def visit_Call(self, node): full_name = str(anno.getanno(node.func, anno.Basic.QN, default='')) function_context_name = self.state[_Function].context_name node = self.generic_visit(node) # TODO(mdan): Refactor converted_call as a 'Call' operator. # Calls to the internal 'ag__' module are never converted (though their # arguments might be). if full_name.startswith('ag__.'): return node # Calls to the function context manager (inserted by function_scopes) are # also safe. if full_name.startswith(function_context_name + '.'): return node # Calls to pdb.set_trace or ipdb.set_trace are never converted. We don't use # the normal mechanisms to bypass these literals because they are sensitive # to the frame they are being called from. # TODO(mdan): Generalize this to a "static whitelist" config. if full_name in ('pdb.set_trace', 'ipdb.set_trace', 'breakpoint'): global set_trace_warned if not set_trace_warned: # TODO(mdan): Update and shorten once available on tensorflow.org. ag_logging.warn( 'Detected `pdb.set_trace()` in converted code. The code' ' generated by AutoGraph is not optimized for step-by-step' ' debugging. See https://github.com/tensorflow/tensorflow/' 'blob/master/tensorflow/python/autograph/g3doc/reference/' 'debugging.md.') set_trace_warned = True return node if (full_name == 'print' and not self.ctx.program.options.uses( converter.Feature.BUILTIN_FUNCTIONS)): return node template = """ ag__.converted_call(func, args, kwargs, function_ctx) """ new_call = templates.replace_as_expression( template, func=node.func, args=self._args_to_tuple(node), kwargs=self._kwargs_to_dict(node), function_ctx=function_context_name) return new_call
def _verify_ineffcient_unroll(self): """Checks for possibly-inefficient creation of ops in a Python loop.""" assert self.ops_before_iteration is not None ops_after_iteration = self._get_ops() new_ops = tuple( op for op in ops_after_iteration if op not in self.ops_before_iteration) if len(new_ops) < INEFFICIENT_UNROLL_MIN_OPS: return False # TODO(mdan): Add location information. ag_logging.warn( 'TensorFlow ops are being created in a Python loop with large number' ' of iterations. This can lead to slow startup. Did you mean to use a' ' TensorFlow loop? For example, `while True:` is a Python loop, and' ' `while tf.constant(True):` is a TensorFlow loop. The following' ' ops were created after iteration %s: %s', self.iterations, new_ops) return True
def _verify_ineffcient_unroll(self): """Checks for possibly-inefficient creation of ops in a Python loop.""" assert self.ops_before_iteration is not None ops_after_iteration = self._get_ops() new_ops = tuple( op for op in ops_after_iteration if op not in self.ops_before_iteration) if len(new_ops) < INEFFICIENT_UNROLL_MIN_OPS: return False # TODO(mdan): Add location information. ag_logging.warn( 'TensorFlow ops are being created in a Python loop with large number' ' of iterations. This can lead to slow startup. Did you mean to use a' ' TensorFlow loop? For example, `while True:` is a Python loop, and' ' `while tf.constant(True):` is a TensorFlow loop. The following' ' ops were created after iteration %s: %s', self.iterations, new_ops) return True
def _fall_back_unconverted(f, args, kwargs, options, exc): """Falls back to calling the function unconverted, in case of error.""" # TODO(mdan): Consider adding an internal metric. warning_template = ( 'AutoGraph could not transform %s and will run it as-is.\n' '%s' 'Cause: %s\n' 'To silence this warning, decorate the function with' ' @tf.autograph.experimental.do_not_convert') if isinstance(exc, errors.UnsupportedLanguageElementError): if not conversion.is_in_whitelist_cache(f, options): logging.warn(warning_template, f, '', exc) else: file_bug_message = ( 'Please report this to the TensorFlow team. When filing the bug, set' ' the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and' ' attach the full output.\n') logging.warn(warning_template, f, file_bug_message, exc) return _call_unconverted(f, args, kwargs, options)
def _verify_ineffcient_unroll(self): """Checks for possibly-inefficient creation of ops in a Python loop.""" assert self.ops_before_iteration is not None ops_after_iteration = self._get_ops() new_ops = tuple(op for op in ops_after_iteration if op not in self.ops_before_iteration) if len(new_ops) < INEFFICIENT_UNROLL_MIN_OPS: return False # TODO(mdan): Add location information. ag_logging.warn( 'Large unrolled loop detected. Did you mean to use a TF loop?' ' The following ops were created after iteration %s: %s\n.' 'See' ' https://github.com/tensorflow/tensorflow/blob/master/' 'tensorflow/python/autograph/g3doc/reference/common_errors.md' '#warning-large-unrolled-loop-detected' '', self.iterations, new_ops) return True
def is_unsupported(o): """Checks whether an entity is supported by AutoGraph at all.""" # TODO(b/122265385): Remove this bypass. if (_is_known_loaded_type(o, 'wrapt', 'FunctionWrapper') or _is_known_loaded_type(o, 'wrapt', 'BoundFunctionWrapper')): logging.warn( '{} appears to be decorated by wrapt, which is not yet supported' ' by AutoGraph. The function will run as-is.' ' You may still apply AutoGraph before the wrapt decorator.'. format(o)) logging.log(2, 'Permanently allowed: %s: wrapt decorated', o) return True if _is_known_loaded_type(o, 'functools', '_lru_cache_wrapper'): logging.log(2, 'Permanently allowed: %s: lru_cache', o) return True # Constructors are permanently allowed. # TODO(mdan): Toggle as experimental feature instead. # TODO(b/124016764): Remove this limitation. if inspect_utils.isconstructor(o): logging.log(2, 'Permanently allowed: %s: constructor', o) return True # Other built-in modules are permanently allowed. # TODO(mdan): Figure out how to do this consistently for all stdlib modules. if any( _is_of_known_loaded_module(o, m) for m in ('collections', 'pdb', 'copy', 'inspect', 're')): logging.log(2, 'Permanently allowed: %s: part of builtin module', o) return True # Custom ops and kernels are also permanently allowed. # See tensorflow.framework.load_library. if (hasattr(o, '__module__') and hasattr(o.__module__, '_IS_TENSORFLOW_PLUGIN')): logging.log(2, 'Permanently allowed: %s: TensorFlow plugin', o) return True return False
def converted_call(f, args, kwargs, caller_fn_scope=None, options=None): """Compiles a function call inline. For internal use only. Note: The argument list is optimized for readability of generated code, which may look like this: ag__.converted_call(f, (arg1, arg2), None, fscope) ag__.converted_call(f, (), dict(arg1=val1, **kwargs), fscope) ag__.converted_call(f, (arg1, arg2) + varargs, dict(**kwargs), lscope) Args: f: The function to convert. args: Tuple, the original positional arguments of f kwargs: Optional[Dict], the original keyword arguments of f caller_fn_scope: Optional[function_wrappers.FunctionScope], the function scope of the converted function in which this call was originally made. options: Optional[converter.ConversionOptions], conversion options. If not specified, the value of caller_fn_scope.callopts is used. Either options or caller_fn_scope must be present. Returns: Any, the result of executing a possibly-converted `f` with the given arguments. """ logging.log(1, 'Converted call: %s\n args: %s\n kwargs: %s\n', f, args, kwargs) if options is None: if caller_fn_scope is None: raise ValueError('either caller_fn_scope or options must have a value') options = caller_fn_scope.callopts if conversion.is_in_whitelist_cache(f, options): logging.log(2, 'Whitelisted %s: from cache', f) return _call_unconverted(f, args, kwargs, options, False) if ag_ctx.control_status_ctx().status == ag_ctx.Status.DISABLED: logging.log(2, 'Whitelisted: %s: AutoGraph is disabled in context', f) return _call_unconverted(f, args, kwargs, options, False) if is_autograph_artifact(f): logging.log(2, 'Permanently whitelisted: %s: AutoGraph artifact', f) return _call_unconverted(f, args, kwargs, options) # If this is a partial, unwrap it and redo all the checks. if isinstance(f, functools.partial): new_kwargs = {} if f.keywords is not None: # Use copy to avoid mutating the underlying keywords. new_kwargs = f.keywords.copy() if kwargs is not None: new_kwargs.update(kwargs) new_args = f.args + args logging.log(3, 'Forwarding call of partial %s with\n%s\n%s\n', f, new_args, new_kwargs) return converted_call( f.func, new_args, new_kwargs, caller_fn_scope=caller_fn_scope, options=options) if inspect_utils.isbuiltin(f): if f is eval: return py_builtins.eval_in_original_context(f, args, caller_fn_scope) if f is super: return py_builtins.super_in_original_context(f, args, caller_fn_scope) if kwargs: return py_builtins.overload_of(f)(*args, **kwargs) else: return py_builtins.overload_of(f)(*args) # TODO(b/122265385): Remove this bypass. if (_is_known_loaded_type(f, 'wrapt', 'FunctionWrapper') or _is_known_loaded_type(f, 'wrapt', 'BoundFunctionWrapper')): logging.warn( '{} appears to be decorated by wrapt, which is not yet supported' ' by AutoGraph. The function will run as-is.' ' You may still apply AutoGraph before the wrapt decorator.'.format(f)) logging.log(2, 'Permanently whitelisted: %s: wrapt decorated', f) return _call_unconverted(f, args, kwargs, options) if _is_known_loaded_type(f, 'functools', '_lru_cache_wrapper'): logging.log(2, 'Permanently whitelisted: %s: lru_cache', f) return _call_unconverted(f, args, kwargs, options) # Constructors are permanently whitelisted. # TODO(mdan): Toggle as experimental feature instead. # TODO(b/124016764): Remove this limitation. if inspect_utils.isconstructor(f): logging.log(2, 'Permanently whitelisted: %s: constructor', f) return _call_unconverted(f, args, kwargs, options) # Other built-in modules are permanently whitelisted. # TODO(mdan): Figure out how to do this consistently for all stdlib modules. if any( f in m.__dict__.values() for m in (collections, pdb, copy, inspect, re)): logging.log(2, 'Permanently whitelisted: %s: part of builtin module', f) return _call_unconverted(f, args, kwargs, options) # Custom ops and kernels are also permanently whitelisted. # See tensorflow.framework.load_library. if (hasattr(f, '__module__') and hasattr(f.__module__, '_IS_TENSORFLOW_PLUGIN')): logging.log(2, 'Permanently whitelisted: %s: TensorFlow plugin', f) return _call_unconverted(f, args, kwargs, options) if not options.user_requested and conversion.is_whitelisted(f): return _call_unconverted(f, args, kwargs, options) # internal_convert_user_code is for example turned off when issuing a dynamic # call conversion from generated code while in nonrecursive mode. In that # case we evidently don't want to recurse, but we still have to convert # things like builtins. if not options.internal_convert_user_code: return _call_unconverted(f, args, kwargs, options) try: if inspect.ismethod(f) or inspect.isfunction(f): target_entity = f effective_args = args f_self = getattr(f, '__self__', None) if f_self is not None: if isinstance(f_self, function.TfMethodTarget): f_self = f_self.target effective_args = (f_self,) + effective_args elif hasattr(f, '__class__') and hasattr(f.__class__, '__call__'): # Callable objects. Dunder methods have special lookup rules, see: # https://docs.python.org/3/reference/datamodel.html#specialnames # TODO(mdan): Recurse into converted_call to simplify other verifications. # This should be handled in the same way as partials. target_entity = f.__class__.__call__ effective_args = (f,) + args else: target_entity = f raise NotImplementedError('unknown callable type "%s"' % type(f)) except Exception as e: # pylint:disable=broad-except logging.log(1, 'Error transforming entity %s', target_entity, exc_info=True) if is_autograph_strict_conversion_mode(): raise return _fall_back_unconverted(f, args, kwargs, options, e) if not hasattr(target_entity, '__code__'): logging.log(2, 'Permanently whitelisted: %s: native binding', target_entity) return _call_unconverted(f, args, kwargs, options) elif (hasattr(target_entity.__code__, 'co_filename') and target_entity.__code__.co_filename == '<string>'): # TODO(mdan): __globals__['txt'] might work in Py3. logging.log(2, 'Permanently whitelisted: %s: dynamic code (exec?)', target_entity) return _call_unconverted(f, args, kwargs, options) try: program_ctx = converter.ProgramContext( options=options, autograph_module=tf_inspect.getmodule(converted_call)) converted_f = conversion.convert(target_entity, program_ctx) if logging.has_verbosity(2): _log_callargs(converted_f, effective_args, kwargs) except Exception as e: # pylint:disable=broad-except logging.log(1, 'Error transforming entity %s', target_entity, exc_info=True) if is_autograph_strict_conversion_mode(): raise return _fall_back_unconverted(f, args, kwargs, options, e) with StackTraceMapper(converted_f), tf_stack.CurrentModuleFilter(): try: if kwargs is not None: result = converted_f(*effective_args, **kwargs) else: result = converted_f(*effective_args) except Exception as e: _attach_metadata(e, converted_f) raise return result
def is_whitelisted(o, check_call_override=True, allow_namedtuple_subclass=False): """Checks whether an entity is whitelisted for use in graph mode. Examples of whitelisted entities include all members of the tensorflow package. Args: o: A Python entity. check_call_override: Reserved for internal use. When set to `False`, it disables the rule according to which classes are whitelisted if their __call__ method is whitelisted. allow_namedtuple_subclass: Reserved for internal use. When `True`, namedtuple subclasses are not whitelisted. Returns: Boolean """ # TODO(b/120224672): Fix this. if isinstance(o, functools.partial): # tf_inspect.getmodule(functools.partial(...)) otherwise returns None since # functools.partial objects do not have a __module__ attribute. m = functools else: m = tf_inspect.getmodule(o) # Examples of callables that lack a __module__ property include builtins. if hasattr(m, '__name__'): for rule in config.CONVERSION_RULES: action = rule.get_action(m) if action == config.Action.CONVERT: logging.log(2, 'Not whitelisted: %s: %s', o, rule) return False elif action == config.Action.DO_NOT_CONVERT: logging.log(2, 'Whitelisted: %s: %s', o, rule) return True if tf_inspect.isgeneratorfunction(o): logging.warn( 'Entity %s appears to be a generator function. It will not be converted' ' by AutoGraph.', o) logging.log(2, 'Whitelisted: %s: generator functions are not converted', o) return True if (check_call_override and not tf_inspect.isclass(o) and hasattr(o, '__call__')): # Callable objects: whitelisted if their __call__ method is. # The type check avoids infinite recursion around the __call__ method # of function objects. if (type(o) != type(o.__call__)) and is_whitelisted(o.__call__): # pylint: disable=unidiomatic-typecheck logging.log(2, 'Whitelisted: %s: object __call__ whitelisted', o) return True owner_class = None if tf_inspect.ismethod(o): # Methods of whitelisted classes are also whitelisted, even if they are # bound via user subclasses. # # For example, suppose `tf.Foo` has a method called `bar`, and `baz` is # defined as below. `tf.Foo` is whitelisted. Then `baz.bar` is also # whitelisted. # # class Custom(tf.Foo): # pass # # baz = Custom() # # For the example above, if `Custom` did overload `bar`, then it would no # longer be whitelisted. owner_class = inspect_utils.getmethodclass(o) if owner_class is function.TfMethodTarget: owner_class = o.__self__.target_class if owner_class is not None: if issubclass(owner_class, unittest.TestCase): logging.log(2, 'Whitelisted: %s: method of TestCase subclass', o) return True owner_class = inspect_utils.getdefiningclass(o, owner_class) if is_whitelisted(owner_class, check_call_override=False, allow_namedtuple_subclass=True): logging.log(2, 'Whitelisted: %s: owner is whitelisted %s', o, owner_class) return True if inspect_utils.isnamedtuple(o): # Due to the way they're constructed, namedtuple types cannot be converted # because they don't expose source code. But we assume they are safe for # graph mode since they are just containers. if allow_namedtuple_subclass: if not any( inspect_utils.isnamedtuple(base) for base in o.__bases__): logging.log(2, 'Whitelisted: %s: named tuple', o) return True else: logging.log(2, 'Whitelisted: %s: named tuple or subclass', o) return True logging.log(2, 'Not whitelisted: %s: default rule', o) return False
def converted_call(f, owner, options, args, kwargs): """Compiles a function call inline. For internal use only.""" logging.log(1, 'Converted call: %s; owner: %s\n args: %s\n kwargs: %s\n', f, owner, args, kwargs) if owner is not None: if not isinstance(f, str): raise ValueError( 'When owner is specified, the function name must be specified as' ' a string: {}'.format(f)) # Special case when the owner is a 'super' object. In that case lookups of # dynamic attributes won't work. See # inspect_utils.SuperWrapperForDynamicAttrs. if isinstance(owner, super): owner = inspect_utils.SuperWrapperForDynamicAttrs(owner) f = getattr(owner, f) if inspect_utils.isbuiltin(f): return py_builtins.overload_of(f)(*args, **kwargs) # TODO(b/122265385): Remove this bypass. if (_is_known_loaded_type(f, 'wrapt', 'FunctionWrapper') or _is_known_loaded_type(f, 'wrapt', 'BoundFunctionWrapper')): logging.warn( 'Entity {} appears to be decorated by wrapt, which is not yet supported' ' by AutoGraph. The function will be called without transformation.' ' You may however apply AutoGraph before the decorator.'.format(f)) logging.log(2, 'Permanently whitelisted: %s: wrapt decorated', f) return _call_unconverted(f, args, kwargs) # Constructors are permanently whitelisted. # TODO(mdan): Toggle as experimental feature instead. # TODO(b/124016764): Remove this limitation. if tf_inspect.isclass(f): logging.log(2, 'Permanently whitelisted: %s: constructor', f) return _call_unconverted(f, args, kwargs) # Other built-in modules are permanently whitelisted. # TODO(mdan): Figure out how to do this consistently for all stdlib modules. # Note: TF linter disallows importing inspect. if any(f in m.__dict__.values() for m in (collections, pdb, copy, tf_inspect._inspect)): # pylint:disable=protected-access logging.log(2, 'Permanently whitelisted: %s: part of builtin module', f) return _call_unconverted(f, args, kwargs) if not options.force_conversion and conversion.is_whitelisted_for_graph(f): return _call_unconverted(f, args, kwargs) # internal_convert_user_code is for example turned off when issuing a dynamic # call conversion from generated code while in nonrecursive mode. In that # case we evidently don't want to recurse, but we still have to convert # things like builtins. if not options.internal_convert_user_code: return _call_unconverted(f, args, kwargs) # TODO(mdan): Move this entire block inside to_graph. try: # Begin of transformation error guards # Unwrap functools.partial objects # TODO(mdan): Consider sharing unwrapping logic with tf_inspect. while isinstance(f, functools.partial): args = f.args + args new_kwargs = {} if f.keywords is not None: new_kwargs.update(f.keywords) new_kwargs.update(kwargs) kwargs = new_kwargs f = f.func if tf_inspect.isfunction(f) or tf_inspect.ismethod(f): # Regular functions target_entity = f arg_map_target = f f_self = inspect_utils.getmethodself(f) # TODO(b/119246461): This may be more elegantly handled using __get__? if f_self is not None: # If this is a method call, it may or may not include self. # # Example when self is included: # converted_call(to_graph(foo.bar), foo) # # Example when self is not included: # super(...).foo(args) # if owner is not None and (not args or args[0] is not owner): effective_args = (owner,) + args else: # When the owner is not specified, use the result of # inspect_utils.getmethodclass. # TODO(b/119246461): Make sure an owner is always specified. if not args or args[0] is not f_self: effective_args = (f_self,) + args else: effective_args = (f_self,) + args[1:] partial_types = (f_self,) else: effective_args = args partial_types = () elif tf_inspect.isclass(f): # Constructors # Note: Until we support class constructurs, and enable whole-class # conversion with an experimental flag, this branch is dead code. # TODO(mdan): Consider removing unless there is a compelling use case. target_entity = f arg_map_target = f.__init__ effective_args = args partial_types = () elif hasattr(f, '__call__') and hasattr(f, '__class__'): # Callable objects target_entity = f.__call__ arg_map_target = f.__call__ effective_args = (f,) + args partial_types = (f.__class__,) else: raise NotImplementedError('unknown callable type "%s"' % type(f)) arg_values = tf_inspect.getcallargs(arg_map_target, *args, **kwargs) arg_types = {} for name, arg in arg_values.items(): arg_class = arg.__class__ arg_types[name] = (arg_class.__name__, arg_class) # When called from within a decorator, this is the only indication that # the function is a method - it appears that the decorator is applied # before the method is bound. if not partial_types: if 'self' in arg_values: if tf_inspect.isclass(arg_values['self'].__class__): partial_types = (arg_values['self'].__class__,) elif 'cls' in arg_values: if tf_inspect.isclass(arg_values['cls']): partial_types = (arg_values['cls'],) logging.log(3, 'Partial types in conversion of %s: %s', target_entity, partial_types) converted_f = to_graph( target_entity, recursive=options.recursive, arg_values=arg_values, arg_types=arg_types, experimental_optional_features=options.optional_features, experimental_strip_decorators=options.strip_decorators, experimental_verbose=options.verbose, experimental_partial_types=partial_types) if logging.has_verbosity(2): logging.log(2, 'Defaults of %s : %s', converted_f, converted_f.__defaults__) callargs = tf_inspect.getcallargs(converted_f, *effective_args, **kwargs) formatted_callargs = '\n'.join( ' {}: {}'.format(k, v) for k, v in callargs.items()) logging.log(2, 'Calling %s with\n%s\n', converted_f, formatted_callargs) # TODO(mdan): Reduce this list. except (errors.AutoGraphError, AssertionError, AttributeError, IndexError, KeyError, NameError, NotImplementedError, SyntaxError, TypeError, ValueError, IOError) as e: logging.log(1, 'Error transforming entity %s', target_entity, exc_info=True) logging.warn( 'Entity %s could not be transformed and will be staged without change.' ' Error details can be found in the logs when running with the env' ' variable AUTOGRAPH_VERBOSITY >= 1. Please report this to the' ' AutoGraph team. Cause: %s', target_entity, e) return _call_unconverted(f, args, kwargs) result = converted_f(*effective_args, **kwargs) # The converted function's closure is simply inserted into the function's # module __dict__. Since modules are permanently cached, that results in # leaking the entire closure. # Normally, it's not safe to delete the module because that may release said # closure as well. However, in the case of converted_call we are certain the # function will not be executed again, so the closure should no longer be # needed so long as the function doesn't return any executable code. # TODO(mdan): Attach the closure properly, using cells. if all(map(_is_not_callable, nest.flatten(result))): del sys.modules[converted_f.__module__] return result
def visit_Call(self, node): full_name = str(anno.getanno(node.func, anno.Basic.QN, default='')) function_context_name = self.state[_Function].context_name node = self.generic_visit(node) # TODO(mdan): Refactor converted_call as a 'Call' operator. # Calls to the internal 'ag__' module are never converted (though their # arguments might be). if full_name.startswith('ag__.'): return node # Calls to the function context manager (inserted by function_scopes) are # also safe. if full_name.startswith(function_context_name + '.'): return node # Calls to pdb.set_trace or ipdb.set_trace are never converted. We don't use # the normal mechanisms to bypass these literals because they are sensitive # to the frame they are being called from. # TODO(mdan): Generalize this to a "static whitelist" config. if full_name in ('pdb.set_trace', 'ipdb.set_trace', 'breakpoint'): global set_trace_warned if not set_trace_warned: # TODO(mdan): Update and shorten once available on tensorflow.org. ag_logging.warn( 'Detected `pdb.set_trace()` in converted code. The code' ' generated by AutoGraph is not optimized for step-by-step' ' debugging. See https://github.com/tensorflow/tensorflow/' 'blob/master/tensorflow/python/autograph/g3doc/reference/' 'debugging.md.') set_trace_warned = True return node if (full_name == 'print' and not self.ctx.program.options.uses( converter.Feature.BUILTIN_FUNCTIONS)): return node func = node.func starred_arg = None normal_args = [] for a in node.args: if isinstance(a, gast.Starred): assert starred_arg is None, 'Multiple *args should be impossible.' starred_arg = a else: normal_args.append(a) if starred_arg is None: args = templates.replace_as_expression('(args,)', args=normal_args) else: args = templates.replace_as_expression('(args,) + tuple(stararg)', stararg=starred_arg.value, args=normal_args) kwargs_arg = None normal_keywords = [] for k in node.keywords: if k.arg is None: assert kwargs_arg is None, 'Multiple **kwargs should be impossible.' kwargs_arg = k else: normal_keywords.append(k) if kwargs_arg is None: if not normal_keywords: kwargs = parser.parse_expression('None') else: kwargs = ast_util.keywords_to_dict(normal_keywords) else: kwargs = templates.replace_as_expression( 'dict(kwargs, **keywords)', kwargs=kwargs_arg.value, keywords=ast_util.keywords_to_dict(normal_keywords)) template = """ ag__.converted_call(func, options, args, kwargs, function_ctx) """ new_call = templates.replace_as_expression( template, func=func, options=parser.parse_expression(function_context_name + '.callopts'), args=args, kwargs=kwargs, function_ctx=function_context_name) return new_call
def converted_call(f, owner, options, args, kwargs): """Compiles a function call inline. For internal use only.""" logging.log( 1, 'Converted call: %s; owner: %s\n args: %s\n kwargs: %s\n', f, owner, args, kwargs) if owner is not None: if not isinstance(f, str): raise ValueError( 'When owner is specified, the function name must be specified as' ' a string: {}'.format(f)) # Special case when the owner is a 'super' object. In that case lookups of # dynamic attributes won't work. See # inspect_utils.SuperWrapperForDynamicAttrs. if isinstance(owner, super): owner = inspect_utils.SuperWrapperForDynamicAttrs(owner) f = getattr(owner, f) if inspect_utils.isbuiltin(f): if kwargs: return py_builtins.overload_of(f)(*args, **kwargs) else: return py_builtins.overload_of(f)(*args) if _is_known_loaded_type(f, 'weakref', 'ref'): logging.log(2, 'Permanently whitelisted: %s: weakref', f) return _call_unconverted(f, args, kwargs) # TODO(b/122265385): Remove this bypass. if (_is_known_loaded_type(f, 'wrapt', 'FunctionWrapper') or _is_known_loaded_type(f, 'wrapt', 'BoundFunctionWrapper')): logging.warn( 'Entity {} appears to be decorated by wrapt, which is not yet supported' ' by AutoGraph. The function will be called without transformation.' ' You may however apply AutoGraph before the decorator.'.format(f)) logging.log(2, 'Permanently whitelisted: %s: wrapt decorated', f) return _call_unconverted(f, args, kwargs) # Constructors are permanently whitelisted. # TODO(mdan): Toggle as experimental feature instead. # TODO(b/124016764): Remove this limitation. if tf_inspect.isclass(f): logging.log(2, 'Permanently whitelisted: %s: constructor', f) return _call_unconverted(f, args, kwargs) # Other built-in modules are permanently whitelisted. # TODO(mdan): Figure out how to do this consistently for all stdlib modules. # Note: TF linter disallows importing inspect. if any(f in m.__dict__.values() for m in (collections, pdb, copy, tf_inspect._inspect)): # pylint:disable=protected-access logging.log(2, 'Permanently whitelisted: %s: part of builtin module', f) return _call_unconverted(f, args, kwargs) if not options.force_conversion and conversion.is_whitelisted_for_graph(f): return _call_unconverted(f, args, kwargs) # internal_convert_user_code is for example turned off when issuing a dynamic # call conversion from generated code while in nonrecursive mode. In that # case we evidently don't want to recurse, but we still have to convert # things like builtins. if not options.internal_convert_user_code: return _call_unconverted(f, args, kwargs) # TODO(mdan): Move this entire block inside to_graph. try: # Begin of transformation error guards # Unwrap functools.partial objects # TODO(mdan): Consider sharing unwrapping logic with tf_inspect. while isinstance(f, functools.partial): args = f.args + args new_kwargs = {} if f.keywords is not None: new_kwargs.update(f.keywords) if kwargs is not None: new_kwargs.update(kwargs) kwargs = new_kwargs f = f.func if tf_inspect.isfunction(f) or tf_inspect.ismethod(f): # Regular functions target_entity = f f_self = inspect_utils.getmethodself(f) # TODO(b/119246461): This may be more elegantly handled using __get__? if f_self is not None: effective_args = (f_self, ) + args else: effective_args = args elif tf_inspect.isclass(f): # Constructors # Note: Until we support class constructurs, and enable whole-class # conversion with an experimental flag, this branch is dead code. # TODO(mdan): Consider removing unless there is a compelling use case. target_entity = f effective_args = args elif hasattr(f, '__call__') and hasattr(f, '__class__'): # Callable objects target_entity = f.__call__ effective_args = (f, ) + args else: target_entity = f raise NotImplementedError('unknown callable type "%s"' % type(f)) converted_f = to_graph( target_entity, recursive=options.recursive, arg_values=None, arg_types=None, experimental_optional_features=options.optional_features) if logging.has_verbosity(2): logging.log(2, 'Defaults of %s : %s', converted_f, converted_f.__defaults__) if kwargs is not None: callargs = tf_inspect.getcallargs(converted_f, *effective_args, **kwargs) else: callargs = tf_inspect.getcallargs(converted_f, *effective_args) formatted_callargs = '\n'.join(' {}: {}'.format(k, v) for k, v in callargs.items()) logging.log(2, 'Calling %s with\n%s\n', converted_f, formatted_callargs) # TODO(mdan): Reduce this list. except (errors.AutoGraphError, AssertionError, AttributeError, IndexError, KeyError, NameError, NotImplementedError, SyntaxError, TypeError, ValueError, IOError) as e: logging.log(1, 'Error transforming entity %s', target_entity, exc_info=True) if is_autograph_strict_conversion_mode(): raise logging.warn( 'Entity %s could not be transformed and will be executed as-is.' ' Some features (e.g. tensor-dependent conditionals and loops) may not' ' work as expected.' ' Error details can be found in the logs when running with the env' ' variable AUTOGRAPH_VERBOSITY >= 1. Please report this to the' ' AutoGraph team. Cause: %s', target_entity, e) return _call_unconverted(f, args, kwargs) if kwargs is not None: result = converted_f(*effective_args, **kwargs) else: result = converted_f(*effective_args) return result
def converted_call(f, owner, options, args, kwargs): """Compiles a function call inline. For internal use only.""" if owner is not None: if not isinstance(f, str): raise ValueError( 'When owner is specified, the function name must be specified as' ' a string: {}'.format(f)) owner_attr = f # Special case when the owner is a 'super' object. In that case lookups of # dynamic attributes won't work. See # inspect_utils.SuperWrapperForDynamicAttrs. if isinstance(owner, super): owner = inspect_utils.SuperWrapperForDynamicAttrs(owner) f = getattr(owner, f) if logging.has_verbosity(1): if owner is not None: composite_desc = '("{}" attr of {})'.format(owner_attr, owner) else: composite_desc = '' logging.log(1, 'Converted call: %s %s\n args: %s\n kwargs: %s\n', f, composite_desc, args, kwargs) if inspect_utils.isbuiltin(f): if kwargs: return py_builtins.overload_of(f)(*args, **kwargs) else: return py_builtins.overload_of(f)(*args) # TODO(b/122265385): Remove this bypass. if (_is_known_loaded_type(f, 'wrapt', 'FunctionWrapper') or _is_known_loaded_type(f, 'wrapt', 'BoundFunctionWrapper')): logging.warn( 'Entity {} appears to be decorated by wrapt, which is not yet supported' ' by AutoGraph. The function will be called without transformation.' ' You may however apply AutoGraph before the decorator.'.format(f)) logging.log(2, 'Permanently whitelisted: %s: wrapt decorated', f) return _call_unconverted(f, args, kwargs) if _is_known_loaded_type(f, 'functools', '_lru_cache_wrapper'): logging.log(2, 'Permanently whitelisted: %s: lru_cache', f) return _call_unconverted(f, args, kwargs) # Constructors are permanently whitelisted. # TODO(mdan): Toggle as experimental feature instead. # TODO(b/124016764): Remove this limitation. if tf_inspect.isclass(f): logging.log(2, 'Permanently whitelisted: %s: constructor', f) return _call_unconverted(f, args, kwargs) # Other built-in modules are permanently whitelisted. # TODO(mdan): Figure out how to do this consistently for all stdlib modules. if any(f in m.__dict__.values() for m in (collections, pdb, copy, inspect)): logging.log(2, 'Permanently whitelisted: %s: part of builtin module', f) return _call_unconverted(f, args, kwargs) if not options.force_conversion and conversion.is_whitelisted_for_graph(f): return _call_unconverted(f, args, kwargs) # internal_convert_user_code is for example turned off when issuing a dynamic # call conversion from generated code while in nonrecursive mode. In that # case we evidently don't want to recurse, but we still have to convert # things like builtins. if not options.internal_convert_user_code: return _call_unconverted(f, args, kwargs) # TODO(mdan): Move this entire block inside to_graph. try: # Begin of transformation error guards # Unwrap functools.partial objects # TODO(mdan): Consider sharing unwrapping logic with tf_inspect. while isinstance(f, functools.partial): args = f.args + args new_kwargs = {} if f.keywords is not None: new_kwargs.update(f.keywords) if kwargs is not None: new_kwargs.update(kwargs) kwargs = new_kwargs f = f.func if tf_inspect.isfunction(f) or tf_inspect.ismethod(f): # Regular functions target_entity = f f_self = inspect_utils.getmethodself(f) # TODO(b/119246461): This may be more elegantly handled using __get__? if f_self is not None: effective_args = (f_self,) + args else: effective_args = args elif tf_inspect.isclass(f): # Constructors # Note: Until we support class constructurs, and enable whole-class # conversion with an experimental flag, this branch is dead code. # TODO(mdan): Consider removing unless there is a compelling use case. target_entity = f effective_args = args elif hasattr(f, '__call__') and hasattr(f, '__class__'): # Callable objects target_entity = f.__call__ effective_args = (f,) + args else: target_entity = f raise NotImplementedError('unknown callable type "%s"' % type(f)) if (not tf_inspect.isclass(target_entity) and not hasattr(target_entity, '__code__')): logging.log( 2, 'Permanently whitelisted: %s: native binding', target_entity) return _call_unconverted(f, args, kwargs) converted_f = to_graph( target_entity, recursive=options.recursive, arg_values=None, arg_types=None, experimental_optional_features=options.optional_features) if logging.has_verbosity(2): logging.log(2, 'Defaults of %s : %s', converted_f, converted_f.__defaults__) if kwargs is not None: callargs = tf_inspect.getcallargs( converted_f, *effective_args, **kwargs) else: callargs = tf_inspect.getcallargs(converted_f, *effective_args) formatted_callargs = '\n'.join( ' {}: {}'.format(k, v) for k, v in callargs.items()) logging.log(2, 'Calling %s with\n%s\n', converted_f, formatted_callargs) # TODO(mdan): Reduce this list. except (errors.AutoGraphError, AssertionError, AttributeError, IndexError, KeyError, NameError, NotImplementedError, SyntaxError, TypeError, ValueError, IOError) as e: logging.log(1, 'Error transforming entity %s', target_entity, exc_info=True) if is_autograph_strict_conversion_mode(): raise logging.warn( 'Entity %s could not be transformed and will be executed as-is.' ' Some features (e.g. tensor-dependent conditionals and loops) may not' ' work as expected.' ' Error details can be found in the logs when running with the env' ' variable AUTOGRAPH_VERBOSITY >= 1. Please report this to the' ' AutoGraph team. Cause: %s', target_entity, e) return _call_unconverted(f, args, kwargs) if kwargs is not None: result = converted_f(*effective_args, **kwargs) else: result = converted_f(*effective_args) return result
def is_whitelisted_for_graph(o): """Checks whether an entity is whitelisted for use in graph mode. Examples of whitelisted entities include all members of the tensorflow package. Args: o: A Python entity. Returns: Boolean """ # TODO(b/120224672): Fix this. if isinstance(o, functools.partial): # tf_inspect.getmodule(functools.partial(...)) otherwise returns None since # functools.partial objects do not have a __module__ attribute. m = functools else: m = tf_inspect.getmodule(o) if hasattr(m, '__name__'): # Builtins typically have unnamed modules. for prefix, in config.DEFAULT_UNCOMPILED_MODULES: if m.__name__.startswith(prefix): logging.log(2, 'Whitelisted: %s: name starts with "%s"', o, prefix) return True # Temporary -- whitelist tensorboard modules. # TODO(b/122731813): Remove. if m.__name__ == 'tensorboard' or '.tensorboard' in m.__name__: logging.log(2, 'Whitelisted: %s: name contains "tensorboard"', o) return True if hasattr(o, 'autograph_info__') or hasattr(o, '__ag_compiled'): logging.log(2, 'Whitelisted: %s: already converted', o) return True if tf_inspect.isgeneratorfunction(o): logging.warn( 'Entity {} appears to be a generator function. It will not be converted' ' by AutoGraph.'.format(o), 1) logging.log(2, 'Whitelisted: %s: generator functions are not converted', o) return True if hasattr(o, '__call__'): # Callable objects: whitelisted if their __call__ method is. # The type check avoids infinite recursion around the __call__ method # of function objects. if (type(o) != type(o.__call__)) and is_whitelisted_for_graph(o.__call__): # pylint: disable=unidiomatic-typecheck logging.log(2, 'Whitelisted: %s: object __call__ whitelisted', o) return True owner_class = None if tf_inspect.ismethod(o): # Methods of whitelisted classes are also whitelisted, even if they are # bound via user subclasses. # # For example, suppose `tf.Foo` has a method called `bar`, and `baz` is # defined as below. `tf.Foo` is whitelisted. Then `baz.bar` is also # whitelisted. # # class Custom(tf.Foo): # pass # # baz = Custom() # # For the example above, if `Custom` did overload `bar`, then it would no # longer be whitelisted. owner_class = inspect_utils.getmethodclass(o) if owner_class is not None: if issubclass(owner_class, unittest.TestCase): logging.log(2, 'Whitelisted: %s: method of TestCase subclass', o) return True owner_class = inspect_utils.getdefiningclass(o, owner_class) if is_whitelisted_for_graph(owner_class): logging.log(2, 'Whitelisted: %s: owner is whitelisted %s', o, owner_class) return True if inspect_utils.isnamedtuple(o): # Due to the way they're constructed, namedtuple types cannot be converted # because they don't expose source code. But we assume they are safe for # graph mode since they are just containers. if tf_inspect.isclass(o) and len(o.__bases__) > 1: logging.warn( 'Entity {} looks like a namedtuple subclass. Its constructor will' ' not be converted by AutoGraph, but if it has any custom methods,' ' those will be.'.format(o), 1) logging.log(2, 'Whitelisted: %s: named tuple', o) return True logging.log(2, 'Not whitelisted: %s: default rule', o) return False
def converted_call(f, options, args, kwargs, caller_fn_scope=None): """Compiles a function call inline. For internal use only. Args: f: The function to convert. options: converter.ConversionOptions args: Tuple, the original positional arguments of f kwargs: Dict, the original keyword arguments of f caller_fn_scope: Optional[function_wrappers.FunctionScope], the function scope of the converted function in which this call was originally made. Returns: Any, the result of executing a possibly-converted `f` with the given arguments. """ logging.log(1, 'Converted call: %s\n args: %s\n kwargs: %s\n', f, args, kwargs) if conversion.check_cached_unconverted(f, options): return _call_unconverted(f, args, kwargs, options, False) if inspect_utils.isbuiltin(f): if f is eval: return py_builtins.eval_in_original_context( f, args, caller_fn_scope) if f is super: return py_builtins.super_in_original_context( f, args, caller_fn_scope) if kwargs: return py_builtins.overload_of(f)(*args, **kwargs) else: return py_builtins.overload_of(f)(*args) # TODO(mdan): Clean up the naming inconsistency. if hasattr(f, 'autograph_info__') or hasattr(f, '__ag_compiled'): logging.log(2, 'Permanently whitelisted: %s: already converted', f) return _call_unconverted(f, args, kwargs, options) # TODO(b/122265385): Remove this bypass. if (_is_known_loaded_type(f, 'wrapt', 'FunctionWrapper') or _is_known_loaded_type(f, 'wrapt', 'BoundFunctionWrapper')): logging.warn( '{} appears to be decorated by wrapt, which is not yet supported' ' by AutoGraph. The function will run as-is.' ' You may still apply AutoGraph before the wrapt decorator.'. format(f)) logging.log(2, 'Permanently whitelisted: %s: wrapt decorated', f) return _call_unconverted(f, args, kwargs, options) if _is_known_loaded_type(f, 'functools', '_lru_cache_wrapper'): logging.log(2, 'Permanently whitelisted: %s: lru_cache', f) return _call_unconverted(f, args, kwargs, options) # Constructors are permanently whitelisted. # TODO(mdan): Toggle as experimental feature instead. # TODO(b/124016764): Remove this limitation. if tf_inspect.isclass(f): logging.log(2, 'Permanently whitelisted: %s: constructor', f) return _call_unconverted(f, args, kwargs, options) # Other built-in modules are permanently whitelisted. # TODO(mdan): Figure out how to do this consistently for all stdlib modules. if any(f in m.__dict__.values() for m in (collections, pdb, copy, inspect, re)): logging.log(2, 'Permanently whitelisted: %s: part of builtin module', f) return _call_unconverted(f, args, kwargs, options) # Custom ops and kernels are also permanently whitelisted. # See tensorflow.framework.load_library. if (hasattr(f, '__module__') and hasattr(f.__module__, '_IS_TENSORFLOW_PLUGIN')): logging.log(2, 'Permanently whitelisted: %s: TensorFlow plugin', f) return _call_unconverted(f, args, kwargs, options) if not options.user_requested and conversion.is_whitelisted_for_graph(f): return _call_unconverted(f, args, kwargs, options) # internal_convert_user_code is for example turned off when issuing a dynamic # call conversion from generated code while in nonrecursive mode. In that # case we evidently don't want to recurse, but we still have to convert # things like builtins. if not options.internal_convert_user_code: return _call_unconverted(f, args, kwargs, options) # TODO(mdan): Move this entire block inside to_graph. try: # Begin of transformation error guards # Unwrap functools.partial objects # TODO(mdan): Consider sharing unwrapping logic with tf_inspect. # TODO(b/120224672): This unwrapping should be done before the checks above. while isinstance(f, functools.partial): args = f.args + args new_kwargs = {} if f.keywords is not None: new_kwargs.update(f.keywords) if kwargs is not None: new_kwargs.update(kwargs) kwargs = new_kwargs f = f.func if tf_inspect.isfunction(f) or tf_inspect.ismethod(f): # Regular functions target_entity = f f_self = inspect_utils.getmethodself(f) # TODO(b/119246461): This may be more elegantly handled using __get__? if f_self is not None: effective_args = (f_self, ) + args else: effective_args = args elif hasattr(f, '__call__') and hasattr(f, '__class__'): # Callable objects target_entity = f.__call__ effective_args = (f, ) + args elif tf_inspect.isclass(f): # Constructors # Note: Until we support class constructurs, and enable whole-class # conversion with an experimental flag, this branch is dead code. # TODO(mdan): Consider removing unless there is a compelling use case. target_entity = f effective_args = args else: target_entity = f raise NotImplementedError('unknown callable type "%s"' % type(f)) if not tf_inspect.isclass(target_entity): if not hasattr(target_entity, '__code__'): logging.log(2, 'Permanently whitelisted: %s: native binding', target_entity) return _call_unconverted(f, args, kwargs, options) elif (hasattr(target_entity.__code__, 'co_filename') and target_entity.__code__.co_filename == '<string>'): # TODO(mdan): __globals__['txt'] might work in Py3. logging.log( 2, 'Permanently whitelisted: %s: dynamic code (exec?)', target_entity) return _call_unconverted(f, args, kwargs, options) program_ctx = converter.ProgramContext( options=options, autograph_module=tf_inspect.getmodule(converted_call)) converted_f = conversion.convert(target_entity, program_ctx) if logging.has_verbosity(2): logging.log(2, 'Defaults of %s : %s', converted_f, converted_f.__defaults__) if six.PY3: logging.log(2, 'KW defaults of %s : %s', converted_f, converted_f.__kwdefaults__) if kwargs is not None: callargs = tf_inspect.getcallargs(converted_f, *effective_args, **kwargs) else: callargs = tf_inspect.getcallargs(converted_f, *effective_args) formatted_callargs = '\n'.join(' {}: {}'.format(k, v) for k, v in callargs.items()) logging.log(2, 'Calling %s with\n%s\n', converted_f, formatted_callargs) except Exception as e: # pylint:disable=broad-except logging.log(1, 'Error transforming entity %s', target_entity, exc_info=True) if is_autograph_strict_conversion_mode(): raise if _errors_are_normally_possible(target_entity, e): logging.warn( 'AutoGraph could not transform %s and will run it as-is.\n' 'Cause: %s', target_entity, e) else: logging.warn( 'AutoGraph could not transform %s and will run it as-is.\n' 'Please report this to the TensorFlow team. When filing the bug, set' ' the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and' ' attach the full output.\n' 'Cause: %s', target_entity, e) return _call_unconverted(f, args, kwargs, options) with StackTraceMapper(converted_f), tf_stack.CurrentModuleFilter(): try: if kwargs is not None: result = converted_f(*effective_args, **kwargs) else: result = converted_f(*effective_args) except Exception as e: _attach_metadata(e, converted_f, True) raise return result
def converted_call(f, owner, options, args, kwargs): """Compiles a function call inline. For internal use only.""" if owner is not None: if not isinstance(f, str): raise ValueError( 'When owner is specified, the function name must be specified as' ' a string: {}'.format(f)) owner_attr = f # Special case when the owner is a 'super' object. In that case lookups of # dynamic attributes won't work. See # inspect_utils.SuperWrapperForDynamicAttrs. if isinstance(owner, super): owner = inspect_utils.SuperWrapperForDynamicAttrs(owner) f = getattr(owner, f) if logging.has_verbosity(1): if owner is not None: composite_desc = '("{}" attr of {})'.format(owner_attr, owner) else: composite_desc = '' logging.log(1, 'Converted call: %s %s\n args: %s\n kwargs: %s\n', f, composite_desc, args, kwargs) if inspect_utils.isbuiltin(f): if kwargs: return py_builtins.overload_of(f)(*args, **kwargs) else: return py_builtins.overload_of(f)(*args) # TODO(mdan): Clean up the naming inconsistency. if hasattr(f, 'autograph_info__') or hasattr(f, '__ag_compiled'): logging.log(2, 'Permanently whitelisted: %s: already converted', f) return _call_unconverted(f, args, kwargs) # TODO(b/122265385): Remove this bypass. if (_is_known_loaded_type(f, 'wrapt', 'FunctionWrapper') or _is_known_loaded_type(f, 'wrapt', 'BoundFunctionWrapper')): logging.warn( 'Entity {} appears to be decorated by wrapt, which is not yet supported' ' by AutoGraph. The function will be called without transformation.' ' You may however apply AutoGraph before the decorator.'.format(f)) logging.log(2, 'Permanently whitelisted: %s: wrapt decorated', f) return _call_unconverted(f, args, kwargs) if _is_known_loaded_type(f, 'functools', '_lru_cache_wrapper'): logging.log(2, 'Permanently whitelisted: %s: lru_cache', f) return _call_unconverted(f, args, kwargs) # Constructors are permanently whitelisted. # TODO(mdan): Toggle as experimental feature instead. # TODO(b/124016764): Remove this limitation. if tf_inspect.isclass(f): logging.log(2, 'Permanently whitelisted: %s: constructor', f) return _call_unconverted(f, args, kwargs) # Other built-in modules are permanently whitelisted. # TODO(mdan): Figure out how to do this consistently for all stdlib modules. if any(f in m.__dict__.values() for m in (collections, pdb, copy, inspect, re)): logging.log(2, 'Permanently whitelisted: %s: part of builtin module', f) return _call_unconverted(f, args, kwargs) # Custom ops and kernels are also permanently whitelisted. # See tensorflow.framework.load_library. if (hasattr(f, '__module__') and hasattr(f.__module__, '_IS_TENSORFLOW_PLUGIN')): logging.log(2, 'Permanently whitelisted: %s: TensorFlow plugin', f) return _call_unconverted(f, args, kwargs) if not options.force_conversion and conversion.is_whitelisted_for_graph(f): return _call_unconverted(f, args, kwargs) # internal_convert_user_code is for example turned off when issuing a dynamic # call conversion from generated code while in nonrecursive mode. In that # case we evidently don't want to recurse, but we still have to convert # things like builtins. if not options.internal_convert_user_code: return _call_unconverted(f, args, kwargs) # TODO(mdan): Move this entire block inside to_graph. try: # Begin of transformation error guards # Unwrap functools.partial objects # TODO(mdan): Consider sharing unwrapping logic with tf_inspect. while isinstance(f, functools.partial): args = f.args + args new_kwargs = {} if f.keywords is not None: new_kwargs.update(f.keywords) if kwargs is not None: new_kwargs.update(kwargs) kwargs = new_kwargs f = f.func if tf_inspect.isfunction(f) or tf_inspect.ismethod(f): # Regular functions target_entity = f f_self = inspect_utils.getmethodself(f) # TODO(b/119246461): This may be more elegantly handled using __get__? if f_self is not None: effective_args = (f_self, ) + args else: effective_args = args elif tf_inspect.isclass(f): # Constructors # Note: Until we support class constructurs, and enable whole-class # conversion with an experimental flag, this branch is dead code. # TODO(mdan): Consider removing unless there is a compelling use case. target_entity = f effective_args = args elif hasattr(f, '__call__') and hasattr(f, '__class__'): # Callable objects target_entity = f.__call__ effective_args = (f, ) + args else: target_entity = f raise NotImplementedError('unknown callable type "%s"' % type(f)) if not tf_inspect.isclass(target_entity): if not hasattr(target_entity, '__code__'): logging.log(2, 'Permanently whitelisted: %s: native binding', target_entity) return _call_unconverted(f, args, kwargs) elif (hasattr(target_entity.__code__, 'co_filename') and target_entity.__code__.co_filename == '<string>'): # TODO(mdan): __globals__['txt'] might work in Py3. logging.log( 2, 'Permanently whitelisted: %s: dynamic code (exec?)', target_entity) return _call_unconverted(f, args, kwargs) converted_f = to_graph( target_entity, recursive=options.recursive, experimental_optional_features=options.optional_features) if logging.has_verbosity(2): logging.log(2, 'Defaults of %s : %s', converted_f, converted_f.__defaults__) if six.PY3: logging.log(2, 'KW defaults of %s : %s', converted_f, converted_f.__kwdefaults__) if kwargs is not None: callargs = tf_inspect.getcallargs(converted_f, *effective_args, **kwargs) else: callargs = tf_inspect.getcallargs(converted_f, *effective_args) formatted_callargs = '\n'.join(' {}: {}'.format(k, v) for k, v in callargs.items()) logging.log(2, 'Calling %s with\n%s\n', converted_f, formatted_callargs) except Exception as e: # pylint:disable=broad-except logging.log(1, 'Error transforming entity %s', target_entity, exc_info=True) if is_autograph_strict_conversion_mode(): raise logging.warn( 'Entity %s could not be transformed and will be executed as-is.' ' Please report this to the AutoGraph team. When filing the bug, set' ' the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and' ' attach the full output. Cause: %s', target_entity, e) return _call_unconverted(f, args, kwargs) with StackTraceMapper(converted_f), tf_stack.CurrentModuleFilter(): try: if kwargs is not None: result = converted_f(*effective_args, **kwargs) else: result = converted_f(*effective_args) except Exception as e: _attach_metadata(e, converted_f, True) raise return result
def is_whitelisted_for_graph(o, check_call_override=True): """Checks whether an entity is whitelisted for use in graph mode. Examples of whitelisted entities include all members of the tensorflow package. Args: o: A Python entity. check_call_override: Reserved for internal use. When set to `False`, it disables the rule according to which classes are whitelisted if their __call__ method is whitelisted. Returns: Boolean """ # TODO(b/120224672): Fix this. if isinstance(o, functools.partial): # tf_inspect.getmodule(functools.partial(...)) otherwise returns None since # functools.partial objects do not have a __module__ attribute. m = functools else: m = tf_inspect.getmodule(o) if hasattr(m, '__name__'): # Builtins typically have unnamed modules. for prefix, in config.DEFAULT_UNCOMPILED_MODULES: if m.__name__.startswith(prefix + '.') or m.__name__ == prefix: logging.log(2, 'Whitelisted: %s: name starts with "%s"', o, prefix) return True if hasattr(o, 'autograph_info__') or hasattr(o, '__ag_compiled'): logging.log(2, 'Whitelisted: %s: already converted', o) return True if tf_inspect.isgeneratorfunction(o): logging.warn( 'Entity {} appears to be a generator function. It will not be converted' ' by AutoGraph.'.format(o), 1) logging.log(2, 'Whitelisted: %s: generator functions are not converted', o) return True if check_call_override and hasattr(o, '__call__'): # Callable objects: whitelisted if their __call__ method is. # The type check avoids infinite recursion around the __call__ method # of function objects. if (type(o) != type(o.__call__)) and is_whitelisted_for_graph( o.__call__): # pylint: disable=unidiomatic-typecheck logging.log(2, 'Whitelisted: %s: object __call__ whitelisted', o) return True owner_class = None if tf_inspect.ismethod(o): # Methods of whitelisted classes are also whitelisted, even if they are # bound via user subclasses. # # For example, suppose `tf.Foo` has a method called `bar`, and `baz` is # defined as below. `tf.Foo` is whitelisted. Then `baz.bar` is also # whitelisted. # # class Custom(tf.Foo): # pass # # baz = Custom() # # For the example above, if `Custom` did overload `bar`, then it would no # longer be whitelisted. owner_class = inspect_utils.getmethodclass(o) if owner_class is not None: if issubclass(owner_class, unittest.TestCase): logging.log(2, 'Whitelisted: %s: method of TestCase subclass', o) return True owner_class = inspect_utils.getdefiningclass(o, owner_class) is_call_override = (o.__name__ == '__call__') if is_whitelisted_for_graph( owner_class, check_call_override=not is_call_override): logging.log(2, 'Whitelisted: %s: owner is whitelisted %s', o, owner_class) return True if inspect_utils.isnamedtuple(o): # Due to the way they're constructed, namedtuple types cannot be converted # because they don't expose source code. But we assume they are safe for # graph mode since they are just containers. if tf_inspect.isclass(o) and len(o.__bases__) > 1: logging.warn( 'Entity {} looks like a namedtuple subclass. Its constructor will' ' not be converted by AutoGraph, but if it has any custom methods,' ' those will be.'.format(o), 1) logging.log(2, 'Whitelisted: %s: named tuple', o) return True logging.log(2, 'Not whitelisted: %s: default rule', o) return False
def converted_call(f, owner, options, args, kwargs): """Compiles a function call inline. For internal use only.""" logging.log(1, 'Converted call: %s; owner: %s\n args: %s\n kwargs: %s\n', f, owner, args, kwargs) if owner is not None: if not isinstance(f, str): raise ValueError( 'When owner is specified, the function name must be specified as' ' a string: {}'.format(f)) # Special case when the owner is a 'super' object. In that case lookups of # dynamic attributes won't work. See # inspect_utils.SuperWrapperForDynamicAttrs. if isinstance(owner, super): owner = inspect_utils.SuperWrapperForDynamicAttrs(owner) f = getattr(owner, f) if inspect_utils.isbuiltin(f): return py_builtins.overload_of(f)(*args, **kwargs) # TODO(b/122265385): Remove this bypass. if ('wrapt' in sys.modules and hasattr(sys.modules['wrapt'], 'FunctionWrapper') and isinstance(f, sys.modules['wrapt'].FunctionWrapper)): logging.warn( 'Entity {} appears to be decorated by wrapt, which is not yet supported' ' by AutoGraph. The function will be called without transformation.' ' You may however apply AutoGraph before the decorator.'.format(f), 1) logging.log(2, 'Permanently whitelisted: %s: wrapt decorated', f) return f(*args, **kwargs) # Constructors are permanently whitelisted. # TODO(mdan): Toggle as experimental feature instead. # TODO(b/124016764): Remove this limitation. if tf_inspect.isclass(f): logging.log(2, 'Permanently whitelisted: %s: constructor', f) return f(*args, **kwargs) # Other built-in modules are permanently whitelisted. # TODO(mdan): Figure out how to do this consistently for all stdlib modules. if (f in collections.__dict__.values() or f in pdb.__dict__.values() or f in copy.__dict__.values()): logging.log(2, 'Permanently whitelisted: %s: part of builtin module', f) return f(*args, **kwargs) # TODO(mdan): This needs cleanup. if not options.force_conversion and conversion.is_whitelisted_for_graph(f): # TODO(mdan): This may be inconsistent in certain situations. # If the function had already been annotated with @tf.function, it # may be bound to the incorrect object. It's unclear if those situations # are possible, but if they happen, we need to check if f is bound # to a shim like WeakrefSelf and unpack it. # Args typically include `self`, as required by the conversion process. # When conversion is skipped, `self` is not necessary, because the # original bound method is being executed. This code removes it. if tf_inspect.ismethod(f) and args: f_self = inspect_utils.getmethodself(f) if args[0] is f_self: args = args[1:] return f(*args, **kwargs) # internal_convert_user_code is for example turned off when issuing a dynamic # call conversion from generated code while in nonrecursive mode. In that # case we evidently don't want to recurse, but we still have to convert # things like builtins. if not options.internal_convert_user_code: return f(*args, **kwargs) # TODO(mdan): Move this entire block inside to_graph. try: # Begin of transformation error guards # Unwrap functools.partial objects # TODO(mdan): Consider sharing unwrapping logic with tf_inspect. while isinstance(f, functools.partial): args = f.args + args new_kwargs = {} if f.keywords is not None: new_kwargs.update(f.keywords) new_kwargs.update(kwargs) kwargs = new_kwargs f = f.func if tf_inspect.isfunction(f) or tf_inspect.ismethod(f): # Regular functions target_entity = f arg_map_target = f f_self = inspect_utils.getmethodself(f) # TODO(b/119246461): This may be more elegantly handled using __get__? if f_self is not None: # If this is a method call, it may or may not include self. # # Example when self is included: # converted_call(to_graph(foo.bar), foo) # # Example when self is not included: # super(...).foo(args) # if owner is not None and (not args or args[0] is not owner): effective_args = (owner,) + args else: # When the owner is not specified, use the result of # inspect_utils.getmethodclass. # TODO(b/119246461): Make sure an owner is always specified. if not args or args[0] is not f_self: effective_args = (f_self,) + args else: effective_args = (f_self,) + args[1:] partial_types = (f_self,) else: effective_args = args partial_types = () elif tf_inspect.isclass(f): # Constructors # Note: Until we support class constructurs, and enable whole-class # conversion with an experimental flag, this branch is dead code. # TODO(mdan): Consider removing unless there is a compelling use case. target_entity = f arg_map_target = f.__init__ effective_args = args partial_types = () elif hasattr(f, '__call__') and hasattr(f, '__class__'): # Callable objects target_entity = f.__call__ arg_map_target = f.__call__ effective_args = (f,) + args partial_types = (f.__class__,) else: raise NotImplementedError('unknown callable type "%s"' % type(f)) arg_values = tf_inspect.getcallargs(arg_map_target, *args, **kwargs) arg_types = {} for name, arg in arg_values.items(): arg_class = arg.__class__ arg_types[name] = (arg_class.__name__, arg_class) # When called from within a decorator, this is the only indication that # the function is a method - it appears that the decorator is applied # before the method is bound. if not partial_types: if 'self' in arg_values: if tf_inspect.isclass(arg_values['self'].__class__): partial_types = (arg_values['self'].__class__,) elif 'cls' in arg_values: if tf_inspect.isclass(arg_values['cls']): partial_types = (arg_values['cls'],) logging.log(3, 'Partial types in conversion of %s: %s', target_entity, partial_types) converted_f = to_graph( target_entity, recursive=options.recursive, arg_values=arg_values, arg_types=arg_types, experimental_optional_features=options.optional_features, experimental_strip_decorators=options.strip_decorators, experimental_verbose=options.verbose, experimental_partial_types=partial_types) if logging.has_verbosity(2): logging.log(2, 'Defaults of %s : %s', converted_f, converted_f.__defaults__) callargs = tf_inspect.getcallargs(converted_f, *effective_args, **kwargs) formatted_callargs = '\n'.join( ' {}: {}'.format(k, v) for k, v in callargs.items()) logging.log(2, 'Calling %s with\n%s\n', converted_f, formatted_callargs) # TODO(mdan): Reduce this list. except (errors.AutoGraphError, AssertionError, AttributeError, IndexError, KeyError, NameError, NotImplementedError, SyntaxError, TypeError, ValueError, IOError) as e: logging.log(1, 'Error transforming entity %s', target_entity, exc_info=True) logging.warn( 'Entity %s could not be transformed and will be staged without change.' ' Error details can be found in the logs when running with the env' ' variable AUTOGRAPH_VERBOSITY=5. Please report this to the AutoGraph' ' team. Cause: %s', target_entity, e) return f(*args, **kwargs) result = converted_f(*effective_args, **kwargs) # The converted function's closure is simply inserted into the function's # module __dict__. Since modules are permanently cached, that results in # leaking the entire closure. # Normally, it's not safe to delete the module because that may release said # closure as well. However, in the case of converted_call we are certain the # function will not be executed again, so the closure should no longer be # needed so long as the function doesn't return any executable code. # TODO(mdan): Attach the closure properly, using cells. if all(map(_is_not_callable, nest.flatten(result))): del sys.modules[converted_f.__module__] return result