mirror of https://github.com/m-labs/artiq.git
compiler.embedding: cache attribute types (fixes #276).
This commit is contained in:
whitequark
parent
d899d7307e
commit
f838b8be49
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@ -295,6 +295,102 @@ class StitchingInferencer(Inferencer):
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super().__init__(engine)
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super().__init__(engine)
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self.value_map = value_map
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self.value_map = value_map
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self.quote = quote
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self.quote = quote
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self.attr_type_cache = {}
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def _compute_value_type(self, object_value, object_type, object_loc, attr_name, loc):
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if not hasattr(object_value, attr_name):
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if attr_name.startswith('_'):
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names = set(filter(lambda name: not name.startswith('_'),
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dir(object_value)))
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else:
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names = set(dir(object_value))
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suggestion = suggest_identifier(attr_name, names)
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note = diagnostic.Diagnostic("note",
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"attribute accessed here", {},
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loc)
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if suggestion is not None:
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diag = diagnostic.Diagnostic("error",
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"host object does not have an attribute '{attr}'; "
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"did you mean '{suggestion}'?",
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{"attr": attr_name, "suggestion": suggestion},
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object_loc, notes=[note])
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else:
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diag = diagnostic.Diagnostic("error",
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"host object does not have an attribute '{attr}'",
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{"attr": attr_name},
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object_loc, notes=[note])
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self.engine.process(diag)
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return
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# Figure out what ARTIQ type does the value of the attribute have.
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# We do this by quoting it, as if to serialize. This has some
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# overhead (i.e. synthesizing a source buffer), but has the advantage
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# of having the host-to-ARTIQ mapping code in only one place and
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# also immediately getting proper diagnostics on type errors.
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attr_value = getattr(object_value, attr_name)
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if inspect.ismethod(attr_value) and types.is_instance(object_type):
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# In cases like:
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# class c:
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# @kernel
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# def f(self): pass
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# we want f to be defined on the class, not on the instance.
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attributes = object_type.constructor.attributes
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attr_value = attr_value.__func__
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else:
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attributes = object_type.attributes
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attr_value_type = None
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if isinstance(attr_value, list):
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# Fast path for lists of scalars.
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IS_FLOAT = 1
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IS_INT32 = 2
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IS_INT64 = 4
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state = 0
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for elt in attr_value:
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if elt.__class__ == float:
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state |= IS_FLOAT
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elif elt.__class__ == int:
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if -2**31 < elt < 2**31-1:
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state |= IS_INT32
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elif -2**63 < elt < 2**63-1:
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state |= IS_INT64
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else:
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state = -1
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break
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else:
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state = -1
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if state == IS_FLOAT:
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attr_value_type = builtins.TList(builtins.TFloat())
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elif state == IS_INT32:
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attr_value_type = builtins.TList(builtins.TInt32())
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elif state == IS_INT64:
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attr_value_type = builtins.TList(builtins.TInt64())
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if attr_value_type is None:
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# Slow path. We don't know what exactly is the attribute value,
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# so we quote it only for the error message that may possibly result.
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ast = self.quote(attr_value, object_loc.expanded_from)
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def proxy_diagnostic(diag):
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note = diagnostic.Diagnostic("note",
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"while inferring a type for an attribute '{attr}' of a host object",
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{"attr": attr_name},
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loc)
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diag.notes.append(note)
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self.engine.process(diag)
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proxy_engine = diagnostic.Engine()
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proxy_engine.process = proxy_diagnostic
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Inferencer(engine=proxy_engine).visit(ast)
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IntMonomorphizer(engine=proxy_engine).visit(ast)
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attr_value_type = ast.type
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return attributes, attr_value_type
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def _unify_attribute(self, result_type, value_node, attr_name, attr_loc, loc):
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def _unify_attribute(self, result_type, value_node, attr_name, attr_loc, loc):
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# The inferencer can only observe types, not values; however,
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# The inferencer can only observe types, not values; however,
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@ -304,108 +400,15 @@ class StitchingInferencer(Inferencer):
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# its type, we now interrogate every host object we have to ensure
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# its type, we now interrogate every host object we have to ensure
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# that we can successfully serialize the value of the attribute we
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# that we can successfully serialize the value of the attribute we
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# are now adding at the code generation stage.
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# are now adding at the code generation stage.
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#
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# FIXME: We perform exhaustive checks of every known host object every
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# time an attribute access is visited, which is potentially quadratic.
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# This is done because it is simpler than performing the checks only when:
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# * a previously unknown attribute is encountered,
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# * a previously unknown host object is encountered;
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# which would be the optimal solution.
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object_type = value_node.type.find()
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object_type = value_node.type.find()
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for object_value, object_loc in self.value_map[object_type]:
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for object_value, object_loc in self.value_map[object_type]:
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attr_value_type = None
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attr_type_key = (id(object_value), attr_name)
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if not hasattr(object_value, attr_name):
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try:
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if attr_name.startswith('_'):
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attributes, attr_value_type = self.attr_type_cache[attr_type_key]
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names = set(filter(lambda name: not name.startswith('_'),
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except KeyError:
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dir(object_value)))
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attributes, attr_value_type = \
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else:
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self._compute_value_type(object_value, object_type, object_loc, attr_name, loc)
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names = set(dir(object_value))
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self.attr_type_cache[attr_type_key] = attributes, attr_value_type
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suggestion = suggest_identifier(attr_name, names)
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note = diagnostic.Diagnostic("note",
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"attribute accessed here", {},
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loc)
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if suggestion is not None:
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diag = diagnostic.Diagnostic("error",
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"host object does not have an attribute '{attr}'; "
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"did you mean '{suggestion}'?",
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{"attr": attr_name, "suggestion": suggestion},
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object_loc, notes=[note])
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else:
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diag = diagnostic.Diagnostic("error",
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"host object does not have an attribute '{attr}'",
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{"attr": attr_name},
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object_loc, notes=[note])
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self.engine.process(diag)
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return
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# Figure out what ARTIQ type does the value of the attribute have.
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# We do this by quoting it, as if to serialize. This has some
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# overhead (i.e. synthesizing a source buffer), but has the advantage
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# of having the host-to-ARTIQ mapping code in only one place and
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# also immediately getting proper diagnostics on type errors.
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attr_value = getattr(object_value, attr_name)
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if inspect.ismethod(attr_value) and types.is_instance(object_type):
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# In cases like:
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# class c:
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# @kernel
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# def f(self): pass
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# we want f to be defined on the class, not on the instance.
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attributes = object_type.constructor.attributes
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attr_value = attr_value.__func__
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is_method = True
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else:
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attributes = object_type.attributes
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is_method = False
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if isinstance(attr_value, list):
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# Fast path for lists of scalars.
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IS_FLOAT = 1
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IS_INT32 = 2
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IS_INT64 = 4
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state = 0
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for elt in attr_value:
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if elt.__class__ == float:
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state |= IS_FLOAT
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elif elt.__class__ == int:
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if -2**31 < elt < 2**31-1:
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state |= IS_INT32
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elif -2**63 < elt < 2**63-1:
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state |= IS_INT64
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else:
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state = -1
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break
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else:
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state = -1
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if state == IS_FLOAT:
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attr_value_type = builtins.TList(builtins.TFloat())
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elif state == IS_INT32:
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attr_value_type = builtins.TList(builtins.TInt32())
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elif state == IS_INT64:
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attr_value_type = builtins.TList(builtins.TInt64())
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if attr_value_type is None:
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# Slow path. We don't know what exactly is the attribute value,
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# so we quote it only for the error message that may possibly result.
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ast = self.quote(attr_value, object_loc.expanded_from)
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def proxy_diagnostic(diag):
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note = diagnostic.Diagnostic("note",
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"while inferring a type for an attribute '{attr}' of a host object",
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{"attr": attr_name},
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loc)
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diag.notes.append(note)
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self.engine.process(diag)
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proxy_engine = diagnostic.Engine()
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proxy_engine.process = proxy_diagnostic
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Inferencer(engine=proxy_engine).visit(ast)
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IntMonomorphizer(engine=proxy_engine).visit(ast)
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attr_value_type = ast.type
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if attr_name not in attributes:
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if attr_name not in attributes:
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# We just figured out what the type should be. Add it.
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# We just figured out what the type should be. Add it.
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