/************************************************************************************* grid physics library, www.github.com/paboyle/Grid Source file: Hadrons/Modules/MDistil/Distil.hpp Copyright (C) 2015-2019 Author: Felix Erben Author: Michael Marshall 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 #include #include #include #include #include #include /****************************************************************************** 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 #elif defined(__FreeBSD__) || defined(__NetBSD__) # include #elif defined(__OpenBSD__) # include # define be16toh(x) betoh16(x) # define be32toh(x) betoh32(x) # define be64toh(x) betoh64(x) #elif defined(__APPLE__) #include #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; Coordinate 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 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 class LinOpPeardonNabla : public LinearOperatorBase, public LinearFunction { 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 > > U; public: // Construct this operator given a gauge field and the number of dimensions it should act on LinOpPeardonNabla( GaugeField& gf, int dimSpatial = Tdir ) : /*_gf(gf),*/ nd{dimSpatial} { assert(dimSpatial>=1); for( int mu = 0 ; mu < nd ; mu++ ) U.push_back(PeekIndex(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 > U(in.Grid()); for( int mu = 0 ; mu < nd ; mu++ ) { //U = PeekIndex(_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 class LinOpPeardonNablaHerm : public LinearFunction { public: OperatorFunction & _poly; LinearOperatorBase &_Linop; LinOpPeardonNablaHerm(OperatorFunction & poly,LinearOperatorBase& 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; // Noise vector index order: nnoise, nt, nvec, ns using NoiseTensor = Eigen::Tensor; struct DistilParameters: Serializable { GRID_SERIALIZABLE_CLASS_MEMBERS(DistilParameters, int, nnoise, int, tsrc, std::string, TI, std::string, LI, std::string, SI ) DistilParameters() = default; template DistilParameters(Reader& 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; } }; #define DISTIL_PARAMETERS_DEFINE( inSetup ) \ const int Nt{env().getDim(Tdir)}; \ const int nvec{par().nvec}; \ const int nnoise{par().Distil.nnoise}; \ const int tsrc{par().Distil.tsrc}; \ const int TI{Hadrons::MDistil::DistilParameters::ParameterDefault(par().Distil.TI, Nt, 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; 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 /****************************************************************************** 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 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; using Index = typename ET::Index; GRID_SERIALIZABLE_CLASS_MEMBERS(NamedTensor , ET, tensor , std::vector, IndexNames ); public: // Named tensors are intended to be a superset of Eigen tensor inline operator ET&() { return tensor; } template inline const Scalar_& operator()(const std::array &Indices) const { return tensor.operator()(Indices); } inline Scalar_& operator()(const std::array &Indices) { return tensor.operator()(Indices); } template 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{{firstDimension, otherDimensions...}}); } template 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{{firstDimension, otherDimensions...}}); } // Construct a named tensor explicitly specifying size of each dimension template EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE NamedTensor(const std::array &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 &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 inline void read (Reader &r, const char * pszTag = nullptr); template 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 // Case insensitive compare of two strings // Pesumably this exists already? Where should this go? static inline 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; } }; // Is this a named tensor template struct is_named_tensor : public std::false_type {}; template struct is_named_tensor> : public std::true_type {}; template struct is_named_tensor, T>::value>::type> : public std::true_type {}; /****************************************************************************** PerambTensor object Endian_Scalar_Size can be removed once (deprecated) NamedTensor::ReadBinary & WriteBinary methods deleted ******************************************************************************/ //template using PerambTensor = NamedTensor; static const std::array 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 void NamedTensor::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(NumElements * Scalar_Size)}; uint64_t u64 = htobe64(static_cast(TotalDataSize)); w.write(reinterpret_cast(&u64), sizeof(u64)); // Size of a Scalar_ uint32_t u32{htobe32(Scalar_Size)}; w.write(reinterpret_cast(&u32), sizeof(u32)); // Endian_Scalar_Size uint16_t u16{htobe16(Endian_Scalar_Size)}; w.write(reinterpret_cast(&u16), sizeof(u16)); // number of dimensions which aren't 1 u16 = static_cast(this->NumIndices); for( auto dim : tensor.dimensions() ) if( dim == 1 ) u16--; u16 = htobe16( u16 ); w.write(reinterpret_cast(&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( dim ) ); w.write(reinterpret_cast(&u16), sizeof(u16)); // length of this dimension name u16 = htobe16( static_cast( IndexNames[d].size() ) ); w.write(reinterpret_cast(&u16), sizeof(u16)); // dimension name w.write(IndexNames[d].c_str(), IndexNames[d].size()); } d++; } // Actual data char * const pStart{reinterpret_cast(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(pStart) ; p < pEnd ; p++ ) * p = htobe64( * p ); else if(Endian_Scalar_Size == 4) for(uint32_t * p = reinterpret_cast(pStart) ; p < pEnd ; p++ ) * p = htobe32( * p ); else if(Endian_Scalar_Size == 2) for(uint16_t * p = reinterpret_cast(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(pStart) ; p < pEnd ; p++ ) * p = be64toh( * p ); else if(Endian_Scalar_Size == 4) for(uint32_t * p = reinterpret_cast(pStart) ; p < pEnd ; p++ ) * p = be32toh( * p ); else if(Endian_Scalar_Size == 2) for(uint16_t * p = reinterpret_cast(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(&u32), sizeof(u32)); } /****************************************************************************** Load NamedTensor binary format (NB: On-disk format is Big Endian) Assumes the Scalar_ objects are contiguous (no padding) ******************************************************************************/ template void NamedTensor::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(NumElements * Scalar_Size)}; uint64_t u64; r.read(reinterpret_cast(&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(&u32), sizeof(u32)); assert( Scalar_Size == be32toh( u32 ) && "NamedTensor error: sizeof(Scalar_)"); // Endian_Scalar_Size uint16_t u16; r.read(reinterpret_cast(&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(&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 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(&thisDim), sizeof(thisDim)); // read dimension name r.read(reinterpret_cast(&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(&u16), sizeof(u16)); u16 = be16toh( u16 ); assert( TensorDims[d] == u16 && "size of dimension" ); // length of dimension name r.read(reinterpret_cast(&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(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(pStart) ; p < pEnd ; p++ ) * p = be64toh( * p ); else if(Endian_Scalar_Size == 4) for(uint32_t * p = reinterpret_cast(pStart) ; p < pEnd ; p++ ) * p = be32toh( * p ); else if(Endian_Scalar_Size == 2) for(uint16_t * p = reinterpret_cast(pStart) ; p < pEnd ; p++ ) * p = be16toh( * p ); // checksum r.read(reinterpret_cast(&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 template void NamedTensor::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 void NamedTensor::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 bool NamedTensor::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 template void NamedTensor::read(Reader &r, const char * pszTag) { if( pszTag == nullptr ) pszTag = "NamedTensor"; // Grab index names and dimensions std::vector 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 void NamedTensor::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(gridHD->_ndimension)}; Coordinate latt_size = gridHD->_gdimensions; latt_size[nd-1] = 1; Coordinate simd_layout = GridDefaultSimd(nd-1, vComplex::Nsimd()); simd_layout.push_back( 1 ); Coordinate 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 & evec) { ColourVector cv0; auto grid = evec[0].Grid(); Coordinate siteFirst(grid->Nd(),0); peekSite(cv0, evec[0], siteFirst); Grid::Complex cplx0 = cv0()()(0); #ifdef GRID_NVCC if( cplx0.imag() == 0 ) #else if( std::imag(cplx0) == 0 ) #endif std::cout << GridLogMessage << "RotateEigen() : Site 0 : " << cplx0 << " => already meets phase convention" << std::endl; else { #ifdef GRID_NVCC const Real cplx0_mag = thrust::abs(cplx0); const Grid::Complex phase = thrust::conj(cplx0 / cplx0_mag); const Real argphase = thrust::arg(phase); #else const Real cplx0_mag = std::abs(cplx0); const Grid::Complex phase = std::conj(cplx0 / cplx0_mag); const Real argphase = std::arg(phase); #endif std::cout << GridLogMessage << "RotateEigen() : Site 0 : |" << cplx0 << "|=" << cplx0_mag << " => phase=" << (argphase / 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_