source/tomopy/util/mproc.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
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"""
Module for multiprocessing tasks.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import numpy as np
import multiprocessing as mp
import math
from contextlib import closing
from .dtype import as_sharedmem, to_numpy_array, get_shared_mem
import logging
import numexpr as ne
logger = logging.getLogger(__name__)
__author__ = "Doga Gursoy"
__copyright__ = "Copyright (c) 2015, UChicago Argonne, LLC."
__docformat__ = 'restructuredtext en'
__all__ = ['distribute_jobs']
# global shared variables
SHARED_ARRAYS = None
SHARED_OUT = None
SHARED_QUEUE = None
INTYPE = None
INSHAPE = None
OUTTYPE = None
OUTSHAPE = None
ON_HOST = False
DEBUG = False
def get_rank():
""" Get the rank of the process """
try:
from mpi4py import MPI
comm_w = MPI.COMM_WORLD
return comm_w.Get_rank()
except ModuleNotFoundError:
return 0
def get_nproc():
""" Get the number of processes """
try:
from mpi4py import MPI
comm_w = MPI.COMM_WORLD
return comm_w.Get_size()
except ModuleNotFoundError:
return 1
def barrier():
""" Barrier for MPI processes """
try:
from mpi4py import MPI
comm_w = MPI.COMM_WORLD
comm_w.Barrier()
except ModuleNotFoundError:
pass
def set_debug(val=True):
"""
Set the global DEBUG variable.
If DEBUG is True, all computations will be run on the host process instead
of distributing work over different processes. This can help to debug
functions that give errors or cause segmentation faults. If DEBUG is False
(the default value), work is distributed over different processes.
"""
global DEBUG
DEBUG = val
def get_ncore_nchunk(axis_size, ncore=None, nchunk=None):
# limit chunk size to size of array along axis
if nchunk and nchunk > axis_size:
nchunk = axis_size
# default ncore to max and limit number of cores to max number
if ncore is None or ncore > mp.cpu_count():
ncore = mp.cpu_count()
# limit number of cores based on nchunk so that all cores are used
if ncore > math.ceil(axis_size / (nchunk or 1)):
ncore = int(math.ceil(axis_size / (nchunk or 1)))
# default nchunk to only use each core for one call
if nchunk is None:
nchunk = int(math.ceil(axis_size / ncore))
return ncore, nchunk
def get_ncore_slices(axis_size, ncore=None, nchunk=None):
# default ncore to max (also defaults ncore == 0)
ncore = min(mp.cpu_count() if not ncore else ncore, axis_size)
if nchunk is None:
# calculate number of slices to send to each GPU
chunk_size = axis_size // ncore
leftover = axis_size % ncore
sizes = np.ones(ncore, dtype=int) * chunk_size
# evenly distribute leftover across workers
sizes[:leftover] += 1
offsets = np.zeros(ncore+1, dtype=int)
offsets[1:] = np.cumsum(sizes)
slcs = [np.s_[offsets[i]:offsets[i+1]] for i in range(offsets.shape[0]-1)]
elif nchunk == 0:
# nchunk == 0 is a special case, we will collapse the dimension
slcs = [np.s_[i] for i in range(axis_size)]
else:
# calculate offsets based on chunk size
slcs = [np.s_[offset:offset+nchunk] for offset in range(0, axis_size, nchunk)]
return ncore, slcs
def get_worker_ncore_slices(axis_size, ncore=None, nchunk=None):
# default ncore to max (also defaults ncore == 0)
if not ncore:
ncore = mp.cpu_count()
if nchunk is None:
# calculate number of slices to send to each GPU
chunk_size = axis_size // ncore
leftover = axis_size % ncore
sizes = np.ones(ncore, dtype=np.int) * chunk_size
# evenly distribute leftover across workers
sizes[:leftover] += 1
offsets = np.zeros(ncore+1, dtype=np.int)
offsets[1:] = np.cumsum(sizes)
slcs = [np.s_[offsets[i]:offsets[i+1]]
for i in range(offsets.shape[0]-1)]
elif nchunk == 0:
# nchunk == 0 is a special case, we will collapse the dimension
slcs = [np.s_[i] for i in range(axis_size)]
else:
# calculate offsets based on chunk size
slcs = [np.s_[offset:offset+nchunk]
for offset in range(0, axis_size, nchunk)]
# create a barrier
barrier()
_size = get_nproc()
if _size > 1:
_nrank = get_rank()
_nsplit = len(slcs) // _size
_nmodulo = len(slcs) % _size
_offset = _nsplit * _nrank
if _nrank == 0:
_nsplit += _nmodulo
else:
_offset += _nmodulo
slcs = slcs[_offset:(_offset+_nsplit)]
return ncore, slcs
def distribute_jobs(arr,
func,
axis,
args=None,
kwargs=None,
ncore=None,
nchunk=None,
out=None):
"""
Distribute N-dimensional shared-memory array into cores by splitting along
an axis.
Parameters
----------
arr : ndarray, or iterable(ndarray)
Array(s) to be split up for processing.
func : func
Function to be parallelized. Should return an ndarray.
args : list
Arguments of the function in a list.
kwargs : list
Keyword arguments of the function in a dictionary.
axis : int
Axis along which parallelization is performed.
ncore : int, optional
Number of cores that will be assigned to jobs.
nchunk : int, optional
Chunk size to use when parallelizing data. None will maximize the chunk
size for the number of cores used. Zero will use a chunk size of one,
but will also remove the dimension from the array.
out : ndarray, optional
Output array. Results of functions will be compiled into this array.
If not provided, new array will be created for output.
Returns
-------
ndarray
Output array.
"""
if isinstance(arr, np.ndarray):
arrs = [arr]
else:
# assume this is multiple arrays
arrs = list(arr)
axis_size = arrs[0].shape[axis]
ncore, nchunk = get_ncore_nchunk(axis_size, ncore, nchunk)
# prepare all args (func, args, kwargs)
# NOTE: args will include shared_arr slice as first arg
args = args or tuple()
kwargs = kwargs or dict()
# prepare global sharedmem arrays
shared_arrays = []
shared_shape = []
shared_out = None
shared_out_shape = None
for arr in arrs:
arr_shared = as_sharedmem(arr)
shared_arrays.append(get_shared_mem(arr_shared))
shared_shape.append(arr.shape)
if out is not None and np.may_share_memory(arr, out) and \
arr.shape == out.shape and arr.dtype == out.dtype:
# assume these are the same array
shared_out = arr_shared
if out is None:
# default out to last array in list
out = shared_arrays[-1]
shared_out = out
out = to_numpy_array(out, arrs[-1].dtype, shared_shape[-1])
shared_out_shape = shared_shape[-1]
shared_out_type = arrs[-1].dtype
else:
shared_out_shape = out.shape
shared_out_type = out.dtype
shared_out = as_sharedmem(out)
# Set up queue
man = mp.Manager()
queue = man.Queue()
# if nchunk is zero, remove dimension from slice.
map_args = []
for i in range(0, axis_size, nchunk or 1):
if nchunk:
map_args.append((func, args, kwargs, np.s_[i:i + nchunk], axis))
else:
map_args.append((func, args, kwargs, i, axis))
init_shared(shared_arrays, shared_out, arr.dtype, shared_shape,
shared_out_type, shared_out_shape, queue=queue, on_host=True)
if ncore > 1 and DEBUG is False:
with closing(mp.Pool(processes=ncore,
initializer=init_shared,
initargs=(shared_arrays, shared_out, arr.dtype,
shared_shape, shared_out_type,
shared_out_shape, queue))) as p:
if p._pool:
proclist = p._pool[:]
res = p.map_async(_arg_parser, map_args)
try:
while not res.ready():
if any(proc.exitcode for proc in proclist):
p.terminate()
raise RuntimeError(
"Child process terminated before finishing")
res.wait(timeout=1)
except KeyboardInterrupt:
p.terminate()
raise
else:
res = p.map_async(_arg_parser, map_args)
try:
p.close()
res.get()
p.join()
except:
p.terminate()
raise
clear_queue(queue, shared_arrays, shared_out)
else:
for m in map_args:
_arg_parser(m)
# NOTE: will only copy if out wasn't sharedmem
out[:] = to_numpy_array(shared_out, shared_out_type, shared_out_shape)
clear_shared()
return out
def init_shared(
shared_arrays, shared_out, intype, inshape,
outtype, outshape, queue=None, on_host=False):
global SHARED_ARRAYS
global SHARED_OUT
global SHARED_QUEUE
global ON_HOST
global INTYPE
global OUTTYPE
global INSHAPE
global OUTSHAPE
SHARED_ARRAYS = shared_arrays
SHARED_OUT = shared_out
SHARED_QUEUE = queue
ON_HOST = on_host
INTYPE = intype
OUTTYPE = outtype
INSHAPE = inshape
OUTSHAPE = outshape
def clear_shared():
global SHARED_ARRAYS
global SHARED_OUT
global SHARED_QUEUE
global INTYPE
global OUTTYPE
SHARED_ARRAYS = None
SHARED_OUT = None
SHARED_QUEUE = None
INTYPE = None
OUTTYPE = None
def _arg_parser(params):
global SHARED_ARRAYS
global SHARED_OUT
global SHARED_QUEUE
global INTYPE
global OUTTYPE
global INSHAPE
global OUTSHAPE
func, args, kwargs, slc, axis = params
func_args = tuple((slice_axis(to_numpy_array(a, INTYPE, INSHAPE[idx]), slc, axis) for idx, a in enumerate(SHARED_ARRAYS))) + args
# NOTE: will only copy if actually different arrays
try:
result = func(*func_args, **kwargs)
if result is not None and isinstance(result, np.ndarray):
outslice = slice_axis(to_numpy_array(SHARED_OUT, OUTTYPE, OUTSHAPE), slc, axis)
outslice[:] = result[:]
except RunOnHostException:
SHARED_QUEUE.put(params)
# apply slice to specific axis on ndarray
def slice_axis(arr, slc, axis):
return arr[tuple(slice(None) if i != axis else slc for i in range(arr.ndim))]
def clear_queue(queue, shared_arrays, shared_out):
while not queue.empty():
params = queue.get(False)
_arg_parser(params)
class RunOnHostException(Exception):
pass
class set_numexpr_threads(object):
def __init__(self, nthreads):
cpu_count = mp.cpu_count()
if nthreads is None or nthreads > cpu_count:
self.n = cpu_count
else:
self.n = nthreads
def __enter__(self):
self.oldn = ne.set_num_threads(self.n)
def __exit__(self, exc_type, exc_value, traceback):
ne.set_num_threads(self.oldn)