Source code for pyscf.df.df

#!/usr/bin/env python
# Copyright 2014-2020 The PySCF Developers. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Author: Qiming Sun <osirpt.sun@gmail.com>
#

'''
J-metric density fitting
'''


import tempfile
import contextlib
import numpy
import h5py
from pyscf import lib
from pyscf import ao2mo
from pyscf.lib import logger
from pyscf.df import incore
from pyscf.df import outcore
from pyscf.df import r_incore
from pyscf.df import addons
from pyscf.df import df_jk
from pyscf.ao2mo import _ao2mo
from pyscf.ao2mo.incore import _conc_mos, iden_coeffs
from pyscf.ao2mo.outcore import _load_from_h5g
from pyscf import __config__

[docs] class DF(lib.StreamObject): r''' Object to hold 3-index tensor Attributes: auxbasis : str or dict Same input format as :attr:`Mole.basis` auxmol : Mole object Read only Mole object to hold the auxiliary basis. auxmol is generated automatically in the initialization step based on the given auxbasis. It is used in the rest part of the code to determine the problem size, the integral batches etc. This object should NOT be modified. _cderi_to_save : str If _cderi_to_save is specified, the DF integral tensor will be saved in this file. _cderi : str or numpy array If _cderi is specified, the DF integral tensor will be read from this HDF5 file (or numpy array). When the DF integral tensor is provided from the HDF5 file, its dataset name should be consistent with DF._dataname, which is 'j3c' by default. The DF integral tensor :math:`V_{x,ij}` should be a 2D array in C (row-major) convention, where x corresponds to index of auxiliary basis, and the composite index ij is the orbital pair index. The hermitian symmetry is assumed for the composite ij index, ie the elements of :math:`V_{x,i,j}` with :math:`i\geq j` are existed in the DF integral tensor. Thus the shape of DF integral tensor is (M,N*(N+1)/2), where M is the number of auxbasis functions and N is the number of basis functions of the orbital basis. blockdim : int When reading DF integrals from disk the chunk size to load. It is used to improve IO performance. ''' blockdim = getattr(__config__, 'df_df_DF_blockdim', 240) # Store DF tensor in a format compatible to pyscf-1.1 - pyscf-1.6 _compatible_format = getattr(__config__, 'df_df_DF_compatible_format', False) _dataname = 'j3c' _keys = set(('mol', 'auxmol')) def __init__(self, mol, auxbasis=None): self.mol = mol self.stdout = mol.stdout self.verbose = mol.verbose self.max_memory = mol.max_memory self._auxbasis = auxbasis ################################################## # Following are not input options self.auxmol = None # If _cderi_to_save is specified, the 3C-integral tensor will be saved in this file. self._cderi_to_save = None # If _cderi is specified, the 3C-integral tensor will be read from this file self._cderi = None self._vjopt = None self._rsh_df = {} # Range separated Coulomb DF objects @property def auxbasis(self): return self._auxbasis @auxbasis.setter def auxbasis(self, x): if self._auxbasis != x: self.reset() self._auxbasis = x
[docs] def dump_flags(self, verbose=None): log = logger.new_logger(self, verbose) log.info('******** %s ********', self.__class__) if self.auxmol is None: log.info('auxbasis = %s', self.auxbasis) else: log.info('auxbasis = auxmol.basis = %s', self.auxmol.basis) log.info('max_memory = %s', self.max_memory) if isinstance(self._cderi, str): log.info('_cderi = %s where DF integrals are loaded (readonly).', self._cderi) return self
[docs] def build(self): t0 = (logger.process_clock(), logger.perf_counter()) log = logger.Logger(self.stdout, self.verbose) self.check_sanity() self.dump_flags() if self._cderi is not None and self.auxmol is None: log.info('Skip DF.build(). Tensor _cderi will be used.') return self mol = self.mol auxmol = self.auxmol = addons.make_auxmol(self.mol, self.auxbasis) nao = mol.nao_nr() naux = auxmol.nao_nr() nao_pair = nao*(nao+1)//2 is_custom_storage = isinstance(self._cderi_to_save, str) max_memory = self.max_memory - lib.current_memory()[0] int3c = mol._add_suffix('int3c2e') int2c = mol._add_suffix('int2c2e') if (nao_pair*naux*8/1e6 < .9*max_memory and not is_custom_storage): self._cderi = incore.cholesky_eri(mol, int3c=int3c, int2c=int2c, auxmol=auxmol, max_memory=max_memory, verbose=log) else: if self._cderi_to_save is None: self._cderi_to_save = tempfile.NamedTemporaryFile(dir=lib.param.TMPDIR) cderi = self._cderi_to_save if is_custom_storage: cderi_name = cderi else: cderi_name = cderi.name if self._cderi is None: log.info('_cderi_to_save = %s', cderi_name) else: # If cderi needs to be saved in log.warn('Value of _cderi is ignored. DF integrals will be ' 'saved in file %s .', cderi_name) if self._compatible_format: outcore.cholesky_eri(mol, cderi, dataname=self._dataname, int3c=int3c, int2c=int2c, auxmol=auxmol, max_memory=max_memory, verbose=log) else: # Store DF tensor in blocks. This is to reduce the # initiailzation overhead outcore.cholesky_eri_b(mol, cderi, dataname=self._dataname, int3c=int3c, int2c=int2c, auxmol=auxmol, max_memory=max_memory, verbose=log) self._cderi = cderi log.timer_debug1('Generate density fitting integrals', *t0) return self
[docs] def kernel(self, *args, **kwargs): return self.build(*args, **kwargs)
[docs] def reset(self, mol=None): '''Reset mol and clean up relevant attributes for scanner mode''' if mol is not None: self.mol = mol self.auxmol = None self._cderi = None self._vjopt = None self._rsh_df = {} return self
[docs] def loop(self, blksize=None): if self._cderi is None: self.build() if blksize is None: blksize = self.blockdim with addons.load(self._cderi, self._dataname) as feri: if isinstance(feri, numpy.ndarray): naoaux = feri.shape[0] for b0, b1 in self.prange(0, naoaux, blksize): yield numpy.asarray(feri[b0:b1], order='C') else: if isinstance(feri, h5py.Group): # starting from pyscf-1.7, DF tensor may be stored in # block format naoaux = feri['0'].shape[0] def load(aux_slice): b0, b1 = aux_slice return _load_from_h5g(feri, b0, b1) else: naoaux = feri.shape[0] def load(aux_slice): b0, b1 = aux_slice return numpy.asarray(feri[b0:b1]) for dat in lib.map_with_prefetch(load, self.prange(0, naoaux, blksize)): yield dat dat = None
[docs] def prange(self, start, end, step): for i in range(start, end, step): yield i, min(i+step, end)
[docs] def get_naoaux(self): # determine naoaux with self._cderi, because DF object may be used as CD # object when self._cderi is provided. if self._cderi is None: self.build() with addons.load(self._cderi, self._dataname) as feri: if isinstance(feri, h5py.Group): return feri['0'].shape[0] else: return feri.shape[0]
[docs] def get_jk(self, dm, hermi=1, with_j=True, with_k=True, direct_scf_tol=getattr(__config__, 'scf_hf_SCF_direct_scf_tol', 1e-13), omega=None): if omega is None: return df_jk.get_jk(self, dm, hermi, with_j, with_k, direct_scf_tol) # A temporary treatment for RSH-DF integrals with self.range_coulomb(omega) as rsh_df: return df_jk.get_jk(rsh_df, dm, hermi, with_j, with_k, direct_scf_tol)
[docs] def get_eri(self): nao = self.mol.nao_nr() nao_pair = nao * (nao+1) // 2 ao_eri = numpy.zeros((nao_pair,nao_pair)) for eri1 in self.loop(): lib.dot(eri1.T, eri1, 1, ao_eri, 1) return ao2mo.restore(8, ao_eri, nao)
get_ao_eri = get_eri
[docs] def ao2mo(self, mo_coeffs, compact=getattr(__config__, 'df_df_DF_ao2mo_compact', True)): if isinstance(mo_coeffs, numpy.ndarray) and mo_coeffs.ndim == 2: mo_coeffs = (mo_coeffs,) * 4 ijmosym, nij_pair, moij, ijslice = _conc_mos(mo_coeffs[0], mo_coeffs[1], compact) klmosym, nkl_pair, mokl, klslice = _conc_mos(mo_coeffs[2], mo_coeffs[3], compact) mo_eri = numpy.zeros((nij_pair,nkl_pair)) sym = (iden_coeffs(mo_coeffs[0], mo_coeffs[2]) and iden_coeffs(mo_coeffs[1], mo_coeffs[3])) Lij = Lkl = None for eri1 in self.loop(): Lij = _ao2mo.nr_e2(eri1, moij, ijslice, aosym='s2', mosym=ijmosym, out=Lij) if sym: Lkl = Lij else: Lkl = _ao2mo.nr_e2(eri1, mokl, klslice, aosym='s2', mosym=klmosym, out=Lkl) lib.dot(Lij.T, Lkl, 1, mo_eri, 1) return mo_eri
get_mo_eri = ao2mo
[docs] @contextlib.contextmanager def range_coulomb(self, omega): '''Creates a temporary density fitting object for RSH-DF integrals. In this context, only LR or SR integrals for mol and auxmol are computed. ''' if omega is None: omega = 0 key = '%.6f' % omega if key in self._rsh_df: rsh_df = self._rsh_df[key] else: rsh_df = self._rsh_df[key] = self.copy().reset() if hasattr(self, '_dataname'): rsh_df._dataname = f'{self._dataname}/lr/{key}' logger.info(self, 'Create RSH-DF object %s for omega=%s', rsh_df, omega) mol = self.mol auxmol = self.auxmol mol_omega = mol.omega mol.omega = omega auxmol_omega = None if auxmol is not None: auxmol_omega = auxmol.omega auxmol.omega = omega assert rsh_df.mol.omega == omega if rsh_df.auxmol is not None: assert rsh_df.auxmol.omega == omega try: yield rsh_df finally: mol.omega = mol_omega if auxmol_omega is not None: auxmol.omega = auxmol_omega
GDF = DF
[docs] class DF4C(DF): '''Relativistic 4-component'''
[docs] def build(self): log = logger.Logger(self.stdout, self.verbose) mol = self.mol auxmol = self.auxmol = addons.make_auxmol(self.mol, self.auxbasis) n2c = mol.nao_2c() naux = auxmol.nao_nr() nao_pair = n2c*(n2c+1)//2 max_memory = (self.max_memory - lib.current_memory()[0]) * .8 if nao_pair*naux*3*16/1e6*2 < max_memory: self._cderi =(r_incore.cholesky_eri(mol, auxmol=auxmol, aosym='s2', int3c='int3c2e_spinor', verbose=log), r_incore.cholesky_eri(mol, auxmol=auxmol, aosym='s2', int3c='int3c2e_spsp1_spinor', verbose=log)) else: raise NotImplementedError return self
[docs] def loop(self, blksize=None): if self._cderi is None: self.build() if blksize is None: blksize = self.blockdim with addons.load(self._cderi[0], self._dataname) as ferill: naoaux = ferill.shape[0] with addons.load(self._cderi[1], self._dataname) as feriss: # python2.6 not support multiple with for b0, b1 in self.prange(0, naoaux, blksize): erill = numpy.asarray(ferill[b0:b1], order='C') eriss = numpy.asarray(feriss[b0:b1], order='C') yield erill, eriss
[docs] def get_jk(self, dm, hermi=1, with_j=True, with_k=True, direct_scf_tol=getattr(__config__, 'scf_hf_SCF_direct_scf_tol', 1e-13), omega=None): if omega is None: return df_jk.r_get_jk(self, dm, hermi, with_j, with_k) with self.range_coulomb(omega) as rsh_df: return df_jk.r_get_jk(rsh_df, dm, hermi, with_j, with_k)
[docs] def ao2mo(self, mo_coeffs): raise NotImplementedError
GDF4C = DF4C