There is a maintenance of MPCDF Gitlab on Thursday, April 22st 2020, 9:00 am CEST - Expect some service interruptions during this time

Commit eda70a19 authored by Martin Reinecke's avatar Martin Reinecke

stage 1

parent f61cb6b2
......@@ -39,17 +39,10 @@ test_serial:
- pytest-3 -q --cov=nifty6 test
- >
python3 -m coverage report --omit "*plot*,*distributed_do*" | tee coverage.txt
python3 -m coverage report --omit "*plot*" | tee coverage.txt
- >
grep TOTAL coverage.txt | awk '{ print "TOTAL: "$4; }'
stage: test
OMPI_MCA_btl_vader_single_copy_mechanism: none
- mpiexec -n 2 --bind-to none pytest-3 -q test
stage: release
from .version import __version__
from . import dobj
from .domains.domain import Domain
from .domains.structured_domain import StructuredDomain
from .domains.unstructured_domain import UnstructuredDomain
......@@ -93,10 +91,5 @@ from .linearization import Linearization
from .operator_spectrum import operator_spectrum
from . import internal_config
_scheme = internal_config.parallelization_scheme()
if _scheme == "Samples":
from .minimization.metric_gaussian_kl_mpi import MetricGaussianKL_MPI
# We deliberately don't set __all__ here, because we don't want people to do a
# "from nifty6 import *"; that would swamp the global namespace.
# 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 3 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
# 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, see <>.
# Copyright(C) 2013-2019 Max-Planck-Society
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik.
import sys
from functools import reduce
import numpy as np
from mpi4py import MPI
from .random import Random
__all__ = ["ntask", "rank", "master", "local_shape", "data_object", "full",
"empty", "zeros", "ones", "empty_like", "vdot", "exp",
"log", "tanh", "sqrt", "from_object", "from_random",
"local_data", "ibegin", "ibegin_from_shape", "np_allreduce_sum",
"np_allreduce_min", "np_allreduce_max",
"distaxis", "from_local_data", "from_global_data", "to_global_data",
"redistribute", "default_distaxis", "is_numpy", "absmax", "norm",
"lock", "locked", "uniform_full", "transpose", "to_global_data_rw",
"ensure_not_distributed", "ensure_default_distributed",
"tanh", "conjugate", "sin", "cos", "tan", "log10", "log1p", "expm1",
"sinh", "cosh", "sinc", "absolute", "sign", "clip"]
ntask = _comm.Get_size()
rank = _comm.Get_rank()
master = (rank == 0)
def is_numpy():
return False
def _shareSize(nwork, nshares, myshare):
return (nwork//nshares) + int(myshare < nwork % nshares)
def _shareRange(nwork, nshares, myshare):
nbase = nwork//nshares
additional = nwork % nshares
lo = myshare*nbase + min(myshare, additional)
hi = lo + nbase + int(myshare < additional)
return lo, hi
def local_shape(shape, distaxis=0):
if len(shape) == 0 or distaxis == -1:
return shape
shape2 = list(shape)
shape2[distaxis] = _shareSize(shape[distaxis], ntask, rank)
return tuple(shape2)
class data_object(object):
def __init__(self, shape, data, distaxis):
self._shape = tuple(shape)
if len(self._shape) == 0:
distaxis = -1
if not isinstance(data, np.ndarray):
data = np.full((), data)
self._distaxis = distaxis
self._data = data
if local_shape(self._shape, self._distaxis) != self._data.shape:
raise ValueError("shape mismatch")
def copy(self):
return data_object(self._shape, self._data.copy(), self._distaxis)
# def _sanity_checks(self):
# # check whether the distaxis is consistent
# if self._distaxis < -1 or self._distaxis >= len(self._shape):
# raise ValueError
# itmp = np.array(self._distaxis)
# otmp = np.empty(ntask,
# _comm.Allgather(itmp, otmp)
# if np.any(otmp != self._distaxis):
# raise ValueError
# # check whether the global shape is consistent
# itmp = np.array(self._shape)
# otmp = np.empty((ntask, len(self._shape)),
# _comm.Allgather(itmp, otmp)
# for i in range(ntask):
# if np.any(otmp[i, :] != self._shape):
# raise ValueError
# # check shape of local data
# if self._distaxis < 0:
# if self._data.shape != self._shape:
# raise ValueError
# else:
# itmp = np.array(self._shape)
# itmp[self._distaxis] = _shareSize(self._shape[self._distaxis],
# ntask, rank)
# if np.any(self._data.shape != itmp):
# raise ValueError
def dtype(self):
return self._data.dtype
def shape(self):
return self._shape
def size(self):
def real(self):
return data_object(self._shape, self._data.real, self._distaxis)
def imag(self):
return data_object(self._shape, self._data.imag, self._distaxis)
def conj(self):
return data_object(self._shape, self._data.conj(), self._distaxis)
def conjugate(self):
return data_object(self._shape, self._data.conjugate(), self._distaxis)
def _contraction_helper(self, op, mpiop, axis):
if axis is not None:
if len(axis) == len(self._data.shape):
axis = None
if axis is None:
res = np.array(getattr(self._data, op)())
if (self._distaxis == -1):
return res[()]
res2 = np.empty((), dtype=res.dtype)
_comm.Allreduce(res, res2, mpiop)
return res2[()]
if self._distaxis in axis:
res = getattr(self._data, op)(axis=axis)
res2 = np.empty_like(res)
_comm.Allreduce(res, res2, mpiop)
return from_global_data(res2, distaxis=0)
# perform the contraction on the local data
res = getattr(self._data, op)(axis=axis)
if self._distaxis == -1:
return from_global_data(res, distaxis=0)
shp = list(res.shape)
shift = 0
for ax in axis:
if ax < self._distaxis:
shift += 1
shp[self._distaxis-shift] = self.shape[self._distaxis]
return from_local_data(shp, res, self._distaxis-shift)
def sum(self, axis=None):
return self._contraction_helper("sum", MPI.SUM, axis)
def prod(self, axis=None):
return self._contraction_helper("prod", MPI.PROD, axis)
# def min(self, axis=None):
# return self._contraction_helper("min", MPI.MIN, axis)
# def max(self, axis=None):
# return self._contraction_helper("max", MPI.MAX, axis)
def mean(self, axis=None):
if axis is None:
sz = self.size
sz = reduce(lambda x, y: x*y, [self.shape[i] for i in axis])
return self.sum(axis)/sz
def std(self, axis=None):
return np.sqrt(self.var(axis))
# FIXME: to be improved!
def var(self, axis=None):
if axis is not None and len(axis) != len(self.shape):
raise ValueError("functionality not yet supported")
return (abs(self-self.mean())**2).mean()
def _binary_helper(self, other, op):
a = self
if isinstance(other, data_object):
b = other
if a._shape != b._shape:
raise ValueError("shapes are incompatible.")
if a._distaxis != b._distaxis:
raise ValueError("distributions are incompatible.")
a = a._data
b = b._data
elif np.isscalar(other):
a = a._data
b = other
return NotImplemented
tval = getattr(a, op)(b)
if tval is a:
return self
return data_object(self._shape, tval, self._distaxis)
def clip(self, min=None, max=None):
return data_object(self._shape, np.clip(self._data, min, max))
def __neg__(self):
return data_object(self._shape, -self._data, self._distaxis)
def __abs__(self):
return data_object(self._shape, abs(self._data), self._distaxis)
def all(self):
return self.sum() == self.size
def any(self):
return self.sum() != 0
def fill(self, value):
for op in ["__add__", "__radd__", "__iadd__",
"__sub__", "__rsub__", "__isub__",
"__mul__", "__rmul__", "__imul__",
"__truediv__", "__rtruediv__", "__itruediv__",
"__floordiv__", "__rfloordiv__", "__ifloordiv__",
"__pow__", "__rpow__", "__ipow__",
"__lt__", "__le__", "__gt__", "__ge__", "__eq__", "__ne__"]:
def func(op):
def func2(self, other):
return self._binary_helper(other, op=op)
return func2
setattr(data_object, op, func(op))
def full(shape, fill_value, dtype=None, distaxis=0):
return data_object(shape, np.full(local_shape(shape, distaxis),
fill_value, dtype), distaxis)
def uniform_full(shape, fill_value, dtype=None, distaxis=0):
return data_object(
shape, np.broadcast_to(fill_value, local_shape(shape, distaxis)),
def empty(shape, dtype=None, distaxis=0):
return data_object(shape, np.empty(local_shape(shape, distaxis),
dtype), distaxis)
def zeros(shape, dtype=None, distaxis=0):
return data_object(shape, np.zeros(local_shape(shape, distaxis), dtype),
def ones(shape, dtype=None, distaxis=0):
return data_object(shape, np.ones(local_shape(shape, distaxis), dtype),
def empty_like(a, dtype=None):
return data_object(np.empty_like(a._data, dtype))
def vdot(a, b):
tmp = np.array(np.vdot(a._data, b._data))
if a._distaxis == -1:
return tmp[()]
res = np.empty((), dtype=tmp.dtype)
_comm.Allreduce(tmp, res, MPI.SUM)
return res[()]
def _math_helper(x, function, out):
function = getattr(np, function)
if out is not None:
function(x._data, out=out._data)
return out
return data_object(x.shape, function(x._data), x._distaxis)
_current_module = sys.modules[__name__]
for f in ["sqrt", "exp", "log", "tanh", "conjugate", "sin", "cos", "tan",
"sinh", "cosh", "sinc", "absolute", "sign", "log10", "log1p",
def func(f):
def func2(x, out=None):
return _math_helper(x, f, out)
return func2
setattr(_current_module, f, func(f))
def clip(x, a_min=None, a_max=None):
return data_object(x.shape, np.clip(x._data, a_min, a_max), x._distaxis)
def from_object(object, dtype, copy, set_locked):
if dtype is None:
dtype = object.dtype
dtypes_equal = dtype == object.dtype
if set_locked and dtypes_equal and locked(object):
return object
if not dtypes_equal and not copy:
raise ValueError("cannot change data type without copying")
if set_locked and not copy:
raise ValueError("cannot lock object without copying")
data = np.array(object._data, dtype=dtype, copy=copy)
if set_locked:
data.flags.writeable = False
return data_object(object._shape, data, distaxis=object._distaxis)
# This function draws all random numbers on all tasks, to produce the same
# array independent on the number of tasks
# MR FIXME: depending on what is really wanted/needed (i.e. same result
# independent of number of tasks, performance etc.) we need to adjust the
# algorithm.
def from_random(random_type, shape, dtype=np.float64, **kwargs):
generator_function = getattr(Random, random_type)
if len(shape) == 0:
ldat = generator_function(dtype=dtype, shape=shape, **kwargs)
ldat = _comm.bcast(ldat)
return from_local_data(shape, ldat, distaxis=-1)
for i in range(ntask):
lshape = list(shape)
lshape[0] = _shareSize(shape[0], ntask, i)
ldat = generator_function(dtype=dtype, shape=lshape, **kwargs)
if i == rank:
outdat = ldat
return from_local_data(shape, outdat, distaxis=0)
def local_data(arr):
return arr._data
def ibegin_from_shape(glob_shape, distaxis=0):
res = [0] * len(glob_shape)
if distaxis < 0:
return res
res[distaxis] = _shareRange(glob_shape[distaxis], ntask, rank)[0]
return tuple(res)
def ibegin(arr):
res = [0] * arr._data.ndim
res[arr._distaxis] = _shareRange(arr._shape[arr._distaxis], ntask, rank)[0]
return tuple(res)
def np_allreduce_sum(arr):
res = np.empty_like(arr)
_comm.Allreduce(arr, res, MPI.SUM)
return res
def np_allreduce_min(arr):
res = np.empty_like(arr)
_comm.Allreduce(arr, res, MPI.MIN)
return res
def np_allreduce_max(arr):
res = np.empty_like(arr)
_comm.Allreduce(arr, res, MPI.MAX)
return res
def distaxis(arr):
return arr._distaxis
def from_local_data(shape, arr, distaxis=0):
if arr.dtype.kind not in "fciub":
raise TypeError
return data_object(shape, arr, distaxis)
def from_global_data(arr, sum_up=False, distaxis=0):
if arr.dtype.kind not in "fciub":
raise TypeError
if sum_up:
arr = np_allreduce_sum(arr)
if arr.ndim == 0:
distaxis = -1
if distaxis == -1:
return data_object(arr.shape, arr, distaxis)
lo, hi = _shareRange(arr.shape[distaxis], ntask, rank)
sl = [slice(None)]*len(arr.shape)
sl[distaxis] = slice(lo, hi)
return data_object(arr.shape, arr[tuple(sl)], distaxis)
def to_global_data(arr):
if arr._distaxis == -1:
return arr._data
tmp = redistribute(arr, dist=-1)
return tmp._data
def to_global_data_rw(arr):
if arr._distaxis == -1:
return arr._data.copy()
tmp = redistribute(arr, dist=-1)
return tmp._data
def redistribute(arr, dist=None, nodist=None):
if dist is not None:
if nodist is not None:
raise ValueError
if dist == arr._distaxis:
return arr
if nodist is None:
raise ValueError
if arr._distaxis not in nodist:
return arr
dist = -1
for i in range(len(arr.shape)):
if i not in nodist:
dist = i
if arr._distaxis == -1: # all data available, just pick the proper subset
return from_global_data(arr._data, distaxis=dist)
if dist == -1: # gather all data on all tasks
tmp = np.moveaxis(arr._data, arr._distaxis, 0)
slabsize =[1:])*tmp.itemsize
sz = np.empty(ntask,
for i in range(ntask):
sz[i] = slabsize*_shareSize(arr.shape[arr._distaxis], ntask, i)
disp = np.empty(ntask,
disp[0] = 0
disp[1:] = np.cumsum(sz[:-1])
tmp = np.require(tmp, requirements="C")
out = np.empty(arr.size, dtype=arr.dtype)
_comm.Allgatherv(tmp, [out, sz, disp, MPI.BYTE])
shp = np.array(arr._shape)
shp[1:arr._distaxis+1] = shp[0:arr._distaxis]
shp[0] = arr.shape[arr._distaxis]
out = out.reshape(shp)
out = np.moveaxis(out, 0, arr._distaxis)
return from_global_data(out, distaxis=-1)
# real redistribution via Alltoallv
ssz0 = arr._data.size//arr.shape[dist]
ssz = np.empty(ntask,
rszall = arr.size//arr.shape[dist]*_shareSize(arr.shape[dist], ntask, rank)
rbuf = np.empty(rszall, dtype=arr.dtype)
rsz0 = rszall//arr.shape[arr._distaxis]
rsz = np.empty(ntask,
if dist == 0: # shortcut possible
sbuf = np.ascontiguousarray(arr._data)
for i in range(ntask):
lo, hi = _shareRange(arr.shape[dist], ntask, i)
ssz[i] = ssz0*(hi-lo)
rsz[i] = rsz0*_shareSize(arr.shape[arr._distaxis], ntask, i)
sbuf = np.empty(arr._data.size, dtype=arr.dtype)
sslice = [slice(None)]*arr._data.ndim
ofs = 0
for i in range(ntask):
lo, hi = _shareRange(arr.shape[dist], ntask, i)
sslice[dist] = slice(lo, hi)
ssz[i] = ssz0*(hi-lo)
sbuf[ofs:ofs+ssz[i]] = arr._data[tuple(sslice)].flat
ofs += ssz[i]
rsz[i] = rsz0*_shareSize(arr.shape[arr._distaxis], ntask, i)
ssz *= arr._data.itemsize
rsz *= arr._data.itemsize
sdisp = np.append(0, np.cumsum(ssz[:-1]))
rdisp = np.append(0, np.cumsum(rsz[:-1]))
s_msg = [sbuf, (ssz, sdisp), MPI.BYTE]
r_msg = [rbuf, (rsz, rdisp), MPI.BYTE]
_comm.Alltoallv(s_msg, r_msg)
del sbuf # free memory
if arr._distaxis == 0:
rbuf = rbuf.reshape(local_shape(arr.shape, dist))
arrnew = from_local_data(arr.shape, rbuf, distaxis=dist)
arrnew = np.empty(local_shape(arr.shape, dist), dtype=arr.dtype)
rslice = [slice(None)]*arr._data.ndim
ofs = 0
for i in range(ntask):
lo, hi = _shareRange(arr.shape[arr._distaxis], ntask, i)
rslice[arr._distaxis] = slice(lo, hi)
sz = rsz[i]//arr._data.itemsize
arrnew[tuple(rslice)].flat = rbuf[ofs:ofs+sz]
ofs += sz
arrnew = from_local_data(arr.shape, arrnew, distaxis=dist)
return arrnew
def transpose(arr):
if len(arr.shape) != 2 or arr._distaxis != 0:
raise ValueError("bad input")
ssz0 = arr._data.size//arr.shape[1]
ssz = np.empty(ntask,
rszall = arr.size//arr.shape[1]*_shareSize(arr.shape[1], ntask, rank)
rbuf = np.empty(rszall, dtype=arr.dtype)
rsz0 = rszall//arr.shape[0]
rsz = np.empty(ntask,
sbuf = np.empty(arr._data.size, dtype=arr.dtype)
ofs = 0
for i in range(ntask):
lo, hi = _shareRange(arr.shape[1], ntask, i)
ssz[i] = ssz0*(hi-lo)
sbuf[ofs:ofs+ssz[i]] = arr._data[:, lo:hi].flat
ofs += ssz[i]
rsz[i] = rsz0*_shareSize(arr.shape[0], ntask, i)
ssz *= arr._data.itemsize
rsz *= arr._data.itemsize
sdisp = np.append(0, np.cumsum(ssz[:-1]))
rdisp = np.append(0, np.cumsum(rsz[:-1]))
s_msg = [sbuf, (ssz, sdisp), MPI.BYTE]
r_msg = [rbuf, (rsz, rdisp), MPI.BYTE]
_comm.Alltoallv(s_msg, r_msg)
del sbuf # free memory
sz2 = _shareSize(arr.shape[1], ntask, rank)
arrnew = np.empty((sz2, arr.shape[0]), dtype=arr.dtype)
ofs = 0
for i in range(ntask):
lo, hi = _shareRange(arr.shape[0], ntask, i)
sz = rsz[i]//arr._data.itemsize
arrnew[:, lo:hi] = rbuf[ofs:ofs+sz].reshape(hi-lo, sz2).T
ofs += sz
return from_local_data((arr.shape[1], arr.shape[0]), arrnew, 0)