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Grid/tests/IO/Test_serialisation.cc
Peter Boyle fa9cd50c5b Merge branch 'develop' into feature/gpu-port
2019-07-16 11:55:17 +01:00

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/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: ./tests/Test_serialisation.cc
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
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>
#include <typeinfo>
using namespace Grid;
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", unsigned short Precision = 0 )
{
std::cout << "IO test: " << name << " -> " << filename << " ...";
// writer needs to be destroyed so that writing physically happens
{
W writer(filename);
if( Precision )
writer.setPrecision(Precision);
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;
exit(EXIT_FAILURE);
}
std::cout << " done." << std::endl;
}
// The only way I could get these iterators to work is to put the begin() and end() functions in the Eigen namespace
// So if Eigen ever defines these, we'll have a conflict and have to change this
namespace Eigen {
template <typename ET>
inline typename std::enable_if<EigenIO::is_tensor<ET>::value, typename EigenIO::Traits<ET>::scalar_type *>::type
begin( ET & et ) { return reinterpret_cast<typename Grid::EigenIO::Traits<ET>::scalar_type *>(et.data()); }
template <typename ET>
inline typename std::enable_if<EigenIO::is_tensor<ET>::value, typename EigenIO::Traits<ET>::scalar_type *>::type
end( ET & et ) { return begin(et) + et.size() * EigenIO::Traits<ET>::count; }
}
// 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)
{
for( auto &s : Perambulator1 ) s = Flag;
for( auto &s : Perambulator2 ) s = Flag;
for( auto &s : tensorR5 ) s = FlagR;
for( auto &s : tensorRank3 ) s = FlagF;
for( auto &s : tensor_9_4_2 ) s = FlagTS;
for( auto &t : atensor_9_4_2 )
for( auto &s : t ) s = FlagTS;
for( auto &s : MyLSCTensor ) s = Flag;
}
// 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...)};
for( auto &s : * pTensor ) s = Flag;
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 four complex numbers
using TensorSimple = Eigen::Tensor<iMatrix<TestScalar,2>, 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 );
TestOne<WTR_, RDR_, TensorR5>(TEST_PARAMS( TensorR5 ), FlagR);
// 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};
const Complex TensorIO::Flag {1,-1};
const ComplexF TensorIO::FlagF {1,-1};
const TensorIO::TestScalar TensorIO::FlagTS{1,-1};
const char * const TensorIO::pszFilePrefix = "tensor_";
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;
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;
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);
{
HMCparameters HMCparams;
HMCparams.StartingType =std::string("CheckpointStart");
HMCparams.StartTrajectory =7;
HMCparams.Trajectories =1000;
HMCparams.NoMetropolisUntil=0;
HMCparams.MD.name =std::string("Force Gradient");
HMCparams.MD.MDsteps = 10;
HMCparams.MD.trajL = 1.0;
XmlWriter HMCwr("HMCparameters.xml");
write(HMCwr,"HMCparameters",HMCparams);
}
Grid_finalize();
}
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