/usr/lib/python2.7/dist-packages/ffc/quadrature/quadratureoptimization.py is in python-ffc 2016.2.0-1.
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# Copyright (C) 2013 Kristian B. Oelgaard
#
# This file is part of FFC.
#
# FFC is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# FFC is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with FFC. If not, see <http://www.gnu.org/licenses/>.
#
# Modified by Marie E. Rognes, 2013
# Modified by Martin Sandve Alnæs, 2013-2014
from ufl.utils.sorting import sorted_by_key
# FFC modules
from ffc.log import info, error
from ffc.cpp import format
from ffc.quadrature.symbolics import optimise_code, BASIS, IP, GEO
from ffc.quadrature.symbolics import create_product, create_sum, create_symbol, create_fraction
def optimize_integral_ir(ir, parameters):
"Compute optimized intermediate representation of integral."
# FIXME: input argument "parameters" has been added to optimize_integral_ir
# FIXME: which shadows a local parameter
# Get integral type and optimization parameters
integral_type = ir["integral_type"]
parameters = ir["optimise_parameters"]
# Check whether we should optimize
if parameters["optimisation"]:
# Get parameters
integrals = ir["trans_integrals"]
integral_type = ir["integral_type"]
num_facets = ir["num_facets"]
num_vertices = ir["num_vertices"]
geo_consts = ir["geo_consts"]
psi_tables_map = ir["psi_tables_map"]
# Optimize based on integral type
if integral_type == "cell":
info("Optimising expressions for cell integral")
if parameters["optimisation"] in ("precompute_ip_const", "precompute_basis_const"):
_precompute_expressions(integrals, geo_consts, parameters["optimisation"])
else:
_simplify_expression(integrals, geo_consts, psi_tables_map)
elif integral_type == "exterior_facet":
for i in range(num_facets):
info("Optimising expressions for facet integral %d" % i)
if parameters["optimisation"] in ("precompute_ip_const", "precompute_basis_const"):
_precompute_expressions(integrals[i], geo_consts, parameters["optimisation"])
else:
_simplify_expression(integrals[i], geo_consts, psi_tables_map)
elif integral_type == "interior_facet":
for i in range(num_facets):
for j in range(num_facets):
info("Optimising expressions for facet integral (%d, %d)" % (i, j))
if parameters["optimisation"] in ("precompute_ip_const", "precompute_basis_const"):
_precompute_expressions(integrals[i][j], geo_consts, parameters["optimisation"])
else:
_simplify_expression(integrals[i][j], geo_consts, psi_tables_map)
elif integral_type == "vertex":
for i in range(num_vertices):
info("Optimising expressions for poin integral %d" % i)
if parameters["optimisation"] in ("precompute_ip_const", "precompute_basis_const"):
_precompute_expressions(integrals[i], geo_consts, parameters["optimisation"])
else:
_simplify_expression(integrals[i], geo_consts, psi_tables_map)
else:
error("Unhandled domain type: " + str(integral_type))
return ir
def _simplify_expression(integral, geo_consts, psi_tables_map):
for points, terms, functions, ip_consts, coordinate, conditionals in integral:
# NOTE: sorted is needed to pass the regression tests on the buildbots
# but it might be inefficient for speed.
# A solution could be to only compare the output of evaluating the
# integral, not the header files.
for loop, (data, entry_vals) in sorted_by_key(terms):
t_set, u_weights, u_psi_tables, u_nzcs, basis_consts = data
new_entry_vals = []
psi_tables = set()
# NOTE: sorted is needed to pass the regression tests on the buildbots
# but it might be inefficient for speed.
# A solution could be to only compare the output of evaluating the
# integral, not the header files.
for entry, val, ops in sorted(entry_vals):
value = optimise_code(val, ip_consts, geo_consts, t_set)
# Check if value is zero
if value.val:
new_entry_vals.append((entry, value, value.ops()))
psi_tables.update(set([psi_tables_map[b] for b in value.get_unique_vars(BASIS)]))
terms[loop][0][2] = psi_tables
terms[loop][1] = new_entry_vals
def _precompute_expressions(integral, geo_consts, optimisation):
for points, terms, functions, ip_consts, coordinate, conditionals in integral:
for loop, (data, entry_vals) in sorted_by_key(terms):
t_set, u_weights, u_psi_tables, u_nzcs, basis_consts = data
new_entry_vals = []
for entry, val, ops in entry_vals:
value = _extract_variables(val, basis_consts, ip_consts, geo_consts, t_set, optimisation)
# Check if value is zero
if value.val:
new_entry_vals.append((entry, value, value.ops()))
terms[loop][1] = new_entry_vals
def _extract_variables(val, basis_consts, ip_consts, geo_consts, t_set, optimisation):
f_G = format["geometry constant"]
f_I = format["ip constant"]
f_B = format["basis constant"]
if val._prec == 0:
return val
elif val._prec == 1:
if val.base_expr is None:
return val
new_base = _extract_variables(val.base_expr, basis_consts, ip_consts, geo_consts, t_set, optimisation)
new_sym = create_symbol(val.v, val.t, new_base, val.base_op)
if new_sym.t == BASIS:
return _reduce_expression(new_sym, [], basis_consts, f_B, True)
elif new_sym.t == IP:
return _reduce_expression(new_sym, [], ip_consts, f_I, True)
elif new_sym.t == GEO:
return _reduce_expression(new_sym, [], geo_consts, f_G, True)
# First handle child classes of product and sum.
elif val._prec in (2, 3):
new_vars = []
for v in val.vrs:
new_vars.append(_extract_variables(v, basis_consts, ip_consts, geo_consts, t_set, optimisation))
if val._prec == 2:
new_val = create_product(new_vars)
if val._prec == 3:
new_val = create_sum(new_vars)
elif val._prec == 4:
num = _extract_variables(val.num, basis_consts, ip_consts, geo_consts, t_set, optimisation)
denom = _extract_variables(val.denom, basis_consts, ip_consts, geo_consts, t_set, optimisation)
return create_fraction(num, denom)
else:
error("Unknown symbolic type: %s" % repr(val))
# Sort variables of product and sum.
b_c, i_c, g_c = [], [], []
for v in new_val.vrs:
if v.t == BASIS:
if optimisation == "precompute_basis_const":
b_c.append(v)
elif v.t == IP:
i_c.append(v)
else:
g_c.append(v)
vrs = new_val.vrs[:]
for v in g_c + i_c + b_c:
vrs.remove(v)
i_c.extend(_reduce_expression(new_val, g_c, geo_consts, f_G))
vrs.extend(_reduce_expression(new_val, i_c, ip_consts, f_I))
vrs.extend(_reduce_expression(new_val, b_c, basis_consts, f_B))
# print "b_c: "
# for b in b_c:
# print b
# print "basis"
# for k,v in basis_consts.items():
# print "k: ", k
# print "v: ", v
# print "geo"
# for k,v in geo_consts.items():
# print "k: ", k
# print "v: ", v
# print "ret val: ", val
if len(vrs) > 1:
if new_val._prec == 2:
new_object = create_product(vrs)
elif new_val._prec == 3:
new_object = create_sum(vrs)
else:
error("Must have product or sum here: %s" % repr(new_val))
if new_object.t == BASIS:
if optimisation == "precompute_ip_const":
return new_object
elif optimisation == "precompute_basis_const":
return _reduce_expression(new_object, [], basis_consts, f_B, True)
elif new_object.t == IP:
return _reduce_expression(new_object, [], ip_consts, f_I, True)
elif new_object.t == GEO:
return _reduce_expression(new_object, [], geo_consts, f_G, True)
return vrs[0]
# if new_val._prec == 2:
# if len(vrs) > 1:
# new_prod = create_product(vrs)
# if new_prod.t == BASIS:
# if optimisation == "precompute_ip_const":
# return new_prod
# elif optimisation == "precompute_basis_const":
# return _reduce_expression(new_prod, [], basis_consts, f_B, True)
# elif new_prod.t == IP:
# return _reduce_expression(new_prod, [], ip_consts, f_I, True)
# elif new_prod.t == GEO:
# return _reduce_expression(new_prod, [], geo_consts, f_G, True)
# return vrs[0]
# elif new_val._prec == 3:
# if len(vrs) > 1:
# new_sum = create_sum(vrs)
# if new_sum.t == BASIS:
# return new_sum
# return _reduce_expression(new_sum, [], basis_consts, f_B, True)
# elif new_sum.t == IP:
# return _reduce_expression(new_sum, [], ip_consts, f_I, True)
# elif new_sum.t == GEO:
# return _reduce_expression(new_sum, [], geo_consts, f_G, True)
# return vrs[0]
# else:
# error("Must have product or sum here: %s" % repr(new_val))
def _reduce_expression(expr, symbols, const_dict, f_name, use_expr_type=False):
if use_expr_type:
if expr not in const_dict:
const_dict[expr] = len(const_dict)
return create_symbol(f_name(const_dict[expr]), expr.t)
# Only something to be done if we have more than one symbol.
if len(symbols) > 1:
sym_type = symbols[0].t
# Create new symbol.
if expr._prec == 2:
new_sym = create_product(symbols)
elif expr._prec == 3:
new_sym = create_sum(symbols)
if new_sym not in const_dict:
const_dict[new_sym] = len(const_dict)
s = create_symbol(f_name(const_dict[new_sym]), sym_type)
return [s]
return symbols
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