def run_definition(self, i, d):
from pecan.lang.typed_ir_lowering import TypedIRLowering
from pecan.lang.optimizer.optimizer import UntypedOptimizer, Optimizer
if isinstance(d, NamedPred):
settings.log(
1, lambda: '[DEBUG] Type inference and IR lowering for: {}'.
format(d.name))
transformed_def = TypedIRLowering(self).transform(
self.type_infer(d))
if settings.opt_enabled():
settings.log(
1, lambda: '[DEBUG] Performing typed optimization on: {}'.
format(d.name))
transformed_def = Optimizer(self).optimize(transformed_def)
transformed_def = TypedIRLowering(self).transform(transformed_def)
settings.log(1, lambda: 'Lowered IR:')
settings.log(1, lambda: transformed_def)
self.defs[i] = transformed_def
self.preds[d.name] = self.defs[i]
self.preds[d.name].evaluate(self)
settings.log(0, lambda: self.preds[d.name])
else:
return d.evaluate(self)
def from_source(source_code, *args, **kwargs):
prog = pecan_parser.parse(source_code)
settings.log(4, lambda: 'Parsed program:')
settings.log(4, lambda: prog)
prog.search_paths = make_search_paths(
filename=kwargs.get('filename', None))
prog.loader = load
if settings.get_extract_implications():
prog.extract_implications()
prog = ASTToIR().transform(prog)
settings.log(0, lambda: 'Search path: {}'.format(prog.search_paths))
# Load the standard library
prog = settings.include_stdlib(prog, load, args, kwargs)
if settings.opt_enabled():
prog = UntypedOptimizer(prog).optimize()
settings.log(1, lambda: '(Untyped) Optimized program:')
settings.log(1, lambda: prog)
return prog