# ___________________________________________________________________________
#
# Pyomo: Python Optimization Modeling Objects
# Copyright 2017 National Technology and Engineering Solutions of Sandia, LLC
# Under the terms of Contract DE-NA0003525 with National Technology and
# Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
# rights in this software.
# This software is distributed under the 3-clause BSD License.
# ___________________________________________________________________________
from pyomo.common.fileutils import find_library
from pyomo.contrib.pynumero.linalg.utils import (validate_index,
validate_value, _NotSet)
import numpy.ctypeslib as npct
import numpy as np
import ctypes
import sys
import os
class MA57Interface(object):
libname = _NotSet
@classmethod
def available(cls):
if cls.libname is _NotSet:
cls.libname = find_library('pynumero_MA57')
if cls.libname is None:
return False
return os.path.exists(cls.libname)
def __init__(self,
work_factor=None,
fact_factor=None,
ifact_factor=None):
if not MA57Interface.available():
raise RuntimeError(
'Could not find pynumero_MA57 library.')
self.work_factor = work_factor
self.fact_factor = fact_factor
self.ifact_factor = ifact_factor
self.lib = ctypes.cdll.LoadLibrary(self.libname)
array_1d_double = npct.ndpointer(dtype=np.double, ndim=1, flags='CONTIGUOUS')
array_2d_double = npct.ndpointer(dtype=np.double, ndim=2, flags='CONTIGUOUS')
array_1d_int = npct.ndpointer(dtype=np.intc, ndim=1, flags='CONTIGUOUS')
# Declare arg and res types of functions:
# Do I need to specify that this function takes no argument?
self.lib.new_MA57_struct.restype = ctypes.c_void_p
# return type is pointer to MA57_struct. Why do I use c_void_p here?
self.lib.free_MA57_struct.argtypes = [ctypes.c_void_p]
self.lib.set_icntl.argtypes = [ctypes.c_void_p, ctypes.c_int, ctypes.c_int]
# Do I need to specify that this function returns nothing?
self.lib.get_icntl.argtypes = [ctypes.c_void_p, ctypes.c_int]
self.lib.get_icntl.restype = ctypes.c_int
self.lib.set_cntl.argtypes = [ctypes.c_void_p, ctypes.c_int, ctypes.c_double]
self.lib.get_cntl.argtypes = [ctypes.c_void_p, ctypes.c_int]
self.lib.get_cntl.restype = ctypes.c_double
self.lib.get_info.argtypes = [ctypes.c_void_p, ctypes.c_int]
self.lib.get_info.restype = ctypes.c_int
self.lib.get_rinfo.argtypes = [ctypes.c_void_p, ctypes.c_int]
self.lib.get_rinfo.restype = ctypes.c_double
self.lib.alloc_keep.argtypes = [ctypes.c_void_p, ctypes.c_int]
self.lib.alloc_work.argtypes = [ctypes.c_void_p, ctypes.c_int]
self.lib.alloc_fact.argtypes = [ctypes.c_void_p, ctypes.c_int]
self.lib.alloc_ifact.argtypes = [ctypes.c_void_p, ctypes.c_int]
self.lib.set_nrhs.argtypes = [ctypes.c_void_p, ctypes.c_int]
self.lib.set_lrhs.argtypes = [ctypes.c_void_p, ctypes.c_int]
self.lib.set_job.argtypes = [ctypes.c_void_p, ctypes.c_int]
self.lib.do_symbolic_factorization.argtypes = [ctypes.c_void_p, ctypes.c_int,
ctypes.c_int, array_1d_int, array_1d_int]
self.lib.do_numeric_factorization.argtypes = [ctypes.c_void_p, ctypes.c_int,
ctypes.c_int, array_1d_double]
self.lib.do_backsolve.argtypes = [ctypes.c_void_p, ctypes.c_int, array_2d_double]
self.lib.do_iterative_refinement.argtypes = [ctypes.c_void_p, ctypes.c_int,
ctypes.c_int, array_1d_double, array_1d_int, array_1d_int,
array_1d_double, array_1d_double, array_1d_double]
self.lib.do_reallocation.argtypes = [ctypes.c_void_p, ctypes.c_int, ctypes.c_double,
ctypes.c_int]
self.icntl_len = 20
self.cntl_len = 5
self.info_len = 40
self.rinfo_len = 20
self._ma57 = self.lib.new_MA57_struct()
def __del__(self):
self.lib.free_MA57_struct(self._ma57)
def set_icntl(self, i, val):
validate_index(i, self.icntl_len, 'ICNTL')
validate_value(i, int, 'ICNTL')
# NOTE: Use the FORTRAN indexing (same as documentation) to
# set and access info/cntl arrays from Python, whereas C
# functions use C indexing. Maybe this is too confusing.
self.lib.set_icntl(self._ma57, i-1, val)
def get_icntl(self, i):
validate_index(i, self.icntl_len, 'ICNTL')
return self.lib.get_icntl(self._ma57, i-1)
def set_cntl(self, i, val):
validate_index(i, self.cntl_len, 'CNTL')
validate_value(val, float, 'CNTL')
self.lib.set_cntl(self._ma57, i-1, val)
def get_cntl(self, i):
validate_index(i, self.cntl_len, 'CNTL')
return self.lib.get_cntl(self._ma57, i-1)
def get_info(self, i):
validate_index(i, self.info_len, 'INFO')
return self.lib.get_info(self._ma57, i-1)
def get_rinfo(self, i):
validate_index(i, self.rinfo_len, 'RINFO')
return self.lib.get_info(self._ma57, i-1)
def do_symbolic_factorization(self, dim, irn, jcn):
irn = irn.astype(np.intc, casting='safe', copy=True)
jcn = jcn.astype(np.intc, casting='safe', copy=True)
# TODO: maybe allow user the option to specify size of KEEP
ne = irn.size
self.ne_cached = ne
self.dim_cached = dim
assert ne == jcn.size, 'Dimension mismatch in row and column arrays'
self.lib.do_symbolic_factorization(self._ma57,
dim, ne, irn, jcn)
return self.get_info(1)
def do_numeric_factorization(self, dim, entries):
entries = entries.astype(np.float64, casting='safe', copy=True)
ne = entries.size
assert ne == self.ne_cached,\
('Wrong number of entries in matrix. Please re-run symbolic'
'factorization with correct nonzero coordinates.')
assert dim == self.dim_cached,\
('Dimension mismatch between symbolic and numeric factorization.'
'Please re-run symbolic factorization with the correct '
'dimension.')
if self.fact_factor is not None:
min_size = self.get_info(9)
self.lib.alloc_fact(self._ma57,
int(self.fact_factor*min_size))
if self.ifact_factor is not None:
min_size = self.get_info(10)
self.lib.alloc_ifact(self._ma57,
int(self.ifact_factor*min_size))
self.lib.do_numeric_factorization(self._ma57,
dim, ne, entries)
return self.get_info(1)
def do_backsolve(self, rhs):
rhs = rhs.astype(np.double, casting='safe', copy=True)
shape = rhs.shape
if len(shape) == 1:
rhs_dim = rhs.size
nrhs = 1
rhs = np.array([rhs])
elif len(shape) == 2:
# FIXME
raise NotImplementedError(
'Funcionality for solving a matrix of right hand '
'is buggy and needs fixing.')
rhs_dim = rhs.shape[0]
nrhs = rhs.shape[1]
else:
raise ValueError(
'Right hand side must be a one or two-dimensional array')
# This does not necessarily need to be true; each RHS could have length
# larger than N (for some reason). In the C interface, however, I assume
# that LRHS == N
assert self.dim_cached == rhs_dim, 'Dimension mismatch in RHS'
# TODO: Option to specify a JOB other than 1. By my understanding,
# different JOBs allow partial factorizations to be performed.
# Currently not supported - unclear if it should be.
if nrhs > 1:
self.lib.set_nrhs(self._ma57, nrhs)
if self.work_factor is not None:
self.lib.alloc_work(self._ma57,
int(self.work_factor*nrhs*rhs_dim))
self.lib.do_backsolve(self._ma57,
rhs_dim, rhs)
if len(shape) == 1:
# If the user input rhs as a 1D array, return the solution
# as a 1D array.
rhs = rhs[0, :]
return rhs