def _gamma1_intermediates(mycc, t1, t2, l1, l2):
t1T = t1.T
t2T = t2.transpose(2, 3, 0, 1)
l1T = l1.T
l2T = l2.transpose(2, 3, 0, 1)
t1 = t2 = l1 = l2 = None
#doo = -np.dot(l1T.T, t1T)
doo = mpi.allreduce(einsum('efim, efjm -> ij', l2T, t2T) * (-0.5))
#dvv = np.dot(t1T, l1T.T)
dvv = mpi.allreduce(einsum('eamn, ebmn -> ab', t2T, l2T) * 0.5)
#xt1 = mpi.allreduce(einsum('efmn, efin -> mi', l2T, t2T) * 0.5)
#xt2 = mpi.allreduce(einsum('famn, femn -> ae', t2T, l2T) * 0.5)
#xt2 += np.dot(t1T, l1T.T)
#dvo = mpi.allgather(np.einsum('aeim, em -> ai', t2T, l1T, optimize=True))
#dvo -= np.dot(t1T, xt1)
#dvo -= np.dot(xt2, t1T)
#dvo += t1T
#dov = l1T.T
nvir, nocc = t1T.shape
dvo = np.zeros((nvir, nocc), dtype=t1T.dtype)
dov = np.zeros((nocc, nvir), dtype=t1T.dtype)
return doo, dov, dvo, dvv
def get_j_kpts(mydf, dm_kpts, hermi=1, kpts=numpy.zeros((1,3)),
kpts_band=None):
mydf = _sync_mydf(mydf)
cell = mydf.cell
mesh = mydf.mesh
dm_kpts = lib.asarray(dm_kpts, order='C')
dms = _format_dms(dm_kpts, kpts)
nset, nkpts, nao = dms.shape[:3]
coulG = tools.get_coulG(cell, mesh=mesh)
ngrids = len(coulG)
vR = rhoR = numpy.zeros((nset,ngrids))
for ao_ks_etc, p0, p1 in mydf.mpi_aoR_loop(mydf.grids, kpts):
ao_ks = ao_ks_etc[0]
for k, ao in enumerate(ao_ks):
for i in range(nset):
rhoR[i,p0:p1] += numint.eval_rho(cell, ao, dms[i,k])
ao = ao_ks = None
rhoR = mpi.allreduce(rhoR)
for i in range(nset):
rhoR[i] *= 1./nkpts
rhoG = tools.fft(rhoR[i], mesh)
vG = coulG * rhoG
vR[i] = tools.ifft(vG, mesh).real
kpts_band, input_band = _format_kpts_band(kpts_band, kpts), kpts_band
nband = len(kpts_band)
weight = cell.vol / ngrids
vR *= weight
if gamma_point(kpts_band):
vj_kpts = numpy.zeros((nset,nband,nao,nao))
else:
vj_kpts = numpy.zeros((nset,nband,nao,nao), dtype=numpy.complex128)
for ao_ks_etc, p0, p1 in mydf.mpi_aoR_loop(mydf.grids, kpts_band):
ao_ks = ao_ks_etc[0]
for k, ao in enumerate(ao_ks):
for i in range(nset):
vj_kpts[i,k] += lib.dot(ao.T.conj()*vR[i,p0:p1], ao)
vj_kpts = mpi.reduce(vj_kpts)
if gamma_point(kpts_band):
vj_kpts = vj_kpts.real
return _format_jks(vj_kpts, dm_kpts, input_band, kpts)
def _make_j3c(mydf, cell, auxcell, kptij_lst, cderi_file):
log = logger.Logger(mydf.stdout, mydf.verbose)
t1 = t0 = (time.clock(), time.time())
fused_cell, fuse = fuse_auxcell(mydf, mydf.auxcell)
ao_loc = cell.ao_loc_nr()
nao = ao_loc[-1]
naux = auxcell.nao_nr()
nkptij = len(kptij_lst)
mesh = mydf.mesh
Gv, Gvbase, kws = cell.get_Gv_weights(mesh)
b = cell.reciprocal_vectors()
gxyz = lib.cartesian_prod([numpy.arange(len(x)) for x in Gvbase])
ngrids = gxyz.shape[0]
kptis = kptij_lst[:, 0]
kptjs = kptij_lst[:, 1]
kpt_ji = kptjs - kptis
uniq_kpts, uniq_index, uniq_inverse = unique(kpt_ji)
log.debug('Num uniq kpts %d', len(uniq_kpts))
log.debug2('uniq_kpts %s', uniq_kpts)
# j2c ~ (-kpt_ji | kpt_ji)
j2c = fused_cell.pbc_intor('int2c2e', hermi=1, kpts=uniq_kpts)
j2ctags = []
t1 = log.timer_debug1('2c2e', *t1)
swapfile = tempfile.NamedTemporaryFile(dir=os.path.dirname(cderi_file))
fswap = lib.H5TmpFile(swapfile.name)
# Unlink swapfile to avoid trash
swapfile = None
mem_now = max(comm.allgather(lib.current_memory()[0]))
max_memory = max(2000, mydf.max_memory - mem_now)
blksize = max(2048, int(max_memory * .5e6 / 16 / fused_cell.nao_nr()))
log.debug2('max_memory %s (MB) blocksize %s', max_memory, blksize)
for k, kpt in enumerate(uniq_kpts):
coulG = mydf.weighted_coulG(kpt, False, mesh)
j2c_k = numpy.zeros_like(j2c[k])
for p0, p1 in mydf.prange(0, ngrids, blksize):
aoaux = ft_ao.ft_ao(fused_cell, Gv[p0:p1], None, b, gxyz[p0:p1],
Gvbase, kpt).T
LkR = numpy.asarray(aoaux.real, order='C')
LkI = numpy.asarray(aoaux.imag, order='C')
aoaux = None
if is_zero(kpt): # kpti == kptj
j2c_k[naux:] += lib.ddot(LkR[naux:] * coulG[p0:p1], LkR.T)
j2c_k[naux:] += lib.ddot(LkI[naux:] * coulG[p0:p1], LkI.T)
else:
j2cR, j2cI = zdotCN(LkR[naux:] * coulG[p0:p1],
LkI[naux:] * coulG[p0:p1], LkR.T, LkI.T)
j2c_k[naux:] += j2cR + j2cI * 1j
kLR = kLI = None
j2c_k[:naux, naux:] = j2c_k[naux:, :naux].conj().T
j2c[k] -= mpi.allreduce(j2c_k)
j2c[k] = fuse(fuse(j2c[k]).T).T
try:
fswap['j2c/%d' % k] = scipy.linalg.cholesky(j2c[k], lower=True)
j2ctags.append('CD')
except scipy.linalg.LinAlgError as e:
#msg =('===================================\n'
# 'J-metric not positive definite.\n'
# 'It is likely that mesh is not enough.\n'
# '===================================')
#log.error(msg)
#raise scipy.linalg.LinAlgError('\n'.join([str(e), msg]))
w, v = scipy.linalg.eigh(j2c[k])
log.debug2('metric linear dependency for kpt %s', k)
log.debug2('cond = %.4g, drop %d bfns', w[0] / w[-1],
numpy.count_nonzero(w < mydf.linear_dep_threshold))
v1 = v[:, w > mydf.linear_dep_threshold].T.conj()
v1 /= numpy.sqrt(w[w > mydf.linear_dep_threshold]).reshape(-1, 1)
fswap['j2c/%d' % k] = v1
if cell.dimension == 2 and cell.low_dim_ft_type != 'inf_vacuum':
idx = numpy.where(w < -mydf.linear_dep_threshold)[0]
if len(idx) > 0:
fswap['j2c-/%d' % k] = (v[:, idx] /
numpy.sqrt(-w[idx])).conj().T
w = v = v1 = None
j2ctags.append('eig')
j2c = coulG = None
aosym_s2 = numpy.einsum('ix->i', abs(kptis - kptjs)) < 1e-9
j_only = numpy.all(aosym_s2)
if gamma_point(kptij_lst):
dtype = 'f8'
else:
dtype = 'c16'
t1 = log.timer_debug1('aoaux and int2c', *t1)
# Estimates the buffer size based on the last contraction in G-space.
# This contraction requires to hold nkptj copies of (naux,?) array
# simultaneously in memory.
mem_now = max(comm.allgather(lib.current_memory()[0]))
max_memory = max(2000, mydf.max_memory - mem_now)
nkptj_max = max((uniq_inverse == x).sum() for x in set(uniq_inverse))
buflen = max(
int(
min(max_memory * .5e6 / 16 / naux / (nkptj_max + 2) / nao,
nao / 3 / mpi.pool.size)), 1)
chunks = (buflen, nao)
j3c_jobs = grids2d_int3c_jobs(cell, auxcell, kptij_lst, chunks, j_only)
log.debug1('max_memory = %d MB (%d in use) chunks %s', max_memory,
mem_now, chunks)
log.debug2('j3c_jobs %s', j3c_jobs)
if j_only:
int3c = wrap_int3c(cell, fused_cell, 'int3c2e', 's2', 1, kptij_lst)
else:
int3c = wrap_int3c(cell, fused_cell, 'int3c2e', 's1', 1, kptij_lst)
idxb = numpy.tril_indices(nao)
idxb = (idxb[0] * nao + idxb[1]).astype('i')
aux_loc = fused_cell.ao_loc_nr('ssc' in 'int3c2e')
def gen_int3c(job_id, ish0, ish1):
dataname = 'j3c-chunks/%d' % job_id
i0 = ao_loc[ish0]
i1 = ao_loc[ish1]
dii = i1 * (i1 + 1) // 2 - i0 * (i0 + 1) // 2
if j_only:
dij = dii
buflen = max(8, int(max_memory * 1e6 / 16 / (nkptij * dii + dii)))
else:
dij = (i1 - i0) * nao
buflen = max(8, int(max_memory * 1e6 / 16 / (nkptij * dij + dij)))
auxranges = balance_segs(aux_loc[1:] - aux_loc[:-1], buflen)
buflen = max([x[2] for x in auxranges])
buf = numpy.empty(nkptij * dij * buflen, dtype=dtype)
buf1 = numpy.empty(dij * buflen, dtype=dtype)
naux = aux_loc[-1]
for kpt_id, kptij in enumerate(kptij_lst):
key = '%s/%d' % (dataname, kpt_id)
if aosym_s2[kpt_id]:
shape = (naux, dii)
else:
shape = (naux, dij)
if gamma_point(kptij):
fswap.create_dataset(key, shape, 'f8')
else:
fswap.create_dataset(key, shape, 'c16')
naux0 = 0
for istep, auxrange in enumerate(auxranges):
log.alldebug2("aux_e1 job_id %d step %d", job_id, istep)
sh0, sh1, nrow = auxrange
sub_slice = (ish0, ish1, 0, cell.nbas, sh0, sh1)
mat = numpy.ndarray((nkptij, dij, nrow), dtype=dtype, buffer=buf)
mat = int3c(sub_slice, mat)
for k, kptij in enumerate(kptij_lst):
h5dat = fswap['%s/%d' % (dataname, k)]
v = lib.transpose(mat[k], out=buf1)
if not j_only and aosym_s2[k]:
idy = idxb[i0 * (i0 + 1) // 2:i1 *
(i1 + 1) // 2] - i0 * nao
out = numpy.ndarray((nrow, dii),
dtype=v.dtype,
buffer=mat[k])
v = numpy.take(v, idy, axis=1, out=out)
if gamma_point(kptij):
h5dat[naux0:naux0 + nrow] = v.real
else:
h5dat[naux0:naux0 + nrow] = v
naux0 += nrow
def ft_fuse(job_id, uniq_kptji_id, sh0, sh1):
kpt = uniq_kpts[uniq_kptji_id] # kpt = kptj - kpti
adapted_ji_idx = numpy.where(uniq_inverse == uniq_kptji_id)[0]
adapted_kptjs = kptjs[adapted_ji_idx]
nkptj = len(adapted_kptjs)
j2c = numpy.asarray(fswap['j2c/%d' % uniq_kptji_id])
j2ctag = j2ctags[uniq_kptji_id]
naux0 = j2c.shape[0]
if ('j2c-/%d' % uniq_kptji_id) in fswap:
j2c_negative = numpy.asarray(fswap['j2c-/%d' % uniq_kptji_id])
else:
j2c_negative = None
if is_zero(kpt):
aosym = 's2'
else:
aosym = 's1'
if aosym == 's2' and cell.dimension == 3:
vbar = fuse(mydf.auxbar(fused_cell))
ovlp = cell.pbc_intor('int1e_ovlp', hermi=1, kpts=adapted_kptjs)
ovlp = [lib.pack_tril(s) for s in ovlp]
j3cR = [None] * nkptj
j3cI = [None] * nkptj
i0 = ao_loc[sh0]
i1 = ao_loc[sh1]
for k, idx in enumerate(adapted_ji_idx):
key = 'j3c-chunks/%d/%d' % (job_id, idx)
v = numpy.asarray(fswap[key])
if aosym == 's2' and cell.dimension == 3:
for i in numpy.where(vbar != 0)[0]:
v[i] -= vbar[i] * ovlp[k][i0 * (i0 + 1) // 2:i1 *
(i1 + 1) // 2].ravel()
j3cR[k] = numpy.asarray(v.real, order='C')
if v.dtype == numpy.complex128:
j3cI[k] = numpy.asarray(v.imag, order='C')
v = None
ncol = j3cR[0].shape[1]
Gblksize = max(16, int(max_memory * 1e6 / 16 / ncol /
(nkptj + 1))) # +1 for pqkRbuf/pqkIbuf
Gblksize = min(Gblksize, ngrids, 16384)
pqkRbuf = numpy.empty(ncol * Gblksize)
pqkIbuf = numpy.empty(ncol * Gblksize)
buf = numpy.empty(nkptj * ncol * Gblksize, dtype=numpy.complex128)
log.alldebug2('job_id %d blksize (%d,%d)', job_id, Gblksize, ncol)
wcoulG = mydf.weighted_coulG(kpt, False, mesh)
fused_cell_slice = (auxcell.nbas, fused_cell.nbas)
if aosym == 's2':
shls_slice = (sh0, sh1, 0, sh1)
else:
shls_slice = (sh0, sh1, 0, cell.nbas)
for p0, p1 in lib.prange(0, ngrids, Gblksize):
Gaux = ft_ao.ft_ao(fused_cell, Gv[p0:p1], fused_cell_slice, b,
gxyz[p0:p1], Gvbase, kpt)
Gaux *= wcoulG[p0:p1, None]
kLR = Gaux.real.copy('C')
kLI = Gaux.imag.copy('C')
Gaux = None
dat = ft_ao._ft_aopair_kpts(cell,
Gv[p0:p1],
shls_slice,
aosym,
b,
gxyz[p0:p1],
Gvbase,
kpt,
adapted_kptjs,
out=buf)
nG = p1 - p0
for k, ji in enumerate(adapted_ji_idx):
aoao = dat[k].reshape(nG, ncol)
pqkR = numpy.ndarray((ncol, nG), buffer=pqkRbuf)
pqkI = numpy.ndarray((ncol, nG), buffer=pqkIbuf)
pqkR[:] = aoao.real.T
pqkI[:] = aoao.imag.T
lib.dot(kLR.T, pqkR.T, -1, j3cR[k][naux:], 1)
lib.dot(kLI.T, pqkI.T, -1, j3cR[k][naux:], 1)
if not (is_zero(kpt) and gamma_point(adapted_kptjs[k])):
lib.dot(kLR.T, pqkI.T, -1, j3cI[k][naux:], 1)
lib.dot(kLI.T, pqkR.T, 1, j3cI[k][naux:], 1)
kLR = kLI = None
for k, idx in enumerate(adapted_ji_idx):
if is_zero(kpt) and gamma_point(adapted_kptjs[k]):
v = fuse(j3cR[k])
else:
v = fuse(j3cR[k] + j3cI[k] * 1j)
if j2ctag == 'CD':
v = scipy.linalg.solve_triangular(j2c,
v,
lower=True,
overwrite_b=True)
fswap['j3c-chunks/%d/%d' % (job_id, idx)][:naux0] = v
else:
fswap['j3c-chunks/%d/%d' % (job_id, idx)][:naux0] = lib.dot(
j2c, v)
# low-dimension systems
if j2c_negative is not None:
fswap['j3c-/%d/%d' % (job_id, idx)] = lib.dot(j2c_negative, v)
_assemble(mydf, kptij_lst, j3c_jobs, gen_int3c, ft_fuse, cderi_file, fswap,
log)
def _assemble(mydf, kptij_lst, j3c_jobs, gen_int3c, ft_fuse, cderi_file, fswap,
log):
t1 = (time.clock(), time.time())
cell = mydf.cell
ao_loc = cell.ao_loc_nr()
nao = ao_loc[-1]
kptis = kptij_lst[:, 0]
kptjs = kptij_lst[:, 1]
kpt_ji = kptjs - kptis
uniq_kpts, uniq_index, uniq_inverse = unique(kpt_ji)
aosym_s2 = numpy.einsum('ix->i', abs(kptis - kptjs)) < 1e-9
t2 = t1
j3c_workers = numpy.zeros(len(j3c_jobs), dtype=int)
#for job_id, ish0, ish1 in mpi.work_share_partition(j3c_jobs):
for job_id, ish0, ish1 in mpi.work_stealing_partition(j3c_jobs):
gen_int3c(job_id, ish0, ish1)
t2 = log.alltimer_debug2('int j3c %d' % job_id, *t2)
for k, kpt in enumerate(uniq_kpts):
ft_fuse(job_id, k, ish0, ish1)
t2 = log.alltimer_debug2('ft-fuse %d k %d' % (job_id, k), *t2)
j3c_workers[job_id] = rank
j3c_workers = mpi.allreduce(j3c_workers)
log.debug2('j3c_workers %s', j3c_workers)
t1 = log.timer_debug1('int3c and fuse', *t1)
# Pass 2
# Transpose 3-index tensor and save data in cderi_file
feri = h5py.File(cderi_file, 'w')
nauxs = [fswap['j2c/%d' % k].shape[0] for k, kpt in enumerate(uniq_kpts)]
segsize = (max(nauxs) + mpi.pool.size - 1) // mpi.pool.size
naux0 = rank * segsize
for k, kptij in enumerate(kptij_lst):
naux1 = min(nauxs[uniq_inverse[k]], naux0 + segsize)
nrow = max(0, naux1 - naux0)
if gamma_point(kptij):
dtype = 'f8'
else:
dtype = 'c16'
if aosym_s2[k]:
nao_pair = nao * (nao + 1) // 2
else:
nao_pair = nao * nao
feri.create_dataset('j3c/%d' % k, (nrow, nao_pair),
dtype,
maxshape=(None, nao_pair))
def get_segs_loc(aosym):
off0 = numpy.asarray([ao_loc[i0] for x, i0, i1 in j3c_jobs])
off1 = numpy.asarray([ao_loc[i1] for x, i0, i1 in j3c_jobs])
if aosym: # s2
dims = off1 * (off1 + 1) // 2 - off0 * (off0 + 1) // 2
else:
dims = (off1 - off0) * nao
#dims = numpy.asarray([ao_loc[i1]-ao_loc[i0] for x,i0,i1 in j3c_jobs])
dims = numpy.hstack(
[dims[j3c_workers == w] for w in range(mpi.pool.size)])
job_idx = numpy.hstack(
[numpy.where(j3c_workers == w)[0] for w in range(mpi.pool.size)])
segs_loc = numpy.append(0, numpy.cumsum(dims))
segs_loc = [(segs_loc[j], segs_loc[j + 1])
for j in numpy.argsort(job_idx)]
return segs_loc
segs_loc_s1 = get_segs_loc(False)
segs_loc_s2 = get_segs_loc(True)
job_ids = numpy.where(rank == j3c_workers)[0]
def load(k, p0, p1):
naux1 = nauxs[uniq_inverse[k]]
slices = [(min(i * segsize + p0, naux1), min(i * segsize + p1, naux1))
for i in range(mpi.pool.size)]
segs = []
for p0, p1 in slices:
val = [
fswap['j3c-chunks/%d/%d' % (job, k)][p0:p1].ravel()
for job in job_ids
]
if val:
segs.append(numpy.hstack(val))
else:
segs.append(numpy.zeros(0))
return segs
def save(k, p0, p1, segs):
segs = mpi.alltoall(segs)
naux1 = nauxs[uniq_inverse[k]]
loc0, loc1 = min(p0, naux1 - naux0), min(p1, naux1 - naux0)
nL = loc1 - loc0
if nL > 0:
if aosym_s2[k]:
segs = numpy.hstack([
segs[i0 * nL:i1 * nL].reshape(nL, -1)
for i0, i1 in segs_loc_s2
])
else:
segs = numpy.hstack([
segs[i0 * nL:i1 * nL].reshape(nL, -1)
for i0, i1 in segs_loc_s1
])
feri['j3c/%d' % k][loc0:loc1] = segs
mem_now = max(comm.allgather(lib.current_memory()[0]))
max_memory = max(2000, min(8000, mydf.max_memory - mem_now))
if numpy.all(aosym_s2):
if gamma_point(kptij_lst):
blksize = max(16, int(max_memory * .5e6 / 8 / nao**2))
else:
blksize = max(16, int(max_memory * .5e6 / 16 / nao**2))
else:
blksize = max(16, int(max_memory * .5e6 / 16 / nao**2 / 2))
log.debug1('max_momory %d MB (%d in use), blksize %d', max_memory, mem_now,
blksize)
t2 = t1
with lib.call_in_background(save) as async_write:
for k, kptji in enumerate(kptij_lst):
for p0, p1 in lib.prange(0, segsize, blksize):
segs = load(k, p0, p1)
async_write(k, p0, p1, segs)
t2 = log.timer_debug1(
'assemble k=%d %d:%d (in %d)' % (k, p0, p1, segsize), *t2)
if 'j2c-' in fswap:
j2c_kpts_lists = []
for k, kpt in enumerate(uniq_kpts):
if ('j2c-/%d' % k) in fswap:
adapted_ji_idx = numpy.where(uniq_inverse == k)[0]
j2c_kpts_lists.append(adapted_ji_idx)
for k in numpy.hstack(j2c_kpts_lists):
val = [
numpy.asarray(fswap['j3c-/%d/%d' % (job, k)]).ravel()
for job in job_ids
]
val = mpi.gather(numpy.hstack(val))
if rank == 0:
naux1 = fswap['j3c-/0/%d' % k].shape[0]
if aosym_s2[k]:
v = [
val[i0 * naux1:i1 * naux1].reshape(naux1, -1)
for i0, i1 in segs_loc_s2
]
else:
v = [
val[i0 * naux1:i1 * naux1].reshape(naux1, -1)
for i0, i1 in segs_loc_s1
]
feri['j3c-/%d' % k] = numpy.hstack(v)
if 'j3c-kptij' in feri: del (feri['j3c-kptij'])
feri['j3c-kptij'] = kptij_lst
t1 = log.alltimer_debug1('assembling j3c', *t1)
feri.close()
def _make_j3c(mydf, cell, auxcell, kptij_lst, cderi_file):
log = logger.Logger(mydf.stdout, mydf.verbose)
t1 = t0 = (time.clock(), time.time())
fused_cell, fuse = fuse_auxcell(mydf, mydf.auxcell)
ao_loc = cell.ao_loc_nr()
nao = ao_loc[-1]
naux = auxcell.nao_nr()
nkptij = len(kptij_lst)
gs = mydf.gs
Gv, Gvbase, kws = cell.get_Gv_weights(gs)
b = cell.reciprocal_vectors()
gxyz = lib.cartesian_prod([numpy.arange(len(x)) for x in Gvbase])
ngs = gxyz.shape[0]
kptis = kptij_lst[:, 0]
kptjs = kptij_lst[:, 1]
kpt_ji = kptjs - kptis
uniq_kpts, uniq_index, uniq_inverse = unique(kpt_ji)
log.debug('Num uniq kpts %d', len(uniq_kpts))
log.debug2('uniq_kpts %s', uniq_kpts)
# j2c ~ (-kpt_ji | kpt_ji)
j2c = fused_cell.pbc_intor('int2c2e_sph', hermi=1, kpts=uniq_kpts)
j2ctags = []
nauxs = []
t1 = log.timer_debug1('2c2e', *t1)
if h5py.is_hdf5(cderi_file):
feri = h5py.File(cderi_file)
else:
feri = h5py.File(cderi_file, 'w')
for k, kpt in enumerate(uniq_kpts):
aoaux = ft_ao.ft_ao(fused_cell, Gv, None, b, gxyz, Gvbase, kpt).T
coulG = numpy.sqrt(mydf.weighted_coulG(kpt, False, gs))
kLR = (aoaux.real * coulG).T
kLI = (aoaux.imag * coulG).T
if not kLR.flags.c_contiguous: kLR = lib.transpose(kLR.T)
if not kLI.flags.c_contiguous: kLI = lib.transpose(kLI.T)
aoaux = None
kLR1 = numpy.asarray(kLR[:, naux:], order='C')
kLI1 = numpy.asarray(kLI[:, naux:], order='C')
if is_zero(kpt): # kpti == kptj
for p0, p1 in mydf.mpi_prange(0, ngs):
j2cR = lib.ddot(kLR1[p0:p1].T, kLR[p0:p1])
j2cR = lib.ddot(kLI1[p0:p1].T, kLI[p0:p1], 1, j2cR, 1)
j2c[k][naux:] -= mpi.allreduce(j2cR)
j2c[k][:naux, naux:] = j2c[k][naux:, :naux].T
else:
for p0, p1 in mydf.mpi_prange(0, ngs):
j2cR, j2cI = zdotCN(kLR1[p0:p1].T, kLI1[p0:p1].T, kLR[p0:p1],
kLI[p0:p1])
j2cR = mpi.allreduce(j2cR)
j2cI = mpi.allreduce(j2cI)
j2c[k][naux:] -= j2cR + j2cI * 1j
j2c[k][:naux, naux:] = j2c[k][naux:, :naux].T.conj()
j2c[k] = fuse(fuse(j2c[k]).T).T
try:
feri['j2c/%d' % k] = scipy.linalg.cholesky(j2c[k], lower=True)
j2ctags.append('CD')
nauxs.append(naux)
except scipy.linalg.LinAlgError as e:
#msg =('===================================\n'
# 'J-metric not positive definite.\n'
# 'It is likely that gs is not enough.\n'
# '===================================')
#log.error(msg)
#raise scipy.linalg.LinAlgError('\n'.join([e.message, msg]))
w, v = scipy.linalg.eigh(j2c)
log.debug2('metric linear dependency for kpt %s', uniq_kptji_id)
log.debug2('cond = %.4g, drop %d bfns', w[0] / w[-1],
numpy.count_nonzero(w < LINEAR_DEP_THR))
v = v[:, w > LINEAR_DEP_THR].T.conj()
v /= numpy.sqrt(w[w > LINEAR_DEP_THR]).reshape(-1, 1)
feri['j2c/%d' % k] = v
j2ctags.append('eig')
nauxs.append(v.shape[0])
kLR = kLI = kLR1 = kLI1 = coulG = None
j2c = None
aosym_s2 = numpy.einsum('ix->i', abs(kptis - kptjs)) < 1e-9
j_only = numpy.all(aosym_s2)
if gamma_point(kptij_lst):
dtype = 'f8'
else:
dtype = 'c16'
vbar = mydf.auxbar(fused_cell)
vbar = fuse(vbar)
ovlp = cell.pbc_intor('int1e_ovlp_sph', hermi=1, kpts=kptjs[aosym_s2])
ovlp = [lib.pack_tril(s) for s in ovlp]
t1 = log.timer_debug1('aoaux and int2c', *t1)
# Estimates the buffer size based on the last contraction in G-space.
# This contraction requires to hold nkptj copies of (naux,?) array
# simultaneously in memory.
mem_now = max(comm.allgather(lib.current_memory()[0]))
max_memory = max(2000, mydf.max_memory - mem_now)
nkptj_max = max((uniq_inverse == x).sum() for x in set(uniq_inverse))
buflen = max(
int(
min(max_memory * .5e6 / 16 / naux / (nkptj_max + 2) / nao,
nao / 3 / mpi.pool.size)), 1)
chunks = (buflen, nao)
j3c_jobs = grids2d_int3c_jobs(cell, auxcell, kptij_lst, chunks, j_only)
log.debug1('max_memory = %d MB (%d in use) chunks %s', max_memory,
mem_now, chunks)
log.debug2('j3c_jobs %s', j3c_jobs)
if j_only:
int3c = wrap_int3c(cell, fused_cell, 'int3c2e_sph', 's2', 1, kptij_lst)
else:
int3c = wrap_int3c(cell, fused_cell, 'int3c2e_sph', 's1', 1, kptij_lst)
idxb = numpy.tril_indices(nao)
idxb = (idxb[0] * nao + idxb[1]).astype('i')
aux_loc = fused_cell.ao_loc_nr('ssc' in 'int3c2e_sph')
def gen_int3c(auxcell, job_id, ish0, ish1):
dataname = 'j3c-chunks/%d' % job_id
if dataname in feri:
del (feri[dataname])
i0 = ao_loc[ish0]
i1 = ao_loc[ish1]
dii = i1 * (i1 + 1) // 2 - i0 * (i0 + 1) // 2
dij = (i1 - i0) * nao
if j_only:
buflen = max(8, int(max_memory * 1e6 / 16 / (nkptij * dii + dii)))
else:
buflen = max(8, int(max_memory * 1e6 / 16 / (nkptij * dij + dij)))
auxranges = balance_segs(aux_loc[1:] - aux_loc[:-1], buflen)
buflen = max([x[2] for x in auxranges])
buf = numpy.empty(nkptij * dij * buflen, dtype=dtype)
buf1 = numpy.empty(dij * buflen, dtype=dtype)
naux = aux_loc[-1]
for kpt_id, kptij in enumerate(kptij_lst):
key = '%s/%d' % (dataname, kpt_id)
if aosym_s2[kpt_id]:
shape = (naux, dii)
else:
shape = (naux, dij)
if gamma_point(kptij):
feri.create_dataset(key, shape, 'f8')
else:
feri.create_dataset(key, shape, 'c16')
naux0 = 0
for istep, auxrange in enumerate(auxranges):
log.alldebug2("aux_e2 job_id %d step %d", job_id, istep)
sh0, sh1, nrow = auxrange
sub_slice = (ish0, ish1, 0, cell.nbas, sh0, sh1)
if j_only:
mat = numpy.ndarray((nkptij, dii, nrow),
dtype=dtype,
buffer=buf)
else:
mat = numpy.ndarray((nkptij, dij, nrow),
dtype=dtype,
buffer=buf)
mat = int3c(sub_slice, mat)
for k, kptij in enumerate(kptij_lst):
h5dat = feri['%s/%d' % (dataname, k)]
v = lib.transpose(mat[k], out=buf1)
if not j_only and aosym_s2[k]:
idy = idxb[i0 * (i0 + 1) // 2:i1 *
(i1 + 1) // 2] - i0 * nao
out = numpy.ndarray((nrow, dii),
dtype=v.dtype,
buffer=mat[k])
v = numpy.take(v, idy, axis=1, out=out)
if gamma_point(kptij):
h5dat[naux0:naux0 + nrow] = v.real
else:
h5dat[naux0:naux0 + nrow] = v
naux0 += nrow
def ft_fuse(job_id, uniq_kptji_id, sh0, sh1):
kpt = uniq_kpts[uniq_kptji_id] # kpt = kptj - kpti
adapted_ji_idx = numpy.where(uniq_inverse == uniq_kptji_id)[0]
adapted_kptjs = kptjs[adapted_ji_idx]
nkptj = len(adapted_kptjs)
shls_slice = (auxcell.nbas, fused_cell.nbas)
Gaux = ft_ao.ft_ao(fused_cell, Gv, shls_slice, b, gxyz, Gvbase, kpt)
Gaux *= mydf.weighted_coulG(kpt, False, gs).reshape(-1, 1)
kLR = Gaux.real.copy('C')
kLI = Gaux.imag.copy('C')
j2c = numpy.asarray(feri['j2c/%d' % uniq_kptji_id])
j2ctag = j2ctags[uniq_kptji_id]
naux0 = j2c.shape[0]
if is_zero(kpt):
aosym = 's2'
else:
aosym = 's1'
j3cR = [None] * nkptj
j3cI = [None] * nkptj
i0 = ao_loc[sh0]
i1 = ao_loc[sh1]
for k, idx in enumerate(adapted_ji_idx):
key = 'j3c-chunks/%d/%d' % (job_id, idx)
v = numpy.asarray(feri[key])
if is_zero(kpt):
for i, c in enumerate(vbar):
if c != 0:
v[i] -= c * ovlp[k][i0 * (i0 + 1) // 2:i1 *
(i1 + 1) // 2].ravel()
j3cR[k] = numpy.asarray(v.real, order='C')
if v.dtype == numpy.complex128:
j3cI[k] = numpy.asarray(v.imag, order='C')
v = None
ncol = j3cR[0].shape[1]
Gblksize = max(16, int(max_memory * 1e6 / 16 / ncol /
(nkptj + 1))) # +1 for pqkRbuf/pqkIbuf
Gblksize = min(Gblksize, ngs, 16384)
pqkRbuf = numpy.empty(ncol * Gblksize)
pqkIbuf = numpy.empty(ncol * Gblksize)
buf = numpy.empty(nkptj * ncol * Gblksize, dtype=numpy.complex128)
log.alldebug2(' blksize (%d,%d)', Gblksize, ncol)
shls_slice = (sh0, sh1, 0, cell.nbas)
for p0, p1 in lib.prange(0, ngs, Gblksize):
dat = ft_ao._ft_aopair_kpts(cell,
Gv[p0:p1],
shls_slice,
aosym,
b,
gxyz[p0:p1],
Gvbase,
kpt,
adapted_kptjs,
out=buf)
nG = p1 - p0
for k, ji in enumerate(adapted_ji_idx):
aoao = dat[k].reshape(nG, ncol)
pqkR = numpy.ndarray((ncol, nG), buffer=pqkRbuf)
pqkI = numpy.ndarray((ncol, nG), buffer=pqkIbuf)
pqkR[:] = aoao.real.T
pqkI[:] = aoao.imag.T
lib.dot(kLR[p0:p1].T, pqkR.T, -1, j3cR[k][naux:], 1)
lib.dot(kLI[p0:p1].T, pqkI.T, -1, j3cR[k][naux:], 1)
if not (is_zero(kpt) and gamma_point(adapted_kptjs[k])):
lib.dot(kLR[p0:p1].T, pqkI.T, -1, j3cI[k][naux:], 1)
lib.dot(kLI[p0:p1].T, pqkR.T, 1, j3cI[k][naux:], 1)
for k, idx in enumerate(adapted_ji_idx):
if is_zero(kpt) and gamma_point(adapted_kptjs[k]):
v = fuse(j3cR[k])
else:
v = fuse(j3cR[k] + j3cI[k] * 1j)
if j2ctag == 'CD':
v = scipy.linalg.solve_triangular(j2c,
v,
lower=True,
overwrite_b=True)
else:
v = lib.dot(j2c, v)
feri['j3c-chunks/%d/%d' % (job_id, idx)][:naux0] = v
t2 = t1
j3c_workers = numpy.zeros(len(j3c_jobs), dtype=int)
#for job_id, ish0, ish1 in mpi.work_share_partition(j3c_jobs):
for job_id, ish0, ish1 in mpi.work_stealing_partition(j3c_jobs):
gen_int3c(fused_cell, job_id, ish0, ish1)
t2 = log.alltimer_debug2('int j3c %d' % job_id, *t2)
for k, kpt in enumerate(uniq_kpts):
ft_fuse(job_id, k, ish0, ish1)
t2 = log.alltimer_debug2('ft-fuse %d k %d' % (job_id, k), *t2)
j3c_workers[job_id] = rank
j3c_workers = mpi.allreduce(j3c_workers)
log.debug2('j3c_workers %s', j3c_workers)
j2c = kLRs = kLIs = ovlp = vbar = fuse = gen_int3c = ft_fuse = None
t1 = log.timer_debug1('int3c and fuse', *t1)
def get_segs_loc(aosym):
off0 = numpy.asarray([ao_loc[i0] for x, i0, i1 in j3c_jobs])
off1 = numpy.asarray([ao_loc[i1] for x, i0, i1 in j3c_jobs])
if aosym: # s2
dims = off1 * (off1 + 1) // 2 - off0 * (off0 + 1) // 2
else:
dims = (off1 - off0) * nao
#dims = numpy.asarray([ao_loc[i1]-ao_loc[i0] for x,i0,i1 in j3c_jobs])
dims = numpy.hstack(
[dims[j3c_workers == w] for w in range(mpi.pool.size)])
job_idx = numpy.hstack(
[numpy.where(j3c_workers == w)[0] for w in range(mpi.pool.size)])
segs_loc = numpy.append(0, numpy.cumsum(dims))
segs_loc = [(segs_loc[j], segs_loc[j + 1])
for j in numpy.argsort(job_idx)]
return segs_loc
segs_loc_s1 = get_segs_loc(False)
segs_loc_s2 = get_segs_loc(True)
if 'j3c' in feri: del (feri['j3c'])
segsize = (max(nauxs) + mpi.pool.size - 1) // mpi.pool.size
naux0 = rank * segsize
for k, kptij in enumerate(kptij_lst):
naux1 = min(nauxs[uniq_inverse[k]], naux0 + segsize)
nrow = max(0, naux1 - naux0)
if gamma_point(kptij):
dtype = 'f8'
else:
dtype = 'c16'
if aosym_s2[k]:
nao_pair = nao * (nao + 1) // 2
else:
nao_pair = nao * nao
feri.create_dataset('j3c/%d' % k, (nrow, nao_pair),
dtype,
maxshape=(None, nao_pair))
def load(k, p0, p1):
naux1 = nauxs[uniq_inverse[k]]
slices = [(min(i * segsize + p0, naux1), min(i * segsize + p1, naux1))
for i in range(mpi.pool.size)]
segs = []
for p0, p1 in slices:
val = []
for job_id, worker in enumerate(j3c_workers):
if rank == worker:
key = 'j3c-chunks/%d/%d' % (job_id, k)
val.append(feri[key][p0:p1].ravel())
if val:
segs.append(numpy.hstack(val))
else:
segs.append(numpy.zeros(0))
return segs
def save(k, p0, p1, segs):
segs = mpi.alltoall(segs)
naux1 = nauxs[uniq_inverse[k]]
loc0, loc1 = min(p0, naux1 - naux0), min(p1, naux1 - naux0)
nL = loc1 - loc0
if nL > 0:
if aosym_s2[k]:
segs = numpy.hstack([
segs[i0 * nL:i1 * nL].reshape(nL, -1)
for i0, i1 in segs_loc_s2
])
else:
segs = numpy.hstack([
segs[i0 * nL:i1 * nL].reshape(nL, -1)
for i0, i1 in segs_loc_s1
])
feri['j3c/%d' % k][loc0:loc1] = segs
mem_now = max(comm.allgather(lib.current_memory()[0]))
max_memory = max(2000, min(8000, mydf.max_memory - mem_now))
if numpy.all(aosym_s2):
if gamma_point(kptij_lst):
blksize = max(16, int(max_memory * .5e6 / 8 / nao**2))
else:
blksize = max(16, int(max_memory * .5e6 / 16 / nao**2))
else:
blksize = max(16, int(max_memory * .5e6 / 16 / nao**2 / 2))
log.debug1('max_momory %d MB (%d in use), blksize %d', max_memory, mem_now,
blksize)
t2 = t1
with lib.call_in_background(save) as async_write:
for k, kptji in enumerate(kptij_lst):
for p0, p1 in lib.prange(0, segsize, blksize):
segs = load(k, p0, p1)
async_write(k, p0, p1, segs)
t2 = log.timer_debug1(
'assemble k=%d %d:%d (in %d)' % (k, p0, p1, segsize), *t2)
if 'j3c-chunks' in feri: del (feri['j3c-chunks'])
if 'j3c-kptij' in feri: del (feri['j3c-kptij'])
feri['j3c-kptij'] = kptij_lst
t1 = log.alltimer_debug1('assembling j3c', *t1)
feri.close()
def _make_j3c(mydf, cell, auxcell, kptij_lst, cderi_file):
log = logger.Logger(mydf.stdout, mydf.verbose)
t1 = t0 = (time.clock(), time.time())
fused_cell, fuse = fuse_auxcell(mydf, mydf.auxcell)
ao_loc = cell.ao_loc_nr()
nao = ao_loc[-1]
naux = auxcell.nao_nr()
nkptij = len(kptij_lst)
mesh = mydf.mesh
Gv, Gvbase, kws = cell.get_Gv_weights(mesh)
b = cell.reciprocal_vectors()
gxyz = lib.cartesian_prod([numpy.arange(len(x)) for x in Gvbase])
ngrids = gxyz.shape[0]
kptis = kptij_lst[:,0]
kptjs = kptij_lst[:,1]
kpt_ji = kptjs - kptis
uniq_kpts, uniq_index, uniq_inverse = unique(kpt_ji)
log.debug('Num uniq kpts %d', len(uniq_kpts))
log.debug2('uniq_kpts %s', uniq_kpts)
# j2c ~ (-kpt_ji | kpt_ji)
j2c = fused_cell.pbc_intor('int2c2e', hermi=1, kpts=uniq_kpts)
j2ctags = []
t1 = log.timer_debug1('2c2e', *t1)
swapfile = tempfile.NamedTemporaryFile(dir=os.path.dirname(cderi_file))
fswap = lib.H5TmpFile(swapfile.name)
# Unlink swapfile to avoid trash
swapfile = None
for k, kpt in enumerate(uniq_kpts):
coulG = mydf.weighted_coulG(kpt, False, mesh)
j2c[k] = fuse(fuse(j2c[k]).T).T.copy()
j2c_k = numpy.zeros_like(j2c[k])
for p0, p1 in mydf.mpi_prange(0, ngrids):
aoaux = ft_ao.ft_ao(fused_cell, Gv[p0:p1], None, b, gxyz[p0:p1], Gvbase, kpt).T
aoaux = fuse(aoaux)
LkR = numpy.asarray(aoaux.real, order='C')
LkI = numpy.asarray(aoaux.imag, order='C')
aoaux = None
if is_zero(kpt): # kpti == kptj
j2cR = lib.dot(LkR*coulG[p0:p1], LkR.T)
j2c_k += lib.dot(LkI*coulG[p0:p1], LkI.T, 1, j2cR, 1)
else:
# aoaux ~ kpt_ij, aoaux.conj() ~ kpt_kl
j2cR, j2cI = zdotCN(LkR*coulG[p0:p1], LkI*coulG[p0:p1], LkR.T, LkI.T)
j2c_k += j2cR + j2cI * 1j
LkR = LkI = None
j2c[k] -= mpi.allreduce(j2c_k)
try:
fswap['j2c/%d'%k] = scipy.linalg.cholesky(j2c[k], lower=True)
j2ctags.append('CD')
except scipy.linalg.LinAlgError:
w, v = scipy.linalg.eigh(j2c[k])
log.debug2('metric linear dependency for kpt %s', k)
log.debug2('cond = %.4g, drop %d bfns',
w[0]/w[-1], numpy.count_nonzero(w<mydf.linear_dep_threshold))
v1 = v[:,w>mydf.linear_dep_threshold].T.conj()
v1 /= numpy.sqrt(w[w>mydf.linear_dep_threshold]).reshape(-1,1)
fswap['j2c/%d'%k] = v1
if cell.dimension == 2 and cell.low_dim_ft_type != 'inf_vacuum':
idx = numpy.where(w < -mydf.linear_dep_threshold)[0]
if len(idx) > 0:
fswap['j2c-/%d'%k] = (v[:,idx]/numpy.sqrt(-w[idx])).conj().T
w = v = v1 = v2 = None
j2ctags.append('eig')
aoaux = kLR = kLI = j2cR = j2cI = coulG = None
j2c = None
aosym_s2 = numpy.einsum('ix->i', abs(kptis-kptjs)) < 1e-9
j_only = numpy.all(aosym_s2)
if gamma_point(kptij_lst):
dtype = 'f8'
else:
dtype = 'c16'
t1 = log.timer_debug1('aoaux and int2c', *t1)
# Estimates the buffer size based on the last contraction in G-space.
# This contraction requires to hold nkptj copies of (naux,?) array
# simultaneously in memory.
mem_now = max(comm.allgather(lib.current_memory()[0]))
max_memory = max(2000, mydf.max_memory - mem_now)
nkptj_max = max((uniq_inverse==x).sum() for x in set(uniq_inverse))
buflen = max(int(min(max_memory*.5e6/16/naux/(nkptj_max+2)/nao,
nao/3/mpi.pool.size)), 1)
chunks = (buflen, nao)
j3c_jobs = mpi_df.grids2d_int3c_jobs(cell, auxcell, kptij_lst, chunks, j_only)
log.debug1('max_memory = %d MB (%d in use) chunks %s',
max_memory, mem_now, chunks)
log.debug2('j3c_jobs %s', j3c_jobs)
if j_only:
int3c = wrap_int3c(cell, fused_cell, 'int3c2e', 's2', 1, kptij_lst)
else:
int3c = wrap_int3c(cell, fused_cell, 'int3c2e', 's1', 1, kptij_lst)
idxb = numpy.tril_indices(nao)
idxb = (idxb[0] * nao + idxb[1]).astype('i')
aux_loc = fused_cell.ao_loc_nr(fused_cell.cart)
def gen_int3c(job_id, ish0, ish1):
dataname = 'j3c-chunks/%d' % job_id
i0 = ao_loc[ish0]
i1 = ao_loc[ish1]
dii = i1*(i1+1)//2 - i0*(i0+1)//2
dij = (i1 - i0) * nao
if j_only:
buflen = max(8, int(max_memory*1e6/16/(nkptij*dii+dii)))
else:
buflen = max(8, int(max_memory*1e6/16/(nkptij*dij+dij)))
auxranges = balance_segs(aux_loc[1:]-aux_loc[:-1], buflen)
buflen = max([x[2] for x in auxranges])
buf = numpy.empty(nkptij*dij*buflen, dtype=dtype)
buf1 = numpy.empty(dij*buflen, dtype=dtype)
naux = aux_loc[-1]
for kpt_id, kptij in enumerate(kptij_lst):
key = '%s/%d' % (dataname, kpt_id)
if aosym_s2[kpt_id]:
shape = (naux, dii)
else:
shape = (naux, dij)
if gamma_point(kptij):
fswap.create_dataset(key, shape, 'f8')
else:
fswap.create_dataset(key, shape, 'c16')
naux0 = 0
for istep, auxrange in enumerate(auxranges):
log.alldebug2("aux_e1 job_id %d step %d", job_id, istep)
sh0, sh1, nrow = auxrange
sub_slice = (ish0, ish1, 0, cell.nbas, sh0, sh1)
if j_only:
mat = numpy.ndarray((nkptij,dii,nrow), dtype=dtype, buffer=buf)
else:
mat = numpy.ndarray((nkptij,dij,nrow), dtype=dtype, buffer=buf)
mat = int3c(sub_slice, mat)
for k, kptij in enumerate(kptij_lst):
h5dat = fswap['%s/%d'%(dataname,k)]
v = lib.transpose(mat[k], out=buf1)
if not j_only and aosym_s2[k]:
idy = idxb[i0*(i0+1)//2:i1*(i1+1)//2] - i0 * nao
out = numpy.ndarray((nrow,dii), dtype=v.dtype, buffer=mat[k])
v = numpy.take(v, idy, axis=1, out=out)
if gamma_point(kptij):
h5dat[naux0:naux0+nrow] = v.real
else:
h5dat[naux0:naux0+nrow] = v
naux0 += nrow
def ft_fuse(job_id, uniq_kptji_id, sh0, sh1):
kpt = uniq_kpts[uniq_kptji_id] # kpt = kptj - kpti
adapted_ji_idx = numpy.where(uniq_inverse == uniq_kptji_id)[0]
adapted_kptjs = kptjs[adapted_ji_idx]
nkptj = len(adapted_kptjs)
Gaux = ft_ao.ft_ao(fused_cell, Gv, None, b, gxyz, Gvbase, kpt).T
Gaux = fuse(Gaux)
Gaux *= mydf.weighted_coulG(kpt, False, mesh)
kLR = lib.transpose(numpy.asarray(Gaux.real, order='C'))
kLI = lib.transpose(numpy.asarray(Gaux.imag, order='C'))
j2c = numpy.asarray(fswap['j2c/%d'%uniq_kptji_id])
j2ctag = j2ctags[uniq_kptji_id]
naux0 = j2c.shape[0]
if ('j2c-/%d' % uniq_kptji_id) in fswap:
j2c_negative = numpy.asarray(fswap['j2c-/%d'%uniq_kptji_id])
else:
j2c_negative = None
if is_zero(kpt):
aosym = 's2'
else:
aosym = 's1'
if aosym == 's2' and cell.dimension == 3:
vbar = fuse(mydf.auxbar(fused_cell))
ovlp = cell.pbc_intor('int1e_ovlp', hermi=1, kpts=adapted_kptjs)
ovlp = [lib.pack_tril(s) for s in ovlp]
j3cR = [None] * nkptj
j3cI = [None] * nkptj
i0 = ao_loc[sh0]
i1 = ao_loc[sh1]
for k, idx in enumerate(adapted_ji_idx):
key = 'j3c-chunks/%d/%d' % (job_id, idx)
v = fuse(numpy.asarray(fswap[key]))
if aosym == 's2' and cell.dimension == 3:
for i in numpy.where(vbar != 0)[0]:
v[i] -= vbar[i] * ovlp[k][i0*(i0+1)//2:i1*(i1+1)//2].ravel()
j3cR[k] = numpy.asarray(v.real, order='C')
if v.dtype == numpy.complex128:
j3cI[k] = numpy.asarray(v.imag, order='C')
v = None
ncol = j3cR[0].shape[1]
Gblksize = max(16, int(max_memory*1e6/16/ncol/(nkptj+1))) # +1 for pqkRbuf/pqkIbuf
Gblksize = min(Gblksize, ngrids, 16384)
pqkRbuf = numpy.empty(ncol*Gblksize)
pqkIbuf = numpy.empty(ncol*Gblksize)
buf = numpy.empty(nkptj*ncol*Gblksize, dtype=numpy.complex128)
log.alldebug2(' blksize (%d,%d)', Gblksize, ncol)
if aosym == 's2':
shls_slice = (sh0, sh1, 0, sh1)
else:
shls_slice = (sh0, sh1, 0, cell.nbas)
for p0, p1 in lib.prange(0, ngrids, Gblksize):
dat = ft_ao._ft_aopair_kpts(cell, Gv[p0:p1], shls_slice, aosym, b,
gxyz[p0:p1], Gvbase, kpt,
adapted_kptjs, out=buf)
nG = p1 - p0
for k, ji in enumerate(adapted_ji_idx):
aoao = dat[k].reshape(nG,ncol)
pqkR = numpy.ndarray((ncol,nG), buffer=pqkRbuf)
pqkI = numpy.ndarray((ncol,nG), buffer=pqkIbuf)
pqkR[:] = aoao.real.T
pqkI[:] = aoao.imag.T
lib.dot(kLR[p0:p1].T, pqkR.T, -1, j3cR[k], 1)
lib.dot(kLI[p0:p1].T, pqkI.T, -1, j3cR[k], 1)
if not (is_zero(kpt) and gamma_point(adapted_kptjs[k])):
lib.dot(kLR[p0:p1].T, pqkI.T, -1, j3cI[k], 1)
lib.dot(kLI[p0:p1].T, pqkR.T, 1, j3cI[k], 1)
for k, idx in enumerate(adapted_ji_idx):
if is_zero(kpt) and gamma_point(adapted_kptjs[k]):
v = j3cR[k]
else:
v = j3cR[k] + j3cI[k] * 1j
if j2ctag == 'CD':
v = scipy.linalg.solve_triangular(j2c, v, lower=True, overwrite_b=True)
fswap['j3c-chunks/%d/%d'%(job_id,idx)][:naux0] = v
else:
fswap['j3c-chunks/%d/%d'%(job_id,idx)][:naux0] = lib.dot(j2c, v)
# low-dimension systems
if j2c_negative is not None:
fswap['j3c-/%d/%d'%(job_id,idx)] = lib.dot(j2c_negative, v)
mpi_df._assemble(mydf, kptij_lst, j3c_jobs, gen_int3c, ft_fuse, cderi_file, fswap, log)
def update_amps(mycc, t1, t2, eris):
time1 = time0 = time.clock(), time.time()
log = logger.Logger(mycc.stdout, mycc.verbose)
cpu1 = time0
t1T = t1.T
t2T = numpy.asarray(t2.transpose(2, 3, 0, 1), order='C')
nvir_seg, nvir, nocc = t2T.shape[:3]
t1 = t2 = None
ntasks = mpi.pool.size
vlocs = [_task_location(nvir, task_id) for task_id in range(ntasks)]
vloc0, vloc1 = vlocs[rank]
log.debug2('vlocs %s', vlocs)
assert (vloc1 - vloc0 == nvir_seg)
fock = eris.fock
mo_e_o = eris.mo_energy[:nocc]
mo_e_v = eris.mo_energy[nocc:] + mycc.level_shift
def _rotate_vir_block(buf):
for task_id, buf in _rotate_tensor_block(buf):
loc0, loc1 = vlocs[task_id]
yield task_id, buf, loc0, loc1
fswap = lib.H5TmpFile()
wVooV = numpy.zeros((nvir_seg, nocc, nocc, nvir))
eris_voov = _cp(eris.ovvo).transpose(1, 0, 3, 2)
tau = t2T * .5
tau += numpy.einsum('ai,bj->abij', t1T[vloc0:vloc1], t1T)
for task_id, tau, p0, p1 in _rotate_vir_block(tau):
wVooV += lib.einsum('bkic,cajk->bija', eris_voov[:, :, :, p0:p1], tau)
fswap['wVooV1'] = wVooV
wVooV = tau = None
time1 = log.timer_debug1('wVooV', *time1)
wVOov = eris_voov
eris_VOov = eris_voov - eris_voov.transpose(0, 2, 1, 3) * .5
tau = t2T.transpose(2, 0, 3, 1) - t2T.transpose(3, 0, 2, 1) * .5
tau -= numpy.einsum('ai,bj->jaib', t1T[vloc0:vloc1], t1T)
for task_id, tau, p0, p1 in _rotate_vir_block(tau):
wVOov += lib.einsum('dlkc,kcjb->dljb', eris_VOov[:, :, :, p0:p1], tau)
fswap['wVOov1'] = wVOov
wVOov = tau = eris_VOov = eris_voov = None
time1 = log.timer_debug1('wVOov', *time1)
t1Tnew = numpy.zeros_like(t1T)
t2Tnew = mycc._add_vvvv(t1T, t2T, eris, t2sym='jiba')
time1 = log.timer_debug1('vvvv', *time1)
#** make_inter_F
fov = fock[:nocc, nocc:].copy()
t1Tnew += fock[nocc:, :nocc]
foo = fock[:nocc, :nocc] - numpy.diag(mo_e_o)
foo += .5 * numpy.einsum('ia,aj->ij', fock[:nocc, nocc:], t1T)
fvv = fock[nocc:, nocc:] - numpy.diag(mo_e_v)
fvv -= .5 * numpy.einsum('ai,ib->ab', t1T, fock[:nocc, nocc:])
foo_priv = numpy.zeros_like(foo)
fov_priv = numpy.zeros_like(fov)
fvv_priv = numpy.zeros_like(fvv)
t1T_priv = numpy.zeros_like(t1T)
max_memory = mycc.max_memory - lib.current_memory()[0]
unit = nocc * nvir**2 * 3 + nocc**2 * nvir + 1
blksize = min(nvir,
max(BLKMIN, int((max_memory * .9e6 / 8 - t2T.size) / unit)))
log.debug1('pass 1, max_memory %d MB, nocc,nvir = %d,%d blksize = %d',
max_memory, nocc, nvir, blksize)
buf = numpy.empty((blksize, nvir, nvir, nocc))
def load_vvvo(p0):
p1 = min(nvir_seg, p0 + blksize)
if p0 < p1:
buf[:p1 - p0] = eris.vvvo[p0:p1]
fswap.create_dataset('wVooV', (nvir_seg, nocc, nocc, nvir), 'f8')
wVOov = []
with lib.call_in_background(load_vvvo) as prefetch:
load_vvvo(0)
for p0, p1 in lib.prange(vloc0, vloc1, blksize):
i0, i1 = p0 - vloc0, p1 - vloc0
eris_vvvo, buf = buf[:p1 - p0], numpy.empty_like(buf)
prefetch(i1)
fvv_priv[p0:p1] += 2 * numpy.einsum('ck,abck->ab', t1T, eris_vvvo)
fvv_priv -= numpy.einsum('ck,cabk->ab', t1T[p0:p1], eris_vvvo)
if not mycc.direct:
raise NotImplementedError
tau = t2T[i0:i1] + numpy.einsum('ai,bj->abij', t1T[p0:p1], t1T)
for task_id, tau, q0, q1 in _rotate_vir_block(tau):
tmp = lib.einsum('bdck,cdij->bkij', eris_vvvo[:, :, q0:q1],
tau)
t2Tnew -= lib.einsum('ak,bkij->baji', t1T, tmp)
tau = tmp = None
fswap['wVooV'][i0:i1] = lib.einsum('cj,baci->bija', -t1T,
eris_vvvo)
theta = t2T[i0:i1].transpose(0, 2, 1, 3) * 2
theta -= t2T[i0:i1].transpose(0, 3, 1, 2)
t1T_priv += lib.einsum('bicj,bacj->ai', theta, eris_vvvo)
wVOov.append(lib.einsum('acbi,cj->abij', eris_vvvo, t1T))
theta = eris_vvvo = None
time1 = log.timer_debug1('vvvo [%d:%d]' % (p0, p1), *time1)
wVOov = numpy.vstack(wVOov)
wVOov = mpi.alltoall([wVOov[:, q0:q1] for q0, q1 in vlocs],
split_recvbuf=True)
wVOov = numpy.vstack([x.reshape(-1, nvir_seg, nocc, nocc) for x in wVOov])
fswap['wVOov'] = wVOov.transpose(1, 2, 3, 0)
wVooV = None
unit = nocc**2 * nvir * 7 + nocc**3 + nocc * nvir**2
max_memory = max(0, mycc.max_memory - lib.current_memory()[0])
blksize = min(nvir,
max(BLKMIN, int((max_memory * .9e6 / 8 - nocc**4) / unit)))
log.debug1('pass 2, max_memory %d MB, nocc,nvir = %d,%d blksize = %d',
max_memory, nocc, nvir, blksize)
woooo = numpy.zeros((nocc, nocc, nocc, nocc))
for p0, p1 in lib.prange(vloc0, vloc1, blksize):
i0, i1 = p0 - vloc0, p1 - vloc0
wVOov = fswap['wVOov'][i0:i1]
wVooV = fswap['wVooV'][i0:i1]
eris_ovoo = eris.ovoo[:, i0:i1]
eris_oovv = numpy.empty((nocc, nocc, i1 - i0, nvir))
def load_oovv(p0, p1):
eris_oovv[:] = eris.oovv[:, :, p0:p1]
with lib.call_in_background(load_oovv) as prefetch_oovv:
#:eris_oovv = eris.oovv[:,:,i0:i1]
prefetch_oovv(i0, i1)
foo_priv += numpy.einsum('ck,kcji->ij', 2 * t1T[p0:p1], eris_ovoo)
foo_priv += numpy.einsum('ck,icjk->ij', -t1T[p0:p1], eris_ovoo)
tmp = lib.einsum('al,jaik->lkji', t1T[p0:p1], eris_ovoo)
woooo += tmp + tmp.transpose(1, 0, 3, 2)
tmp = None
wVOov -= lib.einsum('jbik,ak->bjia', eris_ovoo, t1T)
t2Tnew[i0:i1] += wVOov.transpose(0, 3, 1, 2)
wVooV += lib.einsum('kbij,ak->bija', eris_ovoo, t1T)
eris_ovoo = None
load_oovv = prefetch_oovv = None
eris_ovvo = numpy.empty((nocc, i1 - i0, nvir, nocc))
def load_ovvo(p0, p1):
eris_ovvo[:] = eris.ovvo[:, p0:p1]
with lib.call_in_background(load_ovvo) as prefetch_ovvo:
#:eris_ovvo = eris.ovvo[:,i0:i1]
prefetch_ovvo(i0, i1)
t1T_priv[p0:p1] -= numpy.einsum('bj,jiab->ai', t1T, eris_oovv)
wVooV -= eris_oovv.transpose(2, 0, 1, 3)
wVOov += wVooV * .5 #: bjia + bija*.5
eris_voov = eris_ovvo.transpose(1, 0, 3, 2)
eris_ovvo = None
load_ovvo = prefetch_ovvo = None
def update_wVooV(i0, i1):
wVooV[:] += fswap['wVooV1'][i0:i1]
fswap['wVooV1'][i0:i1] = wVooV
wVOov[:] += fswap['wVOov1'][i0:i1]
fswap['wVOov1'][i0:i1] = wVOov
with lib.call_in_background(update_wVooV) as update_wVooV:
update_wVooV(i0, i1)
t2Tnew[i0:i1] += eris_voov.transpose(0, 3, 1, 2) * .5
t1T_priv[p0:p1] += 2 * numpy.einsum('bj,aijb->ai', t1T, eris_voov)
tmp = lib.einsum('ci,kjbc->bijk', t1T, eris_oovv)
tmp += lib.einsum('bjkc,ci->bjik', eris_voov, t1T)
t2Tnew[i0:i1] -= lib.einsum('bjik,ak->baji', tmp, t1T)
eris_oovv = tmp = None
fov_priv[:,
p0:p1] += numpy.einsum('ck,aikc->ia', t1T, eris_voov) * 2
fov_priv[:, p0:p1] -= numpy.einsum('ck,akic->ia', t1T, eris_voov)
tau = numpy.einsum('ai,bj->abij', t1T[p0:p1] * .5, t1T)
tau += t2T[i0:i1]
theta = tau.transpose(0, 1, 3, 2) * 2
theta -= tau
fvv_priv -= lib.einsum('caij,cjib->ab', theta, eris_voov)
foo_priv += lib.einsum('aikb,abkj->ij', eris_voov, theta)
tau = theta = None
tau = t2T[i0:i1] + numpy.einsum('ai,bj->abij', t1T[p0:p1], t1T)
woooo += lib.einsum('abij,aklb->ijkl', tau, eris_voov)
tau = None
eris_VOov = wVOov = wVooV = update_wVooV = None
time1 = log.timer_debug1('voov [%d:%d]' % (p0, p1), *time1)
wVooV = _cp(fswap['wVooV1'])
for task_id, wVooV, p0, p1 in _rotate_vir_block(wVooV):
tmp = lib.einsum('ackj,ckib->ajbi', t2T[:, p0:p1], wVooV)
t2Tnew += tmp.transpose(0, 2, 3, 1)
t2Tnew += tmp.transpose(0, 2, 1, 3) * .5
wVooV = tmp = None
time1 = log.timer_debug1('contracting wVooV', *time1)
wVOov = _cp(fswap['wVOov1'])
theta = t2T * 2
theta -= t2T.transpose(0, 1, 3, 2)
for task_id, wVOov, p0, p1 in _rotate_vir_block(wVOov):
t2Tnew += lib.einsum('acik,ckjb->abij', theta[:, p0:p1], wVOov)
wVOov = theta = None
fswap = None
time1 = log.timer_debug1('contracting wVOov', *time1)
foo += mpi.allreduce(foo_priv)
fov += mpi.allreduce(fov_priv)
fvv += mpi.allreduce(fvv_priv)
theta = t2T.transpose(0, 1, 3, 2) * 2 - t2T
t1T_priv[vloc0:vloc1] += numpy.einsum('jb,abji->ai', fov, theta)
ovoo = _cp(eris.ovoo)
for task_id, ovoo, p0, p1 in _rotate_vir_block(ovoo):
t1T_priv[vloc0:vloc1] -= lib.einsum('jbki,abjk->ai', ovoo,
theta[:, p0:p1])
theta = ovoo = None
woooo = mpi.allreduce(woooo)
woooo += _cp(eris.oooo).transpose(0, 2, 1, 3)
tau = t2T + numpy.einsum('ai,bj->abij', t1T[vloc0:vloc1], t1T)
t2Tnew += .5 * lib.einsum('abkl,ijkl->abij', tau, woooo)
tau = woooo = None
t1Tnew += mpi.allreduce(t1T_priv)
ft_ij = foo + numpy.einsum('aj,ia->ij', .5 * t1T, fov)
ft_ab = fvv - numpy.einsum('ai,ib->ab', .5 * t1T, fov)
t2Tnew += lib.einsum('acij,bc->abij', t2T, ft_ab)
t2Tnew -= lib.einsum('ki,abkj->abij', ft_ij, t2T)
eia = mo_e_o[:, None] - mo_e_v
t1Tnew += numpy.einsum('bi,ab->ai', t1T, fvv)
t1Tnew -= numpy.einsum('aj,ji->ai', t1T, foo)
t1Tnew /= eia.T
t2tmp = mpi.alltoall([t2Tnew[:, p0:p1] for p0, p1 in vlocs],
split_recvbuf=True)
for task_id, (p0, p1) in enumerate(vlocs):
tmp = t2tmp[task_id].reshape(p1 - p0, nvir_seg, nocc, nocc)
t2Tnew[:, p0:p1] += tmp.transpose(1, 0, 3, 2)
for i in range(vloc0, vloc1):
t2Tnew[i - vloc0] /= lib.direct_sum('i+jb->bij', eia[:, i], eia)
time0 = log.timer_debug1('update t1 t2', *time0)
return t1Tnew.T, t2Tnew.transpose(2, 3, 0, 1)
def update_amps(mycc, t1, t2, eris):
time1 = time0 = time.clock(), time.time()
log = logger.Logger(mycc.stdout, mycc.verbose)
cpu1 = time0
t1T = t1.T
t2T = numpy.asarray(t2.transpose(2,3,0,1), order='C')
nvir_seg, nvir, nocc = t2T.shape[:3]
t1 = t2 = None
ntasks = mpi.pool.size
vlocs = [_task_location(nvir, task_id) for task_id in range(ntasks)]
vloc0, vloc1 = vlocs[rank]
log.debug2('vlocs %s', vlocs)
assert(vloc1-vloc0 == nvir_seg)
fock = eris.fock
mo_e_o = eris.mo_energy[:nocc]
mo_e_v = eris.mo_energy[nocc:] + mycc.level_shift
def _rotate_vir_block(buf):
for task_id, buf in _rotate_tensor_block(buf):
loc0, loc1 = vlocs[task_id]
yield task_id, buf, loc0, loc1
fswap = lib.H5TmpFile()
wVooV = numpy.zeros((nvir_seg,nocc,nocc,nvir))
eris_voov = _cp(eris.ovvo).transpose(1,0,3,2)
tau = t2T * .5
tau += numpy.einsum('ai,bj->abij', t1T[vloc0:vloc1], t1T)
for task_id, tau, p0, p1 in _rotate_vir_block(tau):
wVooV += lib.einsum('bkic,cajk->bija', eris_voov[:,:,:,p0:p1], tau)
fswap['wVooV1'] = wVooV
wVooV = tau = None
time1 = log.timer_debug1('wVooV', *time1)
wVOov = eris_voov
eris_VOov = eris_voov - eris_voov.transpose(0,2,1,3)*.5
tau = t2T.transpose(2,0,3,1) - t2T.transpose(3,0,2,1)*.5
tau -= numpy.einsum('ai,bj->jaib', t1T[vloc0:vloc1], t1T)
for task_id, tau, p0, p1 in _rotate_vir_block(tau):
wVOov += lib.einsum('dlkc,kcjb->dljb', eris_VOov[:,:,:,p0:p1], tau)
fswap['wVOov1'] = wVOov
wVOov = tau = eris_VOov = eris_voov = None
time1 = log.timer_debug1('wVOov', *time1)
t1Tnew = numpy.zeros_like(t1T)
t2Tnew = mycc._add_vvvv(t1T, t2T, eris, t2sym='jiba')
time1 = log.timer_debug1('vvvv', *time1)
#** make_inter_F
fov = fock[:nocc,nocc:].copy()
t1Tnew += fock[nocc:,:nocc]
foo = fock[:nocc,:nocc] - numpy.diag(mo_e_o)
foo += .5 * numpy.einsum('ia,aj->ij', fock[:nocc,nocc:], t1T)
fvv = fock[nocc:,nocc:] - numpy.diag(mo_e_v)
fvv -= .5 * numpy.einsum('ai,ib->ab', t1T, fock[:nocc,nocc:])
foo_priv = numpy.zeros_like(foo)
fov_priv = numpy.zeros_like(fov)
fvv_priv = numpy.zeros_like(fvv)
t1T_priv = numpy.zeros_like(t1T)
max_memory = mycc.max_memory - lib.current_memory()[0]
unit = nocc*nvir**2*3 + nocc**2*nvir + 1
blksize = min(nvir, max(BLKMIN, int((max_memory*.9e6/8-t2T.size)/unit)))
log.debug1('pass 1, max_memory %d MB, nocc,nvir = %d,%d blksize = %d',
max_memory, nocc, nvir, blksize)
buf = numpy.empty((blksize,nvir,nvir,nocc))
def load_vvvo(p0):
p1 = min(nvir_seg, p0+blksize)
if p0 < p1:
buf[:p1-p0] = eris.vvvo[p0:p1]
fswap.create_dataset('wVooV', (nvir_seg,nocc,nocc,nvir), 'f8')
wVOov = []
with lib.call_in_background(load_vvvo) as prefetch:
load_vvvo(0)
for p0, p1 in lib.prange(vloc0, vloc1, blksize):
i0, i1 = p0 - vloc0, p1 - vloc0
eris_vvvo, buf = buf[:p1-p0], numpy.empty_like(buf)
prefetch(i1)
fvv_priv[p0:p1] += 2*numpy.einsum('ck,abck->ab', t1T, eris_vvvo)
fvv_priv -= numpy.einsum('ck,cabk->ab', t1T[p0:p1], eris_vvvo)
if not mycc.direct:
raise NotImplementedError
tau = t2T[i0:i1] + numpy.einsum('ai,bj->abij', t1T[p0:p1], t1T)
for task_id, tau, q0, q1 in _rotate_vir_block(tau):
tmp = lib.einsum('bdck,cdij->bkij', eris_vvvo[:,:,q0:q1], tau)
t2Tnew -= lib.einsum('ak,bkij->baji', t1T, tmp)
tau = tmp = None
fswap['wVooV'][i0:i1] = lib.einsum('cj,baci->bija', -t1T, eris_vvvo)
theta = t2T[i0:i1].transpose(0,2,1,3) * 2
theta -= t2T[i0:i1].transpose(0,3,1,2)
t1T_priv += lib.einsum('bicj,bacj->ai', theta, eris_vvvo)
wVOov.append(lib.einsum('acbi,cj->abij', eris_vvvo, t1T))
theta = eris_vvvo = None
time1 = log.timer_debug1('vvvo [%d:%d]'%(p0, p1), *time1)
wVOov = numpy.vstack(wVOov)
wVOov = mpi.alltoall([wVOov[:,q0:q1] for q0,q1 in vlocs], split_recvbuf=True)
wVOov = numpy.vstack([x.reshape(-1,nvir_seg,nocc,nocc) for x in wVOov])
fswap['wVOov'] = wVOov.transpose(1,2,3,0)
wVooV = None
unit = nocc**2*nvir*7 + nocc**3 + nocc*nvir**2
max_memory = max(0, mycc.max_memory - lib.current_memory()[0])
blksize = min(nvir, max(BLKMIN, int((max_memory*.9e6/8-nocc**4)/unit)))
log.debug1('pass 2, max_memory %d MB, nocc,nvir = %d,%d blksize = %d',
max_memory, nocc, nvir, blksize)
woooo = numpy.zeros((nocc,nocc,nocc,nocc))
for p0, p1 in lib.prange(vloc0, vloc1, blksize):
i0, i1 = p0 - vloc0, p1 - vloc0
wVOov = fswap['wVOov'][i0:i1]
wVooV = fswap['wVooV'][i0:i1]
eris_ovoo = eris.ovoo[:,i0:i1]
eris_oovv = numpy.empty((nocc,nocc,i1-i0,nvir))
def load_oovv(p0, p1):
eris_oovv[:] = eris.oovv[:,:,p0:p1]
with lib.call_in_background(load_oovv) as prefetch_oovv:
#:eris_oovv = eris.oovv[:,:,i0:i1]
prefetch_oovv(i0, i1)
foo_priv += numpy.einsum('ck,kcji->ij', 2*t1T[p0:p1], eris_ovoo)
foo_priv += numpy.einsum('ck,icjk->ij', -t1T[p0:p1], eris_ovoo)
tmp = lib.einsum('al,jaik->lkji', t1T[p0:p1], eris_ovoo)
woooo += tmp + tmp.transpose(1,0,3,2)
tmp = None
wVOov -= lib.einsum('jbik,ak->bjia', eris_ovoo, t1T)
t2Tnew[i0:i1] += wVOov.transpose(0,3,1,2)
wVooV += lib.einsum('kbij,ak->bija', eris_ovoo, t1T)
eris_ovoo = None
load_oovv = prefetch_oovv = None
eris_ovvo = numpy.empty((nocc,i1-i0,nvir,nocc))
def load_ovvo(p0, p1):
eris_ovvo[:] = eris.ovvo[:,p0:p1]
with lib.call_in_background(load_ovvo) as prefetch_ovvo:
#:eris_ovvo = eris.ovvo[:,i0:i1]
prefetch_ovvo(i0, i1)
t1T_priv[p0:p1] -= numpy.einsum('bj,jiab->ai', t1T, eris_oovv)
wVooV -= eris_oovv.transpose(2,0,1,3)
wVOov += wVooV*.5 #: bjia + bija*.5
eris_voov = eris_ovvo.transpose(1,0,3,2)
eris_ovvo = None
load_ovvo = prefetch_ovvo = None
def update_wVooV(i0, i1):
wVooV[:] += fswap['wVooV1'][i0:i1]
fswap['wVooV1'][i0:i1] = wVooV
wVOov[:] += fswap['wVOov1'][i0:i1]
fswap['wVOov1'][i0:i1] = wVOov
with lib.call_in_background(update_wVooV) as update_wVooV:
update_wVooV(i0, i1)
t2Tnew[i0:i1] += eris_voov.transpose(0,3,1,2) * .5
t1T_priv[p0:p1] += 2*numpy.einsum('bj,aijb->ai', t1T, eris_voov)
tmp = lib.einsum('ci,kjbc->bijk', t1T, eris_oovv)
tmp += lib.einsum('bjkc,ci->bjik', eris_voov, t1T)
t2Tnew[i0:i1] -= lib.einsum('bjik,ak->baji', tmp, t1T)
eris_oovv = tmp = None
fov_priv[:,p0:p1] += numpy.einsum('ck,aikc->ia', t1T, eris_voov) * 2
fov_priv[:,p0:p1] -= numpy.einsum('ck,akic->ia', t1T, eris_voov)
tau = numpy.einsum('ai,bj->abij', t1T[p0:p1]*.5, t1T)
tau += t2T[i0:i1]
theta = tau.transpose(0,1,3,2) * 2
theta -= tau
fvv_priv -= lib.einsum('caij,cjib->ab', theta, eris_voov)
foo_priv += lib.einsum('aikb,abkj->ij', eris_voov, theta)
tau = theta = None
tau = t2T[i0:i1] + numpy.einsum('ai,bj->abij', t1T[p0:p1], t1T)
woooo += lib.einsum('abij,aklb->ijkl', tau, eris_voov)
tau = None
eris_VOov = wVOov = wVooV = update_wVooV = None
time1 = log.timer_debug1('voov [%d:%d]'%(p0, p1), *time1)
wVooV = _cp(fswap['wVooV1'])
for task_id, wVooV, p0, p1 in _rotate_vir_block(wVooV):
tmp = lib.einsum('ackj,ckib->ajbi', t2T[:,p0:p1], wVooV)
t2Tnew += tmp.transpose(0,2,3,1)
t2Tnew += tmp.transpose(0,2,1,3) * .5
wVooV = tmp = None
time1 = log.timer_debug1('contracting wVooV', *time1)
wVOov = _cp(fswap['wVOov1'])
theta = t2T * 2
theta -= t2T.transpose(0,1,3,2)
for task_id, wVOov, p0, p1 in _rotate_vir_block(wVOov):
t2Tnew += lib.einsum('acik,ckjb->abij', theta[:,p0:p1], wVOov)
wVOov = theta = None
fswap = None
time1 = log.timer_debug1('contracting wVOov', *time1)
foo += mpi.allreduce(foo_priv)
fov += mpi.allreduce(fov_priv)
fvv += mpi.allreduce(fvv_priv)
theta = t2T.transpose(0,1,3,2) * 2 - t2T
t1T_priv[vloc0:vloc1] += numpy.einsum('jb,abji->ai', fov, theta)
ovoo = _cp(eris.ovoo)
for task_id, ovoo, p0, p1 in _rotate_vir_block(ovoo):
t1T_priv[vloc0:vloc1] -= lib.einsum('jbki,abjk->ai', ovoo, theta[:,p0:p1])
theta = ovoo = None
woooo = mpi.allreduce(woooo)
woooo += _cp(eris.oooo).transpose(0,2,1,3)
tau = t2T + numpy.einsum('ai,bj->abij', t1T[vloc0:vloc1], t1T)
t2Tnew += .5 * lib.einsum('abkl,ijkl->abij', tau, woooo)
tau = woooo = None
t1Tnew += mpi.allreduce(t1T_priv)
ft_ij = foo + numpy.einsum('aj,ia->ij', .5*t1T, fov)
ft_ab = fvv - numpy.einsum('ai,ib->ab', .5*t1T, fov)
t2Tnew += lib.einsum('acij,bc->abij', t2T, ft_ab)
t2Tnew -= lib.einsum('ki,abkj->abij', ft_ij, t2T)
eia = mo_e_o[:,None] - mo_e_v
t1Tnew += numpy.einsum('bi,ab->ai', t1T, fvv)
t1Tnew -= numpy.einsum('aj,ji->ai', t1T, foo)
t1Tnew /= eia.T
t2tmp = mpi.alltoall([t2Tnew[:,p0:p1] for p0,p1 in vlocs],
split_recvbuf=True)
for task_id, (p0, p1) in enumerate(vlocs):
tmp = t2tmp[task_id].reshape(p1-p0,nvir_seg,nocc,nocc)
t2Tnew[:,p0:p1] += tmp.transpose(1,0,3,2)
for i in range(vloc0, vloc1):
t2Tnew[i-vloc0] /= lib.direct_sum('i+jb->bij', eia[:,i], eia)
time0 = log.timer_debug1('update t1 t2', *time0)
return t1Tnew.T, t2Tnew.transpose(2,3,0,1)
def _assemble(mydf, kptij_lst, j3c_jobs, gen_int3c, ft_fuse, cderi_file, fswap, log):
t1 = (time.clock(), time.time())
cell = mydf.cell
ao_loc = cell.ao_loc_nr()
nao = ao_loc[-1]
kptis = kptij_lst[:,0]
kptjs = kptij_lst[:,1]
kpt_ji = kptjs - kptis
uniq_kpts, uniq_index, uniq_inverse = unique(kpt_ji)
aosym_s2 = numpy.einsum('ix->i', abs(kptis-kptjs)) < 1e-9
t2 = t1
j3c_workers = numpy.zeros(len(j3c_jobs), dtype=int)
#for job_id, ish0, ish1 in mpi.work_share_partition(j3c_jobs):
for job_id, ish0, ish1 in mpi.work_stealing_partition(j3c_jobs):
gen_int3c(job_id, ish0, ish1)
t2 = log.alltimer_debug2('int j3c %d' % job_id, *t2)
for k, kpt in enumerate(uniq_kpts):
ft_fuse(job_id, k, ish0, ish1)
t2 = log.alltimer_debug2('ft-fuse %d k %d' % (job_id, k), *t2)
j3c_workers[job_id] = rank
j3c_workers = mpi.allreduce(j3c_workers)
log.debug2('j3c_workers %s', j3c_workers)
t1 = log.timer_debug1('int3c and fuse', *t1)
# Pass 2
# Transpose 3-index tensor and save data in cderi_file
feri = h5py.File(cderi_file, 'w')
nauxs = [fswap['j2c/%d'%k].shape[0] for k, kpt in enumerate(uniq_kpts)]
segsize = (max(nauxs)+mpi.pool.size-1) // mpi.pool.size
naux0 = rank * segsize
for k, kptij in enumerate(kptij_lst):
naux1 = min(nauxs[uniq_inverse[k]], naux0+segsize)
nrow = max(0, naux1-naux0)
if gamma_point(kptij):
dtype = 'f8'
else:
dtype = 'c16'
if aosym_s2[k]:
nao_pair = nao * (nao+1) // 2
else:
nao_pair = nao * nao
feri.create_dataset('j3c/%d'%k, (nrow,nao_pair), dtype, maxshape=(None,nao_pair))
def get_segs_loc(aosym):
off0 = numpy.asarray([ao_loc[i0] for x,i0,i1 in j3c_jobs])
off1 = numpy.asarray([ao_loc[i1] for x,i0,i1 in j3c_jobs])
if aosym: # s2
dims = off1*(off1+1)//2 - off0*(off0+1)//2
else:
dims = (off1-off0) * nao
#dims = numpy.asarray([ao_loc[i1]-ao_loc[i0] for x,i0,i1 in j3c_jobs])
dims = numpy.hstack([dims[j3c_workers==w] for w in range(mpi.pool.size)])
job_idx = numpy.hstack([numpy.where(j3c_workers==w)[0]
for w in range(mpi.pool.size)])
segs_loc = numpy.append(0, numpy.cumsum(dims))
segs_loc = [(segs_loc[j], segs_loc[j+1]) for j in numpy.argsort(job_idx)]
return segs_loc
segs_loc_s1 = get_segs_loc(False)
segs_loc_s2 = get_segs_loc(True)
job_ids = numpy.where(rank == j3c_workers)[0]
def load(k, p0, p1):
naux1 = nauxs[uniq_inverse[k]]
slices = [(min(i*segsize+p0,naux1), min(i*segsize+p1,naux1))
for i in range(mpi.pool.size)]
segs = []
for p0, p1 in slices:
val = [fswap['j3c-chunks/%d/%d' % (job, k)][p0:p1].ravel()
for job in job_ids]
if val:
segs.append(numpy.hstack(val))
else:
segs.append(numpy.zeros(0))
return segs
def save(k, p0, p1, segs):
segs = mpi.alltoall(segs)
naux1 = nauxs[uniq_inverse[k]]
loc0, loc1 = min(p0, naux1-naux0), min(p1, naux1-naux0)
nL = loc1 - loc0
if nL > 0:
if aosym_s2[k]:
segs = numpy.hstack([segs[i0*nL:i1*nL].reshape(nL,-1)
for i0,i1 in segs_loc_s2])
else:
segs = numpy.hstack([segs[i0*nL:i1*nL].reshape(nL,-1)
for i0,i1 in segs_loc_s1])
feri['j3c/%d'%k][loc0:loc1] = segs
mem_now = max(comm.allgather(lib.current_memory()[0]))
max_memory = max(2000, min(8000, mydf.max_memory - mem_now))
if numpy.all(aosym_s2):
if gamma_point(kptij_lst):
blksize = max(16, int(max_memory*.5e6/8/nao**2))
else:
blksize = max(16, int(max_memory*.5e6/16/nao**2))
else:
blksize = max(16, int(max_memory*.5e6/16/nao**2/2))
log.debug1('max_momory %d MB (%d in use), blksize %d',
max_memory, mem_now, blksize)
t2 = t1
with lib.call_in_background(save) as async_write:
for k, kptji in enumerate(kptij_lst):
for p0, p1 in lib.prange(0, segsize, blksize):
segs = load(k, p0, p1)
async_write(k, p0, p1, segs)
t2 = log.timer_debug1('assemble k=%d %d:%d (in %d)' %
(k, p0, p1, segsize), *t2)
if 'j2c-' in fswap:
j2c_kpts_lists = []
for k, kpt in enumerate(uniq_kpts):
if ('j2c-/%d' % k) in fswap:
adapted_ji_idx = numpy.where(uniq_inverse == k)[0]
j2c_kpts_lists.append(adapted_ji_idx)
for k in numpy.hstack(j2c_kpts_lists):
val = [numpy.asarray(fswap['j3c-/%d/%d' % (job, k)]).ravel()
for job in job_ids]
val = mpi.gather(numpy.hstack(val))
if rank == 0:
naux1 = fswap['j3c-/0/%d'%k].shape[0]
if aosym_s2[k]:
v = [val[i0*naux1:i1*naux1].reshape(naux1,-1)
for i0,i1 in segs_loc_s2]
else:
v = [val[i0*naux1:i1*naux1].reshape(naux1,-1)
for i0,i1 in segs_loc_s1]
feri['j3c-/%d'%k] = numpy.hstack(v)
if 'j3c-kptij' in feri: del(feri['j3c-kptij'])
feri['j3c-kptij'] = kptij_lst
t1 = log.alltimer_debug1('assembling j3c', *t1)
feri.close()