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template <typename U, int N>
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void read(const std::string &s, iMatrix<U, N> &output);
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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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read(const std::string &s, ETensor &output);
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template <typename ETensor>
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typename std::enable_if<EigenIO::is_tensor_fixed<ETensor>::value, void>::type
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Reshape(ETensor &t, const std::array<typename ETensor::Index, ETensor::NumDimensions> &dims );
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template <typename ETensor>
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typename std::enable_if<EigenIO::is_tensor_variable<ETensor>::value, void>::type
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Reshape(ETensor &t, const std::array<typename ETensor::Index, ETensor::NumDimensions> &dims );
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// Helper functions for Scalar vs Container specialisations
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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(S &Dest, const S * &pSource)
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{
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Dest = * pSource ++;
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}
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template <typename S>
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inline typename std::enable_if<isGridTensor<S>::value, void>::type
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copyScalars(S &Dest, const typename GridTypeMapper<S>::scalar_type * &pSource)
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{
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for( typename GridTypeMapper<S>::scalar_type &item : Dest )
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item = * pSource ++;
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}
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protected:
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template <typename U>
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void fromString(U &output, const std::string &s);
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@@ -135,12 +252,14 @@ namespace Grid {
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template <typename T>
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template <typename U>
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typename std::enable_if<!std::is_base_of<Serializable, U>::value, void>::type
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typename std::enable_if<!std::is_base_of<Serializable, U>::value
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&& !EigenIO::is_tensor<U>::value, void>::type
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Writer<T>::write(const std::string &s, const U &output)
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{
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upcast->writeDefault(s, output);
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}
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template <typename T>
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template <typename U>
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void Writer<T>::write(const std::string &s, const iScalar<U> &output)
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@@ -162,6 +281,58 @@ namespace Grid {
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upcast->writeDefault(s, tensorToVec(output));
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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>
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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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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<ETensor>;
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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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// 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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pWriteBuffer = getFirstScalar( output );
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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 = new Scalar[TotalNumElements];
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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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const Container & c = output( MyIndex );
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copyScalars( pCopy, c );
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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 ) delete [] pCopyBuffer;
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}
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template <typename T>
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void Writer<T>::scientificFormat(const bool set)
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{
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@@ -215,7 +386,8 @@ namespace Grid {
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template <typename T>
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template <typename U>
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typename std::enable_if<!std::is_base_of<Serializable, U>::value, void>::type
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typename std::enable_if<!std::is_base_of<Serializable, U>::value
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&& !EigenIO::is_tensor<U>::value, void>::type
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Reader<T>::read(const std::string &s, U &output)
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{
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upcast->readDefault(s, output);
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@@ -251,6 +423,79 @@ namespace Grid {
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vecToTensor(output, v);
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}
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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<ETensor>::value, void>::type
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Reader<T>::read(const std::string &s, ETensor &output)
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{
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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<ETensor>;
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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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using ETDims = std::array<Index, TensorRank>; // Dimensions of the tensor
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// read the (flat) data and dimensionality
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std::vector<std::size_t> dimData;
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std::vector<Scalar> buf;
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upcast->readMultiDim( s, buf, dimData );
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assert(dimData.size() == TotalRank && "EigenIO: Tensor rank mismatch" );
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// Make sure that the number of elements read matches dimensions read
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std::size_t NumContainers = 1;
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for( auto i = 0 ; i < TensorRank ; i++ )
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NumContainers *= dimData[i];
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// If our scalar object is a Container, make sure it's dimensions match what we read back
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std::size_t ElementsPerContainer = 1;
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for( auto i = 0 ; i < ContainerRank ; i++ ) {
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assert( dimData[TensorRank+i] == Traits::Dimension(i) && "Tensor Container dimensions don't match data" );
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ElementsPerContainer *= dimData[TensorRank+i];
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}
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assert( NumContainers * ElementsPerContainer == buf.size() && "EigenIO: Number of elements != product of dimensions" );
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// Now see whether the tensor is the right shape, or can be made to be
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const auto & dims{output.dimensions()};
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bool bShapeOK = (output.data() != nullptr);
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for( auto i = 0; bShapeOK && i < TensorRank ; i++ )
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if( dims[i] != dimData[i] )
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bShapeOK = false;
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// Make the tensor the same size as the data read
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ETDims MyIndex;
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if( !bShapeOK ) {
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for( auto i = 0 ; i < TensorRank ; i++ )
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MyIndex[i] = dimData[i];
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Reshape(output, MyIndex);
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}
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// Copy the data into the tensor
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for( auto &d : MyIndex ) d = 0;
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const Scalar * pSource = &buf[0];
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for( std::size_t n = 0 ; n < NumContainers ; n++ ) {
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Container & c = output( MyIndex );
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copyScalars( c, pSource );
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// Now increment the index
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for( int i = TensorRank - 1; i != -1 && ++MyIndex[i] == dims[i]; i-- )
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MyIndex[i] = 0;
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}
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assert( pSource == &buf[NumContainers * ElementsPerContainer] );
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}
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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_fixed<ETensor>::value, void>::type
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Reader<T>::Reshape(ETensor &t, const std::array<typename ETensor::Index, ETensor::NumDimensions> &dims )
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{
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assert( 0 && "EigenIO: Fixed tensor dimensions can't be changed" );
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}
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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_variable<ETensor>::value, void>::type
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Reader<T>::Reshape(ETensor &t, const std::array<typename ETensor::Index, ETensor::NumDimensions> &dims )
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{
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//t.reshape( dims );
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t.resize( dims );
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}
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template <typename T>
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template <typename U>
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void Reader<T>::fromString(U &output, const std::string &s)
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@@ -289,6 +534,48 @@ namespace Grid {
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{
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return os;
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}
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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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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 = (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, 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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}
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template <typename T>
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static inline typename std::enable_if<EigenIO::is_tensor<T>::value, bool>::type
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CompareMember(const std::vector<T> &lhs, const std::vector<T> &rhs) {
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const auto NumElements{lhs.size()};
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bool bResult = ( NumElements == rhs.size() );
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for( auto i = 0 ; i < NumElements && bResult ; i++ )
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bResult = CompareMember(lhs[i], rhs[i]);
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return bResult;
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}
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template <typename T>
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static inline typename std::enable_if<!EigenIO::is_tensor<T>::value, void>::type
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WriteMember(std::ostream &os, const T &object) {
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os << object;
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}
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template <typename T>
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static inline typename std::enable_if<EigenIO::is_tensor<T>::value, void>::type
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WriteMember(std::ostream &os, const T &object) {
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os << "Eigen::Tensor";
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}
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};
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// Generic writer interface //////////////////////////////////////////////////
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@@ -51,6 +51,8 @@ namespace Grid {
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template <typename U>
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void writeDefault(const std::string &s, const std::vector<U> &x);
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||||
void writeDefault(const std::string &s, const char *x);
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template <typename U>
|
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void writeMultiDim(const std::string &s, const std::vector<size_t> & Dimensions, const U * pDataRowMajor, size_t NumElements);
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private:
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std::ofstream file_;
|
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};
|
||||
@@ -66,6 +68,8 @@ namespace Grid {
|
||||
void readDefault(const std::string &s, U &output);
|
||||
template <typename U>
|
||||
void readDefault(const std::string &s, std::vector<U> &output);
|
||||
template <typename U>
|
||||
void readMultiDim(const std::string &s, std::vector<U> &buf, std::vector<size_t> &dim);
|
||||
private:
|
||||
std::ifstream file_;
|
||||
};
|
||||
@@ -92,6 +96,27 @@ namespace Grid {
|
||||
}
|
||||
}
|
||||
|
||||
template <typename U>
|
||||
void BinaryWriter::writeMultiDim(const std::string &s, const std::vector<size_t> & Dimensions, const U * pDataRowMajor, size_t NumElements)
|
||||
{
|
||||
uint64_t rank = static_cast<uint64_t>( Dimensions.size() );
|
||||
uint64_t tmp = 1;
|
||||
for( auto i = 0 ; i < rank ; i++ )
|
||||
tmp *= Dimensions[i];
|
||||
assert( tmp == NumElements && "Dimensions don't match size of data being written" );
|
||||
// Total number of elements
|
||||
write("", tmp);
|
||||
// Number of dimensions
|
||||
write("", rank);
|
||||
// Followed by each dimension
|
||||
for( auto i = 0 ; i < rank ; i++ ) {
|
||||
tmp = Dimensions[i];
|
||||
write("", tmp);
|
||||
}
|
||||
for( auto i = 0; i < NumElements; ++i)
|
||||
write("", pDataRowMajor[i]);
|
||||
}
|
||||
|
||||
// Reader template implementation ////////////////////////////////////////////
|
||||
template <typename U>
|
||||
void BinaryReader::readDefault(const std::string &s, U &output)
|
||||
@@ -114,6 +139,30 @@ namespace Grid {
|
||||
read("", output[i]);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename U>
|
||||
void BinaryReader::readMultiDim(const std::string &s, std::vector<U> &buf, std::vector<size_t> &dim)
|
||||
{
|
||||
// Number of elements
|
||||
uint64_t NumElements;
|
||||
read("", NumElements);
|
||||
// Number of dimensions
|
||||
uint64_t rank;
|
||||
read("", rank);
|
||||
// Followed by each dimension
|
||||
uint64_t count = 1;
|
||||
dim.resize(rank);
|
||||
uint64_t tmp;
|
||||
for( auto i = 0 ; i < rank ; i++ ) {
|
||||
read("", tmp);
|
||||
dim[i] = tmp;
|
||||
count *= tmp;
|
||||
}
|
||||
assert( count == NumElements && "Dimensions don't match size of data being read" );
|
||||
buf.resize(count);
|
||||
for( auto i = 0; i < count; ++i)
|
||||
read("", buf[i]);
|
||||
}
|
||||
}
|
||||
|
||||
#endif
|
||||
|
||||
@@ -3,6 +3,7 @@
|
||||
|
||||
#include <stack>
|
||||
#include <string>
|
||||
#include <list>
|
||||
#include <vector>
|
||||
#include <H5Cpp.h>
|
||||
#include <Grid/tensors/Tensors.h>
|
||||
@@ -38,6 +39,8 @@ namespace Grid
|
||||
template <typename U>
|
||||
typename std::enable_if<!element<std::vector<U>>::is_number, void>::type
|
||||
writeDefault(const std::string &s, const std::vector<U> &x);
|
||||
template <typename U>
|
||||
void writeMultiDim(const std::string &s, const std::vector<size_t> & Dimensions, const U * pDataRowMajor, size_t NumElements);
|
||||
H5NS::Group & getGroup(void);
|
||||
private:
|
||||
template <typename U>
|
||||
@@ -66,6 +69,8 @@ namespace Grid
|
||||
template <typename U>
|
||||
typename std::enable_if<!element<std::vector<U>>::is_number, void>::type
|
||||
readDefault(const std::string &s, std::vector<U> &x);
|
||||
template <typename U>
|
||||
void readMultiDim(const std::string &s, std::vector<U> &buf, std::vector<size_t> &dim);
|
||||
H5NS::Group & getGroup(void);
|
||||
private:
|
||||
template <typename U>
|
||||
@@ -101,6 +106,90 @@ namespace Grid
|
||||
template <>
|
||||
void Hdf5Writer::writeDefault(const std::string &s, const std::string &x);
|
||||
|
||||
template <typename U>
|
||||
void Hdf5Writer::writeMultiDim(const std::string &s, const std::vector<size_t> & Dimensions, const U * pDataRowMajor, size_t NumElements)
|
||||
{
|
||||
// Hdf5 needs the dimensions as hsize_t
|
||||
const int rank = static_cast<int>(Dimensions.size());
|
||||
std::vector<hsize_t> dim(rank);
|
||||
for(int i = 0; i < rank; i++)
|
||||
dim[i] = Dimensions[i];
|
||||
// write the entire dataset to file
|
||||
H5NS::DataSpace dataSpace(rank, dim.data());
|
||||
|
||||
size_t DataSize = NumElements * sizeof(U);
|
||||
if (DataSize > dataSetThres_)
|
||||
{
|
||||
// First few prime numbers from https://oeis.org/A000040
|
||||
static const unsigned short Primes[] = { 2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31,
|
||||
37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97, 101, 103, 107, 109,
|
||||
113, 127, 131, 137, 139, 149, 151, 157, 163, 167, 173, 179, 181, 191, 193,
|
||||
197, 199, 211, 223, 227, 229, 233, 239, 241, 251, 257, 263, 269, 271 };
|
||||
constexpr int NumPrimes = sizeof( Primes ) / sizeof( Primes[0] );
|
||||
// Make sure 1) each dimension; and 2) chunk size is < 4GB
|
||||
const hsize_t MaxElements = ( sizeof( U ) == 1 ) ? 0xffffffff : 0x100000000 / sizeof( U );
|
||||
hsize_t ElementsPerChunk = 1;
|
||||
bool bTooBig = false;
|
||||
for( int i = rank - 1 ; i != -1 ; i-- ) {
|
||||
auto &d = dim[i];
|
||||
if( bTooBig )
|
||||
d = 1; // Chunk size is already as big as can be - remaining dimensions = 1
|
||||
else {
|
||||
// If individual dimension too big, reduce by prime factors if possible
|
||||
for( int PrimeIdx = 0; d > MaxElements && PrimeIdx < NumPrimes; ) {
|
||||
if( d % Primes[PrimeIdx] )
|
||||
PrimeIdx++;
|
||||
else
|
||||
d /= Primes[PrimeIdx];
|
||||
}
|
||||
const char ErrorMsg[] = " dimension > 4GB without small prime factors. "
|
||||
"Hdf5IO chunk size will be inefficient. NB Serialisation is not intended for large datasets - please consider alternatives.";
|
||||
if( d > MaxElements ) {
|
||||
std::cout << GridLogWarning << "Individual" << ErrorMsg << std::endl;
|
||||
hsize_t quotient = d / MaxElements;
|
||||
if( d % MaxElements )
|
||||
quotient++;
|
||||
d /= quotient;
|
||||
}
|
||||
// Now make sure overall size is not too big
|
||||
hsize_t OverflowCheck = ElementsPerChunk;
|
||||
ElementsPerChunk *= d;
|
||||
assert( OverflowCheck == ElementsPerChunk / d && "Product of dimensions overflowed hsize_t" );
|
||||
// If product of dimensions too big, reduce by prime factors
|
||||
for( int PrimeIdx = 0; ElementsPerChunk > MaxElements && PrimeIdx < NumPrimes; ) {
|
||||
bTooBig = true;
|
||||
if( d % Primes[PrimeIdx] )
|
||||
PrimeIdx++;
|
||||
else {
|
||||
d /= Primes[PrimeIdx];
|
||||
ElementsPerChunk /= Primes[PrimeIdx];
|
||||
}
|
||||
}
|
||||
if( ElementsPerChunk > MaxElements ) {
|
||||
std::cout << GridLogMessage << "Product of" << ErrorMsg << std::endl;
|
||||
hsize_t quotient = ElementsPerChunk / MaxElements;
|
||||
if( ElementsPerChunk % MaxElements )
|
||||
quotient++;
|
||||
d /= quotient;
|
||||
ElementsPerChunk /= quotient;
|
||||
}
|
||||
}
|
||||
}
|
||||
H5NS::DataSet dataSet;
|
||||
H5NS::DSetCreatPropList plist;
|
||||
plist.setChunk(rank, dim.data());
|
||||
plist.setFletcher32();
|
||||
dataSet = group_.createDataSet(s, Hdf5Type<U>::type(), dataSpace, plist);
|
||||
dataSet.write(pDataRowMajor, Hdf5Type<U>::type());
|
||||
}
|
||||
else
|
||||
{
|
||||
H5NS::Attribute attribute;
|
||||
attribute = group_.createAttribute(s, Hdf5Type<U>::type(), dataSpace);
|
||||
attribute.write(Hdf5Type<U>::type(), pDataRowMajor);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename U>
|
||||
typename std::enable_if<element<std::vector<U>>::is_number, void>::type
|
||||
Hdf5Writer::writeDefault(const std::string &s, const std::vector<U> &x)
|
||||
@@ -110,34 +199,11 @@ namespace Grid
|
||||
|
||||
// flatten the vector and getting dimensions
|
||||
Flatten<std::vector<U>> flat(x);
|
||||
std::vector<hsize_t> dim;
|
||||
std::vector<size_t> dim;
|
||||
const auto &flatx = flat.getFlatVector();
|
||||
|
||||
for (auto &d: flat.getDim())
|
||||
{
|
||||
dim.push_back(d);
|
||||
}
|
||||
|
||||
// write to file
|
||||
H5NS::DataSpace dataSpace(dim.size(), dim.data());
|
||||
|
||||
if (flatx.size() > dataSetThres_)
|
||||
{
|
||||
H5NS::DataSet dataSet;
|
||||
H5NS::DSetCreatPropList plist;
|
||||
|
||||
plist.setChunk(dim.size(), dim.data());
|
||||
plist.setFletcher32();
|
||||
dataSet = group_.createDataSet(s, Hdf5Type<Element>::type(), dataSpace, plist);
|
||||
dataSet.write(flatx.data(), Hdf5Type<Element>::type());
|
||||
}
|
||||
else
|
||||
{
|
||||
H5NS::Attribute attribute;
|
||||
|
||||
attribute = group_.createAttribute(s, Hdf5Type<Element>::type(), dataSpace);
|
||||
attribute.write(Hdf5Type<Element>::type(), flatx.data());
|
||||
}
|
||||
writeMultiDim<Element>(s, dim, &flatx[0], flatx.size());
|
||||
}
|
||||
|
||||
template <typename U>
|
||||
@@ -175,8 +241,7 @@ namespace Grid
|
||||
void Hdf5Reader::readDefault(const std::string &s, std::string &x);
|
||||
|
||||
template <typename U>
|
||||
typename std::enable_if<element<std::vector<U>>::is_number, void>::type
|
||||
Hdf5Reader::readDefault(const std::string &s, std::vector<U> &x)
|
||||
void Hdf5Reader::readMultiDim(const std::string &s, std::vector<U> &buf, std::vector<size_t> &dim)
|
||||
{
|
||||
// alias to element type
|
||||
typedef typename element<std::vector<U>>::type Element;
|
||||
@@ -184,7 +249,6 @@ namespace Grid
|
||||
// read the dimensions
|
||||
H5NS::DataSpace dataSpace;
|
||||
std::vector<hsize_t> hdim;
|
||||
std::vector<size_t> dim;
|
||||
hsize_t size = 1;
|
||||
|
||||
if (group_.attrExists(s))
|
||||
@@ -204,9 +268,9 @@ namespace Grid
|
||||
}
|
||||
|
||||
// read the flat vector
|
||||
std::vector<Element> buf(size);
|
||||
buf.resize(size);
|
||||
|
||||
if (size > dataSetThres_)
|
||||
if (size * sizeof(Element) > dataSetThres_)
|
||||
{
|
||||
H5NS::DataSet dataSet;
|
||||
|
||||
@@ -220,6 +284,18 @@ namespace Grid
|
||||
attribute = group_.openAttribute(s);
|
||||
attribute.read(Hdf5Type<Element>::type(), buf.data());
|
||||
}
|
||||
}
|
||||
|
||||
template <typename U>
|
||||
typename std::enable_if<element<std::vector<U>>::is_number, void>::type
|
||||
Hdf5Reader::readDefault(const std::string &s, std::vector<U> &x)
|
||||
{
|
||||
// alias to element type
|
||||
typedef typename element<std::vector<U>>::type Element;
|
||||
|
||||
std::vector<size_t> dim;
|
||||
std::vector<Element> buf;
|
||||
readMultiDim( s, buf, dim );
|
||||
|
||||
// reconstruct the multidimensional vector
|
||||
Reconstruct<std::vector<U>> r(buf, dim);
|
||||
|
||||
@@ -109,8 +109,8 @@ THE SOFTWARE.
|
||||
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
#define GRID_MACRO_MEMBER(A,B) A B;
|
||||
#define GRID_MACRO_COMP_MEMBER(A,B) result = (result and (lhs. B == rhs. B));
|
||||
#define GRID_MACRO_OS_WRITE_MEMBER(A,B) os<< #A <<" " #B << " = " << obj. B << " ; " <<std::endl;
|
||||
#define GRID_MACRO_COMP_MEMBER(A,B) result = (result and CompareMember(lhs. B, rhs. B));
|
||||
#define GRID_MACRO_OS_WRITE_MEMBER(A,B) os<< #A <<" " #B << " = "; WriteMember( os, obj. B ); os << " ; " <<std::endl;
|
||||
#define GRID_MACRO_READ_MEMBER(A,B) Grid::read(RD,#B,obj. B);
|
||||
#define GRID_MACRO_WRITE_MEMBER(A,B) Grid::write(WR,#B,obj. B);
|
||||
|
||||
|
||||
@@ -51,6 +51,8 @@ namespace Grid
|
||||
void writeDefault(const std::string &s, const U &x);
|
||||
template <typename U>
|
||||
void writeDefault(const std::string &s, const std::vector<U> &x);
|
||||
template <typename U>
|
||||
void writeMultiDim(const std::string &s, const std::vector<size_t> & Dimensions, const U * pDataRowMajor, size_t NumElements);
|
||||
private:
|
||||
void indent(void);
|
||||
private:
|
||||
@@ -69,6 +71,8 @@ namespace Grid
|
||||
void readDefault(const std::string &s, U &output);
|
||||
template <typename U>
|
||||
void readDefault(const std::string &s, std::vector<U> &output);
|
||||
template <typename U>
|
||||
void readMultiDim(const std::string &s, std::vector<U> &buf, std::vector<size_t> &dim);
|
||||
private:
|
||||
void checkIndent(void);
|
||||
private:
|
||||
@@ -96,6 +100,17 @@ namespace Grid
|
||||
}
|
||||
}
|
||||
|
||||
template <typename U>
|
||||
void TextWriter::writeMultiDim(const std::string &s, const std::vector<size_t> & Dimensions, const U * pDataRowMajor, size_t NumElements)
|
||||
{
|
||||
uint64_t Rank = Dimensions.size();
|
||||
write(s, Rank);
|
||||
for( uint64_t d : Dimensions )
|
||||
write(s, d);
|
||||
while( NumElements-- )
|
||||
write(s, *pDataRowMajor++);
|
||||
}
|
||||
|
||||
// Reader template implementation ////////////////////////////////////////////
|
||||
template <typename U>
|
||||
void TextReader::readDefault(const std::string &s, U &output)
|
||||
@@ -121,6 +136,23 @@ namespace Grid
|
||||
read("", output[i]);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename U>
|
||||
void TextReader::readMultiDim(const std::string &s, std::vector<U> &buf, std::vector<size_t> &dim)
|
||||
{
|
||||
const char sz[] = "";
|
||||
uint64_t Rank;
|
||||
read(sz, Rank);
|
||||
dim.resize( Rank );
|
||||
size_t NumElements = 1;
|
||||
for( auto &d : dim ) {
|
||||
read(sz, d);
|
||||
NumElements *= d;
|
||||
}
|
||||
buf.resize( NumElements );
|
||||
for( auto &x : buf )
|
||||
read(s, x);
|
||||
}
|
||||
}
|
||||
|
||||
#endif
|
||||
|
||||
@@ -53,6 +53,17 @@ namespace Grid {
|
||||
return os;
|
||||
}
|
||||
|
||||
// std::vector<std:vector<...>> nested to specified Rank //////////////////////////////////
|
||||
template<typename T, unsigned int Rank>
|
||||
struct NestedStdVector {
|
||||
typedef typename std::vector<typename NestedStdVector<T, Rank - 1>::type> type;
|
||||
};
|
||||
|
||||
template<typename T>
|
||||
struct NestedStdVector<T,0> {
|
||||
typedef T type;
|
||||
};
|
||||
|
||||
// Grid scalar tensors to nested std::vectors //////////////////////////////////
|
||||
template <typename T>
|
||||
struct TensorToVec
|
||||
|
||||
@@ -57,6 +57,8 @@ namespace Grid
|
||||
void writeDefault(const std::string &s, const U &x);
|
||||
template <typename U>
|
||||
void writeDefault(const std::string &s, const std::vector<U> &x);
|
||||
template <typename U>
|
||||
void writeMultiDim(const std::string &s, const std::vector<size_t> & Dimensions, const U * pDataRowMajor, size_t NumElements);
|
||||
std::string docString(void);
|
||||
std::string string(void);
|
||||
private:
|
||||
@@ -79,6 +81,8 @@ namespace Grid
|
||||
void readDefault(const std::string &s, U &output);
|
||||
template <typename U>
|
||||
void readDefault(const std::string &s, std::vector<U> &output);
|
||||
template <typename U>
|
||||
void readMultiDim(const std::string &s, std::vector<U> &buf, std::vector<size_t> &dim);
|
||||
void readCurrentSubtree(std::string &s);
|
||||
private:
|
||||
void checkParse(const pugi::xml_parse_result &result, const std::string name);
|
||||
@@ -122,9 +126,41 @@ namespace Grid
|
||||
void XmlWriter::writeDefault(const std::string &s, const std::vector<U> &x)
|
||||
{
|
||||
push(s);
|
||||
for (auto &x_i: x)
|
||||
for( auto &u : x )
|
||||
{
|
||||
write("elem", x_i);
|
||||
write("elem", u);
|
||||
}
|
||||
pop();
|
||||
}
|
||||
|
||||
template <typename U>
|
||||
void XmlWriter::writeMultiDim(const std::string &s, const std::vector<size_t> & Dimensions, const U * pDataRowMajor, size_t NumElements)
|
||||
{
|
||||
push(s);
|
||||
size_t count = 1;
|
||||
const int Rank = static_cast<int>( Dimensions.size() );
|
||||
write("rank", Rank );
|
||||
std::vector<size_t> MyIndex( Rank );
|
||||
for( auto d : Dimensions ) {
|
||||
write("dim", d);
|
||||
count *= d;
|
||||
}
|
||||
assert( count == NumElements && "XmlIO : element count doesn't match dimensions" );
|
||||
static const char sName[] = "tensor";
|
||||
for( int i = 0 ; i < Rank ; i++ ) {
|
||||
MyIndex[i] = 0;
|
||||
push(sName);
|
||||
}
|
||||
while (NumElements--) {
|
||||
write("elem", *pDataRowMajor++);
|
||||
int i;
|
||||
for( i = Rank - 1 ; i != -1 && ++MyIndex[i] == Dimensions[i] ; i-- )
|
||||
MyIndex[i] = 0;
|
||||
int Rollover = Rank - 1 - i;
|
||||
for( i = 0 ; i < Rollover ; i++ )
|
||||
pop();
|
||||
for( i = 0 ; NumElements && i < Rollover ; i++ )
|
||||
push(sName);
|
||||
}
|
||||
pop();
|
||||
}
|
||||
@@ -145,25 +181,66 @@ namespace Grid
|
||||
template <typename U>
|
||||
void XmlReader::readDefault(const std::string &s, std::vector<U> &output)
|
||||
{
|
||||
std::string buf;
|
||||
unsigned int i = 0;
|
||||
|
||||
if (!push(s))
|
||||
{
|
||||
std::cout << GridLogWarning << "XML: cannot open node '" << s << "'";
|
||||
std::cout << std::endl;
|
||||
|
||||
return;
|
||||
}
|
||||
while (node_.child("elem"))
|
||||
} else {
|
||||
for(unsigned int i = 0; node_.child("elem"); )
|
||||
{
|
||||
output.resize(i + 1);
|
||||
read("elem", output[i]);
|
||||
read("elem", output[i++]);
|
||||
node_.child("elem").set_name("elem-done");
|
||||
i++;
|
||||
}
|
||||
pop();
|
||||
}
|
||||
}
|
||||
|
||||
template <typename U>
|
||||
void XmlReader::readMultiDim(const std::string &s, std::vector<U> &buf, std::vector<size_t> &dim)
|
||||
{
|
||||
if (!push(s))
|
||||
{
|
||||
std::cout << GridLogWarning << "XML: cannot open node '" << s << "'";
|
||||
std::cout << std::endl;
|
||||
} else {
|
||||
static const char sName[] = "tensor";
|
||||
static const char sNameDone[] = "tensor-done";
|
||||
int Rank;
|
||||
read("rank", Rank);
|
||||
dim.resize( Rank );
|
||||
size_t NumElements = 1;
|
||||
for( auto &d : dim )
|
||||
{
|
||||
read("dim", d);
|
||||
node_.child("dim").set_name("dim-done");
|
||||
NumElements *= d;
|
||||
}
|
||||
buf.resize( NumElements );
|
||||
std::vector<size_t> MyIndex( Rank );
|
||||
for( int i = 0 ; i < Rank ; i++ ) {
|
||||
MyIndex[i] = 0;
|
||||
push(sName);
|
||||
}
|
||||
|
||||
for( auto &x : buf )
|
||||
{
|
||||
NumElements--;
|
||||
read("elem", x);
|
||||
node_.child("elem").set_name("elem-done");
|
||||
int i;
|
||||
for( i = Rank - 1 ; i != -1 && ++MyIndex[i] == dim[i] ; i-- )
|
||||
MyIndex[i] = 0;
|
||||
int Rollover = Rank - 1 - i;
|
||||
for( i = 0 ; i < Rollover ; i++ ) {
|
||||
node_.set_name(sNameDone);
|
||||
pop();
|
||||
}
|
||||
for( i = 0 ; NumElements && i < Rollover ; i++ )
|
||||
push(sName);
|
||||
}
|
||||
pop();
|
||||
}
|
||||
}
|
||||
}
|
||||
#endif
|
||||
|
||||
@@ -143,6 +143,7 @@ namespace Grid {
|
||||
typedef vRealD DoublePrecision;
|
||||
};
|
||||
template<> struct GridTypeMapper<vRealH> : public GridTypeMapper_Base {
|
||||
// Fixme this is incomplete until Grid supports fp16 or bfp16 arithmetic types
|
||||
typedef RealF scalar_type;
|
||||
typedef vRealH vector_type;
|
||||
typedef vRealD vector_typeD;
|
||||
@@ -153,6 +154,7 @@ namespace Grid {
|
||||
typedef vRealD DoublePrecision;
|
||||
};
|
||||
template<> struct GridTypeMapper<vComplexH> : public GridTypeMapper_Base {
|
||||
// Fixme this is incomplete until Grid supports fp16 or bfp16 arithmetic types
|
||||
typedef ComplexF scalar_type;
|
||||
typedef vComplexH vector_type;
|
||||
typedef vComplexD vector_typeD;
|
||||
|
||||
198
Grid/util/EigenUtil.h
Normal file
198
Grid/util/EigenUtil.h
Normal file
@@ -0,0 +1,198 @@
|
||||
/*************************************************************************************
|
||||
|
||||
Grid physics library, www.github.com/paboyle/Grid
|
||||
|
||||
Source file: Grid/util/EigenUtil.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_EIGENUTIL_H
|
||||
#define GRID_UTIL_EIGENUTIL_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,
|
||||
const std::array<typename ETensor::Index, ETensor::NumIndices> &MyIndex,
|
||||
const std::array<int, EigenIO::Traits<ETensor>::Rank> &DimGridTensor,
|
||||
std::array<int, EigenIO::Traits<ETensor>::Rank> &GridTensorIndex)
|
||||
{
|
||||
lambda( scalar, Seq++, MyIndex, GridTensorIndex );
|
||||
}
|
||||
|
||||
// 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,
|
||||
const std::array<typename ETensor::Index, ETensor::NumIndices> &MyIndex,
|
||||
const std::array<int, EigenIO::Traits<ETensor>::Rank> &DimGridTensor,
|
||||
std::array<int, EigenIO::Traits<ETensor>::Rank> &GridTensorIndex)
|
||||
{
|
||||
using Traits = EigenIO::Traits<ETensor>;
|
||||
const auto InnerRank = Traits::Rank;
|
||||
for( typename Traits::scalar_type &Source : container ) {
|
||||
lambda(Source, Seq++, MyIndex, GridTensorIndex );
|
||||
// Now increment SubIndex
|
||||
for( auto i = InnerRank - 1; i != -1 && ++GridTensorIndex[i] == DimGridTensor[i]; i-- )
|
||||
GridTensorIndex[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
|
||||
using Index = typename ETensor::Index;
|
||||
using Traits = EigenIO::Traits<ETensor>;
|
||||
// Check that the number of elements in the container is the product of tensor dimensions
|
||||
const Index NumScalars = ET.size();
|
||||
assert( NumScalars > 0 && "EigenUtil: tensor has no elements" );
|
||||
Index ScalarElementCount{1};
|
||||
const auto rank{ETensor::NumIndices};
|
||||
std::array<Index, rank> DimTensor, MyIndex;
|
||||
for(int i = 0; i < rank; i++ ) {
|
||||
DimTensor[i] = ET.dimension(i);
|
||||
ScalarElementCount *= DimTensor[i];
|
||||
MyIndex[i] = 0;
|
||||
}
|
||||
assert( NumScalars == ScalarElementCount && "EigenUtil: tensor size not product of dimensions" );
|
||||
// Save the GridTensor dimensions
|
||||
const auto InnerRank{Traits::Rank};
|
||||
std::array<int, InnerRank> DimGridTensor, GridTensorIndex;
|
||||
for(int i = 0; i < InnerRank; i++ ) {
|
||||
DimGridTensor[i] = Traits::Dimension(i);
|
||||
GridTensorIndex[i] = 0;
|
||||
}
|
||||
// Now walk the tensor in memory order
|
||||
Index Seq = 0;
|
||||
Scalar * pScalar = ET.data();
|
||||
for( Index j = 0; j < NumScalars; j++ ) {
|
||||
for_all_do_lambda<ETensor, Lambda>( lambda, * pScalar, Seq, MyIndex, DimGridTensor, GridTensorIndex );
|
||||
// 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] == DimTensor[i]; i-- )
|
||||
MyIndex[i] = 0;
|
||||
} else {
|
||||
for( auto i = 0; i < rank && ++MyIndex[i] == DimTensor[i]; i++ )
|
||||
MyIndex[i] = 0;
|
||||
Seq = 0;
|
||||
for( auto i = 0; i < rank; i++ ) {
|
||||
Seq *= DimTensor[i];
|
||||
Seq += MyIndex[i];
|
||||
}
|
||||
Seq *= Traits::count;
|
||||
}
|
||||
pScalar++;
|
||||
}
|
||||
}
|
||||
|
||||
// Sequential initialisation of tensors up to specified precision
|
||||
// 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 && !EigenIO::Traits<ETensor>::is_complex>::type
|
||||
SequentialInit( ETensor &ET, typename EigenIO::Traits<ETensor>::scalar_type Inc = 1, unsigned short Precision = 0 )
|
||||
{
|
||||
using Traits = EigenIO::Traits<ETensor>;
|
||||
using scalar_type = typename Traits::scalar_type;
|
||||
using Index = typename ETensor::Index;
|
||||
for_all( ET, [&](scalar_type &c, Index n, const std::array<Index, ETensor::NumIndices> &TensorIndex,
|
||||
const std::array<int, Traits::Rank> &ScalarIndex ) {
|
||||
scalar_type x = Inc * static_cast<scalar_type>(n);
|
||||
if( Precision ) {
|
||||
std::stringstream s;
|
||||
s << std::setprecision(Precision) << x;
|
||||
s >> x;
|
||||
}
|
||||
c = x;
|
||||
} );
|
||||
}
|
||||
|
||||
template <typename ETensor>
|
||||
typename std::enable_if<EigenIO::is_tensor<ETensor>::value && EigenIO::Traits<ETensor>::is_complex>::type
|
||||
SequentialInit( ETensor &ET, typename EigenIO::Traits<ETensor>::scalar_type Inc={1,-1}, unsigned short Precision = 0 )
|
||||
{
|
||||
using Traits = EigenIO::Traits<ETensor>;
|
||||
using scalar_type = typename Traits::scalar_type;
|
||||
using Index = typename ETensor::Index;
|
||||
for_all( ET, [&](scalar_type &c, Index n, const std::array<Index, ETensor::NumIndices> &TensorIndex,
|
||||
const std::array<int, Traits::Rank> &ScalarIndex ) {
|
||||
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
|
||||
template <typename T>
|
||||
typename std::enable_if<EigenIO::is_tensor<T>::value, void>::type
|
||||
dump_tensor(T &t, const char * pName = nullptr)
|
||||
{
|
||||
using Traits = EigenIO::Traits<T>;
|
||||
using scalar_type = typename Traits::scalar_type;
|
||||
using Index = typename T::Index;
|
||||
const auto rank{T::NumIndices};
|
||||
const auto &dims = t.dimensions();
|
||||
std::cout << "Dumping rank " << rank + Traits::Rank << ((T::Options & Eigen::RowMajor) ? ", row" : ", column") << "-major tensor ";
|
||||
if( pName )
|
||||
std::cout << pName;
|
||||
for( int i = 0 ; i < rank; i++ ) std::cout << "[" << dims[i] << "]";
|
||||
for( int i = 0 ; i < Traits::Rank; i++ ) std::cout << "(" << Traits::Dimension(i) << ")";
|
||||
std::cout << " in memory order:" << std::endl;
|
||||
for_all( t, [&](scalar_type &c, Index n, const std::array<Index, rank> &TensorIndex,
|
||||
const std::array<int, Traits::Rank> &ScalarIndex ){
|
||||
std::cout << " ";
|
||||
for( auto dim : TensorIndex )
|
||||
std::cout << "[" << dim << "]";
|
||||
for( auto dim : ScalarIndex )
|
||||
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(T &t, const char * pName = nullptr)
|
||||
{
|
||||
std::cout << "Dumping non-tensor object ";
|
||||
if( pName ) std::cout << pName;
|
||||
std::cout << "=" << t;
|
||||
}
|
||||
}
|
||||
#endif
|
||||
@@ -4,11 +4,12 @@
|
||||
|
||||
Source file: ./tests/Test_serialisation.cc
|
||||
|
||||
Copyright (C) 2015-2016
|
||||
Copyright (C) 2015-2019
|
||||
|
||||
Author: Guido Cossu <guido.cossu@ed.ac.uk>
|
||||
Author: Antonin Portelli <antonin.portelli@me.com>
|
||||
Author: Peter Boyle <paboyle@ph.ed.ac.uk>
|
||||
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
|
||||
@@ -28,6 +29,8 @@ Author: Peter Boyle <paboyle@ph.ed.ac.uk>
|
||||
*************************************************************************************/
|
||||
/* END LEGAL */
|
||||
#include <Grid/Grid.h>
|
||||
#include <Grid/util/EigenUtil.h>
|
||||
#include <typeinfo>
|
||||
|
||||
using namespace Grid;
|
||||
using namespace Grid::QCD;
|
||||
@@ -80,25 +83,175 @@ double d = 2*M_PI;
|
||||
bool b = false;
|
||||
|
||||
template <typename W, typename R, typename O>
|
||||
void ioTest(const std::string &filename, const O &object, const std::string &name)
|
||||
void ioTest(const std::string &filename, const O &object, const std::string &name,
|
||||
const char * tag = "testobject", unsigned short Precision = 0 )
|
||||
{
|
||||
std::cout << "IO test: " << name << " -> " << filename << " ...";
|
||||
// writer needs to be destroyed so that writing physically happens
|
||||
{
|
||||
W writer(filename);
|
||||
|
||||
write(writer, "testobject", object);
|
||||
if( Precision )
|
||||
writer.setPrecision(Precision);
|
||||
write(writer, tag , object);
|
||||
}
|
||||
|
||||
std::cout << " done. reading ...";
|
||||
R reader(filename);
|
||||
O buf;
|
||||
bool good;
|
||||
std::unique_ptr<O> buf( new O ); // In case object too big for stack
|
||||
|
||||
read(reader, "testobject", buf);
|
||||
good = (object == buf);
|
||||
std::cout << name << " IO test: " << (good ? "success" : "failure");
|
||||
std::cout << std::endl;
|
||||
if (!good) exit(EXIT_FAILURE);
|
||||
read(reader, tag, *buf);
|
||||
bool good = Serializable::CompareMember(object, *buf);
|
||||
if (!good) {
|
||||
std::cout << " failure!" << std::endl;
|
||||
if (EigenIO::is_tensor<O>::value)
|
||||
dump_tensor(*buf);
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
std::cout << " done." << std::endl;
|
||||
}
|
||||
|
||||
// Perform I/O tests on a range of tensor types
|
||||
// Test coverage: scalars, complex and GridVectors in single, double and default precision
|
||||
class TensorIO : public Serializable {
|
||||
using TestScalar = ComplexD;
|
||||
using SR3 = Eigen::Sizes<9,4,2>;
|
||||
using SR5 = Eigen::Sizes<5,4,3,2,1>;
|
||||
using ESO = Eigen::StorageOptions;
|
||||
using TensorRank3 = Eigen::Tensor<ComplexF, 3, ESO::RowMajor>;
|
||||
using TensorR5 = Eigen::TensorFixedSize<Real, SR5>;
|
||||
using TensorR5Alt = Eigen::TensorFixedSize<Real, SR5, ESO::RowMajor>;
|
||||
using Tensor942 = Eigen::TensorFixedSize<TestScalar, SR3, ESO::RowMajor>;
|
||||
using aTensor942 = std::vector<Tensor942>;
|
||||
using Perambulator = Eigen::Tensor<SpinColourVector, 6, ESO::RowMajor>;
|
||||
using LSCTensor = Eigen::TensorFixedSize<SpinColourMatrix, Eigen::Sizes<6,5>>;
|
||||
|
||||
static const Real FlagR;
|
||||
static const Complex Flag;
|
||||
static const ComplexF FlagF;
|
||||
static const TestScalar FlagTS;
|
||||
static const char * const pszFilePrefix;
|
||||
|
||||
void Init(unsigned short Precision)
|
||||
{
|
||||
SequentialInit(Perambulator1, Flag, Precision);
|
||||
SequentialInit(Perambulator2, Flag, Precision);
|
||||
SequentialInit(tensorR5, FlagR, Precision);
|
||||
SequentialInit(tensorRank3, FlagF, Precision);
|
||||
SequentialInit(tensor_9_4_2, FlagTS, Precision);
|
||||
for( auto &t : atensor_9_4_2 )
|
||||
SequentialInit(t, FlagTS, Precision);
|
||||
SequentialInit(MyLSCTensor, Flag, Precision);
|
||||
}
|
||||
|
||||
// Perform an I/O test for a single Eigen tensor (of any type)
|
||||
template <typename W, typename R, typename T, typename... IndexTypes>
|
||||
static void TestOne(const char * MyTypeName, unsigned short Precision, std::string &filename,
|
||||
const char * pszExtension, unsigned int &TestNum,
|
||||
typename EigenIO::Traits<T>::scalar_type Flag, IndexTypes... otherDims)
|
||||
{
|
||||
using Traits = EigenIO::Traits<T>;
|
||||
using scalar_type = typename Traits::scalar_type;
|
||||
std::unique_ptr<T> pTensor{new T(otherDims...)};
|
||||
SequentialInit( * pTensor, Flag, Precision );
|
||||
filename = pszFilePrefix + std::to_string(++TestNum) + "_" + MyTypeName + pszExtension;
|
||||
ioTest<W, R, T>(filename, * pTensor, MyTypeName, MyTypeName);
|
||||
}
|
||||
|
||||
public:
|
||||
GRID_SERIALIZABLE_CLASS_MEMBERS(TensorIO
|
||||
, SpinColourVector, spinColourVector
|
||||
, SpinColourMatrix, spinColourMatrix
|
||||
, std::vector<std::string>, DistilParameterNames
|
||||
, std::vector<int>, DistilParameterValues
|
||||
, Perambulator, Perambulator1
|
||||
, Perambulator, Perambulator2
|
||||
, TensorR5, tensorR5
|
||||
, TensorRank3, tensorRank3
|
||||
, Tensor942, tensor_9_4_2
|
||||
, aTensor942, atensor_9_4_2
|
||||
, LSCTensor, MyLSCTensor
|
||||
);
|
||||
TensorIO()
|
||||
: DistilParameterNames {"do", "androids", "dream", "of", "electric", "sheep?"}
|
||||
, DistilParameterValues{2,3,1,4,5,1}
|
||||
, Perambulator1(2,3,1,4,5,1)
|
||||
, Perambulator2(7,1,6,1,5,1)
|
||||
, tensorRank3(7,3,2)
|
||||
, atensor_9_4_2(3) {}
|
||||
|
||||
#define TEST_PARAMS( T ) #T, Precision, filename, pszExtension, TestNum
|
||||
|
||||
// Perform a series of I/O tests for Eigen tensors, including a serialisable object
|
||||
template <typename WTR_, typename RDR_>
|
||||
static void Test(const char * pszExtension, unsigned short Precision = 0)
|
||||
{
|
||||
// Perform a series of tests on progressively more complex tensors
|
||||
unsigned int TestNum = 0;
|
||||
std::string filename;
|
||||
// Rank 1 tensor containing a single integer
|
||||
using TensorSingle = Eigen::TensorFixedSize<Integer, Eigen::Sizes<1>>;
|
||||
TestOne<WTR_, RDR_, TensorSingle>( TEST_PARAMS( TensorSingle ), 7 ); // lucky!
|
||||
// Rather convoluted way of defining a single complex number
|
||||
using TensorSimple = Eigen::Tensor<iMatrix<TestScalar,1>, 6>;
|
||||
using I = typename TensorSimple::Index; // NB: Never specified, so same for all my test tensors
|
||||
// Try progressively more complicated tensors
|
||||
TestOne<WTR_, RDR_, TensorSimple, I,I,I,I,I,I>( TEST_PARAMS( TensorSimple ), FlagTS, 1,1,1,1,1,1 );
|
||||
TestOne<WTR_, RDR_, TensorRank3, I, I, I>( TEST_PARAMS( TensorRank3 ), FlagF, 6, 3, 2 );
|
||||
TestOne<WTR_, RDR_, Tensor942>(TEST_PARAMS( Tensor942 ), FlagTS);
|
||||
TestOne<WTR_, RDR_, LSCTensor>(TEST_PARAMS( LSCTensor ), Flag );
|
||||
|
||||
// Now see whether we can write a tensor in one memory order and read back in the other
|
||||
{
|
||||
TestOne<WTR_, RDR_, TensorR5>(TEST_PARAMS( TensorR5 ), FlagR);
|
||||
std::cout << " Testing alternate memory order read ... ";
|
||||
TensorR5Alt t2;
|
||||
RDR_ reader(filename);
|
||||
::Grid::read(reader, "TensorR5", t2);
|
||||
bool good = true;
|
||||
TensorR5 cf;
|
||||
SequentialInit( cf, FlagR, Precision );
|
||||
for_all( t2, [&](typename EigenIO::Traits<TensorR5Alt>::scalar_type c, I n,
|
||||
const std::array<I, TensorR5Alt::NumIndices> &TensorIndex,
|
||||
const std::array<int, EigenIO::Traits<TensorR5Alt>::Rank> &GridTensorIndex ){
|
||||
Real &r = cf(TensorIndex);
|
||||
if( c != r ){
|
||||
good = false;
|
||||
std::cout << "\nError: " << n << ": " << c << " != " << r;
|
||||
}
|
||||
} );
|
||||
if (!good) {
|
||||
std::cout << std::endl;
|
||||
dump_tensor(t2,"t2");
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
std::cout << " done." << std::endl;
|
||||
}
|
||||
// Now test a serialisable object containing a number of tensors
|
||||
{
|
||||
static const char MyTypeName[] = "TensorIO";
|
||||
filename = pszFilePrefix + std::to_string(++TestNum) + "_" + MyTypeName + pszExtension;
|
||||
std::unique_ptr<TensorIO> pObj{new TensorIO()};
|
||||
pObj->Init(Precision);
|
||||
ioTest<WTR_, RDR_, TensorIO>(filename, * pObj, MyTypeName, MyTypeName, Precision);
|
||||
}
|
||||
// Stress test. Too large for the XML or text readers and writers!
|
||||
#ifdef STRESS_TEST
|
||||
const std::type_info &tw = typeid( WTR_ );
|
||||
if( tw == typeid( Hdf5Writer ) || tw == typeid( BinaryWriter ) ) {
|
||||
using LCMTensor=Eigen::TensorFixedSize<iMatrix<iVector<iMatrix<iVector<LorentzColourMatrix,5>,2>,7>,3>,
|
||||
Eigen::Sizes<2,4,11,10,9>, Eigen::StorageOptions::RowMajor>;
|
||||
std::cout << "sizeof( LCMTensor ) = " << sizeof( LCMTensor ) / 1024 / 1024 << " MB" << std::endl;
|
||||
TestOne<WTR_, RDR_, LCMTensor>(TEST_PARAMS( LCMTensor ), Flag);
|
||||
}
|
||||
#endif
|
||||
}
|
||||
};
|
||||
|
||||
const Real TensorIO::FlagR {-1.001};
|
||||
const Complex TensorIO::Flag {1,-3.1415927};
|
||||
const ComplexF TensorIO::FlagF {1,-3.1415927};
|
||||
const TensorIO::TestScalar TensorIO::FlagTS{1,-3.1415927};
|
||||
const char * const TensorIO::pszFilePrefix = "tensor_";
|
||||
|
||||
template <typename T>
|
||||
void tensorConvTestFn(GridSerialRNG &rng, const std::string label)
|
||||
@@ -121,12 +274,12 @@ void tensorConvTestFn(GridSerialRNG &rng, const std::string label)
|
||||
int main(int argc,char **argv)
|
||||
{
|
||||
Grid_init(&argc,&argv);
|
||||
std::cout << std::boolalpha << "==== basic IO" << std::endl; // display true / false for boolean
|
||||
|
||||
GridSerialRNG rng;
|
||||
|
||||
rng.SeedFixedIntegers(std::vector<int>({42,10,81,9}));
|
||||
|
||||
std::cout << "==== basic IO" << std::endl;
|
||||
XmlWriter WR("bother.xml");
|
||||
|
||||
// test basic type writing
|
||||
@@ -146,7 +299,6 @@ int main(int argc,char **argv)
|
||||
// test serializable class writing
|
||||
myclass obj(1234); // non-trivial constructor
|
||||
std::vector<myclass> vec;
|
||||
std::pair<myenum, myenum> pair;
|
||||
|
||||
std::cout << "-- serialisable class writing to 'bother.xml'..." << std::endl;
|
||||
write(WR,"obj",obj);
|
||||
@@ -154,15 +306,15 @@ int main(int argc,char **argv)
|
||||
vec.push_back(obj);
|
||||
vec.push_back(myclass(5678));
|
||||
vec.push_back(myclass(3838));
|
||||
pair = std::make_pair(myenum::red, myenum::blue);
|
||||
|
||||
write(WR, "objvec", vec);
|
||||
std::cout << "-- serialisable class writing to std::cout:" << std::endl;
|
||||
std::cout << obj << std::endl;
|
||||
std::cout << "-- serialisable class comparison:" << std::endl;
|
||||
std::cout << "vec[0] == obj: " << ((vec[0] == obj) ? "true" : "false") << std::endl;
|
||||
std::cout << "vec[1] == obj: " << ((vec[1] == obj) ? "true" : "false") << std::endl;
|
||||
std::cout << "vec[0] == obj: " << (vec[0] == obj) << std::endl;
|
||||
std::cout << "vec[1] == obj: " << (vec[1] == obj) << std::endl;
|
||||
std::cout << "-- pair writing to std::cout:" << std::endl;
|
||||
std::pair<myenum, myenum> pair = std::make_pair(myenum::red, myenum::blue);
|
||||
std::cout << pair << std::endl;
|
||||
|
||||
// read tests
|
||||
@@ -184,7 +336,15 @@ int main(int argc,char **argv)
|
||||
#ifdef HAVE_HDF5
|
||||
ioTest<Hdf5Writer, Hdf5Reader>("iotest.h5", obj, "HDF5 (object) ");
|
||||
ioTest<Hdf5Writer, Hdf5Reader>("iotest.h5", vec, "HDF5 (vector of objects)");
|
||||
std::cout << "\n==== detailed Hdf5 tensor tests (Grid::EigenIO)" << std::endl;
|
||||
TensorIO::Test<Hdf5Writer, Hdf5Reader>(".h5");
|
||||
#endif
|
||||
std::cout << "\n==== detailed binary tensor tests (Grid::EigenIO)" << std::endl;
|
||||
TensorIO::Test<BinaryWriter, BinaryReader>(".bin");
|
||||
std::cout << "\n==== detailed xml tensor tests (Grid::EigenIO)" << std::endl;
|
||||
TensorIO::Test<XmlWriter, XmlReader>(".xml", 6);
|
||||
std::cout << "\n==== detailed text tensor tests (Grid::EigenIO)" << std::endl;
|
||||
TensorIO::Test<TextWriter, TextReader>(".dat", 5);
|
||||
|
||||
std::cout << "\n==== vector flattening/reconstruction" << std::endl;
|
||||
typedef std::vector<std::vector<std::vector<double>>> vec3d;
|
||||
@@ -227,4 +387,6 @@ int main(int argc,char **argv)
|
||||
tensorConvTest(rng, ColourVector);
|
||||
tensorConvTest(rng, SpinMatrix);
|
||||
tensorConvTest(rng, SpinVector);
|
||||
|
||||
Grid_finalize();
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user