pypy / pypy / annotation / annrpython.py

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from __future__ import absolute_import

import types
from pypy.tool.ansi_print import ansi_log
from pypy.tool.pairtype import pair
from pypy.tool.error import (format_blocked_annotation_error,
                             AnnotatorError, gather_error, ErrorWrapper)
from pypy.objspace.flow.model import (Variable, Constant, FunctionGraph,
                                      c_last_exception, checkgraph)
from pypy.translator import simplify, transform
from pypy.annotation import model as annmodel, signature, unaryop, binaryop
from pypy.annotation.bookkeeper import Bookkeeper
import py
log = py.log.Producer("annrpython")
py.log.setconsumer("annrpython", ansi_log)


FAIL = object()

class RPythonAnnotator(object):
    """Block annotator for RPython.
    See description in doc/translation.txt."""

    def __init__(self, translator=None, policy=None, bookkeeper=None):
        import pypy.rpython.ootypesystem.ooregistry # has side effects
        import pypy.rpython.extfuncregistry # has side effects
        import pypy.rlib.nonconst # has side effects

        if translator is None:
            # interface for tests
            from pypy.translator.translator import TranslationContext
            translator = TranslationContext()
            translator.annotator = self
        self.translator = translator
        self.pendingblocks = {}  # map {block: graph-containing-it}
        self.bindings = {}       # map Variables to SomeValues
        self.annotated = {}      # set of blocks already seen
        self.added_blocks = None # see processblock() below
        self.links_followed = {} # set of links that have ever been followed
        self.notify = {}        # {block: {positions-to-reflow-from-when-done}}
        self.fixed_graphs = {}  # set of graphs not to annotate again
        self.blocked_blocks = {} # set of {blocked_block: (graph, index)}
        # --- the following information is recorded for debugging ---
        self.blocked_graphs = {} # set of graphs that have blocked blocks
        # --- end of debugging information ---
        self.frozen = False
        if policy is None:
            from pypy.annotation.policy import AnnotatorPolicy
            self.policy = AnnotatorPolicy()
        else:
            self.policy = policy
        if bookkeeper is None:
            bookkeeper = Bookkeeper(self)
        self.bookkeeper = bookkeeper

    def __getstate__(self):
        attrs = """translator pendingblocks bindings annotated links_followed
        notify bookkeeper frozen policy added_blocks""".split()
        ret = self.__dict__.copy()
        for key, value in ret.items():
            if key not in attrs:
                assert type(value) is dict, (
                    "%r is not dict. please update %s.__getstate__" %
                    (key, self.__class__.__name__))
                ret[key] = {}
        return ret

    #___ convenience high-level interface __________________

    def build_types(self, function, input_arg_types, complete_now=True,
                    main_entry_point=False):
        """Recursively build annotations about the specific entry point."""
        assert isinstance(function, types.FunctionType), "fix that!"

        from pypy.annotation.policy import AnnotatorPolicy
        policy = AnnotatorPolicy()
        # make input arguments and set their type
        args_s = [self.typeannotation(t) for t in input_arg_types]

        # XXX hack
        annmodel.TLS.check_str_without_nul = (
            self.translator.config.translation.check_str_without_nul)

        flowgraph, inputcells = self.get_call_parameters(function, args_s, policy)
        if not isinstance(flowgraph, FunctionGraph):
            assert isinstance(flowgraph, annmodel.SomeObject)
            return flowgraph

        if main_entry_point:
            self.translator.entry_point_graph = flowgraph
        return self.build_graph_types(flowgraph, inputcells, complete_now=complete_now)

    def get_call_parameters(self, function, args_s, policy):
        desc = self.bookkeeper.getdesc(function)
        args = self.bookkeeper.build_args("simple_call", args_s[:])
        result = []
        def schedule(graph, inputcells):
            result.append((graph, inputcells))
            return annmodel.s_ImpossibleValue

        prevpolicy = self.policy
        self.policy = policy
        self.bookkeeper.enter(None)
        try:
            desc.pycall(schedule, args, annmodel.s_ImpossibleValue)
        finally:
            self.bookkeeper.leave()
            self.policy = prevpolicy
        [(graph, inputcells)] = result
        return graph, inputcells

    def annotate_helper(self, function, args_s, policy=None):
        if policy is None:
            from pypy.annotation.policy import AnnotatorPolicy
            policy = AnnotatorPolicy()
        graph, inputcells = self.get_call_parameters(function, args_s, policy)
        self.build_graph_types(graph, inputcells, complete_now=False)
        self.complete_helpers(policy)
        return graph

    def complete_helpers(self, policy):
        saved = self.policy, self.added_blocks
        self.policy = policy
        try:
            self.added_blocks = {}
            self.complete()
            # invoke annotation simplifications for the new blocks
            self.simplify(block_subset=self.added_blocks)
        finally:
            self.policy, self.added_blocks = saved

    def build_graph_types(self, flowgraph, inputcells, complete_now=True):
        checkgraph(flowgraph)

        nbarg = len(flowgraph.getargs())
        if len(inputcells) != nbarg: 
            raise TypeError("%s expects %d args, got %d" %(       
                            flowgraph, nbarg, len(inputcells)))
        
        # register the entry point
        self.addpendinggraph(flowgraph, inputcells)
        # recursively proceed until no more pending block is left
        if complete_now:
            self.complete()
        return self.binding(flowgraph.getreturnvar(), None)

    def gettype(self, variable):
        """Return the known type of a control flow graph variable,
        defaulting to 'object'."""
        if isinstance(variable, Constant):
            return type(variable.value)
        elif isinstance(variable, Variable):
            cell = self.bindings.get(variable)
            if cell:
                return cell.knowntype
            else:
                return object
        else:
            raise TypeError, ("Variable or Constant instance expected, "
                              "got %r" % (variable,))

    def getuserclassdefinitions(self):
        """Return a list of ClassDefs."""
        return self.bookkeeper.classdefs

    #___ medium-level interface ____________________________

    def addpendinggraph(self, flowgraph, inputcells):
        self.addpendingblock(flowgraph, flowgraph.startblock, inputcells)

    def addpendingblock(self, graph, block, cells):
        """Register an entry point into block with the given input cells."""
        if graph in self.fixed_graphs:
            # special case for annotating/rtyping in several phases: calling
            # a graph that has already been rtyped.  Safety-check the new
            # annotations that are passed in, and don't annotate the old
            # graph -- it's already low-level operations!
            for a, s_newarg in zip(graph.getargs(), cells):
                s_oldarg = self.binding(a)
                assert annmodel.unionof(s_oldarg, s_newarg) == s_oldarg
        else:
            assert not self.frozen
            for a in cells:
                assert isinstance(a, annmodel.SomeObject)
            if block not in self.annotated:
                self.bindinputargs(graph, block, cells)
            else:
                self.mergeinputargs(graph, block, cells)
            if not self.annotated[block]:
                self.pendingblocks[block] = graph

    def complete(self):
        """Process pending blocks until none is left."""
        while True:
            while self.pendingblocks:
                block, graph = self.pendingblocks.popitem()
                self.processblock(graph, block)
            self.policy.no_more_blocks_to_annotate(self)
            if not self.pendingblocks:
                break   # finished
        # make sure that the return variables of all graphs is annotated
        if self.added_blocks is not None:
            newgraphs = [self.annotated[block] for block in self.added_blocks]
            newgraphs = dict.fromkeys(newgraphs)
            got_blocked_blocks = False in newgraphs
        else:
            newgraphs = self.translator.graphs  #all of them
            got_blocked_blocks = False in self.annotated.values()
        if got_blocked_blocks:
            for graph in self.blocked_graphs.values():
                self.blocked_graphs[graph] = True

            blocked_blocks = [block for block, done in self.annotated.items()
                                    if done is False]
            assert len(blocked_blocks) == len(self.blocked_blocks)

            text = format_blocked_annotation_error(self, self.blocked_blocks)
            #raise SystemExit()
            raise AnnotatorError(text)
        for graph in newgraphs:
            v = graph.getreturnvar()
            if v not in self.bindings:
                self.setbinding(v, annmodel.s_ImpossibleValue)
        # policy-dependent computation
        self.bookkeeper.compute_at_fixpoint()

    def binding(self, arg, default=FAIL):
        "Gives the SomeValue corresponding to the given Variable or Constant."
        if isinstance(arg, Variable):
            try:
                return self.bindings[arg]
            except KeyError:
                if default is not FAIL:
                    return default
                else:
                    raise
        elif isinstance(arg, Constant):
            #if arg.value is undefined_value:   # undefined local variables
            #    return annmodel.s_ImpossibleValue
            return self.bookkeeper.immutableconstant(arg)
        else:
            raise TypeError, 'Variable or Constant expected, got %r' % (arg,)

    def typeannotation(self, t):
        return signature.annotation(t, self.bookkeeper)

    def setbinding(self, arg, s_value):
        if arg in self.bindings:
            assert s_value.contains(self.bindings[arg])
        self.bindings[arg] = s_value

    def transfer_binding(self, v_target, v_source):
        assert v_source in self.bindings
        self.bindings[v_target] = self.bindings[v_source]

    def warning(self, msg, pos=None):
        if pos is None:
            try:
                pos = self.bookkeeper.position_key
            except AttributeError:
                pos = '?'
        if pos != '?':
            pos = self.whereami(pos)
 
        log.WARNING("%s/ %s" % (pos, msg))


    #___ interface for annotator.bookkeeper _______

    def recursivecall(self, graph, whence, inputcells):
        if isinstance(whence, tuple):
            parent_graph, parent_block, parent_index = whence
            tag = parent_block, parent_index
            self.translator.update_call_graph(parent_graph, graph, tag)
        # self.notify[graph.returnblock] is a dictionary of call
        # points to this func which triggers a reflow whenever the
        # return block of this graph has been analysed.
        callpositions = self.notify.setdefault(graph.returnblock, {})
        if whence is not None:
            if callable(whence):
                def callback():
                    whence(self, graph)
            else:
                callback = whence
            callpositions[callback] = True

        # generalize the function's input arguments
        self.addpendingblock(graph, graph.startblock, inputcells)

        # get the (current) return value
        v = graph.getreturnvar()
        try:
            return self.bindings[v]
        except KeyError: 
            # the function didn't reach any return statement so far.
            # (some functions actually never do, they always raise exceptions)
            return annmodel.s_ImpossibleValue

    def reflowfromposition(self, position_key):
        graph, block, index = position_key
        self.reflowpendingblock(graph, block)


    #___ simplification (should be moved elsewhere?) _______

    def simplify(self, block_subset=None, extra_passes=None):
        # Generic simplifications
        transform.transform_graph(self, block_subset=block_subset,
                                  extra_passes=extra_passes)
        if block_subset is None:
            graphs = self.translator.graphs
        else:
            graphs = {}
            for block in block_subset:
                graph = self.annotated.get(block)
                if graph:
                    graphs[graph] = True
        for graph in graphs:
            simplify.eliminate_empty_blocks(graph)


    #___ flowing annotations in blocks _____________________

    def processblock(self, graph, block):
        # Important: this is not called recursively.
        # self.flowin() can only issue calls to self.addpendingblock().
        # The analysis of a block can be in three states:
        #  * block not in self.annotated:
        #      never seen the block.
        #  * self.annotated[block] == False:
        #      the input variables of the block are in self.bindings but we
        #      still have to consider all the operations in the block.
        #  * self.annotated[block] == graph-containing-block:
        #      analysis done (at least until we find we must generalize the
        #      input variables).

        #print '* processblock', block, cells
        self.annotated[block] = graph
        if block in self.blocked_blocks:
            del self.blocked_blocks[block]
        try:
            self.flowin(graph, block)
        except BlockedInference, e:
            self.annotated[block] = False   # failed, hopefully temporarily
            self.blocked_blocks[block] = (graph, e.opindex)
        except Exception, e:
            # hack for debug tools only
            if not hasattr(e, '__annotator_block'):
                setattr(e, '__annotator_block', block)
            raise

        # The dict 'added_blocks' is used by rpython.annlowlevel to
        # detect which are the new blocks that annotating an additional
        # small helper creates.
        if self.added_blocks is not None:
            self.added_blocks[block] = True

    def reflowpendingblock(self, graph, block):
        assert not self.frozen
        assert graph not in self.fixed_graphs
        self.pendingblocks[block] = graph
        assert block in self.annotated
        self.annotated[block] = False  # must re-flow
        self.blocked_blocks[block] = (graph, None)

    def bindinputargs(self, graph, block, inputcells):
        # Create the initial bindings for the input args of a block.
        assert len(block.inputargs) == len(inputcells)
        for a, cell in zip(block.inputargs, inputcells):
            self.setbinding(a, cell)
        self.annotated[block] = False  # must flowin.
        self.blocked_blocks[block] = (graph, None)

    def mergeinputargs(self, graph, block, inputcells):
        # Merge the new 'cells' with each of the block's existing input
        # variables.
        oldcells = [self.binding(a) for a in block.inputargs]
        try:
            unions = [annmodel.unionof(c1,c2) for c1, c2 in zip(oldcells,inputcells)]
        except annmodel.UnionError, e:
            e.args = e.args + (
                ErrorWrapper(gather_error(self, graph, block, None)),)
            raise
        # if the merged cells changed, we must redo the analysis
        if unions != oldcells:
            self.bindinputargs(graph, block, unions)

    def whereami(self, position_key):
        graph, block, i = position_key
        blk = ""
        if block:
            at = block.at()
            if at:
                blk = " block"+at
        opid=""
        if i is not None:
            opid = " op=%d" % i
        return repr(graph) + blk + opid

    def flowin(self, graph, block):
        #print 'Flowing', block, [self.binding(a) for a in block.inputargs]
        try:
            for i in range(len(block.operations)):
                try:
                    self.bookkeeper.enter((graph, block, i))
                    self.consider_op(block, i)
                finally:
                    self.bookkeeper.leave()

        except BlockedInference, e:
            if (e.op is block.operations[-1] and
                block.exitswitch == c_last_exception):
                # this is the case where the last operation of the block will
                # always raise an exception which is immediately caught by
                # an exception handler.  We then only follow the exceptional
                # branches.
                exits = [link for link in block.exits
                              if link.exitcase is not None]

            elif e.op.opname in ('simple_call', 'call_args', 'next'):
                # XXX warning, keep the name of the call operations in sync
                # with the flow object space.  These are the operations for
                # which it is fine to always raise an exception.  We then
                # swallow the BlockedInference and that's it.
                # About 'next': see test_annotate_iter_empty_container().
                return

            else:
                # other cases are problematic (but will hopefully be solved
                # later by reflowing).  Throw the BlockedInference up to
                # processblock().
                raise

        except annmodel.HarmlesslyBlocked:
            return

        else:
            # dead code removal: don't follow all exits if the exitswitch
            # is known
            exits = block.exits
            if isinstance(block.exitswitch, Variable):
                s_exitswitch = self.bindings[block.exitswitch]
                if s_exitswitch.is_constant():
                    exits = [link for link in exits
                                  if link.exitcase == s_exitswitch.const]

        # mapping (exitcase, variable) -> s_annotation
        # that can be attached to booleans, exitswitches
        knowntypedata = getattr(self.bindings.get(block.exitswitch),
                                "knowntypedata", {})

        # filter out those exceptions which cannot
        # occour for this specific, typed operation.
        if block.exitswitch == c_last_exception:
            op = block.operations[-1]
            if op.opname in binaryop.BINARY_OPERATIONS:
                arg1 = self.binding(op.args[0])
                arg2 = self.binding(op.args[1])
                binop = getattr(pair(arg1, arg2), op.opname, None)
                can_only_throw = annmodel.read_can_only_throw(binop, arg1, arg2)
            elif op.opname in unaryop.UNARY_OPERATIONS:
                arg1 = self.binding(op.args[0])
                opname = op.opname
                if opname == 'contains': opname = 'op_contains'
                unop = getattr(arg1, opname, None)
                can_only_throw = annmodel.read_can_only_throw(unop, arg1)
            else:
                can_only_throw = None

            if can_only_throw is not None:
                candidates = can_only_throw
                candidate_exits = exits
                exits = []
                for link in candidate_exits:
                    case = link.exitcase
                    if case is None:
                        exits.append(link)
                        continue
                    covered = [c for c in candidates if issubclass(c, case)]
                    if covered:
                        exits.append(link)
                        candidates = [c for c in candidates if c not in covered]

        for link in exits:
            in_except_block = False

            last_exception_var = link.last_exception # may be None for non-exception link
            last_exc_value_var = link.last_exc_value # may be None for non-exception link

            if isinstance(link.exitcase, (types.ClassType, type)) \
                   and issubclass(link.exitcase, py.builtin.BaseException):
                assert last_exception_var and last_exc_value_var
                last_exc_value_object = self.bookkeeper.valueoftype(link.exitcase)
                last_exception_object = annmodel.SomeType()
                if isinstance(last_exception_var, Constant):
                    last_exception_object.const = last_exception_var.value
                last_exception_object.is_type_of = [last_exc_value_var]

                if isinstance(last_exception_var, Variable):
                    self.setbinding(last_exception_var, last_exception_object)
                if isinstance(last_exc_value_var, Variable):
                    self.setbinding(last_exc_value_var, last_exc_value_object)

                last_exception_object = annmodel.SomeType()
                if isinstance(last_exception_var, Constant):
                    last_exception_object.const = last_exception_var.value
                #if link.exitcase is Exception:
                #    last_exc_value_object = annmodel.SomeObject()
                #else:
                last_exc_value_vars = []
                in_except_block = True

            ignore_link = False
            cells = []
            renaming = {}
            for a,v in zip(link.args,link.target.inputargs):
                renaming.setdefault(a, []).append(v)
            for a,v in zip(link.args,link.target.inputargs):
                if a == last_exception_var:
                    assert in_except_block
                    cells.append(last_exception_object)
                elif a == last_exc_value_var:
                    assert in_except_block
                    cells.append(last_exc_value_object)
                    last_exc_value_vars.append(v)
                else:
                    cell = self.binding(a)
                    if (link.exitcase, a) in knowntypedata:
                        knownvarvalue = knowntypedata[(link.exitcase, a)]
                        cell = pair(cell, knownvarvalue).improve()
                        # ignore links that try to pass impossible values
                        if cell == annmodel.s_ImpossibleValue:
                            ignore_link = True

                    if hasattr(cell,'is_type_of'):
                        renamed_is_type_of = []
                        for v in cell.is_type_of:
                            new_vs = renaming.get(v,[])
                            renamed_is_type_of += new_vs
                        assert cell.knowntype is type
                        newcell = annmodel.SomeType()
                        if cell.is_constant():
                            newcell.const = cell.const
                        cell = newcell
                        cell.is_type_of = renamed_is_type_of

                    if hasattr(cell, 'knowntypedata'):
                        renamed_knowntypedata = {}
                        for (value, v), s in cell.knowntypedata.items():
                            new_vs = renaming.get(v, [])
                            for new_v in new_vs:
                                renamed_knowntypedata[value, new_v] = s
                        assert isinstance(cell, annmodel.SomeBool)
                        newcell = annmodel.SomeBool()
                        if cell.is_constant():
                            newcell.const = cell.const
                        cell = newcell
                        cell.set_knowntypedata(renamed_knowntypedata)

                    cells.append(cell)

            if ignore_link:
                continue

            if in_except_block:
                last_exception_object.is_type_of = last_exc_value_vars

            self.links_followed[link] = True
            self.addpendingblock(graph, link.target, cells)

        if block in self.notify:
            # reflow from certain positions when this block is done
            for callback in self.notify[block]:
                if isinstance(callback, tuple):
                    self.reflowfromposition(callback) # callback is a position
                else:
                    callback()


    #___ creating the annotations based on operations ______

    def consider_op(self, block, opindex):
        op = block.operations[opindex]
        argcells = [self.binding(a) for a in op.args]
        consider_meth = getattr(self,'consider_op_'+op.opname,
                                None)
        if not consider_meth:
            raise Exception,"unknown op: %r" % op

        # let's be careful about avoiding propagated SomeImpossibleValues
        # to enter an op; the latter can result in violations of the
        # more general results invariant: e.g. if SomeImpossibleValue enters is_
        #  is_(SomeImpossibleValue, None) -> SomeBool
        #  is_(SomeInstance(not None), None) -> SomeBool(const=False) ...
        # boom -- in the assert of setbinding()
        for arg in argcells:
            if isinstance(arg, annmodel.SomeImpossibleValue):
                raise BlockedInference(self, op, opindex)
        try:
            resultcell = consider_meth(*argcells)
        except Exception, e:
            graph = self.bookkeeper.position_key[0]
            e.args = e.args + (
                ErrorWrapper(gather_error(self, graph, block, opindex)),)
            raise
        if resultcell is None:
            resultcell = self.noreturnvalue(op)
        elif resultcell == annmodel.s_ImpossibleValue:
            raise BlockedInference(self, op, opindex) # the operation cannot succeed
        assert isinstance(resultcell, annmodel.SomeObject)
        assert isinstance(op.result, Variable)
        self.setbinding(op.result, resultcell)  # bind resultcell to op.result

    def noreturnvalue(self, op):
        return annmodel.s_ImpossibleValue  # no return value (hook method)

    # XXX "contains" clash with SomeObject method
    def consider_op_contains(self, seq, elem):
        self.bookkeeper.count("contains", seq)
        return seq.op_contains(elem)

    def consider_op_newtuple(self, *args):
        return annmodel.SomeTuple(items = args)

    def consider_op_newlist(self, *args):
        return self.bookkeeper.newlist(*args)

    def consider_op_newdict(self):
        return self.bookkeeper.newdict()


    def _registeroperations(cls, unary_ops, binary_ops):
        # All unary operations
        d = {}
        for opname in unary_ops:
            fnname = 'consider_op_' + opname
            exec py.code.Source("""
def consider_op_%s(self, arg, *args):
    return arg.%s(*args)
""" % (opname, opname)).compile() in globals(), d
            setattr(cls, fnname, d[fnname])
        # All binary operations
        for opname in binary_ops:
            fnname = 'consider_op_' + opname
            exec py.code.Source("""
def consider_op_%s(self, arg1, arg2, *args):
    return pair(arg1,arg2).%s(*args)
""" % (opname, opname)).compile() in globals(), d
            setattr(cls, fnname, d[fnname])
    _registeroperations = classmethod(_registeroperations)

# register simple operations handling
RPythonAnnotator._registeroperations(unaryop.UNARY_OPERATIONS, binaryop.BINARY_OPERATIONS)


class BlockedInference(Exception):
    """This exception signals the type inference engine that the situation
    is currently blocked, and that it should try to progress elsewhere."""

    def __init__(self, annotator, op, opindex):
        self.annotator = annotator
        try:
            self.break_at = annotator.bookkeeper.position_key
        except AttributeError:
            self.break_at = None
        self.op = op
        self.opindex = opindex

    def __repr__(self):
        if not self.break_at:
            break_at = "?"
        else:
            break_at = self.annotator.whereami(self.break_at)
        return "<BlockedInference break_at %s [%s]>" %(break_at, self.op)

    __str__ = __repr__
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