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Grid/tests/IO/Test_serialisation.cc

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/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: ./tests/Test_serialisation.cc
Copyright (C) 2015-2016
Author: Guido Cossu <guido.cossu@ed.ac.uk>
Author: Antonin Portelli <antonin.portelli@me.com>
Author: Peter Boyle <paboyle@ph.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 */
#include <Grid/Grid.h>
using namespace Grid;
using namespace Grid::QCD;
GRID_SERIALIZABLE_ENUM(myenum, undef, red, 1, blue, 2, green, 3);
class myclass: Serializable {
public:
GRID_SERIALIZABLE_CLASS_MEMBERS(myclass,
myenum, e,
std::vector<myenum>, ve,
std::string, name,
int, x,
double, y,
bool , b,
std::vector<double>, array,
std::vector<std::vector<double> >, twodimarray,
std::vector<std::vector<std::vector<Complex> > >, cmplx3darray,
SpinColourMatrix, scm
);
myclass() {}
myclass(int i)
: array(4,5.1)
, twodimarray(3,std::vector<double>(5, 1.23456))
, cmplx3darray(3,std::vector<std::vector<Complex>>(5, std::vector<Complex>(7, Complex(1.2, 3.4))))
, ve(2, myenum::blue)
{
e=myenum::red;
x=i;
y=2*i;
b=true;
name="bother said pooh";
scm()(0, 1)(2, 1) = 2.356;
scm()(3, 0)(1, 1) = 1.323;
scm()(2, 1)(0, 1) = 5.3336;
scm()(0, 2)(1, 1) = 6.336;
scm()(2, 1)(2, 2) = 7.344;
scm()(1, 1)(2, 0) = 8.3534;
}
};
int16_t i16 = 1;
uint16_t u16 = 2;
int32_t i32 = 3;
uint32_t u32 = 4;
int64_t i64 = 5;
uint64_t u64 = 6;
float f = M_PI;
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, const char * tag = "testobject" )
{
std::cout << "IO test: " << name << " -> " << filename << " ...";
// writer needs to be destroyed so that writing physically happens
{
W writer(filename);
write(writer, tag , object);
}
std::cout << " done. reading...";
R reader(filename);
std::unique_ptr<O> buf( new O ); // In case object too big for stack
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;
}
#ifdef HAVE_HDF5
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>, Eigen::StorageOptions::RowMajor> TensorRank5UShortAlt;
typedef Eigen::Tensor<TestScalar, 3, Eigen::StorageOptions::RowMajor> TensorRank3;
typedef Eigen::TensorFixedSize<TestScalar, Eigen::Sizes<9,4,2>, Eigen::StorageOptions::RowMajor> Tensor_9_4_2;
typedef std::vector<Tensor_9_4_2> aTensor_9_4_2;
typedef Eigen::TensorFixedSize<SpinColourVector, Eigen::Sizes<6,5>> LSCTensor;
#ifdef DEBUG
typedef Eigen::TensorFixedSize<iMatrix<iVector<iMatrix<iVector<LorentzColourMatrix,5>,2>,7>,3>, Eigen::Sizes<2,2,11,10,9>, Eigen::StorageOptions::RowMajor> LCMTensor;
#endif
class PerambIOTestClass: Serializable {
public:
using PerambTensor = Eigen::Tensor<SpinColourVector, 6, Eigen::StorageOptions::RowMajor>;
GRID_SERIALIZABLE_CLASS_MEMBERS(PerambIOTestClass
, SpinColourVector, spinColourVector
, SpinColourMatrix, spinColourMatrix
, std::vector<std::string>, DistilParameterNames
, std::vector<int>, DistilParameterValues
, PerambTensor, Perambulator
, PerambTensor, Perambulator2
, TensorRank5UShort, tensorRank5UShort
, TensorRank3, tensorRank3
, Tensor_9_4_2, tensor_9_4_2
, aTensor_9_4_2, atensor_9_4_2
, LSCTensor, MyLSCTensor
#ifdef DEBUG
, LCMTensor, MyLCMTensor
#endif
);
PerambIOTestClass()
: DistilParameterNames {"alpha", "beta", "gamma", "delta", "epsilon", "zeta"}
, DistilParameterValues{2,3,1,4,5,1}
, Perambulator(2,3,1,4,5,1)
, Perambulator2(7,1,6,1,5,1)
, tensorRank3(7,3,2)
, atensor_9_4_2(3)
{
Grid_complex<double> Flag{1,-3.1415927};
SequentialInit(Perambulator, Flag);
SequentialInit(Perambulator2, Flag);
SequentialInit(tensorRank5UShort);
SequentialInit(tensorRank3, Flag);
SequentialInit(tensor_9_4_2, Flag);
for( auto &t : atensor_9_4_2 )
SequentialInit(t, Flag);
SequentialInit( MyLSCTensor );
#ifdef DEBUG
SequentialInit( MyLCMTensor );
#endif
}
};
#define RDR_ Hdf5Reader
#define WTR_ Hdf5Writer
#define TensorWriteReadInnerNoInit( T ) \
filename = "iotest_"s + std::to_string(++TestNum) + "_" #T ".h5"; \
ioTest<WTR_, RDR_, T>(filename, t, #T, #T);
#define TensorWriteReadInner( T ) SequentialInit( t ); TensorWriteReadInnerNoInit( T )
#define TensorWriteRead( T ) { T t ; TensorWriteReadInner( T ) }
#define TensorWriteReadV(T, ... ) { T t( __VA_ARGS__ ); TensorWriteReadInner( T ) }
#define TensorWriteReadLarge( T ) { std::unique_ptr<T> p{new T}; T &t{*p}; TensorWriteReadInnerNoInit(T) }
void EigenHdf5IOTest(void)
{
using namespace std::string_literals;
unsigned int TestNum = 0;
std::string filename;
using TensorSingle = Eigen::TensorFixedSize<int, Eigen::Sizes<1>>;
TensorWriteRead( TensorSingle )
using TensorSimple = Eigen::Tensor<iMatrix<TestScalar,1>, 6>;
TensorWriteReadV( TensorSimple, 1, 1, 1, 1, 1, 1 )
TensorWriteReadV( TensorRank3, 6, 3, 2 )
TensorWriteRead ( Tensor_9_4_2 )
{
TensorRank5UShort t;
TensorWriteReadInner ( TensorRank5UShort );
std::cout << " Testing alternate memory order read ... ";
TensorRank5UShortAlt t2;
RDR_ reader(filename);
read(reader, "TensorRank5UShort", t2);
bool good = true;
for_all( t2, [&](unsigned short c, unsigned short n,
const std::array<size_t, TensorRank5UShortAlt::NumIndices> &Dims ) {
good = good && ( c == n );
} );
if (!good) {
std::cout << " failure!" << std::endl;
dump_tensor(t2,"t2");
exit(EXIT_FAILURE);
}
std::cout << " done." << std::endl;
}
TensorWriteRead ( LSCTensor )
TensorWriteReadLarge( PerambIOTestClass )
#ifdef DEBUG
std::cout << "sizeof( LCMTensor ) = " << sizeof( LCMTensor ) / 1024 / 1024 << " MB" << std::endl;
TensorWriteReadLarge ( LCMTensor )
// Also write > 4GB of complex numbers (I suspect this will fail inside Hdf5)
{
static constexpr size_t Num = 0x11000000;
std::cout << "Stress test: " << Num * sizeof( Grid_complex<double> ) / 1024 / 1024
<< " MB array of complex<double>" << std::endl;
using Stress = std::vector<Grid_complex<double>>;
Stress t (Num);
TensorWriteReadInnerNoInit( Stress );
}
#endif
}
#endif
template <typename T>
void tensorConvTestFn(GridSerialRNG &rng, const std::string label)
{
T t, ft;
Real n;
bool good;
random(rng, t);
auto tv = tensorToVec(t);
vecToTensor(ft, tv);
n = norm2(t - ft);
good = (n == 0);
std::cout << label << " norm 2 diff: " << n << " -- "
<< (good ? "success" : "failure") << std::endl;
}
#define tensorConvTest(rng, type) tensorConvTestFn<type>(rng, #type)
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}));
XmlWriter WR("bother.xml");
// test basic type writing
std::cout << "-- basic writing to 'bother.xml'..." << std::endl;
push(WR,"BasicTypes");
write(WR,std::string("i16"),i16);
write(WR,"u16",u16);
write(WR,"i32",i32);
write(WR,"u32",u32);
write(WR,"i64",i64);
write(WR,"u64",u64);
write(WR,"f",f);
write(WR,"d",d);
write(WR,"b",b);
pop(WR);
// test serializable class writing
myclass obj(1234); // non-trivial constructor
std::vector<myclass> vec;
std::cout << "-- serialisable class writing to 'bother.xml'..." << std::endl;
write(WR,"obj",obj);
WR.write("obj2", obj);
vec.push_back(obj);
vec.push_back(myclass(5678));
vec.push_back(myclass(3838));
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) << 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
std::cout << "\n==== IO self-consistency tests" << std::endl;
//// XML
ioTest<XmlWriter, XmlReader>("iotest.xml", obj, "XML (object) ");
ioTest<XmlWriter, XmlReader>("iotest.xml", vec, "XML (vector of objects)");
//// binary
ioTest<BinaryWriter, BinaryReader>("iotest.bin", obj, "binary (object) ");
ioTest<BinaryWriter, BinaryReader>("iotest.bin", vec, "binary (vector of objects)");
//// text
ioTest<TextWriter, TextReader>("iotest.dat", obj, "text (object) ");
ioTest<TextWriter, TextReader>("iotest.dat", vec, "text (vector of objects)");
//// text
ioTest<JSONWriter, JSONReader>("iotest.json", obj, "JSON (object) ");
ioTest<JSONWriter, JSONReader>("iotest.json", vec, "JSON (vector of objects)");
//// HDF5
#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;
EigenHdf5IOTest();
#endif
std::cout << "\n==== vector flattening/reconstruction" << std::endl;
typedef std::vector<std::vector<std::vector<double>>> vec3d;
vec3d dv, buf;
double d = 0.;
dv.resize(4);
for (auto &v1: dv)
{
v1.resize(3);
for (auto &v2: v1)
{
v2.resize(5);
for (auto &x: v2)
{
x = d++;
}
}
}
std::cout << "original 3D vector:" << std::endl;
std::cout << dv << std::endl;
Flatten<vec3d> flatdv(dv);
std::cout << "\ndimensions:" << std::endl;
std::cout << flatdv.getDim() << std::endl;
std::cout << "\nflattened vector:" << std::endl;
std::cout << flatdv.getFlatVector() << std::endl;
Reconstruct<vec3d> rec(flatdv.getFlatVector(), flatdv.getDim());
std::cout << "\nreconstructed vector:" << std::endl;
std::cout << flatdv.getVector() << std::endl;
std::cout << std::endl;
std::cout << "==== Grid tensor to vector test" << std::endl;
tensorConvTest(rng, SpinColourMatrix);
tensorConvTest(rng, SpinColourVector);
tensorConvTest(rng, ColourMatrix);
tensorConvTest(rng, ColourVector);
tensorConvTest(rng, SpinMatrix);
tensorConvTest(rng, SpinVector);
Grid_finalize();
}
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