incomp-flame-post/code/code_gen/code_gen.py
2019-04-28 01:10:57 +09:00

522 lines
12 KiB
Python

#
# This example shows how to write a basic calculator with variables.
#
from lark import Lark, Visitor, Transformer, v_args, Token
try:
input = raw_input # For Python2 compatibility
except NameError:
pass
calc_grammar = """
?varlist: "[" [NAME ("," NAME)*] "]"
?start: statement*
?statement: NAME "=" sum -> assign_var
| avg "{" [NAME ("," NAME)*] "}" -> assign_avg_var
| varlist
?sum: product
| sum "+" product -> add
| sum "-" product -> sub
?product: atom
| product "*" atom -> mul
| product "/" atom -> div
?atom: NUMBER -> number
| "-" atom -> neg
| NAME -> var
| NAME "'" -> fluc
| "$" NAME -> env
| "(" sum ")"
| inlinefunc "(" sum ")" -> icall
| mathfunc "(" sum ")" -> fcall
| derivative "(" NAME ")" -> dnx
avg: "avg" [NAME]
?inlinefunc: "sqr" -> sqr
| "pow3" -> pow3
?mathfunc: "log" -> log
| "exp" -> exp
| "sqrt" -> sqrt
| "rxn_rate" -> rxn_rate
?derivative: "ddx" -> ddx
| "dd2x" -> dd2x
| "ddy" -> ddy
| "dd2y" -> dd2y
| "ddz" -> ddz
| "dd2z" -> dd2z
%import common.CNAME -> NAME
%import common.NUMBER
%import common.WS
%ignore WS
"""
real_array_decl = "real*8, allocatable, dimension(:,:,:) :: {}"
real_array_alloc = "allocate({0}(nxp,nyp,nzp), stat=ierr) ; {0} = 0."
avg_array_decl = "real*8, allocatable, dimension(:) :: {}"
avg_array_alloc = "allocate({0}(nxp), stat=ierr) ; {0} = 0."
real_array_free = "deallocate({})"
real_array_loop = """
do k = 1, nzp
do j = 1, nyp
do i = 1, nxp
{0[0]}(i,j,k) = {0[1]}
end do
end do
end do
"""
avg_array_sum = """
do k = 1, nzp
do j = 1, nyp
do i = 1, nxp
{0}(i) = {0}(i) + {1} {2}
end do
end do
end do
"""
avg_array_divide = "{0} = {0} / denum {1}"
real_array_diff = "call {0[0]} ( {0[0]}_{0[1]}, {0[1]} )"
@v_args(inline=True) # Affects the signatures of the methods
class ToFortran(Transformer):
def __init__(self, primary_set):
self.primary = primary_set
self.derivatives = {}
self.dependency = {}
def number(self, numeral):
return (str(float(numeral)), [])
def env(self, name):
return (name.value, [])
def var(self, name):
return (name + "(i,j,k)",
[name.value] if name.value not in self.primary else [])
def fluc(self, name):
fmt = "({0}(i,j,k) - {{}}avg_{0}(i))"
return (fmt.format(name),
[name.value] if name.value not in self.primary else [])
def dnx (self, partial, b):
signature = "{}_{}".format(partial.data, b)
self.derivatives[signature] = (partial.data, b.value)
self.dependency[signature] = [b.value]
return (signature + "(i,j,k)", [signature])
def icall (self, a, (b, dep)):
fcode = "({0})".format(b)
if a.data == "sqr":
fcode = "(({0})*({0}))".format(b)
elif a.data == "pow3":
fcode = "(({0})*({0})*({0}))".format(b)
return (fcode, dep)
def fcall (self, a, (b, dep)):
fcode = "( {} ( {} ) )".format(a, b)
return (fcode, dep)
def neg(self, (b, dep)):
fcode = "( - {} )".format(b)
return (fcode, dep)
def add(self, (a, adep), (b, bdep)):
fcode = "( {} + {} )".format(a, b)
return (fcode, adep + bdep)
def sub(self, (a, adep), (b, bdep)):
fcode = "( {} - {} )".format(a, b)
return (fcode, adep + bdep)
def mul(self, (a, adep), (b, bdep)):
fcode = "( {} * {} )".format(a, b)
return (fcode, adep + bdep)
def div(self, (a, adep), (b, bdep)):
fcode = "( {} / {} )".format(a, b)
return (fcode, adep + bdep)
log = lambda self : "log"
exp = lambda self : "exp"
sqrt = lambda self : "sqrt"
rxn_rate = lambda self : "rxn_rate"
class CheckPass(Visitor):
def __init__(self):
self.hasFluc = False
@classmethod
def check(cls, tree):
self = cls()
return self(tree)
def __call__(self, tree):
self.visit(tree)
return self.hasFluc
def fluc(self, tree):
self.hasFluc = True
@v_args(inline=True) # Affects the signatures of the methods
class CalculateTree(Transformer):
def __init__(self):
self.primary = []
self.derived = {}
self.averaged = {}
self.derivatives = {}
self.dependency = {}
self.definitions = {}
self.exp_parser = ToFortran([])
def varlist(self, *args):
for arg in args:
self.primary.append(arg.value)
self.dependency[arg.value] = []
# return self.primary
return ""
def assign_var(self, *args): # name, (value, dep)):
vname, vdef = args
self.definitions[vname.value] = vdef
code, dep = self.exp_parser.transform(vdef)
self.dependency[vname.value] = dep
return ""
def assign_avg_var(self, *args): # weight, *args): #name, (value, dep)):
weight = args[0]
vlist = args[1:]
self.averaged[str(weight)] = map(str, vlist)
w = str(weight)
for v in vlist:
avg_var = ( "" if w == str(None) else w + "_" ) + "avg_" + v
self.dependency[avg_var] = [str(v)]
# average_names.append(avg_var)
# self.depsDict[avg_var] = [v]
# self.flucDict[avg_var] = False
'''
fmt = "avg_{}"
if weight is not None:
wvalue, wdep = self.var(weight)
self.averaged[fmt.format(weight)] = wvalue, None
self.dependency[fmt.format(weight)] = wdep
fmt = weight + "_" + fmt
for i, (value, dep) in enumerate(args):
self.averaged[fmt.format(i)] = value, weight
self.dependency[fmt.format(i)] = dep + (wdep if weight is not None else [])
'''
return ""
def avg(self, *args):
try:
return args[0]
except IndexError:
return None
def dep_graph (self):
return dict(
self.dependency.items()
+ self.exp_parser.dependency.items()
)
def array_decl (self):
f_code = ""
for var in self.derived.iterkeys():
f_code = f_code + real_array_decl.format(var) + "\n"
for var in self.derivatives.iterkeys():
f_code = f_code + real_array_decl.format(var) + "\n"
for var in self.averaged.iterkeys():
f_code = f_code + avg_array_decl.format(var) + "\n"
return f_code
def array_init (self):
f_code = ""
for var in self.derived.iterkeys():
f_code = f_code + real_array_alloc.format(var) + "\n"
for var in self.derivatives.iterkeys():
f_code = f_code + real_array_alloc.format(var) + "\n"
for var in self.averaged.iterkeys():
f_code = f_code + avg_array_alloc.format(var) + "\n"
return f_code
def array_final (self):
f_code = ""
for var in self.derived.iterkeys():
f_code = f_code + real_array_free.format(var) + "\n"
for var in self.derivatives.iterkeys():
f_code = f_code + real_array_free.format(var) + "\n"
for var in self.averaged.iterkeys():
f_code = f_code + real_array_free.format(var) + "\n"
return f_code
def array_pass1 (self):
f_code = ""
code_dict = {}
for tup in self.derived.iteritems():
code_dict[tup[0]] = real_array_loop.format(tup) + "\n"
for tup in self.derivatives.iteritems():
code_dict[tup[0]] = real_array_diff.format(tup[1]) + "\n"
wfmt = "* {}(i,j,k)"
for k, (v,w) in self.averaged.iteritems():
code_dict[k] = avg_array_sum.format(k, v, wfmt.format(w) if w is not None else "") + "\n"
for var in self.sort_vars():
f_code = f_code + code_dict[var]
return f_code
def array_pass1_avg (self):
f_code = ""
meanw = "/ avg_{}"
for k, (v,w) in self.averaged.iteritems():
if k.startswith("avg"):
f_code = avg_array_divide.format(k, meanw.format(w) if w is not None else "") + "\n" + f_code
else:
f_code = f_code + avg_array_divide.format(k, meanw.format(w) if w is not None else "") + "\n"
return f_code
def module_dict (self):
md = {}
md["module_name"] = "terms"
md["module_data"] = self.array_decl()
md["module_init"] = self.array_init()
md["module_finalize"] = self.array_final()
md["module_pass1"] = self.array_pass1()
md["module_pass1_avg"] = self.array_pass1_avg()
return md
def sort_vars (self):
order = []
remain = set(self.derived.iterkeys()) | set(self.derivatives.iterkeys()) | set(self.averaged.iterkeys())
while len(remain) > 0:
for v in remain:
if len(set(self.dependency[v]) & remain) == 0:
order.append(v)
remain.remove(v)
break
return order
tf=CalculateTree()
ft=ToFortran(['u','v','w','y'])
calc_parser = Lark(calc_grammar, parser='lalr' ) # , transformer=tf)
calc = calc_parser.parse
import sys
import pprint
pp = pprint.PrettyPrinter()
class FortranProgram:
def __init__ (self, terms_input):
self.tree_parser = Lark(calc_grammar, parser='lalr' )
self.parser = CalculateTree()
tree = self.tree_parser.parse(terms_input)
self.parser.transform(tree)
dg = self.parser.dep_graph()
fd = {}
for v in dg.iterkeys():
fd[v] = False
for n, d in (self.parser.definitions.iteritems()):
fd[n] = CheckPass.check(d)
average_names = []
for w, vlist in self.parser.averaged.iteritems():
for v in vlist:
avg_var = ( "" if w == "None" else w + "_" ) + "avg_" + v
average_names.append(avg_var)
def isFluc (a):
for x in dg[a]:
fd[a] = fd[a] or isFluc(x)
return fd[a]
pass1var = filter(lambda x: not isFluc(x), average_names)
pass2var = filter(isFluc, average_names)
def dep_set (varset):
c = set([])
for var in varset:
c.update(dg[var])
return c
def dep_closer (s):
c = set(s)
while len(dep_set(s)) > 0:
s = dep_set(s)
c.update(s)
return c
self.pass1set = dep_closer(set(pass1var))
self.pass2set = dep_closer(set(pass2var))
print self.pass1set
print self.pass2set
def main():
while True:
try:
s = input('> ')
except EOFError:
break
print(calc(s))
def test():
with open("resources/m_template.f90") as template_file:
mod_form = template_file.read()
with open("terms.input") as inputfile:
terms_raw = ((inputfile.read()))
parsed_tree = (calc(terms_raw))
'''
tf.transform(parsed_tree)
namelist, deflist = zip(*list(tf.definitions.iteritems()))
hasFluc = dict([ (n, hf) for n, hf in zip(namelist, map(CheckPass.check, deflist)) ])
codes, deps = zip(*map ( ft.transform, deflist ))
depsDict = dict(zip(namelist, deps))
visited = {n: False for n in namelist}
def isFluc (a, graph, visit, hf):
try:
if visit[a]:
pass
elif len(graph[a]) < 1:
visit[a] = True
else:
hf[a] = any([hf[a]] + [isFluc(x, graph, visit, hf) for x in graph[a]])
visit[a] = True
return hf[a]
except KeyError:
return False
flucDict = {n: isFluc(n, depsDict, visited, hasFluc) for n in namelist}
'''
FortranProgram(terms_raw)
'''
for f, ts in zip(namelist, deps):
for t in ts:
print "{} -> {}".format(f, t)
for d, (op, phi) in ft.derivatives.iteritems():
print "{} -> {}".format(d, phi)
print tf.averaged
'''
#print mod_form.format(tf.module_dict())
#print "! ", tf.derived
#print "! ", tf.derivatives
#print "! ", tf.dependency
#print "! ", tf.sort_vars()
if __name__ == '__main__':
test()
# main()