def _rename_compilable_function(self, node):
assert anno.hasanno(node.func, 'live_val')
assert anno.hasanno(node.func, 'fqn')
target_entity = anno.getanno(node.func, 'live_val')
target_fqn = anno.getanno(node.func, 'fqn')
if not self._should_compile(node, target_fqn):
return node
if anno.hasanno(node, 'is_constructor'):
new_name = self.context.namer.compiled_class_name(
target_fqn, live_entity=target_entity)
do_rename = True
else:
owner_type = self._determine_function_owner(target_entity)
new_name, do_rename = self.context.namer.compiled_function_name(
target_fqn, live_entity=target_entity, owner_type=owner_type)
if do_rename:
if target_entity is not None:
if tf_inspect.ismethod(target_entity):
# The renaming process will transform it into a regular function.
# TODO(mdan): Is this complete? How does it work with nested members?
node.args = [node.func.value] + node.args
node.func = gast.Name(new_name, gast.Load(), None)
return node
def fn_args(fn):
"""Get argument names for function-like object.
Args:
fn: Function, or function-like object (e.g., result of `functools.partial`).
Returns:
`tuple` of string argument names.
Raises:
ValueError: if partial function has positionally bound arguments
"""
_, fn = tf_decorator.unwrap(fn)
# Handle callables.
if hasattr(fn, '__call__') and tf_inspect.ismethod(fn.__call__):
return tuple(tf_inspect.getargspec(fn.__call__).args)
# Handle functools.partial and similar objects.
if hasattr(fn, 'func') and hasattr(fn, 'keywords') and hasattr(fn, 'args'):
# Handle nested partial.
original_args = fn_args(fn.func)
if not original_args:
return tuple()
return tuple([
arg for arg in original_args[len(fn.args):]
if arg not in set((fn.keywords or {}).keys())
])
# Handle function.
return tuple(tf_inspect.getargspec(fn).args)
def convert_inline(f, *args, **kwargs):
"""Shorthand to convert and call a function.
For example, the following two statements are equivalent:
@convert()
def foo():
...
foo(bar)
def foo():
...
convert_inline(foo, bar)
Args:
f: Function to convert. Only this call will be converted.
*args: Passed through to f.
**kwargs: Passed through to f, with the following exceptions:
* arg_value_hints: A dict mapping parameter names to objects that can
hint at the type of those parameters.
Returns:
The result of the converted f applied to args and kwargs.
"""
if 'arg_value_hints' in kwargs:
arg_value_hints = kwargs['arg_value_hints']
del kwargs['arg_value_hints']
else:
arg_value_hints = None
if tf_inspect.ismethod(f):
# When converting methods, the result is still an unbound function.
args = (f.__self__,) + args
return convert(arg_value_hints)(f)(*args, **kwargs)
def _is_known_loaded_type(f, module_name, entity_name):
"""Tests whether the function or method is an instance of a known type."""
if (module_name not in sys.modules or
not hasattr(sys.modules[module_name], entity_name)):
return False
type_entity = getattr(sys.modules[module_name], entity_name)
if isinstance(f, type_entity):
# The method if of this type. Example:
#
# o = ClassType()
# function(o.method)()
return True
if tf_inspect.ismethod(f):
f = six.get_unbound_function(f)
# The the unbound method if of this type. Example:
#
# class ClassType:
# @function
# def method(self):
# ...
# o = ClassType()
# o.method()
if isinstance(f, type_entity):
return True
return False
def _rename_compilable_function(self, node):
assert anno.hasanno(node.func, 'live_val')
assert anno.hasanno(node.func, 'fqn')
target_entity = anno.getanno(node.func, 'live_val')
target_fqn = anno.getanno(node.func, 'fqn')
if not self._should_compile(node, target_fqn):
return node
if anno.hasanno(node, 'is_constructor'):
new_name = self.ctx.namer.compiled_class_name(
target_fqn, live_entity=target_entity)
do_rename = True
else:
if anno.hasanno(node.func, 'parent_type'):
owner_type = anno.getanno(node.func, 'parent_type')
else:
# Fallback - not reliable.
owner_type = inspect_utils.getmethodclass(target_entity)
new_name, do_rename = self.ctx.namer.compiled_function_name(
target_fqn, live_entity=target_entity, owner_type=owner_type)
if do_rename:
if target_entity is not None:
if tf_inspect.ismethod(target_entity):
# The renaming process will transform it into a regular function.
# TODO(mdan): Is this complete? How does it work with nested members?
node.args = [node.func.value] + node.args
node.func = templates.replace('func_name', func_name=new_name)[0]
return node
def __call__(self, func):
# Various sanity checks on the callable func.
if not callable(func):
raise ValueError("func %s must be callable" % func)
# Func should not use kwargs and defaults.
argspec = tf_inspect.getargspec(func)
if argspec.keywords or argspec.defaults:
raise ValueError("Functions with argument defaults or keyword "
"arguments are not supported.")
# Computes how many arguments 'func' has.
min_args = len(argspec.args)
max_args = min_args
if argspec.varargs:
max_args = 1000000
argnames = argspec.args
if tf_inspect.ismethod(func):
# 1st argument is the "class" type.
min_args -= 1
argnames = argnames[1:]
if self._input_types:
# If Defun is given a list of types for the inputs, the number
# of input types should be compatible with 'func'.
num = len(self._input_types)
if num < min_args or num > max_args:
raise ValueError(
"The function has fewer arguments than the number of specified "
"input types.")
return _DefinedFunction(
func,
argnames,
self._input_types,
self._func_name,
self._grad_func,
self._python_grad_func,
out_names=self._out_names,
**self._extra_kwargs)
# 'func' expects no arguments and input types is an empty list.
if min_args == 0 and max_args == 0:
return _DefinedFunction(
func, [], [],
self._func_name,
self._grad_func,
self._python_grad_func,
out_names=self._out_names,
**self._extra_kwargs)
# Input types are unknown. It's an overloaded function and hence
# its definition needs to be deferred until it's called.
return _OverloadedFunction(
func,
argnames,
self._func_name,
self._grad_func,
self._python_grad_func,
out_names=self._out_names,
**self._extra_kwargs)
def class_to_graph(c, conversion_map):
"""Specialization of `entity_to_graph` for classes."""
converted_members = {}
method_filter = lambda m: tf_inspect.isfunction(m) or tf_inspect.ismethod(m)
members = tf_inspect.getmembers(c, predicate=method_filter)
if not members:
raise ValueError('Cannot convert %s: it has no member methods.' % c)
class_namespace = None
for _, m in members:
node, _ = function_to_graph(
m,
conversion_map=conversion_map,
arg_values={},
arg_types={'self': (c.__name__, c)},
owner_type=c)
# TODO(mdan): Do not assume all members have the same view of globals.
if class_namespace is None:
class_namespace = inspect_utils.getnamespace(m)
converted_members[m] = node
namer = conversion_map.new_namer(class_namespace)
class_name = namer.compiled_class_name(c.__name__, c)
node = gast.ClassDef(
class_name,
bases=[],
keywords=[],
body=list(converted_members.values()),
decorator_list=[])
return node, class_name
def entity_to_graph(o, conversion_map, arg_values, arg_types):
"""Compile a Python entity into equivalent TensorFlow.
The function will also recursively compile all the entities that `o`
references, updating `dependency_cache`.
This function is reentrant, and relies on dependency_cache to avoid
generating duplicate code.
Args:
o: A Python entity.
conversion_map: A ConversionMap object.
arg_values: A dict containing value hints for symbols like function
parameters.
arg_types: A dict containing type hints for symbols like function
parameters.
Returns:
A tuple (ast, new_name, namespace):
* ast: An AST representing an entity with interface equivalent to `o`,
but which when executed it creates TF a graph.
* new_name: The symbol name under which the new entity can be found.
* namespace: A dict mapping all symbols visible to the converted entity,
keyed by their symbol name.
Raises:
ValueError: if the entity type is not supported.
"""
if tf_inspect.isclass(o):
node, name, ns = class_to_graph(o, conversion_map)
elif tf_inspect.isfunction(o):
node, name, ns = function_to_graph(o, conversion_map, arg_values, arg_types)
elif tf_inspect.ismethod(o):
node, name, ns = function_to_graph(o, conversion_map, arg_values, arg_types)
else:
raise ValueError(
'Entity "%s" has unsupported type "%s". Only functions and classes are '
'supported for now.' % (o, type(o)))
conversion_map.add_to_cache(o, node)
if conversion_map.recursive:
while True:
candidate = None
for obj in conversion_map.name_map.keys():
if obj not in conversion_map.dependency_cache:
candidate = obj
break
if candidate is None:
break
if (hasattr(candidate, 'im_class') and
getattr(candidate, 'im_class') not in conversion_map.partial_types):
# Class members are converted with their objects, unless they're
# only converted partially.
continue
entity_to_graph(candidate, conversion_map, {}, {})
return node, name, ns
def _verify_metric_fn_args(metric_fn):
args = set(estimator_util.fn_args(metric_fn))
if tf_inspect.ismethod(metric_fn):
if 'self' in args:
args.remove('self')
invalid_args = list(args - _VALID_METRIC_FN_ARGS)
if invalid_args:
raise ValueError('metric_fn (%s) has following not expected args: %s' %
(metric_fn, invalid_args))
def _get_func_name(func):
_, func = tf_decorator.unwrap(func)
if callable(func):
if tf_inspect.isfunction(func):
return func.__name__
elif tf_inspect.ismethod(func):
return "%s.%s" % (func.__self__.__name__, func.__name__)
else: # Probably a class instance with __call__
return type(func)
else:
raise ValueError("Argument must be callable")
def get_func_code(func):
"""Returns func_code of passed callable."""
_, func = tf_decorator.unwrap(func)
if callable(func):
if tf_inspect.isfunction(func) or tf_inspect.ismethod(func):
return six.get_function_code(func)
elif hasattr(func, '__call__'):
return six.get_function_code(func.__call__)
else:
raise ValueError('Unhandled callable, type=%s' % type(func))
else:
raise ValueError('Argument must be callable')
def getfutureimports(entity):
"""Detects what future imports are necessary to safely execute entity source.
Args:
entity: Any object
Returns:
A tuple of future strings
"""
if not (tf_inspect.isfunction(entity) or tf_inspect.ismethod(entity)):
return tuple()
return tuple(sorted(name for name, value in entity.__globals__.items()
if getattr(value, '__module__', None) == '__future__'))
def get_func_name(func):
"""Returns name of passed callable."""
_, func = tf_decorator.unwrap(func)
if callable(func):
if tf_inspect.isfunction(func):
return func.__name__
elif tf_inspect.ismethod(func):
return '%s.%s' % (six.get_method_self(func).__class__.__name__,
six.get_method_function(func).__name__)
else: # Probably a class instance with __call__
return str(type(func))
else:
raise ValueError('Argument must be callable')
def convert_entity_to_ast(o, program_ctx):
"""Compile a Python entity into equivalent TensorFlow.
Args:
o: A Python entity.
program_ctx: A ProgramContext object.
Returns:
A tuple (ast, new_name, namespace):
* ast: An AST representing an entity with interface equivalent to `o`,
but which when executed it creates TF a graph.
* new_name: The symbol name under which the new entity can be found.
* namespace: A dict mapping all symbols visible to the converted entity,
keyed by their symbol name.
Raises:
ValueError: if the entity type is not supported.
"""
logging.log(1, 'Converting %s', o)
if tf_inspect.isclass(o):
nodes, name, entity_info = convert_class_to_ast(o, program_ctx)
elif tf_inspect.isfunction(o):
nodes, name, entity_info = convert_func_to_ast(o, program_ctx)
elif tf_inspect.ismethod(o):
nodes, name, entity_info = convert_func_to_ast(o, program_ctx)
# TODO(mdan,yashkatariya): Remove when object conversion is implemented.
elif hasattr(o, '__class__'):
raise NotImplementedError(
'Object conversion is not yet supported. If you are '
'trying to convert code that uses an existing object, '
'try including the creation of that object in the '
'conversion. For example, instead of converting the method '
'of a class, try converting the entire class instead. '
'See https://github.com/tensorflow/tensorflow/blob/master/tensorflow/'
'python/autograph/README.md#using-the-functional-api '
'for more information.')
else:
raise ValueError(
'Entity "%s" has unsupported type "%s". Only functions and classes are '
'supported for now.' % (o, type(o)))
if logging.has_verbosity(2):
logging.log(2, 'Compiled output of %s:\n\n%s\n', o,
compiler.ast_to_source(nodes))
if logging.has_verbosity(4):
for n in nodes:
logging.log(4, 'Compiled AST of %s:\n\n%s\n\n', o,
pretty_printer.fmt(n, color=False))
return nodes, name, entity_info
def _instantiate(entity, converted_entity_info, free_nonglobal_var_names):
"""Creates a converted instance and binds it to match original entity."""
factory = converted_entity_info.get_factory()
# `factory` is currently bound to the empty module it was loaded from.
# It must instead be bound to the globals and closure from the original
# entity.
if tf_inspect.isfunction(entity) or tf_inspect.ismethod(entity):
entity_globals = entity.__globals__
entity_closure = entity.__closure__ or ()
elif hasattr(entity, '__module__'):
entity_globals = sys.modules[entity.__module__].__dict__
entity_closure = ()
assert len(entity_closure) == len(free_nonglobal_var_names)
# Fit the original entity's cells to match the order of factory's cells.
original_names_and_cells = dict(zip(free_nonglobal_var_names, entity_closure))
new_factory_cells = tuple(
original_names_and_cells[name] for name in factory.__code__.co_freevars)
bound_factory = types.FunctionType(
code=factory.__code__,
globals=entity_globals,
name=factory.__name__,
argdefs=(),
closure=new_factory_cells)
# Two other free vars: the internal "ag__" module and the source
# map. These are wired via the parameters of the factory.
converted_entity = bound_factory( # pylint:disable=not-callable
ag_internal, converted_entity_info.source_map,
converted_entity_info.get_module())
if tf_inspect.isfunction(entity) or tf_inspect.ismethod(entity):
# Attach the default argument to the converted function.
converted_entity.__defaults__ = entity.__defaults__
return converted_entity
def object_to_graph(o, conversion_map, value_hints):
"""Compile a Python object into equivalent TensorFlow.
The function will also recursively compile all the objects that `o`
references, updating `dependency_cache`.
This function is reentrant, and relies on dependency_cache to avoid
generating duplicate code.
Args:
o: A Python object.
conversion_map: A ConversionMap object.
value_hints: A dict containing value hints for symbols like function
parameters.
Returns:
A tuple (ast, new_name):
* ast: An AST representing an object with interface equivalent to `o`,
but which when executed it creates TF a graph.
* new_name: The symbol name under which the new object can be found.
Raises:
ValueError: if the object is not supported.
"""
if value_hints is None:
value_hints = {}
if tf_inspect.isclass(o):
node, new_name = class_to_graph(o, conversion_map, value_hints)
elif tf_inspect.isfunction(o):
node, new_name = function_to_graph(o, conversion_map, value_hints)
elif tf_inspect.ismethod(o):
node, new_name = function_to_graph(o, conversion_map, value_hints)
else:
raise ValueError(
'Entity "%s" has unsupported type "%s". Only functions and classes are '
'supported for now.' % (o, type(o)))
conversion_map.add_to_cache(o, node)
if conversion_map.recursive:
for obj in conversion_map.name_map.keys():
if obj not in conversion_map.dependency_cache:
if (hasattr(obj, 'im_class') and
getattr(obj, 'im_class') not in conversion_map.partial_types):
# Class members are converted with their objects, unless they're
# only converted partially.
continue
object_to_graph(obj, conversion_map, None)
return node, new_name
def get_func_code(func):
"""Returns func_code of passed callable, or None if not available."""
_, func = tf_decorator.unwrap(func)
if callable(func):
if tf_inspect.isfunction(func) or tf_inspect.ismethod(func):
return six.get_function_code(func)
# Since the object is not a function or method, but is a callable, we will
# try to access the __call__method as a function. This works with callable
# classes but fails with functool.partial objects despite their __call__
# attribute.
try:
return six.get_function_code(func.__call__)
except AttributeError:
return None
else:
raise ValueError('Argument must be callable')
def convert_entity_to_ast(o, program_ctx):
"""Compile a Python entity into equivalent TensorFlow.
Args:
o: A Python entity.
program_ctx: A ProgramContext object.
Returns:
A tuple (ast, new_name, namespace):
* ast: An AST representing an entity with interface equivalent to `o`,
but which when executed it creates TF a graph.
* new_name: The symbol name under which the new entity can be found.
* namespace: A dict mapping all symbols visible to the converted entity,
keyed by their symbol name.
Raises:
ValueError: if the entity type is not supported.
"""
logging.log(1, 'Converting %s', o)
if tf_inspect.isclass(o):
nodes, name, entity_info = convert_class_to_ast(o, program_ctx)
elif tf_inspect.isfunction(o):
nodes, name, entity_info = convert_func_to_ast(o, program_ctx)
elif tf_inspect.ismethod(o):
nodes, name, entity_info = convert_func_to_ast(o, program_ctx)
elif hasattr(o, '__class__'):
# Note: this should only be raised when attempting to convert the object
# directly. converted_call should still support it.
raise NotImplementedError(
'cannot convert entity "{}": object conversion is not yet'
' supported.'.format(o))
else:
raise ValueError(
'Entity "%s" has unsupported type "%s". Only functions and classes are '
'supported for now.' % (o, type(o)))
if logging.has_verbosity(2):
logging.log(2, 'Compiled output of %s:\n\n%s\n', o,
compiler.ast_to_source(nodes))
if logging.has_verbosity(4):
for n in nodes:
logging.log(4, 'Compiled AST of %s:\n\n%s\n\n', o,
pretty_printer.fmt(n, color=False))
return nodes, name, entity_info
def _get_raw_docstring(py_object):
"""Get the docs for a given python object.
Args:
py_object: A python object to retrieve the docs for (class, function/method,
or module).
Returns:
The docstring, or the empty string if no docstring was found.
"""
# For object instances, tf_inspect.getdoc does give us the docstring of their
# type, which is not what we want. Only return the docstring if it is useful.
if (tf_inspect.isclass(py_object) or tf_inspect.ismethod(py_object) or
tf_inspect.isfunction(py_object) or tf_inspect.ismodule(py_object) or
isinstance(py_object, property)):
return tf_inspect.getdoc(py_object) or ''
else:
return ''
def convert(entity, program_ctx):
"""Converts an entity into an equivalent entity."""
if tf_inspect.isfunction(entity) or tf_inspect.ismethod(entity):
free_nonglobal_var_names = entity.__code__.co_freevars
else:
free_nonglobal_var_names = ()
for i, name in enumerate(free_nonglobal_var_names):
if (name == 'ag__' and
entity.__closure__[i].cell_contents is not ag_internal):
raise ValueError('entity {} uses the reserved symbol "{}"'.format(
entity, name))
# TODO(mdan): In extreme cases, other ag__ symbols may also be clobbered.
converted_entity_info = _convert_with_cache(
entity, program_ctx, free_nonglobal_var_names)
return _instantiate(entity, converted_entity_info, free_nonglobal_var_names)
def _verify_model_fn_args(model_fn, params):
"""Verifies model fn arguments."""
args = set(util.fn_args(model_fn))
if 'features' not in args:
raise ValueError('model_fn (%s) must include features argument.' % model_fn)
if params is not None and 'params' not in args:
raise ValueError('model_fn (%s) does not include params argument, '
'but params (%s) is passed to Estimator.' % (model_fn,
params))
if params is None and 'params' in args:
logging.warning('Estimator\'s model_fn (%s) includes params '
'argument, but params are not passed to Estimator.',
model_fn)
if tf_inspect.ismethod(model_fn):
if 'self' in args:
args.remove('self')
non_valid_args = list(args - _VALID_MODEL_FN_ARGS)
if non_valid_args:
raise ValueError('model_fn (%s) has following not expected args: %s' %
(model_fn, non_valid_args))
def converted_call(f, recursive, verbose, arg_types, *args, **kwargs):
"""Compiles a function call inline."""
# TODO(mdan): This needs cleanup.
# In particular, we may want to avoid renaming functions altogether.
if conversion.is_whitelisted_for_graph(f):
return f(*args, **kwargs)
unknown_arg_value = object() # Sentinel for arguments of unknown value
if inspect_utils.isbuiltin(f):
return builtins.dynamic_builtin(f, *args, **kwargs)
if tf_inspect.isfunction(f) or tf_inspect.ismethod(f):
# Regular functions
target_entity = f
arg_map_target = f
effective_args = args
f_class = inspect_utils.getmethodclass(f)
if f_class is not None:
partial_types = (f_class,)
else:
partial_types = ()
elif tf_inspect.isclass(f):
# Constructors
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:
NotImplementedError('unknown callable type "%s"' % type(f))
arg_values = tf_inspect.getcallargs(arg_map_target, *args, **kwargs)
for name, arg in arg_values.items():
if arg is unknown_arg_value:
continue
arg_class = arg.__class__
# If arg_value_hints specifies any name, use that instead.
if name not in arg_types:
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'],)
converted_f = to_graph(
target_entity,
recursive=recursive,
verbose=verbose,
arg_values=arg_values,
arg_types=arg_types,
partial_types=partial_types)
return converted_f(*effective_args, **kwargs)
def entity_to_graph(o, program_ctx, arg_values, arg_types):
"""Compile a Python entity into equivalent TensorFlow.
The function will also recursively compile all the entities that `o`
references, updating `dependency_cache`.
This function is reentrant, and relies on dependency_cache to avoid
generating duplicate code.
Args:
o: A Python entity.
program_ctx: A ProgramContext object.
arg_values: A dict containing value hints for symbols like function
parameters.
arg_types: A dict containing type hints for symbols like function
parameters.
Returns:
A tuple (ast, new_name, namespace):
* ast: An AST representing an entity with interface equivalent to `o`,
but which when executed it creates TF a graph.
* new_name: The symbol name under which the new entity can be found.
* namespace: A dict mapping all symbols visible to the converted entity,
keyed by their symbol name.
Raises:
ValueError: if the entity type is not supported.
"""
if program_ctx.options.verbose == converter.Verbosity.VERBOSE:
logging.info('Converting {}'.format(o))
if tf_inspect.isclass(o):
node, name, ns = class_to_graph(o, program_ctx)
elif tf_inspect.isfunction(o):
node, name, ns = function_to_graph(o, program_ctx, arg_values, arg_types)
elif tf_inspect.ismethod(o):
node, name, ns = function_to_graph(o, program_ctx, arg_values, arg_types)
# TODO(mdan,yashkatariya): Remove when object conversion is implemented.
elif hasattr(o, '__class__'):
raise NotImplementedError(
'Object conversion is not yet supported. If you are '
'trying to convert code that uses an existing object, '
'try including the creation of that object in the '
'conversion. For example, instead of converting the method '
'of a class, try converting the entire class instead. '
'See https://github.com/tensorflow/tensorflow/blob/master/tensorflow/'
'contrib/autograph/README.md#using-the-functional-api '
'for more information.')
else:
raise ValueError(
'Entity "%s" has unsupported type "%s". Only functions and classes are '
'supported for now.' % (o, type(o)))
# TODO(mdan): This is temporary. it should be created using a converter.
# TODO(mdan): The attribute should be added with a helper, not directly.
# The helper can ensure there are no collisions.
template = '''
entity.autograph_info__ = {}
'''
node.extend(templates.replace(template, entity=name))
program_ctx.add_to_cache(o, node)
if program_ctx.options.verbose == converter.Verbosity.VERBOSE:
logging.info('Compiled output of {}:\n\n{}\n'.format(
o, compiler.ast_to_source(node)))
if program_ctx.options.recursive:
while True:
candidate = None
for obj in program_ctx.name_map.keys():
if obj not in program_ctx.dependency_cache:
candidate = obj
break
if candidate is None:
break
if (hasattr(candidate, 'im_class') and
getattr(candidate, 'im_class') not in program_ctx.partial_types):
# Class members are converted with their objects, unless they're
# only converted partially.
continue
entity_to_graph(candidate, program_ctx, {}, {})
return node, name, ns
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 docs_for_object(full_name, py_object, parser_config):
"""Return a PageInfo object describing a given object from the TF API.
This function uses _parse_md_docstring to parse the docs pertaining to
`object`.
This function resolves '@{symbol}' references in the docstrings into links to
the appropriate location. It also adds a list of alternative names for the
symbol automatically.
It assumes that the docs for each object live in a file given by
`documentation_path`, and that relative links to files within the
documentation are resolvable.
Args:
full_name: The fully qualified name of the symbol to be
documented.
py_object: The Python object to be documented. Its documentation is sourced
from `py_object`'s docstring.
parser_config: A ParserConfig object.
Returns:
Either a `_FunctionPageInfo`, `_ClassPageInfo`, or a `_ModulePageInfo`
depending on the type of the python object being documented.
Raises:
RuntimeError: If an object is encountered for which we don't know how
to make docs.
"""
# Which other aliases exist for the object referenced by full_name?
master_name = parser_config.reference_resolver.py_master_name(full_name)
duplicate_names = parser_config.duplicates.get(master_name, [full_name])
# TODO(wicke): Once other pieces are ready, enable this also for partials.
if (tf_inspect.ismethod(py_object) or tf_inspect.isfunction(py_object) or
# Some methods in classes from extensions come in as routines.
tf_inspect.isroutine(py_object)):
page_info = _FunctionPageInfo(master_name)
page_info.set_signature(py_object, parser_config.reverse_index)
elif tf_inspect.isclass(py_object):
page_info = _ClassPageInfo(master_name)
page_info.collect_docs_for_class(py_object, parser_config)
elif tf_inspect.ismodule(py_object):
page_info = _ModulePageInfo(master_name)
page_info.collect_docs_for_module(parser_config)
else:
raise RuntimeError('Cannot make docs for object %s: %r' %
(full_name, py_object))
relative_path = os.path.relpath(
path='.', start=os.path.dirname(documentation_path(full_name)) or '.')
page_info.set_doc(
_parse_md_docstring(py_object, relative_path,
parser_config.reference_resolver))
page_info.set_aliases(duplicate_names)
page_info.set_guides(
_get_guides_markdown(duplicate_names, parser_config.guide_index,
relative_path))
page_info.set_defined_in(_get_defined_in(py_object, parser_config))
return page_info
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 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))
# 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)
# TODO(mdan): This needs cleanup.
# In particular, we may want to avoid renaming functions altogether.
if not options.force_conversion and conversion.is_whitelisted_for_graph(f):
return f(*args, **kwargs)
unknown_arg_value = object() # Sentinel for arguments of unknown value
if inspect_utils.isbuiltin(f):
return py_builtins.overload_of(f)(*args, **kwargs)
if tf_inspect.isfunction(f) or tf_inspect.ismethod(f):
# Regular functions
target_entity = f
arg_map_target = f
f_class = inspect_utils.getmethodclass(f)
if f_class 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:
effective_args = args
partial_types = (f_class,)
else:
effective_args = args
partial_types = ()
elif tf_inspect.isclass(f):
# Constructors
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:
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():
if arg is unknown_arg_value:
continue
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'],)
converted_f = to_graph(
target_entity,
recursive=options.recursive,
verbose=options.verbose,
arg_values=arg_values,
arg_types=arg_types,
partial_types=partial_types,
strip_decorators=options.strip_decorators)
return converted_f(*effective_args, **kwargs)
def convert_class_to_ast(c, program_ctx):
"""Specialization of `convert_entity_to_ast` for classes."""
# TODO(mdan): Revisit this altogether. Not sure we still need it.
converted_members = {}
method_filter = lambda m: tf_inspect.isfunction(m) or tf_inspect.ismethod(m
)
members = tf_inspect.getmembers(c, predicate=method_filter)
if not members:
raise ValueError('cannot convert %s: no member methods' % c)
# TODO(mdan): Don't clobber namespaces for each method in one class namespace.
# The assumption that one namespace suffices for all methods only holds if
# all methods were defined in the same module.
# If, instead, functions are imported from multiple modules and then spliced
# into the class, then each function has its own globals and __future__
# imports that need to stay separate.
# For example, C's methods could both have `global x` statements referring to
# mod1.x and mod2.x, but using one namespace for C would cause a conflict.
# from mod1 import f1
# from mod2 import f2
# class C(object):
# method1 = f1
# method2 = f2
class_namespace = {}
future_features = None
for _, m in members:
# Only convert the members that are directly defined by the class.
if inspect_utils.getdefiningclass(m, c) is not c:
continue
(node, ), _, entity_info = convert_func_to_ast(m,
program_ctx=program_ctx,
do_rename=False)
class_namespace.update(entity_info.namespace)
converted_members[m] = node
# TODO(mdan): Similarly check the globals.
if future_features is None:
future_features = entity_info.future_features
elif frozenset(future_features) ^ frozenset(
entity_info.future_features):
# Note: we can support this case if ever needed.
raise ValueError(
'cannot convert {}: if has methods built with mismatched future'
' features: {} and {}'.format(c, future_features,
entity_info.future_features))
namer = naming.Namer(class_namespace)
class_name = namer.class_name(c.__name__)
# Process any base classes: if the superclass if of a whitelisted type, an
# absolute import line is generated.
output_nodes = []
renames = {}
base_names = []
for base in c.__bases__:
if isinstance(object, base):
base_names.append('object')
continue
if is_whitelisted_for_graph(base):
alias = namer.new_symbol(base.__name__, ())
output_nodes.append(
gast.ImportFrom(
module=base.__module__,
names=[gast.alias(name=base.__name__, asname=alias)],
level=0))
else:
raise NotImplementedError(
'Conversion of classes that do not directly extend classes from'
' whitelisted modules is temporarily suspended. If this breaks'
' existing code please notify the AutoGraph team immediately.')
base_names.append(alias)
renames[qual_names.QN(base.__name__)] = qual_names.QN(alias)
# Generate the definition of the converted class.
bases = [gast.Name(n, gast.Load(), None) for n in base_names]
class_def = gast.ClassDef(class_name,
bases=bases,
keywords=[],
body=list(converted_members.values()),
decorator_list=[])
# Make a final pass to replace references to the class or its base classes.
# Most commonly, this occurs when making super().__init__() calls.
# TODO(mdan): Making direct references to superclass' superclass will fail.
class_def = qual_names.resolve(class_def)
renames[qual_names.QN(c.__name__)] = qual_names.QN(class_name)
class_def = ast_util.rename_symbols(class_def, renames)
output_nodes.append(class_def)
# TODO(mdan): Find a way better than forging this object.
entity_info = transformer.EntityInfo(source_code=None,
source_file=None,
future_features=future_features,
namespace=class_namespace)
return output_nodes, class_name, entity_info
def converted_call(f, owner, options, *args, **kwargs):
"""Compiles a function call inline. For internal use only."""
if options.verbose:
logging.info('Converted call: {}; owner: {}'.format(f, owner))
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)
# TODO(mdan): This needs cleanup.
# In particular, we may want to avoid renaming functions altogether.
if not options.force_conversion and conversion.is_whitelisted_for_graph(f):
return f(*args, **kwargs)
unknown_arg_value = object() # Sentinel for arguments of unknown value
if inspect_utils.isbuiltin(f):
return py_builtins.overload_of(f)(*args, **kwargs)
if tf_inspect.isfunction(f) or tf_inspect.ismethod(f):
# Regular functions
target_entity = f
arg_map_target = f
f_class = inspect_utils.getmethodclass(f)
if f_class 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:
effective_args = args
partial_types = (f_class, )
else:
effective_args = args
partial_types = ()
elif tf_inspect.isclass(f):
# Constructors
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:
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():
if arg is unknown_arg_value:
continue
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'], )
converted_f = to_graph(target_entity,
recursive=options.recursive,
verbose=options.verbose,
arg_values=arg_values,
arg_types=arg_types,
partial_types=partial_types,
strip_decorators=options.strip_decorators,
optional_features=options.optional_features)
return converted_f(*effective_args, **kwargs)
def converted_call(f, owner, options, *args, **kwargs):
"""Compiles a function call inline. For internal use only."""
if options.verbose:
logging.info('Converted call: {}; owner: {}'.format(f, owner))
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)
# TODO(mdan): This needs cleanup.
# In particular, we may want to avoid renaming functions altogether.
if not options.force_conversion and conversion.is_whitelisted_for_graph(f):
return f(*args, **kwargs)
if inspect_utils.isbuiltin(f):
return py_builtins.overload_of(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)
if tf_inspect.isfunction(f) or tf_inspect.ismethod(f):
# Regular functions
target_entity = f
arg_map_target = f
f_class = inspect_utils.getmethodclass(f)
if f_class 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:
effective_args = args
partial_types = (f_class, )
else:
effective_args = args
partial_types = ()
elif tf_inspect.isclass(f):
# Constructors
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:
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'], )
converted_f = to_graph(target_entity,
recursive=options.recursive,
verbose=options.verbose,
arg_values=arg_values,
arg_types=arg_types,
partial_types=partial_types,
strip_decorators=options.strip_decorators,
optional_features=options.optional_features)
result = converted_f(*effective_args, **kwargs)
# When converting a function, we write a tmp file and import it as a module.
# This leaks the module's closure. Once we've executed the converted_f module
# and there is no more code left to be executed, we can clean up the module.
# TODO(mdan): Look into workarounds that don't suffer from refcount leaks.
# Possibly attach the closure as a regular closure cell, instead of relying on
# module globals.
# If there are callables in the result, they will fail to find their closure
# when called, so only delete module if all returned types are not callable.
flat_results = nest.flatten(result)
if all(map(_is_not_callable, flat_results)):
del sys.modules[converted_f.__module__]
return result
def __call__(self, func):
# Various sanity checks on the callable func.
if not callable(func):
raise ValueError("function %s must be callable" % func)
# Func should not use kwargs and defaults.
argspec = tf_inspect.getargspec(func)
if argspec.keywords or argspec.defaults:
raise ValueError(
"function with argument defaults or keywords arguments are not"
" supported. {} has defaults {} and keywords {}.".format(
func, argspec.defaults, argspec.keywords))
# Computes how many arguments 'func' has.
min_args = len(argspec.args)
max_args = min_args
if argspec.varargs:
max_args = 1000000
argnames = argspec.args
if tf_inspect.ismethod(func):
# 1st argument is the "class" type.
min_args -= 1
argnames = argnames[1:]
if self._input_types:
# If Defun is given a list of types for the inputs, the number
# of input types should be compatible with 'func'.
num = len(self._input_types)
if num < min_args or num > max_args:
raise ValueError(
"The function has fewer arguments than the number of specified "
"input types.")
return _DefinedFunction(
func,
argnames,
self._input_types,
self._func_name,
self._grad_func,
self._python_grad_func,
out_names=self._out_names,
**self._extra_kwargs)
# 'func' expects no arguments and input types is an empty list.
if min_args == 0 and max_args == 0:
return _DefinedFunction(
func, [], [],
self._func_name,
self._grad_func,
self._python_grad_func,
out_names=self._out_names,
**self._extra_kwargs)
# Input types are unknown. It's an overloaded function and hence
# its definition needs to be deferred until it's called.
return _OverloadedFunction(
func,
argnames,
self._func_name,
self._grad_func,
self._python_grad_func,
out_names=self._out_names,
**self._extra_kwargs)
def class_to_graph(c, conversion_map):
"""Specialization of `entity_to_graph` for classes."""
converted_members = {}
method_filter = lambda m: tf_inspect.isfunction(m) or tf_inspect.ismethod(m
)
members = tf_inspect.getmembers(c, predicate=method_filter)
if not members:
raise ValueError('Cannot convert %s: it has no member methods.' % c)
class_namespace = {}
for _, m in members:
# Only convert the members that are directly defined by the class.
if inspect_utils.getdefiningclass(m, c) is not c:
continue
node, _, namespace = function_to_graph(
m,
conversion_map=conversion_map,
arg_values={},
arg_types={'self': (c.__name__, c)},
owner_type=c)
if class_namespace is None:
class_namespace = namespace
else:
class_namespace.update(namespace)
converted_members[m] = node
namer = conversion_map.new_namer(class_namespace)
class_name = namer.compiled_class_name(c.__name__, c)
# TODO (mdan): This needs to be explained more thoroughly. id:673
# https://github.com/imdone/tensorflow/issues/674
# Process any base classes: if the sueprclass if of a whitelisted type, an
# absolute import line is generated. Otherwise, it is marked for conversion
# (as a side effect of the call to namer.compiled_class_name() followed by
# conversion_map.update_name_map(namer)).
output_nodes = []
renames = {}
bases = []
for base in c.__bases__:
if isinstance(object, base):
bases.append('object')
continue
if is_whitelisted_for_graph(base):
alias = namer.new_symbol(base.__name__, ())
output_nodes.append(
gast.ImportFrom(
module=base.__module__,
names=[gast.alias(name=base.__name__, asname=alias)],
level=0))
else:
# This will trigger a conversion into a class with this name.
alias = namer.compiled_class_name(base.__name__, base)
bases.append(alias)
renames[qual_names.QN(base.__name__)] = qual_names.QN(alias)
conversion_map.update_name_map(namer)
# Generate the definition of the converted class.
output_nodes.append(
gast.ClassDef(class_name,
bases=bases,
keywords=[],
body=list(converted_members.values()),
decorator_list=[]))
node = gast.Module(output_nodes)
# Make a final pass to replace references to the class or its base classes.
# Most commonly, this occurs when making super().__init__() calls.
# TODO (mdan): Making direct references to superclass' superclass will fail. id:521
# https://github.com/imdone/tensorflow/issues/522
node = qual_names.resolve(node)
renames[qual_names.QN(c.__name__)] = qual_names.QN(class_name)
node = ast_util.rename_symbols(node, renames)
return node, class_name, class_namespace
def entity_to_graph(o, conversion_map, arg_values, arg_types):
"""Compile a Python entity into equivalent TensorFlow.
The function will also recursively compile all the entities that `o`
references, updating `dependency_cache`.
This function is reentrant, and relies on dependency_cache to avoid
generating duplicate code.
Args:
o: A Python entity.
conversion_map: A ConversionMap object.
arg_values: A dict containing value hints for symbols like function
parameters.
arg_types: A dict containing type hints for symbols like function
parameters.
Returns:
A tuple (ast, new_name, namespace):
* ast: An AST representing an entity with interface equivalent to `o`,
but which when executed it creates TF a graph.
* new_name: The symbol name under which the new entity can be found.
* namespace: A dict mapping all symbols visible to the converted entity,
keyed by their symbol name.
Raises:
ValueError: if the entity type is not supported.
"""
if tf_inspect.isclass(o):
node, name, ns = class_to_graph(o, conversion_map)
elif tf_inspect.isfunction(o):
# TODO (mdan): This is not a reliable mechanism. id:619
# https://github.com/imdone/tensorflow/issues/620
# The most reliable way is to check the source code, the AST will contain
# a Lambda node instead of a FunctionDef
if o.__name__ == '<lambda>':
raise NotImplementedError(
'lambda functions are not yet supported; declare the function'
' using def instead: %s' % o)
else:
node, name, ns = function_to_graph(o, conversion_map, arg_values,
arg_types)
elif tf_inspect.ismethod(o):
node, name, ns = function_to_graph(o, conversion_map, arg_values,
arg_types)
else:
raise ValueError(
'Entity "%s" has unsupported type "%s". Only functions and classes are '
'supported for now.' % (o, type(o)))
conversion_map.add_to_cache(o, node)
if conversion_map.recursive:
while True:
candidate = None
for obj in conversion_map.name_map.keys():
if obj not in conversion_map.dependency_cache:
candidate = obj
break
if candidate is None:
break
if (hasattr(candidate, 'im_class')
and getattr(candidate, 'im_class')
not in conversion_map.partial_types):
# Class members are converted with their objects, unless they're
# only converted partially.
continue
entity_to_graph(candidate, conversion_map, {}, {})
return node, name, ns
def testIsMethod(self):
self.assertTrue(tf_inspect.ismethod(TestDecoratedClass().two))
self.assertFalse(tf_inspect.ismethod(test_decorated_function))
def converted_call(f, recursive, verbose, arg_types, *args, **kwargs):
"""Compiles a function call inline."""
# TODO(mdan): This needs cleanup.
# In particular, we may want to avoid renaming functions altogether.
if conversion.is_whitelisted_for_graph(f):
return f(*args, **kwargs)
unknown_arg_value = object() # Sentinel for arguments of unknown value
if tf_inspect.isbuiltin(f):
return builtins.dynamic_builtin(f, *args, **kwargs)
if tf_inspect.isfunction(f) or tf_inspect.ismethod(f):
# Regular functions
target_entity = f
arg_map_target = f
effective_args = args
f_class = inspect_utils.getmethodclass(f)
if f_class is not None:
partial_types = (f_class, )
else:
partial_types = ()
elif tf_inspect.isclass(f):
# Constructors
target_entity = f
arg_map_target = f.__init__
effective_args = (unknown_arg_value, ) + 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:
NotImplementedError('unknown callable type "%s"' % type(f))
arg_values = tf_inspect.getcallargs(arg_map_target, *args, **kwargs)
for name, arg in arg_values.items():
if arg is unknown_arg_value:
continue
arg_class = arg.__class__
# If arg_value_hints specifies any name, use that instead.
if name not in arg_types:
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'], )
converted_f = to_graph(target_entity,
recursive=recursive,
verbose=verbose,
arg_values=arg_values,
arg_types=arg_types,
partial_types=partial_types)
return converted_f(*effective_args, **kwargs)
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 _is_callable_object(obj): return hasattr(obj, '__call__') and tf_inspect.ismethod(obj.__call__)
def _is_callable_object(obj):
return hasattr(obj, '__call__') and tf_inspect.ismethod(obj.__call__)
def converted_call(f, owner, options, *args, **kwargs):
"""Compiles a function call inline. For internal use only."""
if options.verbose >= converter.Verbosity.VERBOSE:
logging.info('Converted call: {}; owner: {}'.format(f, owner))
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(mdan): This needs cleanup.
# In particular, we may want to avoid renaming functions altogether.
if not options.force_conversion and conversion.is_whitelisted_for_graph(f):
# 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_class = inspect_utils.getmethodclass(f)
if args[0] is f_class:
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)
if tf_inspect.isfunction(f) or tf_inspect.ismethod(f):
# Regular functions
target_entity = f
arg_map_target = f
f_class = inspect_utils.getmethodclass(f)
# TODO(mdan): This may be more elegantly handled using __get__?
if f_class 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:
# Always override the self arg, because it might be different from
# what the method was bound to - see inspect_utils.getmethodclass.
assert args, 'Bound function call without self argument?'
effective_args = (f_class, ) + args[1:]
partial_types = (f_class, )
else:
effective_args = args
partial_types = ()
elif tf_inspect.isclass(f):
# Constructors
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:
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'], )
converted_f = to_graph(target_entity,
recursive=options.recursive,
verbose=options.verbose,
arg_values=arg_values,
arg_types=arg_types,
partial_types=partial_types,
strip_decorators=options.strip_decorators,
optional_features=options.optional_features)
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 _is_bounded_method(fn):
_, fn = tf_decorator.unwrap(fn)
return tf_inspect.ismethod(fn) and (fn.__self__ is not None)
def entity_to_graph(o, program_ctx, arg_values, arg_types):
"""Compile a Python entity into equivalent TensorFlow.
The function will also recursively compile all the entities that `o`
references, updating `dependency_cache`.
This function is reentrant, and relies on dependency_cache to avoid
generating duplicate code.
Args:
o: A Python entity.
program_ctx: A ProgramContext object.
arg_values: A dict containing value hints for symbols like function
parameters.
arg_types: A dict containing type hints for symbols like function
parameters.
Returns:
A tuple (ast, new_name, namespace):
* ast: An AST representing an entity with interface equivalent to `o`,
but which when executed it creates TF a graph.
* new_name: The symbol name under which the new entity can be found.
* namespace: A dict mapping all symbols visible to the converted entity,
keyed by their symbol name.
Raises:
ValueError: if the entity type is not supported.
"""
if tf_inspect.isclass(o):
node, name, ns = class_to_graph(o, program_ctx)
elif tf_inspect.isfunction(o):
# TODO(mdan): This is not a reliable mechanism.
# The most reliable way is to check the source code, the AST will contain
# a Lambda node instead of a FunctionDef
if o.__name__ == '<lambda>':
raise NotImplementedError(
'lambda functions are not yet supported; declare the function'
' using def instead: %s' % o)
else:
node, name, ns = function_to_graph(o, program_ctx, arg_values, arg_types)
elif tf_inspect.ismethod(o):
node, name, ns = function_to_graph(o, program_ctx, arg_values, arg_types)
else:
raise ValueError(
'Entity "%s" has unsupported type "%s". Only functions and classes are '
'supported for now.' % (o, type(o)))
# TODO(mdan): This is temporary. it should be created using a converter.
# TODO(mdan): The attribute should be added with a helper, not directly.
# The helper can ensure there are no collisions.
template = '''
entity.autograph_info__ = {}
'''
node.extend(templates.replace(template, entity=name))
program_ctx.add_to_cache(o, node)
if program_ctx.recursive:
while True:
candidate = None
for obj in program_ctx.name_map.keys():
if obj not in program_ctx.dependency_cache:
candidate = obj
break
if candidate is None:
break
if (hasattr(candidate, 'im_class') and
getattr(candidate, 'im_class') not in program_ctx.partial_types):
# Class members are converted with their objects, unless they're
# only converted partially.
continue
entity_to_graph(candidate, program_ctx, {}, {})
return node, name, ns
def is_allowlisted(o,
check_call_override=True,
allow_namedtuple_subclass=False):
"""Checks whether an entity is allowed for use in graph mode.
Examples of allowed 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 allowed if their
__call__ method is allowed.
allow_namedtuple_subclass: Reserved for internal use. When `True`,
namedtuple subclasses are not allowed.
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 allowed: %s: %s', o, rule)
return False
elif action == config.Action.DO_NOT_CONVERT:
logging.log(2, 'Allowlisted: %s: %s', o, rule)
return True
# The check for __code__ below is because isgeneratorfunction crashes
# without one.
if hasattr(o, '__code__') and tf_inspect.isgeneratorfunction(o):
logging.log(2,
'Allowlisted: %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: allowed 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_allowlisted(o.__call__): # pylint: disable=unidiomatic-typecheck
logging.log(2, 'Allowlisted: %s: object __call__ allowed', o)
return True
owner_class = None
if tf_inspect.ismethod(o):
# Methods of allowed classes are also allowed, 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 allowed. Then `baz.bar` is also
# allowed.
#
# class Custom(tf.Foo):
# pass
#
# baz = Custom()
#
# For the example above, if `Custom` did overload `bar`, then it would no
# longer be allowed.
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, 'Allowlisted: %s: method of TestCase subclass',
o)
return True
owner_class = inspect_utils.getdefiningclass(o, owner_class)
if is_allowlisted(owner_class,
check_call_override=False,
allow_namedtuple_subclass=True):
logging.log(2, 'Allowlisted: %s: owner is allowed %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, 'Allowlisted: %s: named tuple', o)
return True
else:
logging.log(2, 'Allowlisted: %s: named tuple or subclass', o)
return True
logging.log(2, 'Not allowed: %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.
# 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 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 >= 1. 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
def entity_to_graph(o, program_ctx, arg_values, arg_types):
"""Compile a Python entity into equivalent TensorFlow.
The function will also recursively compile all the entities that `o`
references, updating `dependency_cache`.
This function is reentrant, and relies on dependency_cache to avoid
generating duplicate code.
Args:
o: A Python entity.
program_ctx: A ProgramContext object.
arg_values: A dict containing value hints for symbols like function
parameters.
arg_types: A dict containing type hints for symbols like function
parameters.
Returns:
A tuple (ast, new_name, namespace):
* ast: An AST representing an entity with interface equivalent to `o`,
but which when executed it creates TF a graph.
* new_name: The symbol name under which the new entity can be found.
* namespace: A dict mapping all symbols visible to the converted entity,
keyed by their symbol name.
Raises:
ValueError: if the entity type is not supported.
"""
if tf_inspect.isclass(o):
node, name, ns = class_to_graph(o, program_ctx)
elif tf_inspect.isfunction(o):
# TODO(mdan): This is not a reliable mechanism.
# The most reliable way is to check the source code, the AST will contain
# a Lambda node instead of a FunctionDef
if o.__name__ == '<lambda>':
raise NotImplementedError(
'lambda functions are not yet supported; declare the function'
' using def instead: %s' % o)
else:
node, name, ns = function_to_graph(o, program_ctx, arg_values,
arg_types)
elif tf_inspect.ismethod(o):
node, name, ns = function_to_graph(o, program_ctx, arg_values,
arg_types)
# TODO(mdan,yashkatariya): Remove when object conversion is implemented.
elif hasattr(o, '__class__'):
raise NotImplementedError(
'Object conversion is not yet supported. If you are '
'trying to convert code that uses an existing object, '
'try including the creation of that object in the '
'conversion. For example, instead of converting the method '
'of a class, try converting the entire class instead. '
'See https://github.com/tensorflow/tensorflow/blob/master/tensorflow/'
'contrib/autograph/README.md#using-the-functional-api '
'for more information.')
else:
raise ValueError(
'Entity "%s" has unsupported type "%s". Only functions and classes are '
'supported for now.' % (o, type(o)))
# TODO(mdan): This is temporary. it should be created using a converter.
# TODO(mdan): The attribute should be added with a helper, not directly.
# The helper can ensure there are no collisions.
template = '''
entity.autograph_info__ = {}
'''
node.extend(templates.replace(template, entity=name))
program_ctx.add_to_cache(o, node)
if program_ctx.recursive:
while True:
candidate = None
for obj in program_ctx.name_map.keys():
if obj not in program_ctx.dependency_cache:
candidate = obj
break
if candidate is None:
break
if (hasattr(candidate, 'im_class') and getattr(
candidate, 'im_class') not in program_ctx.partial_types):
# Class members are converted with their objects, unless they're
# only converted partially.
continue
entity_to_graph(candidate, program_ctx, {}, {})
return node, name, ns
def collect_docs_for_class(self, py_class, parser_config):
"""Collect information necessary specifically for a class's doc page.
Mainly, this is details about information about the class's members.
Args:
py_class: the class object being documented
parser_config: An instance of ParserConfig.
"""
doc_path = documentation_path(self.full_name)
relative_path = os.path.relpath(path='.',
start=os.path.dirname(doc_path) or '.')
for short_name in parser_config.tree[self.full_name]:
# Remove builtin members that we never want to document.
if short_name in [
'__class__', '__base__', '__weakref__', '__doc__',
'__module__', '__dict__', '__abstractmethods__',
'__slots__', '__getnewargs__'
]:
continue
child_name = '.'.join([self.full_name, short_name])
child = parser_config.py_name_to_object(child_name)
# Don't document anything that is defined in object or by protobuf.
defining_class = _get_defining_class(py_class, short_name)
if (defining_class is object or defining_class is type
or defining_class is tuple
or defining_class is BaseException
or defining_class is Exception or
# The following condition excludes most protobuf-defined symbols.
defining_class and defining_class.__name__
in ['CMessage', 'Message', 'MessageMeta']):
continue
# TODO(markdaoust): Add a note in child docs showing the defining class.
child_doc = _parse_md_docstring(child, relative_path,
parser_config.reference_resolver)
if isinstance(child, property):
self._add_property(short_name, child_name, child, child_doc)
elif tf_inspect.isclass(child):
if defining_class is None:
continue
url = parser_config.reference_resolver.reference_to_url(
child_name, relative_path)
self._add_class(short_name, child_name, child, child_doc, url)
elif (tf_inspect.ismethod(child) or tf_inspect.isfunction(child)
or tf_inspect.isroutine(child)):
if defining_class is None:
continue
# Omit methods defined by namedtuple.
original_method = defining_class.__dict__[short_name]
if (hasattr(original_method, '__module__')
and (original_method.__module__
or '').startswith('namedtuple')):
continue
# Some methods are often overridden without documentation. Because it's
# obvious what they do, don't include them in the docs if there's no
# docstring.
if not child_doc.brief.strip() and short_name in [
'__str__', '__repr__', '__hash__', '__del__',
'__copy__'
]:
print('Skipping %s, defined in %s, no docstring.' %
(child_name, defining_class))
continue
try:
child_signature = _generate_signature(
child, parser_config.reverse_index)
except TypeError:
# If this is a (dynamically created) slot wrapper, tf_inspect will
# raise typeerror when trying to get to the code. Ignore such
# functions.
continue
self._add_method(short_name, child_name, child, child_doc,
child_signature)
else:
# Exclude members defined by protobuf that are useless
if issubclass(py_class, ProtoMessage):
if (short_name.endswith('_FIELD_NUMBER')
or short_name in ['__slots__', 'DESCRIPTOR']):
continue
# TODO(wicke): We may want to also remember the object itself.
self._add_other_member(short_name, child_name, child,
child_doc)
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)
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)
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__)
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)
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)
result = converted_f(*effective_args, **kwargs)
return result
def is_whitelisted_for_graph(o):
"""Check 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 not hasattr(m, '__name__'):
# Note: typically it's builtins that fall in this category. Builtins will
# be handled by specific code that follows this screening layer.
logging.log(2, '%s is NOT whitelisted: unknown module name', o)
return False
for prefix, in config.DEFAULT_UNCOMPILED_MODULES:
if m.__name__.startswith(prefix):
logging.log(2, '%s is whitelisted: name starts with "%s"', o,
prefix)
return True
if hasattr(o, 'autograph_info__') or hasattr(o, '__ag_compiled'):
logging.log(2, '%s is whitelisted: already converted', o)
return True
if (not inspect_utils.isweakrefself(o) and not tf_inspect.isclass(o)
and hasattr(o, '__call__') and hasattr(o, '__class__')):
# Callable objects: whitelisted if their __call__ method is.
call_whitelisted = is_whitelisted_for_graph(o.__call__)
if call_whitelisted:
logging.log(2, '%s is whitelisted: object __call__ whitelisted', o)
return call_whitelisted
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:
owner_class = inspect_utils.getdefiningclass(o, owner_class)
if is_whitelisted_for_graph(owner_class):
logging.log(2, '%s is whitelisted: 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_first_n(
'Entity {} looks like a namedtuple subclass. If it has any custom'
' methods, they will not be converted by AutoGraph.'.format(o),
1)
logging.log(2, '%s is whitelisted: named tuple', o)
return True
logging.log(2, '%s is NOT whitelisted', 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)
# 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():
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)
# 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)
if owner is not None:
partial_types = (type(owner), )
elif f_self is not None:
partial_types = (type(f_self), )
else:
partial_types = ()
# 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:]
else:
effective_args = args
elif tf_inspect.isclass(f):
# Constructors
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):
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)
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 class_to_graph(c, program_ctx):
"""Specialization of `entity_to_graph` for classes."""
converted_members = {}
method_filter = lambda m: tf_inspect.isfunction(m) or tf_inspect.ismethod(m)
members = tf_inspect.getmembers(c, predicate=method_filter)
if not members:
raise ValueError('Cannot convert %s: it has no member methods.' % c)
class_namespace = {}
for _, m in members:
# Only convert the members that are directly defined by the class.
if inspect_utils.getdefiningclass(m, c) is not c:
continue
node, _, namespace = function_to_graph(
m,
program_ctx=program_ctx,
arg_values={},
arg_types={'self': (c.__name__, c)},
owner_type=c)
if class_namespace is None:
class_namespace = namespace
else:
class_namespace.update(namespace)
converted_members[m] = node[0]
namer = program_ctx.new_namer(class_namespace)
class_name = namer.compiled_class_name(c.__name__, c)
# TODO(mdan): This needs to be explained more thoroughly.
# Process any base classes: if the superclass if of a whitelisted type, an
# absolute import line is generated. Otherwise, it is marked for conversion
# (as a side effect of the call to namer.compiled_class_name() followed by
# program_ctx.update_name_map(namer)).
output_nodes = []
renames = {}
base_names = []
for base in c.__bases__:
if isinstance(object, base):
base_names.append('object')
continue
if is_whitelisted_for_graph(base):
alias = namer.new_symbol(base.__name__, ())
output_nodes.append(
gast.ImportFrom(
module=base.__module__,
names=[gast.alias(name=base.__name__, asname=alias)],
level=0))
else:
# This will trigger a conversion into a class with this name.
alias = namer.compiled_class_name(base.__name__, base)
base_names.append(alias)
renames[qual_names.QN(base.__name__)] = qual_names.QN(alias)
program_ctx.update_name_map(namer)
# Generate the definition of the converted class.
bases = [gast.Name(n, gast.Load(), None) for n in base_names]
class_def = gast.ClassDef(
class_name,
bases=bases,
keywords=[],
body=list(converted_members.values()),
decorator_list=[])
# Make a final pass to replace references to the class or its base classes.
# Most commonly, this occurs when making super().__init__() calls.
# TODO(mdan): Making direct references to superclass' superclass will fail.
class_def = qual_names.resolve(class_def)
renames[qual_names.QN(c.__name__)] = qual_names.QN(class_name)
class_def = ast_util.rename_symbols(class_def, renames)
output_nodes.append(class_def)
return output_nodes, class_name, class_namespace
def _is_bounded_method(fn): _, fn = tf_decorator.unwrap(fn) return tf_inspect.ismethod(fn) and (fn.__self__ is not None)
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.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)
try:
with StackTraceMapper(converted_f), tf_stack.CurrentModuleFilter():
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