def get_constraints(current_spec):
'''Generates a new distinguishing input which is not a part of the existing
specification
Arguments:
- current_spec (str): The specification for the problem including the
existing constraints for the problem.
Returns:
- list: contains all the specifications
'''
file_sexp = parser.sexpFromFile(current_spec)
benchmark_tuple = parser.extract_benchmark(file_sexp)
(
theories,
syn_ctx,
synth_instantiator,
macro_instantiator,
uf_instantiator,
constraints,
grammar_map,
forall_vars_map,
default_grammar_sfs
) = benchmark_tuple
inputs = []
vals= []
for constraint in constraints:
inputs.append(constraint.children[0].children[0].value_object.value_object)
vals.append(constraint.children[1].value_object.value_object)
result=[]
for i in range(0,len(inputs)):
result.append(inputs[i]+'; '+vals[i])
#print(result)
return result
def test_make_solver(benchmark_files):
for benchmark_file in benchmark_files:
# print(benchmark_file)
file_sexp = parser.sexpFromFile(benchmark_file)
# import cProfile, pstats
# pr = cProfile.Profile()
# pr.enable()
make_solver(file_sexp)
def evaluate(expr, input, synth_file='./euphony/benchmarks/string/test/phone-5-test.sl'):
'''Evaluate an expression for a given input
Arguments:
- expr(FunctionExpression): The expression to evaluate
- input(str): the input to the given expression.
- synth_file(str): path to a synthesis file defining the available
available grammar. This file doesn't need to correspond to the same
file that the expression was generated from. It just needs to have the
same grammar definition. Constraints are not used in any way. It's
required in order to load the proper logic into the expression
evaluator. This is set to a file containing string logic by default.
Returns:
- (str): output from evaluating expression against given input
'''
eval_ctx = EvaluationContext()
val_map = (Value(input, value_type=StringType()),)
eval_ctx.set_valuation_map(val_map)
# print('attempting to eval: {}'.format(expr))
# print('eval ctx: {} -- {}'.format(val_map, type(val_map)))
# This is pretty flow since it needs to re-read the synthesis file to know
# the grammar that it needs to parse. Personally, I am not so concerned
# about performance, but rather just getting it to work in the first place.
file_sexp = parser.sexpFromFile(synth_file)
benchmark_tuple = parser.extract_benchmark(file_sexp)
(
theories,
syn_ctx,
synth_instantiator,
macro_instantiator,
uf_instantiator,
constraints,
grammar_map,
forall_vars_map,
default_grammar_sfs
) = benchmark_tuple
# We should only have one grammar, so just grab the only key to this map.
# The type of the object is a SynthFun
#print(constraints)
synth_fun = list(grammar_map.keys())[0]
# Pass this into the interpretation along with the expression we're
# attempting to evaluate, or else we'll get an UnboundLetException
eval_ctx.set_interpretation(synth_fun, expr)
return evaluate_expression_raw(expr, eval_ctx)
def print_stat(benchmark_files, phog_file):
from os.path import basename
for benchmark_file in benchmark_files:
# print('loading: ', benchmark_file)
file_sexp = parser.sexpFromFile(benchmark_file)
benchmark_tuple = parser.extract_benchmark(file_sexp)
(theories, syn_ctx, synth_instantiator, macro_instantiator,
uf_instantiator, constraints, grammar_map, forall_vars_map,
default_grammar_sfs) = benchmark_tuple
assert len(theories) == 1
theory = theories[0]
specification = get_specification(file_sexp)
synth_funs = list(synth_instantiator.get_functions().values())
grammar = grammar_map[synth_funs[0]]
phog = SPhog(grammar, phog_file, synth_funs[0].range_type, specification) if options.use_sphog() else \
Phog(grammar, phog_file, synth_funs[0].range_type)
defs, _ = parser.filter_sexp_for('define-fun', file_sexp)
if defs is None or len(defs) == 0:
print('cannot find a solution!')
exit(0)
[name, args_data, ret_type_data, interpretation] = defs[-1]
((arg_vars, arg_types, arg_var_map),
return_type) = parser._process_function_defintion(
args_data, ret_type_data)
expr = parser.sexp_to_expr(interpretation, syn_ctx, arg_var_map)
i = 0
subs_pairs = []
for (var_expr, ty) in zip(arg_vars, arg_types):
param_expr = exprs.FormalParameterExpression(None, ty, i)
subs_pairs.append((var_expr, param_expr))
i += 1
expr = exprs.substitute_all(expr, subs_pairs)
score = phog.get_score(expr)
print(basename(benchmark_file), ' \t', score)
def get_string_solutions(input_file, num_sols=1):
'''Returns a set of FunctionExpressions representing solutions from the
euphony synthesizer.
The outputs of this function can be then evaluated and/or fed into a neural
network for ranking
Arguments:
- input_file (str): The file containing the logic set and constraints
for euphony to synthesize
- num_sols (int): The number of solutions that the solver should
generate
Returns:
- list: <- FunctionExpression (see euphony.exprs.FunctionExpression)
'''
if not os.path.exists(input_file):
print("Input file does not exist: {}".format(input_file))
sys.exit(1)
benchmark_files = input_file
sphog_file = "./euphony/phog_str"
rcfg_file = ''
options.noindis = True
options.inc = False
options.allex = False
options.stat = False
options.noheuristic = False
options.numsols = num_sols
options.rewrite = False
if not os.path.exists(sphog_file):
print("Can't find sphog file! {}".format(sphog_file))
sys.exit(1)
file_sexp = parser.sexpFromFile(input_file)
# result is a list of lists
sols = benchmarks.make_solver(file_sexp, sphog_file, rcfg_file, options=options)
return [sol for innersols in sols for sol in innersols]
def get_func_exprs_grammars(benchmark_files):
global eu
# Grammars
results = []
# for eusolver
ite_related_macros = []
for benchmark_file in benchmark_files:
fun_exprs = []
print('Loading : ', benchmark_file)
file_sexp = parser.sexpFromFile(benchmark_file)
if file_sexp is None:
continue
core_instantiator = semantics_core.CoreInstantiator()
theory_instantiators = [
parser.get_theory_instantiator(theory)
for theory in parser._known_theories
]
macro_instantiator = semantics_core.MacroInstantiator()
uf_instantiator = semantics_core.UninterpretedFunctionInstantiator()
synth_instantiator = semantics_core.SynthFunctionInstantiator()
syn_ctx = synthesis_context.SynthesisContext(core_instantiator,
*theory_instantiators,
macro_instantiator,
uf_instantiator,
synth_instantiator)
syn_ctx.set_macro_instantiator(macro_instantiator)
defs, _ = parser.filter_sexp_for('define-fun', file_sexp)
if defs is None: defs = []
for [name, args_data, ret_type_data, interpretation] in defs:
for eusolver in ([True, False] if eu else [False]):
((arg_vars, arg_types, arg_var_map),
return_type) = parser._process_function_defintion(
args_data, ret_type_data)
expr = parser.sexp_to_expr(interpretation, syn_ctx,
arg_var_map)
macro_func = semantics_types.MacroFunction(
name, len(arg_vars), tuple(arg_types), return_type, expr,
arg_vars)
# for eusolver (recording macro functions of which definition include ite)
if eusolver:
app = exprs.find_application(expr, 'ite')
if app is not None: ite_related_macros.append(name)
macro_instantiator.add_function(name, macro_func)
i = 0
subs_pairs = []
for (var_expr, ty) in zip(arg_vars, arg_types):
param_expr = exprs.FormalParameterExpression(None, ty, i)
subs_pairs.append((var_expr, param_expr))
i += 1
expr = exprs.substitute_all(expr, subs_pairs)
# resolve macro functions involving ite (for enumeration of pred exprs (eusolver))
if eusolver:
for fname in ite_related_macros:
app = exprs.find_application(expr, fname)
if app is None: continue
expr = macro_instantiator.instantiate_macro(
expr, fname)
fun_exprs.append(expr)
@static_var("cnt", 0)
def rename(synth_funs_data):
for synth_fun_data in synth_funs_data:
# to avoid duplicated names
synth_fun_data[0] = "__aux_name__" + benchmark_file + str(
rename.cnt)
rename.cnt += 1
# collect grammars
synth_funs_data, _ = parser.filter_sexp_for('synth-fun', file_sexp)
if len(synth_funs_data) == 0:
synth_funs_data, _ = parser.filter_sexp_for('synth-inv', file_sexp)
rename(synth_funs_data)
synth_funs_grammar_data = parser.process_synth_invs(
synth_funs_data, synth_instantiator, syn_ctx)
else:
rename(synth_funs_data)
synth_funs_grammar_data = parser.process_synth_funcs(
synth_funs_data, synth_instantiator, syn_ctx)
grammar = None
for synth_fun, arg_vars, grammar_data in synth_funs_grammar_data:
if grammar_data != 'Default grammar':
# we only consider a single function synthesis for now
grammar = parser.sexp_to_grammar(arg_vars, grammar_data,
synth_fun, syn_ctx)
break
results.append((fun_exprs, grammar))
return results
def test_make_solver(benchmark_files, phog_file, rcfg_file):
for benchmark_file in benchmark_files:
file_sexp = parser.sexpFromFile(benchmark_file)
make_solver(file_sexp, phog_file, rcfg_file)
def get_func_exprs_grammars(benchmark_files):
# expected format:
# sygus format problem
# (check-synth)
# a single solution
global eu
@static_var("cnt", 0)
def rename(synth_funs_data):
for synth_fun_data in synth_funs_data:
# to avoid duplicated names
synth_fun_data[0] = "__aux_name__" + benchmark_file + str(
rename.cnt)
rename.cnt += 1
exprs_per_category = {}
# decision tree : label -> exprs
## label : (ret_type, eu, STD spec / PBE spec, spec information ... )
# for eusolver
ite_related_macros = []
# all vocabs
all_vocabs = set([])
for benchmark_file in benchmark_files:
print('Loading : ', benchmark_file)
file_sexp = parser.sexpFromFile(benchmark_file)
if file_sexp is None:
continue
## specification
specification = get_specification(file_sexp)
all_vocabs.update(basic_vocabs_for_spec(specification))
core_instantiator = semantics_core.CoreInstantiator()
theory_instantiators = [
parser.get_theory_instantiator(theory)
for theory in parser._known_theories
]
macro_instantiator = semantics_core.MacroInstantiator()
uf_instantiator = semantics_core.UninterpretedFunctionInstantiator()
synth_instantiator = semantics_core.SynthFunctionInstantiator()
syn_ctx = synthesis_context.SynthesisContext(core_instantiator,
*theory_instantiators,
macro_instantiator,
uf_instantiator,
synth_instantiator)
syn_ctx.set_macro_instantiator(macro_instantiator)
# collect grammars
synth_funs_data, _ = parser.filter_sexp_for('synth-fun', file_sexp)
if len(synth_funs_data) == 0:
synth_funs_data, _ = parser.filter_sexp_for('synth-inv', file_sexp)
# rename(synth_funs_data)
synth_funs_grammar_data = parser.process_synth_invs(
synth_funs_data, synth_instantiator, syn_ctx)
else:
# rename(synth_funs_data)
synth_funs_grammar_data = parser.process_synth_funcs(
synth_funs_data, synth_instantiator, syn_ctx)
# handling only single function problems for now
fetchop_func = fetchop
spec_flag = ()
synth_fun_name = ''
for synth_fun, arg_vars, grammar_data in synth_funs_grammar_data:
if grammar_data != 'Default grammar':
synth_fun_name = synth_fun.function_name
grammar = parser.sexp_to_grammar(arg_vars, grammar_data,
synth_fun, syn_ctx)
# spec flag
spec_flag = get_spec_flag(specification, grammar)
# fetchop func
fetchop_func = get_fetchop_func(specification, grammar)
all_vocabs.update(
get_vocabs_from_grammar(grammar, fetchop_func))
defs, _ = parser.filter_sexp_for('define-fun', file_sexp)
if defs is None: defs = []
if len(defs) > 0:
for [name, args_data, ret_type_data, interpretation] in defs:
print(name, ' ', synth_fun_name)
if synth_fun_name in name:
for eusolver in ([True] if eu else [False]):
((arg_vars, arg_types, arg_var_map),
return_type) = parser._process_function_defintion(
args_data, ret_type_data)
# category flag
flag = (return_type, eusolver, spec_flag)
expr = parser.sexp_to_expr(interpretation, syn_ctx,
arg_var_map)
macro_func = semantics_types.MacroFunction(
name, len(arg_vars), tuple(arg_types), return_type,
expr, arg_vars)
# for eusolver (recording macro functions of which definition include ite)
if eusolver:
app = exprs.find_application(expr, 'ite')
if app is not None: ite_related_macros.append(name)
macro_instantiator.add_function(name, macro_func)
i = 0
subs_pairs = []
for (var_expr, ty) in zip(arg_vars, arg_types):
param_expr = exprs.FormalParameterExpression(
None, ty, i)
subs_pairs.append((var_expr, param_expr))
i += 1
expr = exprs.substitute_all(expr, subs_pairs)
# resolve macro functions involving ite (for enumeration of pred exprs (eusolver))
if eusolver:
for fname in ite_related_macros:
app = exprs.find_application(expr, fname)
if app is None: continue
expr = macro_instantiator.instantiate_macro(
expr, fname)
if flag not in exprs_per_category:
exprs_per_category[flag] = set([])
exprs_per_category[flag].add((expr, fetchop_func))
return exprs_per_category, all_vocabs
core_instantiator = semantics_core.CoreInstantiator()
theory_instantiators = [
parser.get_theory_instantiator(theory)
for theory in parser._known_theories
]
macro_instantiator = semantics_core.MacroInstantiator()
uf_instantiator = semantics_core.UninterpretedFunctionInstantiator()
synth_instantiator = semantics_core.SynthFunctionInstantiator()
syn_ctx = synthesis_context.SynthesisContext(core_instantiator,
*theory_instantiators,
macro_instantiator,
uf_instantiator,
synth_instantiator)
file_sexp = parser.sexpFromFile(benchmark_file)
defs, _ = parser.filter_sexp_for('define-fun', file_sexp)
if defs is None or len(defs) == 0:
print('No function can be found!')
exit(0)
[name, args_data, ret_type_data, interpretation] = defs[-1]
((arg_vars, arg_types, arg_var_map),
return_type) = parser._process_function_defintion(args_data,
ret_type_data)
expr = parser.sexp_to_expr(interpretation, syn_ctx, arg_var_map)
# if basename(benchmark_file) in solved:
print(exprs.get_expression_size(expr))
# print(exprs.get_expression_size(expr), '\t', prog2runtime[basename(benchmark_file)])
def generate_distinguishing_input(current_spec, candidate_programs, failure_threshold=100, args=None):
'''Generates a new distinguishing input which is not a part of the existing
specification
Arguments:
- current_spec (str): The specification for the problem including the
existing constraints for the problem.
Returns:
- str: a string representing the distinguishing input not part of the
current specification. Also can return None if no distinguishing input can be found.
'''
file_sexp = parser.sexpFromFile(current_spec)
benchmark_tuple = parser.extract_benchmark(file_sexp)
(
theories,
syn_ctx,
synth_instantiator,
macro_instantiator,
uf_instantiator,
constraints,
grammar_map,
forall_vars_map,
default_grammar_sfs
) = benchmark_tuple
inputs = []
for constraint in constraints:
inputs.append(constraint.children[0].children[0].value_object.value_object)
#print(inputs)
import string_builder
inputs_r = [string_builder.RString(inp) for inp in inputs]
#print(inputs_r)
print_grid = lambda x: [print(item) for item in x]
def distances(input_list, distfunc):
dists = []
for x in input_list:
# print(x)
# print(x.groupstr)
curr_dists = []
for y in input_list:
curr_dists.append(distfunc(x, y))
dists.append(curr_dists)
# print_grid(dists)
return dists
# find the metric which varies the least between all the examples
char_dist = distances(inputs_r, lambda x, y: x.orig_distance(y))
class_dist = distances(inputs_r, lambda x, y: x.class_distance(y))
group_dist = distances(inputs_r, lambda x, y: x.group_distance(y))
vals = {
'char': char_dist,
'class': class_dist,
'group': group_dist
}
stats = {}
for key in vals:
val = vals[key]
tril = np.tril(np.array(val))
mu = tril.mean()
std = tril.std()
stats[key] = string_builder.TextStats(mu, std)
# print("{}: mean: {}; std: {}".format(key, mu, std))
stat_set = string_builder.EditStatSet(stats['char'], stats['class'], stats['group'])
distinguishing_input = None
loop_count = 0
total_generated = 0
while True:
distinguished = None
for constraint_input in inputs_r:
mutated = constraint_input.generate_mutation(stat_set)
total_generated += 1
# run input across all outputs to see if there is an input which returns more
# than one unique result.
results = set([evaluate(expression, mutated, current_spec) for expression in candidate_programs])
if len(results) > 1:
if args is not None and args.auto:
distinguishing_input = mutated
distinguished = True
break
selected = input('Found a distinguishing input ({}). Programs generated outputs: {}. Acceptable? (Y)es/(n)o/(f)inish: '.format(mutated, results)).lower()
if selected == '' or selected == 'y' or selected == 'f':
distinguishing_input = mutated
distinguished = True
break
if distinguished is not None:
break
loop_count += 1
if loop_count > failure_threshold:
break
if args is not None and args.verbose:
print("returning input {}. Total generated inputs: {}".format(distinguishing_input, total_generated))
return distinguishing_input
default_grammar_sfs
) = benchmark_tuple
inputs = []
vals= []
for constraint in constraints:
inputs.append(constraint.children[0].children[0].value_object.value_object)
vals.append(constraint.children[1].value_object.value_object)
result=[]
for i in range(0,len(inputs)):
result.append(inputs[i]+'; '+vals[i])
print(result)
return result
def get_programs(current_spec):
'
file_sexp = parser.sexpFromFile(current_spec)
core_instantiator = semantics_core.CoreInstantiator()
theory_instantiators = [parser.get_theory_instantiator(theory) for theory in parser._known_theories]
macro_instantiator = semantics_core.MacroInstantiator()
uf_instantiator = semantics_core.UninterpretedFunctionInstantiator()
synth_instantiator = semantics_core.SynthFunctionInstantiator()
syn_ctx = synthesis_context.SynthesisContext(
core_instantiator,
*theory_instantiators,
macro_instantiator,
uf_instantiator,
synth_instantiator)
