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Grid/Hadrons/Distil.hpp

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/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: Hadrons/Modules/MDistil/Distil.hpp
Copyright (C) 2015-2019
Author: Felix Erben <ferben@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 */
#ifndef Hadrons_MDistil_Distil_hpp_
#define Hadrons_MDistil_Distil_hpp_
#include <Hadrons/Global.hpp>
#include <Hadrons/Module.hpp>
#include <Hadrons/ModuleFactory.hpp>
#include <Hadrons/Solver.hpp>
#include <Hadrons/EigenPack.hpp>
#include <Hadrons/A2AVectors.hpp>
#include <Hadrons/DilutedNoise.hpp>
/******************************************************************************
A consistent set of cross-platform methods for big endian <-> host byte ordering
I imagine this exists already?
This can be removed once the (deprecated) NamedTensor::ReadBinary & WriteBinary methods are deleted
******************************************************************************/
#if defined(__linux__)
# include <endian.h>
#elif defined(__FreeBSD__) || defined(__NetBSD__)
# include <sys/endian.h>
#elif defined(__OpenBSD__)
# include <sys/types.h>
# define be16toh(x) betoh16(x)
# define be32toh(x) betoh32(x)
# define be64toh(x) betoh64(x)
#elif defined(__APPLE__)
#include <libkern/OSByteOrder.h>
#define htobe16(x) OSSwapHostToBigInt16(x)
#define htole16(x) OSSwapHostToLittleInt16(x)
#define be16toh(x) OSSwapBigToHostInt16(x)
#define le16toh(x) OSSwapLittleToHostInt16(x)
#define htobe32(x) OSSwapHostToBigInt32(x)
#define htole32(x) OSSwapHostToLittleInt32(x)
#define be32toh(x) OSSwapBigToHostInt32(x)
#define le32toh(x) OSSwapLittleToHostInt32(x)
#define htobe64(x) OSSwapHostToBigInt64(x)
#define htole64(x) OSSwapHostToLittleInt64(x)
#define be64toh(x) OSSwapBigToHostInt64(x)
#define le64toh(x) OSSwapLittleToHostInt64(x)
#endif
/******************************************************************************
This potentially belongs in CartesianCommunicator
******************************************************************************/
BEGIN_MODULE_NAMESPACE(Grid)
inline void SliceShare( GridBase * gridLowDim, GridBase * gridHighDim, void * Buffer, int BufferSize )
{
// Work out which dimension is the spread-out dimension
assert(gridLowDim);
assert(gridHighDim);
const int iNumDims{(const int)gridHighDim->_gdimensions.size()};
assert(iNumDims == gridLowDim->_gdimensions.size());
int dimSpreadOut = -1;
std::vector<int> coor(iNumDims);
for( int i = 0 ; i < iNumDims ; i++ ) {
coor[i] = gridHighDim->_processor_coor[i];
if( gridLowDim->_gdimensions[i] != gridHighDim->_gdimensions[i] ) {
assert( dimSpreadOut == -1 );
assert( gridLowDim->_processors[i] == 1 ); // easiest assumption to make for now
dimSpreadOut = i;
}
}
if( dimSpreadOut != -1 && gridHighDim->_processors[dimSpreadOut] != gridLowDim->_processors[dimSpreadOut] ) {
// Make sure the same number of data elements exist on each slice
const int NumSlices{gridHighDim->_processors[dimSpreadOut] / gridLowDim->_processors[dimSpreadOut]};
assert(gridHighDim->_processors[dimSpreadOut] == gridLowDim->_processors[dimSpreadOut] * NumSlices);
const int SliceSize{BufferSize/NumSlices};
//CCC_DEBUG_DUMP(Buffer, NumSlices, SliceSize);
assert(BufferSize == SliceSize * NumSlices);
//#ifndef USE_LOCAL_SLICES
// assert(0); // Can't do this without MPI (should really test whether MPI is defined)
//#else
const auto MyRank = gridHighDim->ThisRank();
std::vector<CommsRequest_t> reqs(0);
int MySlice{coor[dimSpreadOut]};
char * const _buffer{(char *)Buffer};
char * const MyData{_buffer + MySlice * SliceSize};
for(int i = 1; i < NumSlices ; i++ ){
int SendSlice = ( MySlice + i ) % NumSlices;
int RecvSlice = ( MySlice - i + NumSlices ) % NumSlices;
char * const RecvData{_buffer + RecvSlice * SliceSize};
coor[dimSpreadOut] = SendSlice;
const auto SendRank = gridHighDim->RankFromProcessorCoor(coor);
coor[dimSpreadOut] = RecvSlice;
const auto RecvRank = gridHighDim->RankFromProcessorCoor(coor);
std::cout << GridLogMessage << "Send slice " << MySlice << " (" << MyRank << ") to " << SendSlice << " (" << SendRank
<< "), receive slice from " << RecvSlice << " (" << RecvRank << ")" << std::endl;
gridHighDim->SendToRecvFromBegin(reqs,MyData,SendRank,RecvData,RecvRank,SliceSize);
//memcpy(RecvData,MyData,SliceSize); // Debug
}
gridHighDim->SendToRecvFromComplete(reqs);
std::cout << GridLogMessage << "Slice data shared." << std::endl;
//CCC_DEBUG_DUMP(Buffer, NumSlices, SliceSize);
//#endif
}
}
/*************************************************************************************
Not sure where the right home for this is? But presumably in Grid
-Grad^2 (Peardon, 2009, pg 2, equation 3, https://arxiv.org/abs/0905.2160)
Field Type of field the operator will be applied to
GaugeField Gauge field the operator will smear using
*************************************************************************************/
template<typename Field, typename GaugeField=LatticeGaugeField>
class LinOpPeardonNabla : public LinearOperatorBase<Field>, public LinearFunction<Field> {
typedef typename GaugeField::vector_type vCoeff_t;
protected: // I don't really mind if _gf is messed with ... so make this public?
//GaugeField & _gf;
int nd; // number of spatial dimensions
std::vector<Lattice<iColourMatrix<vCoeff_t> > > U;
public:
// Construct this operator given a gauge field and the number of dimensions it should act on
LinOpPeardonNabla( GaugeField& gf, int dimSpatial = Grid::QCD::Tdir ) : /*_gf(gf),*/ nd{dimSpatial} {
assert(dimSpatial>=1);
for( int mu = 0 ; mu < nd ; mu++ )
U.push_back(PeekIndex<LorentzIndex>(gf,mu));
}
// Apply this operator to "in", return result in "out"
void operator()(const Field& in, Field& out) {
assert( nd <= in._grid->Nd() );
conformable( in, out );
out = ( ( Real ) ( 2 * nd ) ) * in;
Field _tmp(in._grid);
typedef typename GaugeField::vector_type vCoeff_t;
//Lattice<iColourMatrix<vCoeff_t> > U(in._grid);
for( int mu = 0 ; mu < nd ; mu++ ) {
//U = PeekIndex<LorentzIndex>(_gf,mu);
out -= U[mu] * Cshift( in, mu, 1);
_tmp = adj( U[mu] ) * in;
out -= Cshift(_tmp,mu,-1);
}
}
void OpDiag (const Field &in, Field &out) { assert(0); };
void OpDir (const Field &in, Field &out,int dir,int disp) { assert(0); };
void Op (const Field &in, Field &out) { assert(0); };
void AdjOp (const Field &in, Field &out) { assert(0); };
void HermOpAndNorm(const Field &in, Field &out,RealD &n1,RealD &n2) { assert(0); };
void HermOp(const Field &in, Field &out) { operator()(in,out); };
};
template<typename Field>
class LinOpPeardonNablaHerm : public LinearFunction<Field> {
public:
OperatorFunction<Field> & _poly;
LinearOperatorBase<Field> &_Linop;
LinOpPeardonNablaHerm(OperatorFunction<Field> & poly,LinearOperatorBase<Field>& linop)
: _poly{poly}, _Linop{linop} {}
void operator()(const Field& in, Field& out) {
_poly(_Linop,in,out);
}
};
END_MODULE_NAMESPACE // Grid
/******************************************************************************
Common elements for distillation
******************************************************************************/
BEGIN_HADRONS_NAMESPACE
BEGIN_MODULE_NAMESPACE(MDistil)
using LapEvecs = Grid::Hadrons::EigenPack<LatticeColourVector>;
// Noise vector index order: nnoise, nt, nvec, ns
using NoiseTensor = Eigen::Tensor<Complex, 4, Eigen::RowMajor>;
struct DistilParameters: Serializable {
GRID_SERIALIZABLE_CLASS_MEMBERS(DistilParameters,
int, nnoise,
int, tsrc,
std::string, TI,
std::string, LI,
std::string, SI )
DistilParameters() = default;
template <class ReaderClass> DistilParameters(Reader<ReaderClass>& Reader){read(Reader,"Distil",*this);}
// Numeric parameter is allowed to be empty (in which case it = Default),
// but assert during setup() if specified but not numeric
static int ParameterDefault( const std::string & s, int Default, bool bCalledFromSetup )
{
int i = Default;
if( s.length() > 0 ) {
std::istringstream ss( s );
ss >> i;
if( bCalledFromSetup )
assert( !ss.fail() && "Parameter should either be empty or integer" );
}
return i;
}
int getTI( const Environment & env, bool bCalledFromSetup ) const {
return ParameterDefault( TI, env.getDim(Tdir), bCalledFromSetup ); }
};
#define DISTIL_PARAMETERS_DEFINE( inSetup ) \
const int Nt{env().getDim(Tdir)}; \
const int nvec{par().nvec}; \
const int Ns{Grid::QCD::Ns}; \
const int nnoise{par().Distil.nnoise}; \
const int tsrc{par().Distil.tsrc}; \
const int TI{par().Distil.getTI(env(), inSetup)}; \
const int LI{Hadrons::MDistil::DistilParameters::ParameterDefault(par().Distil.LI, nvec, inSetup)}; \
const int SI{Hadrons::MDistil::DistilParameters::ParameterDefault(par().Distil.SI, Ns, inSetup)}; \
const bool full_tdil{ TI == Nt }; \
const bool exact_distillation{ full_tdil && LI == nvec }; \
const int Nt_inv{ full_tdil ? 1 : TI }
class BFieldIO: Serializable{
public:
using BaryonTensorSet = Eigen::Tensor<ComplexD, 6>;
GRID_SERIALIZABLE_CLASS_MEMBERS(BFieldIO, BaryonTensorSet, BField );
};
/******************************************************************************
Default for distillation file operations. For now only used by NamedTensor
******************************************************************************/
#ifdef HAVE_HDF5
using Default_Reader = Grid::Hdf5Reader;
using Default_Writer = Grid::Hdf5Writer;
static const char * FileExtension = ".h5";
#else
using Default_Reader = Grid::BinaryReader;
using Default_Writer = Grid::BinaryWriter;
static const char * FileExtension = ".dat";
#endif
/******************************************************************************
Case insensitive compare of two strings
******************************************************************************/
bool CompareCaseInsensitive( const std::string &s1, const std::string &s2 ) {
auto Len = s1.size();
bool bSame{ Len == s2.size() };
for( int j = 0; bSame && j < Len; j++ ) {
wchar_t c1 = s1[j];
if( c1 >= 'a' && c1 <= 'z' )
c1 -= 'a' - 'A';
wchar_t c2 = s2[j];
if( c2 >= 'a' && c1 <= 'z' )
c2 -= 'a' - 'A';
bSame = ( c1 == c2 );
}
return bSame;
}
/******************************************************************************
NamedTensor object
This is an Eigen::Tensor of type Scalar_ and rank NumIndices_ (row-major order)
They can be persisted to disk
Scalar_ objects are assumed to be composite objects of size Endian_Scalar_Size.
(Disable big-endian by setting Endian_Scalar_Size=1).
NB: Endian_Scalar_Size will disappear when ReadBinary & WriteBinary retired
IndexNames contains one name for each index, and IndexNames are validated on load.
WHAT TO SAVE / VALIDATE ON LOAD (Override to warn instead of assert on load)
Ensemble string
Configuration number
Noise unique string
Distillation parameters
******************************************************************************/
template<typename Scalar_, int NumIndices_, uint16_t Endian_Scalar_Size_ = sizeof(Scalar_)>
class NamedTensor : Serializable
{
public:
using Scalar = Scalar_;
static constexpr int NumIndices = NumIndices_;
static constexpr uint16_t Endian_Scalar_Size = Endian_Scalar_Size_;
using ET = Eigen::Tensor<Scalar_, NumIndices_, Eigen::RowMajor>;
using Index = typename ET::Index;
GRID_SERIALIZABLE_CLASS_MEMBERS(NamedTensor
, ET, tensor
, std::vector<std::string>, IndexNames
);
public:
// Named tensors are intended to be a superset of Eigen tensor
inline operator ET&() { return tensor; }
template<typename... IndexTypes>
inline const Scalar_& operator()(const std::array<Eigen::Index, NumIndices_> &Indices) const
{ return tensor.operator()(Indices); }
inline Scalar_& operator()(const std::array<Eigen::Index, NumIndices_> &Indices)
{ return tensor.operator()(Indices); }
template<typename... IndexTypes>
inline const Scalar_& operator()(Eigen::Index firstDimension, IndexTypes... otherDimensions) const
{
// The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
assert(sizeof...(otherDimensions) + 1 == NumIndices_ && "NamedTensor: dimensions != tensor rank");
return tensor.operator()(std::array<Eigen::Index, NumIndices_>{{firstDimension, otherDimensions...}});
}
template<typename... IndexTypes>
inline Scalar_& operator()(Eigen::Index firstDimension, IndexTypes... otherDimensions)
{
// The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
assert(sizeof...(otherDimensions) + 1 == NumIndices_ && "NamedTensor: dimensions != tensor rank");
return tensor.operator()(std::array<Eigen::Index, NumIndices_>{{firstDimension, otherDimensions...}});
}
// Construct a named tensor explicitly specifying size of each dimension
template<typename... IndexTypes>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE NamedTensor(const std::array<std::string,NumIndices_> &IndexNames_, Eigen::Index firstDimension, IndexTypes... otherDimensions)
: tensor(firstDimension, otherDimensions...), IndexNames{NumIndices}
{
// The number of dimensions used to construct a tensor must be equal to the rank of the tensor.
assert(sizeof...(otherDimensions) + 1 == NumIndices_ && "NamedTensor: dimensions != tensor rank");
for( int i = 0; i < NumIndices_; i++ )
IndexNames[i] = IndexNames_[i];
}
// Default constructor (assumes tensor will be loaded from file)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE NamedTensor() : IndexNames{NumIndices_} {}
// Construct a named tensor without specifying size of each dimension (because it will be loaded from file)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE NamedTensor(const std::array<std::string,NumIndices_> &IndexNames_)
: IndexNames{NumIndices_}
{
for( int i = 0; i < NumIndices_; i++ )
IndexNames[i] = IndexNames_[i];
}
// Share data for timeslices we calculated with other nodes
inline void SliceShare( GridCartesian * gridLowDim, GridCartesian * gridHighDim ) {
Grid::SliceShare( gridLowDim, gridHighDim, tensor.data(), (int) (tensor.size() * sizeof(Scalar_)));
}
bool ValidateIndexNames( int iNumNames, const std::string * MatchNames ) const;
// Read/Write in any format
template<typename Reader> inline void read (Reader &r, const char * pszTag = nullptr);
template<typename Writer> inline void write(Writer &w, const char * pszTag = nullptr) const;
// Read/Write in default format, i.e. HDF5 if present, else binary
inline void read (const char * filename, const char * pszTag = nullptr);
inline void write(const char * filename, const char * pszTag = nullptr) const;
// Original I/O implementation. This will be removed when we're sure it's no longer needed
EIGEN_DEPRECATED inline void ReadBinary (const std::string filename); // To be removed
EIGEN_DEPRECATED inline void WriteBinary(const std::string filename); // To be removed
};
// Is this a named tensor
template<typename T, typename V = void> struct is_named_tensor : public std::false_type {};
template<typename Scalar_, int NumIndices_, uint16_t Endian_Scalar_Size_> struct is_named_tensor<NamedTensor<Scalar_, NumIndices_, Endian_Scalar_Size_>> : public std::true_type {};
template<typename T> struct is_named_tensor<T, typename std::enable_if<std::is_base_of<NamedTensor<typename T::Scalar, T::NumIndices, T::Endian_Scalar_Size_>, T>::value>::type> : public std::true_type {};
/******************************************************************************
PerambTensor object
Endian_Scalar_Size can be removed once (deprecated) NamedTensor::ReadBinary & WriteBinary methods deleted
******************************************************************************/
//template<typename Scalar_, int NumIndices_, uint16_t Endian_Scalar_Size = sizeof(Scalar_)>
using PerambTensor = NamedTensor<SpinVector, 6, sizeof(Real)>;
static const std::array<std::string, 6> PerambIndexNames{"nT", "nVec", "LI", "nNoise", "nT_inv", "SI"};
/******************************************************************************
Save NamedTensor binary format (NB: On-disk format is Big Endian)
Assumes the Scalar_ objects are contiguous (no padding)
******************************************************************************/
template<typename Scalar_, int NumIndices_, uint16_t Endian_Scalar_Size>
void NamedTensor<Scalar_, NumIndices_, Endian_Scalar_Size>::WriteBinary(const std::string filename) {
LOG(Message) << "Writing NamedTensor to \"" << filename << "\"" << std::endl;
std::ofstream w(filename, std::ios::binary);
// Enforce assumption that the scalar is composed of fundamental elements of size Endian_Scalar_Size
assert((Endian_Scalar_Size == 1 || Endian_Scalar_Size == 2 || Endian_Scalar_Size == 4 || Endian_Scalar_Size == 8 )
&& "NamedTensor error: Endian_Scalar_Size should be 1, 2, 4 or 8");
assert((sizeof(Scalar_) % Endian_Scalar_Size) == 0 && "NamedTensor error: Scalar_ is not composed of Endian_Scalar_Size" );
// Size of the data (in bytes)
const uint32_t Scalar_Size{sizeof(Scalar_)};
const auto NumElements = tensor.size();
const std::streamsize TotalDataSize{static_cast<std::streamsize>(NumElements * Scalar_Size)};
uint64_t u64 = htobe64(static_cast<uint64_t>(TotalDataSize));
w.write(reinterpret_cast<const char *>(&u64), sizeof(u64));
// Size of a Scalar_
uint32_t u32{htobe32(Scalar_Size)};
w.write(reinterpret_cast<const char *>(&u32), sizeof(u32));
// Endian_Scalar_Size
uint16_t u16{htobe16(Endian_Scalar_Size)};
w.write(reinterpret_cast<const char *>(&u16), sizeof(u16));
// number of dimensions which aren't 1
u16 = static_cast<uint16_t>(this->NumIndices);
for( auto dim : tensor.dimensions() )
if( dim == 1 )
u16--;
u16 = htobe16( u16 );
w.write(reinterpret_cast<const char *>(&u16), sizeof(u16));
// dimensions together with names
int d = 0;
for( auto dim : tensor.dimensions() ) {
if( dim != 1 ) {
// size of this dimension
u16 = htobe16( static_cast<uint16_t>( dim ) );
w.write(reinterpret_cast<const char *>(&u16), sizeof(u16));
// length of this dimension name
u16 = htobe16( static_cast<uint16_t>( IndexNames[d].size() ) );
w.write(reinterpret_cast<const char *>(&u16), sizeof(u16));
// dimension name
w.write(IndexNames[d].c_str(), IndexNames[d].size());
}
d++;
}
// Actual data
char * const pStart{reinterpret_cast<char *>(tensor.data())};
// Swap to network byte order in place (alternative is to copy memory - still slow)
void * const pEnd{pStart + TotalDataSize};
if(Endian_Scalar_Size == 8)
for(uint64_t * p = reinterpret_cast<uint64_t *>(pStart) ; p < pEnd ; p++ )
* p = htobe64( * p );
else if(Endian_Scalar_Size == 4)
for(uint32_t * p = reinterpret_cast<uint32_t *>(pStart) ; p < pEnd ; p++ )
* p = htobe32( * p );
else if(Endian_Scalar_Size == 2)
for(uint16_t * p = reinterpret_cast<uint16_t *>(pStart) ; p < pEnd ; p++ )
* p = htobe16( * p );
w.write(pStart, TotalDataSize);
// Swap back from network byte order
if(Endian_Scalar_Size == 8)
for(uint64_t * p = reinterpret_cast<uint64_t *>(pStart) ; p < pEnd ; p++ )
* p = be64toh( * p );
else if(Endian_Scalar_Size == 4)
for(uint32_t * p = reinterpret_cast<uint32_t *>(pStart) ; p < pEnd ; p++ )
* p = be32toh( * p );
else if(Endian_Scalar_Size == 2)
for(uint16_t * p = reinterpret_cast<uint16_t *>(pStart) ; p < pEnd ; p++ )
* p = be16toh( * p );
// checksum
#ifdef USE_IPP
u32 = htobe32(GridChecksum::crc32c(tensor.data(), TotalDataSize));
#else
u32 = htobe32(GridChecksum::crc32(tensor.data(), TotalDataSize));
#endif
w.write(reinterpret_cast<const char *>(&u32), sizeof(u32));
}
/******************************************************************************
Load NamedTensor binary format (NB: On-disk format is Big Endian)
Assumes the Scalar_ objects are contiguous (no padding)
******************************************************************************/
template<typename Scalar_, int NumIndices_, uint16_t Endian_Scalar_Size>
void NamedTensor<Scalar_, NumIndices_, Endian_Scalar_Size>::ReadBinary(const std::string filename) {
LOG(Message) << "Reading NamedTensor from \"" << filename << "\"" << std::endl;
std::ifstream r(filename, std::ios::binary);
// Enforce assumption that the scalar is composed of fundamental elements of size Endian_Scalar_Size
assert((Endian_Scalar_Size == 1 || Endian_Scalar_Size == 2 || Endian_Scalar_Size == 4 || Endian_Scalar_Size == 8 )
&& "NamedTensor error: Endian_Scalar_Size should be 1, 2, 4 or 8");
assert((sizeof(Scalar_) % Endian_Scalar_Size) == 0 && "NamedTensor error: Scalar_ is not composed of Endian_Scalar_Size" );
// Size of the data in bytes
const uint32_t Scalar_Size{sizeof(Scalar_)};
Index NumElements{tensor.size()};
std::streamsize TotalDataSize{static_cast<std::streamsize>(NumElements * Scalar_Size)};
uint64_t u64;
r.read(reinterpret_cast<char *>(&u64), sizeof(u64));
assert( TotalDataSize == 0 || TotalDataSize == be64toh( u64 ) && "NamedTensor error: Size of the data in bytes" );
// Size of a Scalar_
uint32_t u32;
r.read(reinterpret_cast<char *>(&u32), sizeof(u32));
assert( Scalar_Size == be32toh( u32 ) && "NamedTensor error: sizeof(Scalar_)");
// Endian_Scalar_Size
uint16_t u16;
r.read(reinterpret_cast<char *>(&u16), sizeof(u16));
assert( Endian_Scalar_Size == be16toh( u16 ) && "NamedTensor error: Scalar_Unit_size");
// number of dimensions which aren't 1
uint16_t NumFileDimensions;
r.read(reinterpret_cast<char *>(&NumFileDimensions), sizeof(NumFileDimensions));
NumFileDimensions = be16toh( NumFileDimensions );
/*for( auto dim : tensor.dimensions() )
if( dim == 1 )
u16++;*/
assert( ( TotalDataSize == 0 && this->NumIndices >= NumFileDimensions || this->NumIndices == NumFileDimensions )
&& "NamedTensor error: number of dimensions which aren't 1" );
if( TotalDataSize == 0 ) {
// Read each dimension, using names to skip past dimensions == 1
std::array<Index,NumIndices_> NewDimensions;
for( Index &i : NewDimensions ) i = 1;
int d = 0;
for( int FileDimension = 0; FileDimension < NumFileDimensions; FileDimension++ ) {
// read dimension
uint16_t thisDim;
r.read(reinterpret_cast<char *>(&thisDim), sizeof(thisDim));
// read dimension name
r.read(reinterpret_cast<char *>(&u16), sizeof(u16));
size_t l = be16toh( u16 );
std::string s( l, '?' );
r.read(&s[0], l);
// skip forward to matching name
while( IndexNames[d].size() > 0 && !CompareCaseInsensitive( s, IndexNames[d] ) )
assert(++d < NumIndices && "NamedTensor error: dimension name" );
if( IndexNames[d].size() == 0 )
IndexNames[d] = s;
NewDimensions[d++] = be16toh( thisDim );
}
tensor.resize(NewDimensions);
NumElements = 1;
for( Index i : NewDimensions ) NumElements *= i;
TotalDataSize = NumElements * Scalar_Size;
} else {
// dimensions together with names
const auto & TensorDims = tensor.dimensions();
for( int d = 0; d < NumIndices_; d++ ) {
// size of dimension
r.read(reinterpret_cast<char *>(&u16), sizeof(u16));
u16 = be16toh( u16 );
assert( TensorDims[d] == u16 && "size of dimension" );
// length of dimension name
r.read(reinterpret_cast<char *>(&u16), sizeof(u16));
size_t l = be16toh( u16 );
assert( l == IndexNames[d].size() && "NamedTensor error: length of dimension name" );
// dimension name
std::string s( l, '?' );
r.read(&s[0], l);
assert( s == IndexNames[d] && "NamedTensor error: dimension name" );
}
}
// Actual data
char * const pStart{reinterpret_cast<char *>(tensor.data())};
void * const pEnd{pStart + TotalDataSize};
r.read(pStart,TotalDataSize);
// Swap back from network byte order
if(Endian_Scalar_Size == 8)
for(uint64_t * p = reinterpret_cast<uint64_t *>(pStart) ; p < pEnd ; p++ )
* p = be64toh( * p );
else if(Endian_Scalar_Size == 4)
for(uint32_t * p = reinterpret_cast<uint32_t *>(pStart) ; p < pEnd ; p++ )
* p = be32toh( * p );
else if(Endian_Scalar_Size == 2)
for(uint16_t * p = reinterpret_cast<uint16_t *>(pStart) ; p < pEnd ; p++ )
* p = be16toh( * p );
// checksum
r.read(reinterpret_cast<char *>(&u32), sizeof(u32));
u32 = be32toh( u32 );
#ifdef USE_IPP
u32 -= GridChecksum::crc32c(tensor.data(), TotalDataSize);
#else
u32 -= GridChecksum::crc32(tensor.data(), TotalDataSize);
#endif
assert( u32 == 0 && "NamedTensor error: PerambTensor checksum invalid");
}
/******************************************************************************
Write NamedTensor
******************************************************************************/
template<typename Scalar_, int NumIndices_, uint16_t Endian_Scalar_Size>
template<typename Writer>
void NamedTensor<Scalar_, NumIndices_, Endian_Scalar_Size>::write(Writer &w, const char * pszTag)const{
if( pszTag == nullptr )
pszTag = "NamedTensor";
LOG(Message) << "Writing NamedTensor to tag " << pszTag << std::endl;
write(w, pszTag, *this);
}
template<typename Scalar_, int NumIndices_, uint16_t Endian_Scalar_Size>
void NamedTensor<Scalar_, NumIndices_, Endian_Scalar_Size>::write(const char * filename, const char * pszTag)const{
std::string sFileName{filename};
sFileName.append( MDistil::FileExtension );
LOG(Message) << "Writing NamedTensor to file " << sFileName << std::endl;
MDistil::Default_Writer w( sFileName );
write( w, pszTag );
}
/******************************************************************************
Validate named tensor index names
******************************************************************************/
template<typename Scalar_, int NumIndices_, uint16_t Endian_Scalar_Size>
bool NamedTensor<Scalar_, NumIndices_, Endian_Scalar_Size>::ValidateIndexNames( int iNumNames, const std::string * MatchNames ) const {
bool bSame{ iNumNames == NumIndices_ && IndexNames.size() == NumIndices_ };
for( int i = 0; bSame && i < NumIndices_; i++ )
bSame = CompareCaseInsensitive( MatchNames[i], IndexNames[i] );
return bSame;
}
/******************************************************************************
Read NamedTensor
******************************************************************************/
template<typename Scalar_, int NumIndices_, uint16_t Endian_Scalar_Size>
template<typename Reader>
void NamedTensor<Scalar_, NumIndices_, Endian_Scalar_Size>::read(Reader &r, const char * pszTag) {
if( pszTag == nullptr )
pszTag = "NamedTensor";
// Grab index names and dimensions
std::vector<std::string> OldIndexNames{std::move(IndexNames)};
typename ET::Dimensions OldDimensions{tensor.dimensions()};
LOG(Message) << "Reading NamedTensor from tag " << pszTag << std::endl;
read(r, pszTag, *this);
const typename ET::Dimensions & NewDimensions{tensor.dimensions()};
for( int i = 0; i < NumIndices_; i++ )
assert(OldDimensions[i] == 0 || OldDimensions[i] == NewDimensions[i] && "NamedTensor::load dimension size");
assert( ValidateIndexNames( OldIndexNames.size(), &OldIndexNames[0] ) && "NamedTensor::load dimension name" );
}
template<typename Scalar_, int NumIndices_, uint16_t Endian_Scalar_Size>
void NamedTensor<Scalar_, NumIndices_, Endian_Scalar_Size>::read(const char * filename, const char * pszTag) {
std::string sFileName{filename};
sFileName.append( MDistil::FileExtension );
LOG(Message) << "Reading NamedTensor from file " << sFileName << std::endl;
MDistil::Default_Reader r( sFileName );
read( r, pszTag );
}
/******************************************************************************
Make a lower dimensional grid in preparation for local slice operations
******************************************************************************/
inline GridCartesian * MakeLowerDimGrid( GridCartesian * gridHD )
{
int nd{static_cast<int>(gridHD->_ndimension)};
std::vector<int> latt_size = gridHD->_gdimensions;
latt_size[nd-1] = 1;
std::vector<int> simd_layout = GridDefaultSimd(nd-1, vComplex::Nsimd());
simd_layout.push_back( 1 );
std::vector<int> mpi_layout = gridHD->_processors;
mpi_layout[nd-1] = 1;
GridCartesian * gridLD = new GridCartesian(latt_size,simd_layout,mpi_layout,*gridHD);
return gridLD;
}
/*************************************************************************************
Rotate eigenvectors into our phase convention
First component of first eigenvector is real and positive
*************************************************************************************/
inline void RotateEigen(std::vector<LatticeColourVector> & evec)
{
ColourVector cv0;
auto grid = evec[0]._grid;
std::vector<int> siteFirst(grid->Nd(),0);
peekSite(cv0, evec[0], siteFirst);
auto & cplx0 = cv0()()(0);
if( std::imag(cplx0) == 0 )
std::cout << GridLogMessage << "RotateEigen() : Site 0 : " << cplx0 << " => already meets phase convention" << std::endl;
else {
const auto cplx0_mag = std::abs(cplx0);
const auto phase = std::conj(cplx0 / cplx0_mag);
std::cout << GridLogMessage << "RotateEigen() : Site 0 : |" << cplx0 << "|=" << cplx0_mag << " => phase=" << (std::arg(phase) / 3.14159265) << " pi" << std::endl;
{
// TODO: Only really needed on the master slice
for( int k = 0 ; k < evec.size() ; k++ )
evec[k] *= phase;
if(grid->IsBoss()){
for( int c = 0 ; c < Nc ; c++ )
cv0()()(c) *= phase;
cplx0.imag(0); // This assumes phase convention is real, positive (so I get rid of rounding error)
//pokeSite(cv0, evec[0], siteFirst);
pokeLocalSite(cv0, evec[0], siteFirst);
}
}
}
}
END_MODULE_NAMESPACE
END_HADRONS_NAMESPACE
#endif // Hadrons_MDistil_Distil_hpp_
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