def setUpClass(cls):
cls.v2_symbols = {}
cls.v1_symbols = {}
if hasattr(tf.compat, "v2"):
def symbol_collector(unused_path, unused_parent, children):
for child in children:
_, attr = tf_decorator.unwrap(child[1])
api_names_v2 = tf_export.get_v2_names(attr)
for name in api_names_v2:
cls.v2_symbols["tf." + name] = attr
visitor = public_api.PublicAPIVisitor(symbol_collector)
traverse.traverse(tf.compat.v2, visitor)
if hasattr(tf.compat, "v1"):
def symbol_collector_v1(unused_path, unused_parent, children):
for child in children:
_, attr = tf_decorator.unwrap(child[1])
api_names_v1 = tf_export.get_v1_names(attr)
for name in api_names_v1:
cls.v1_symbols["tf." + name] = attr
visitor = public_api.PublicAPIVisitor(symbol_collector_v1)
traverse.traverse(tf.compat.v1, visitor)
def testAllAPI(self):
if not hasattr(tf.compat, "v2"):
return
v2_symbols = set([])
def symbol_collector(unused_path, unused_parent, children):
for child in children:
_, attr = tf_decorator.unwrap(child[1])
api_names_v2 = get_v2_names(attr)
for name in api_names_v2:
v2_symbols.add("tf." + name)
visitor = public_api.PublicAPIVisitor(symbol_collector)
traverse.traverse(tf.compat.v2, visitor)
# Converts all symbols in the v1 namespace to the v2 namespace, raising
# an error if the target of the conversion is not in the v2 namespace.
def conversion_visitor(unused_path, unused_parent, children):
for child in children:
_, attr = tf_decorator.unwrap(child[1])
api_names = get_v1_names(attr)
for name in api_names:
_, _, _, text = self._upgrade("tf." + name)
if (text and not text.startswith("tf.compat.v1")
and text not in v2_symbols):
self.assertFalse(
True, "Symbol %s generated from %s not in v2 API" %
(text, name))
visitor = public_api.PublicAPIVisitor(conversion_visitor)
visitor.do_not_descend_map["tf"].append("contrib")
visitor.private_map["tf.compat"] = ["v1", "v2"]
traverse.traverse(tf.compat.v1, visitor)
def collect_function_arg_names(function_names):
"""Determines argument names for reordered function signatures.
Args:
function_names: Functions to collect arguments for.
Returns:
Dictionary mapping function name to its arguments.
"""
# Map from reordered function name to its arguments.
function_to_args = {}
def visit(unused_path, unused_parent, children):
"""Visitor that collects arguments for reordered functions."""
for child in children:
_, attr = tf_decorator.unwrap(child[1])
api_names_v1 = tf_export.get_v1_names(attr)
api_names_v1 = ['tf.%s' % name for name in api_names_v1]
matches_function_names = any(
name in function_names for name in api_names_v1)
if matches_function_names:
arg_list = tf_inspect.getargspec(attr)[0]
for name in api_names_v1:
function_to_args[name] = arg_list
visitor = public_api.PublicAPIVisitor(visit)
visitor.do_not_descend_map['tf'].append('contrib')
visitor.do_not_descend_map['tf.compat'] = ['v1', 'v2']
traverse.traverse(tf, visitor)
return function_to_args
def testAPIBackwardsCompatibility(self):
# Extract all API stuff.
visitor = python_object_to_proto_visitor.PythonObjectToProtoVisitor()
public_api_visitor = public_api.PublicAPIVisitor(visitor)
public_api_visitor.do_not_descend_map[''].append('contrib')
traverse.traverse(tf, public_api_visitor)
proto_dict = visitor.GetProtos()
# Read all golden files.
expression = os.path.join(
resource_loader.get_root_dir_with_all_resources(),
_KeyToFilePath('*'))
golden_file_list = file_io.get_matching_files(expression)
def _ReadFileToProto(filename):
"""Read a filename, create a protobuf from its contents."""
ret_val = api_objects_pb2.TFAPIObject()
text_format.Merge(file_io.read_file_to_string(filename), ret_val)
return ret_val
golden_proto_dict = {
_FileNameToKey(filename): _ReadFileToProto(filename)
for filename in golden_file_list
}
# Diff them. Do not fail if called with update.
# If the test is run to update goldens, only report diffs but do not fail.
self._AssertProtoDictEquals(
golden_proto_dict,
proto_dict,
verbose=FLAGS.verbose_diffs,
update_goldens=FLAGS.update_goldens)
def checkBackwardsCompatibility(self, root, golden_file_pattern, api_version):
# Extract all API stuff.
visitor = python_object_to_proto_visitor.PythonObjectToProtoVisitor()
public_api_visitor = public_api.PublicAPIVisitor(visitor)
public_api_visitor.do_not_descend_map['tf'].append('contrib')
public_api_visitor.do_not_descend_map['tf.GPUOptions'] = ['Experimental']
traverse.traverse(root, public_api_visitor)
proto_dict = visitor.GetProtos()
# Read all golden files.
golden_file_list = file_io.get_matching_files(golden_file_pattern)
def _ReadFileToProto(filename):
"""Read a filename, create a protobuf from its contents."""
ret_val = api_objects_pb2.TFAPIObject()
text_format.Merge(file_io.read_file_to_string(filename), ret_val)
return ret_val
golden_proto_dict = {
_FileNameToKey(filename): _ReadFileToProto(filename)
for filename in golden_file_list
}
# Diff them. Do not fail if called with update.
# If the test is run to update goldens, only report diffs but do not fail.
self._AssertProtoDictEquals(
golden_proto_dict,
proto_dict,
verbose=FLAGS.verbose_diffs,
update_goldens=FLAGS.update_goldens,
api_version=api_version)
def testAllAPI(self):
if not hasattr(tf.compat, "v2"):
return
# Converts all symbols in the v1 namespace to the v2 namespace, raising
# an error if the target of the conversion is not in the v2 namespace.
# Please regenerate the renames file or edit any manual renames if this
# test fails.
def conversion_visitor(unused_path, unused_parent, children):
for child in children:
_, attr = tf_decorator.unwrap(child[1])
api_names = tf_export.get_v1_names(attr)
for name in api_names:
_, _, _, text = self._upgrade("tf." + name)
if (text and
not text.startswith("tf.compat.v1") and
text not in self.v2_symbols and
# Builds currently install old version of estimator that doesn't
# have some 2.0 symbols.
not text.startswith("tf.estimator")):
self.assertFalse(
True, "Symbol %s generated from %s not in v2 API" % (
text, name))
visitor = public_api.PublicAPIVisitor(conversion_visitor)
visitor.do_not_descend_map["tf"].append("contrib")
visitor.private_map["tf.compat"] = ["v1", "v2"]
traverse.traverse(tf.compat.v1, visitor)
def collect_function_renames():
"""Looks for functions/classes that need to be renamed in TF 2.0.
Returns:
Set of tuples of the form (current name, new name).
"""
# Set of rename lines to write to output file in the form:
# 'tf.deprecated_name': 'tf.canonical_name'
renames = set()
def visit(unused_path, unused_parent, children):
"""Visitor that collects rename strings to add to rename_line_set."""
for child in children:
_, attr = tf_decorator.unwrap(child[1])
api_names_v1 = tf_export.get_v1_names(attr)
api_names_v2 = tf_export.get_v2_names(attr)
deprecated_api_names = set(api_names_v1) - set(api_names_v2)
for name in deprecated_api_names:
renames.add((name, get_canonical_name(api_names_v2, name)))
visitor = public_api.PublicAPIVisitor(visit)
visitor.do_not_descend_map['tf'].append('contrib')
visitor.do_not_descend_map['tf.compat'] = ['v1', 'v2']
traverse.traverse(tf, visitor)
# It is possible that a different function is exported with the
# same name. For e.g. when creating a different function to
# rename arguments. Exclude it from renames in this case.
v2_names = get_all_v2_names()
renames = set((name, new_name) for name, new_name in renames
if name not in v2_names)
return renames
def testV1KeywordArgNames(self):
all_keyword_renames = (
tf_upgrade_v2.TFAPIChangeSpec().function_keyword_renames)
# Visitor that verifies V1 argument names.
def arg_test_visitor(unused_path, unused_parent, children):
for child in children:
_, attr = tf_decorator.unwrap(child[1])
names_v1 = tf_export.get_v1_names(attr)
for name in names_v1:
name = "tf.%s" % name
if name not in all_keyword_renames:
continue
arg_names_v1 = tf_inspect.getargspec(attr)[0]
keyword_renames = all_keyword_renames[name]
self.assertEqual(type(keyword_renames), dict)
# Assert that v1 function has valid v1 argument names.
for from_name, _ in keyword_renames.items():
self.assertIn(
from_name, arg_names_v1,
"%s not found in %s arguments: %s" %
(from_name, name, str(arg_names_v1)))
visitor = public_api.PublicAPIVisitor(arg_test_visitor)
visitor.do_not_descend_map["tf"].append("contrib")
visitor.private_map["tf.compat"] = ["v1", "v2"]
traverse.traverse(tf.compat.v1, visitor)
def testNoSubclassOfMessageV2(self):
if not hasattr(tf.compat, 'v2'):
return
visitor = public_api.PublicAPIVisitor(
_VerifyNoSubclassOfMessageVisitor)
visitor.do_not_descend_map['tf'].append('contrib')
traverse.traverse(tf.compat.v2, visitor)
def extract():
"""Extract docs from tf namespace and write them to disk."""
visitor = doc_generator_visitor.DocGeneratorVisitor('tf')
api_visitor = public_api.PublicAPIVisitor(visitor)
# Access something in contrib so tf.contrib is properly loaded (it's hidden
# behind lazy loading)
_ = tf.contrib.__name__
# Exclude some libaries in contrib from the documentation altogether.
# TODO(wicke): Shrink this list.
api_visitor.do_not_descend_map.update({
'contrib': [
'compiler',
'factorization',
'grid_rnn',
'labeled_tensor',
'ndlstm',
'quantization',
'session_bundle',
'slim',
'solvers',
'specs',
'tensor_forest',
'tensorboard',
'testing',
'tfprof',
],
'contrib.bayesflow': [
'entropy', 'monte_carlo', 'special_math',
'stochastic_gradient_estimators', 'stochastic_graph',
'stochastic_tensor', 'stochastic_variables',
'variational_inference'
],
'contrib.distributions': ['bijector'],
'contrib.ffmpeg': ['ffmpeg_ops'],
'contrib.graph_editor':
['edit', 'match', 'subgraph', 'transform', 'select', 'util'],
'contrib.layers': ['feature_column', 'summaries'],
'contrib.learn': [
'datasets',
'head',
'graph_actions',
'io',
'models',
'monitors',
'ops',
'preprocessing',
'utils',
],
'contrib.util': ['loader'],
})
traverse.traverse(tf, api_visitor)
# tf_debug is not imported with tf, it's a separate module altogether
visitor.set_root_name('tfdbg')
traverse.traverse(tf_debug, api_visitor)
return visitor
def test_private_child_removal(self):
visitor = self.TestVisitor()
children = [('name1', 'thing1'), ('_name2', 'thing2')]
public_api.PublicAPIVisitor(visitor)('test', 'dummy', children)
# Make sure the private symbols are removed before the visitor is called.
self.assertEqual([('name1', 'thing1')], visitor.last_children)
self.assertEqual([('name1', 'thing1')], children)
def collect_function_renames():
"""Looks for functions/classes that need to be renamed in TF 2.0.
Returns:
List of tuples of the form (current name, new name).
"""
# Set of rename lines to write to output file in the form:
# 'tf.deprecated_name': 'tf.canonical_name'
renames = set()
def visit(unused_path, unused_parent, children):
"""Visitor that collects rename strings to add to rename_line_set."""
for child in children:
_, attr = tf_decorator.unwrap(child[1])
if not hasattr(attr, '__dict__'):
continue
api_names_v1 = attr.__dict__.get(_TENSORFLOW_API_ATTR_V1, [])
api_names_v2 = attr.__dict__.get(_TENSORFLOW_API_ATTR, [])
deprecated_api_names = set(api_names_v1) - set(api_names_v2)
for name in deprecated_api_names:
renames.add((name, get_canonical_name(api_names_v2, name)))
visitor = public_api.PublicAPIVisitor(visit)
visitor.do_not_descend_map['tf'].append('contrib')
visitor.do_not_descend_map['tf.compat'] = ['v1', 'v2']
traverse.traverse(tf, visitor)
return renames
def testNoSubclassOfMessage(self):
visitor = public_api.PublicAPIVisitor(
_VerifyNoSubclassOfMessageVisitor)
visitor.do_not_descend_map['tf'].append('contrib')
# Skip compat.v1 and compat.v2 since they are validated in separate tests.
visitor.private_map['tf.compat'] = ['v1', 'v2']
traverse.traverse(tf, visitor)
def testAllAPIV1(self):
collect = True
v1_symbols = set([])
# Symbols which may be generated by the conversion script which do not exist
# in TF 1.x. This should be a very short list of symbols which are
# experimental in 1.x but stable for 2.x.
whitelisted_v2_only_symbols = set(["tf.saved_model.save"])
# Converts all symbols in the v1 namespace to the v2 namespace, raising
# an error if the target of the conversion is not in the v1 namespace.
def conversion_visitor(unused_path, unused_parent, children):
for child in children:
_, attr = tf_decorator.unwrap(child[1])
api_names = tf_export.get_v1_names(attr)
for name in api_names:
if collect:
v1_symbols.add("tf." + name)
else:
_, _, _, text = self._upgrade("tf." + name)
if (text and not text.startswith("tf.compat.v1")
and not text.startswith("tf.estimator")
and text not in v1_symbols
and text not in whitelisted_v2_only_symbols):
self.assertFalse(
True,
"Symbol %s generated from %s not in v1 API" %
(text, name))
visitor = public_api.PublicAPIVisitor(conversion_visitor)
visitor.do_not_descend_map["tf"].append("contrib")
visitor.private_map["tf.compat"] = ["v1", "v2"]
traverse.traverse(tf.compat.v1, visitor)
collect = False
traverse.traverse(tf.compat.v1, visitor)
def _checkBackwardsCompatibility(self,
root,
golden_file_patterns,
api_version,
additional_private_map=None,
omit_golden_symbols_map=None):
# Extract all API stuff.
visitor = python_object_to_proto_visitor.PythonObjectToProtoVisitor()
public_api_visitor = public_api.PublicAPIVisitor(visitor)
public_api_visitor.private_map['tf'].append('contrib')
if api_version == 2:
public_api_visitor.private_map['tf'].append('enable_v2_behavior')
public_api_visitor.do_not_descend_map['tf.GPUOptions'] = [
'Experimental'
]
# Do not descend into these numpy classes because their signatures may be
# different between internal and OSS.
public_api_visitor.do_not_descend_map['tf.experimental.numpy'] = [
'bool_', 'complex_', 'complex128', 'complex64', 'float_',
'float16', 'float32', 'float64', 'inexact', 'int_', 'int16',
'int32', 'int64', 'int8', 'object_', 'string_', 'uint16', 'uint32',
'uint64', 'uint8', 'unicode_', 'iinfo'
]
if FLAGS.only_test_core_api:
public_api_visitor.do_not_descend_map['tf'].extend(
_NON_CORE_PACKAGES)
if additional_private_map:
public_api_visitor.private_map.update(additional_private_map)
traverse.traverse(root, public_api_visitor)
proto_dict = visitor.GetProtos()
# Read all golden files.
golden_file_list = file_io.get_matching_files(golden_file_patterns)
if FLAGS.only_test_core_api:
golden_file_list = _FilterNonCoreGoldenFiles(golden_file_list)
def _ReadFileToProto(filename):
"""Read a filename, create a protobuf from its contents."""
ret_val = api_objects_pb2.TFAPIObject()
text_format.Merge(file_io.read_file_to_string(filename), ret_val)
return ret_val
golden_proto_dict = {
_FileNameToKey(filename): _ReadFileToProto(filename)
for filename in golden_file_list
}
golden_proto_dict = _FilterGoldenProtoDict(golden_proto_dict,
omit_golden_symbols_map)
# Diff them. Do not fail if called with update.
# If the test is run to update goldens, only report diffs but do not fail.
self._AssertProtoDictEquals(golden_proto_dict,
proto_dict,
verbose=FLAGS.verbose_diffs,
update_goldens=FLAGS.update_goldens,
api_version=api_version)
def test_call_forward(self):
visitor = self.TestVisitor()
children = [('name1', 'thing1'), ('name2', 'thing2')]
public_api.PublicAPIVisitor(visitor)('test', 'dummy', children)
self.assertEqual(set(['test']), visitor.symbols)
self.assertEqual('dummy', visitor.last_parent)
self.assertEqual([('name1', 'thing1'), ('name2', 'thing2')],
visitor.last_children)
def testNoSubclassOfMessageV2(self):
if not hasattr(tf.compat, 'v2'):
return
visitor = public_api.PublicAPIVisitor(_VerifyNoSubclassOfMessageVisitor)
visitor.do_not_descend_map['tf'].append('contrib')
if FLAGS.only_test_core_api:
visitor.do_not_descend_map['tf'].extend(_NON_CORE_PACKAGES)
traverse.traverse(tf.compat.v2, visitor)
def testKeywordArgNames(self):
if not hasattr(tf.compat, "v2"):
return
all_keyword_renames = (
tf_upgrade_v2.TFAPIChangeSpec().function_keyword_renames)
v2_name_exceptions = {"verify_shape_is_now_always_true"}
# Visitor that verifies V1 argument names, converts to V2 and checks
# V2 argument names.
def conversion_visitor(unused_path, unused_parent, children):
for child in children:
_, attr = tf_decorator.unwrap(child[1])
names_v1 = get_v1_names(attr)
for name in names_v1:
name = "tf.%s" % name
if name not in all_keyword_renames:
continue
arg_names_v1 = tf_inspect.getargspec(attr)[0]
keyword_renames = all_keyword_renames[name]
self.assertEqual(type(keyword_renames), dict)
# Assert that v1 function has valid v1 argument names.
for from_name, _ in keyword_renames.items():
self.assertIn(
from_name, arg_names_v1,
"%s not found in %s arguments: %s" %
(from_name, name, str(arg_names_v1)))
# Assert that arg names after converting to v2 are present in
# v2 function.
# 1. First, create an input of the form:
# tf.foo(arg1=val1, arg2=val2, ...)
args = ",".join([
"%s=%d" % (from_name, from_index) for from_index,
from_name in enumerate(keyword_renames.keys())
])
text_input = "%s(%s)" % (name, args)
# 2. Convert the input to V2.
_, _, _, text = self._upgrade(text_input)
new_function_name, new_args = get_func_and_args_from_str(
text)
# 3. Verify V2 function and arguments.
# Note: If we rename arguments, new function must be available in 2.0.
# We should not be using compat.v1 in this case.
self.assertIn(new_function_name, self.v2_symbols)
args_v2 = tf_inspect.getargspec(
self.v2_symbols[new_function_name])[0]
args_v2.extend(v2_name_exceptions)
for new_arg in new_args:
self.assertIn(new_arg, args_v2)
visitor = public_api.PublicAPIVisitor(conversion_visitor)
visitor.do_not_descend_map["tf"].append("contrib")
visitor.private_map["tf.compat"] = ["v1", "v2"]
traverse.traverse(tf.compat.v1, visitor)
def test_no_descent_child_removal(self):
visitor = self.TestVisitor()
children = [('name1', 'thing1'), ('mock', 'thing2')]
public_api.PublicAPIVisitor(visitor)('test', 'dummy', children)
# Make sure not-to-be-descended-into symbols are removed after the visitor
# is called.
self.assertEqual([('name1', 'thing1'), ('mock', 'thing2')],
visitor.last_children)
self.assertEqual([('name1', 'thing1')], children)
def testNewAPIBackwardsCompatibility(self):
# Extract all API stuff.
visitor = python_object_to_proto_visitor.PythonObjectToProtoVisitor()
public_api_visitor = public_api.PublicAPIVisitor(visitor)
public_api_visitor.do_not_descend_map['tf'].append('contrib')
public_api_visitor.do_not_descend_map['tf.GPUOptions'] = [
'Experimental'
]
# TODO(annarev): Make slide_dataset available in API.
public_api_visitor.private_map['tf'] = ['slide_dataset']
traverse.traverse(api, public_api_visitor)
proto_dict = visitor.GetProtos()
# Read all golden files.
expression = os.path.join(
resource_loader.get_root_dir_with_all_resources(),
_KeyToFilePath('*'))
golden_file_list = file_io.get_matching_files(expression)
def _ReadFileToProto(filename):
"""Read a filename, create a protobuf from its contents."""
ret_val = api_objects_pb2.TFAPIObject()
text_format.Merge(file_io.read_file_to_string(filename), ret_val)
return ret_val
golden_proto_dict = {
_FileNameToKey(filename): _ReadFileToProto(filename)
for filename in golden_file_list
}
# user_ops is an empty module. It is currently available in TensorFlow API
# but we don't keep empty modules in the new API.
# We delete user_ops from golden_proto_dict to make sure assert passes
# when diffing new API against goldens.
# TODO(annarev): remove user_ops from goldens once we switch to new API.
tf_module = golden_proto_dict['tensorflow'].tf_module
for i in range(len(tf_module.member)):
if tf_module.member[i].name == 'user_ops':
del tf_module.member[i]
break
# Diff them. Do not fail if called with update.
# If the test is run to update goldens, only report diffs but do not fail.
self._AssertProtoDictEquals(
golden_proto_dict,
proto_dict,
verbose=FLAGS.verbose_diffs,
update_goldens=False,
additional_missing_object_message=
'Check if tf_export decorator/call is missing for this symbol.')
def extract(py_modules, do_not_descend_map):
"""Extract docs from tf namespace and write them to disk."""
# Traverse the first module.
visitor = doc_generator_visitor.DocGeneratorVisitor(py_modules[0][0])
api_visitor = public_api.PublicAPIVisitor(visitor)
add_dict_to_dict(do_not_descend_map, api_visitor.do_not_descend_map)
traverse.traverse(py_modules[0][1], api_visitor)
# Traverse all py_modules after the first:
for module_name, module in py_modules[1:]:
visitor.set_root_name(module_name)
traverse.traverse(module, api_visitor)
return visitor
def get_all_v2_names():
"""Get a set of function/class names available in TensorFlow 2.0."""
v2_names = set() # All op names in TensorFlow 2.0
def visit(unused_path, unused_parent, children):
"""Visitor that collects TF 2.0 names."""
for child in children:
_, attr = tf_decorator.unwrap(child[1])
api_names_v2 = get_v2_names(attr)
for name in api_names_v2:
v2_names.add(name)
visitor = public_api.PublicAPIVisitor(visit)
visitor.do_not_descend_map['tf'].append('contrib')
traverse.traverse(tf.compat.v2, visitor)
return v2_names
def _checkBackwardsCompatibility(
self,
root,
golden_file_patterns,
api_version,
additional_private_map=None,
omit_golden_symbols_map=None,
):
# Extract all API stuff.
visitor = python_object_to_proto_visitor.PythonObjectToProtoVisitor(
default_path="tensorflow.keras")
public_api_visitor = public_api.PublicAPIVisitor(visitor)
if additional_private_map:
public_api_visitor.private_map.update(additional_private_map)
public_api_visitor.set_root_name("tf.keras")
traverse.traverse(root, public_api_visitor)
proto_dict = visitor.GetProtos()
# Read all golden files.
golden_file_list = tf.compat.v1.gfile.Glob(golden_file_patterns)
def _ReadFileToProto(filename):
"""Read a filename, create a protobuf from its contents."""
ret_val = api_objects_pb2.TFAPIObject()
text_format.Merge(file_io.read_file_to_string(filename), ret_val)
return ret_val
golden_proto_dict = {
_FileNameToKey(filename): _ReadFileToProto(filename)
for filename in golden_file_list
}
golden_proto_dict = _FilterGoldenProtoDict(golden_proto_dict,
omit_golden_symbols_map)
# Diff them. Do not fail if called with update.
# If the test is run to update goldens, only report diffs but do not
# fail.
self._AssertProtoDictEquals(
golden_proto_dict,
proto_dict,
verbose=FLAGS.verbose_diffs,
update_goldens=FLAGS.update_goldens,
api_version=api_version,
)
def testNoSubclassOfMessage(self):
def Visit(path, parent, unused_children):
"""A Visitor that crashes on subclasses of generated proto classes."""
# If the traversed object is a proto Message class
if not (isinstance(parent, type) and
issubclass(parent, message.Message)):
return
if parent is message.Message:
return
# Check that it is a direct subclass of Message.
if message.Message not in parent.__bases__:
raise NotImplementedError(
'Object tf.%s is a subclass of a generated proto Message. '
'They are not yet supported by the API tools.' % path)
visitor = public_api.PublicAPIVisitor(Visit)
visitor.do_not_descend_map['tf'].append('contrib')
traverse.traverse(tf, visitor)
def testAPIBackwardsCompatibility(self):
# Extract all API stuff.
visitor = python_object_to_proto_visitor.PythonObjectToProtoVisitor()
public_api_visitor = public_api.PublicAPIVisitor(visitor)
public_api_visitor.do_not_descend_map['tf'].append('contrib')
public_api_visitor.do_not_descend_map['tf.GPUOptions'] = ['Experimental']
traverse.traverse(tf, public_api_visitor)
proto_dict = visitor.GetProtos()
# Read all golden files.
expression = os.path.join(
resource_loader.get_root_dir_with_all_resources(),
_KeyToFilePath('*'))
golden_file_list = file_io.get_matching_files(expression)
def _ReadFileToProto(filename):
"""Read a filename, create a protobuf from its contents."""
ret_val = api_objects_pb2.TFAPIObject()
text_format.Merge(file_io.read_file_to_string(filename), ret_val)
return ret_val
golden_proto_dict = {
_FileNameToKey(filename): _ReadFileToProto(filename)
for filename in golden_file_list
}
# TODO(annarev): remove once we switch to using tf_export decorators.
tf_module = golden_proto_dict['tensorflow'].tf_module
for i in range(len(tf_module.member)):
if tf_module.member[i].name == 'math':
del tf_module.member[i]
break
# Diff them. Do not fail if called with update.
# If the test is run to update goldens, only report diffs but do not fail.
self._AssertProtoDictEquals(
golden_proto_dict,
proto_dict,
verbose=FLAGS.verbose_diffs,
update_goldens=FLAGS.update_goldens)
def collect_function_arg_names(function_names):
"""Determines argument names for reordered function signatures.
Args:
function_names: Functions to collect arguments for.
Returns:
Dictionary mapping function name to its arguments.
"""
# Map from reordered function name to its arguments.
function_to_args = {}
def visit(unused_path, unused_parent, children):
"""Visitor that collects arguments for reordered functions."""
for child in children:
_, attr = tf_decorator.unwrap(child[1])
api_names_v1 = tf_export.get_v1_names(attr)
api_names_v1 = ['tf.%s' % name for name in api_names_v1]
matches_function_names = any(name in function_names
for name in api_names_v1)
if matches_function_names:
if tf_inspect.isclass(attr):
# Get constructor arguments if attr is a class
arg_list = tf_inspect.getargspec(getattr(attr,
'__init__'))[0]
arg_list = arg_list[1:] # skip 'self' argument
else:
# Get function arguments.
# getargspec returns a tuple of (args, varargs, keywords, defaults)
# we just look at args.
arg_list = tf_inspect.getargspec(attr)[0]
for name in api_names_v1:
function_to_args[name] = arg_list
visitor = public_api.PublicAPIVisitor(visit)
visitor.do_not_descend_map['tf'].append('contrib')
visitor.do_not_descend_map['tf.compat'] = ['v1', 'v2']
traverse.traverse(tf, visitor)
return function_to_args
def testNewAPIBackwardsCompatibility(self):
# Extract all API stuff.
visitor = python_object_to_proto_visitor.PythonObjectToProtoVisitor()
public_api_visitor = public_api.PublicAPIVisitor(visitor)
public_api_visitor.do_not_descend_map['tf'].append('contrib')
public_api_visitor.do_not_descend_map['tf.GPUOptions'] = ['Experimental']
# TODO(annarev): Make slide_dataset available in API.
public_api_visitor.private_map['tf'] = ['slide_dataset']
traverse.traverse(api, public_api_visitor)
proto_dict = visitor.GetProtos()
# Read all golden files.
expression = os.path.join(
resource_loader.get_root_dir_with_all_resources(),
_KeyToFilePath('*'))
golden_file_list = file_io.get_matching_files(expression)
def _ReadFileToProto(filename):
"""Read a filename, create a protobuf from its contents."""
ret_val = api_objects_pb2.TFAPIObject()
text_format.Merge(file_io.read_file_to_string(filename), ret_val)
return ret_val
golden_proto_dict = {
_FileNameToKey(filename): _ReadFileToProto(filename)
for filename in golden_file_list
}
# Diff them. Do not fail if called with update.
# If the test is run to update goldens, only report diffs but do not fail.
self._AssertProtoDictEquals(
golden_proto_dict,
proto_dict,
verbose=FLAGS.verbose_diffs,
update_goldens=False,
additional_missing_object_message=
'Check if tf_export decorator/call is missing for this symbol.')
def collect_function_renames():
"""Looks for functions/classes that need to be renamed in TF 2.0.
Returns:
List of tuples of the form (current name, new name).
"""
# Set of rename lines to write to output file in the form:
# 'tf.deprecated_name': 'tf.canonical_name'
renames = set()
v2_names = set() # All op names in TensorFlow 2.0
def visit(unused_path, unused_parent, children):
"""Visitor that collects rename strings to add to rename_line_set."""
for child in children:
_, attr = tf_decorator.unwrap(child[1])
if not hasattr(attr, '__dict__'):
continue
api_names_v1 = attr.__dict__.get(_TENSORFLOW_API_ATTR_V1, [])
api_names_v2 = attr.__dict__.get(_TENSORFLOW_API_ATTR, [])
deprecated_api_names = set(api_names_v1) - set(api_names_v2)
for name in deprecated_api_names:
renames.add((name, get_canonical_name(api_names_v2, name)))
for name in api_names_v2:
v2_names.add(name)
visitor = public_api.PublicAPIVisitor(visit)
visitor.do_not_descend_map['tf'].append('contrib')
visitor.do_not_descend_map['tf.compat'] = ['v1', 'v2']
traverse.traverse(tf, visitor)
# It is possible that a different function is exported with the
# same name. For e.g. when creating a different function to
# rename arguments. Exclude it from renames in this case.
renames = {
name: new_name
for name, new_name in renames.items() if name not in v2_names
}
return renames
def collect_constant_renames():
"""Looks for constants that need to be renamed in TF 2.0.
Args:
output_file_path: File path to write output to. Any existing contents
would be replaced.
"""
# Set of rename lines to write to output file in the form:
# 'tf.deprecated_name': 'tf.canonical_name'
rename_line_set = set()
# _tf_api_names attribute name
tensorflow_api_attr = tf_export.API_ATTRS[
tf_export.TENSORFLOW_API_NAME].names
def visit(unused_path, unused_parent, children):
"""Visitor that collects rename strings to add to rename_line_set."""
for child in children:
_, attr = tf_decorator.unwrap(child[1])
if not hasattr(attr, '__dict__'):
continue
api_names = attr.__dict__.get(tensorflow_api_attr, [])
deprecated_api_names = attr.__dict__.get(
'_tf_deprecated_api_names', [])
canonical_name = tf_export.get_canonical_name(
api_names, deprecated_api_names)
for name in deprecated_api_names:
rename_line_set.add(' \'tf.%s\': \'tf.%s\'' %
(name, canonical_name))
visitor = public_api.PublicAPIVisitor(visit)
visitor.do_not_descend_map['tf'].append('contrib')
visitor.do_not_descend_map['tf.compat'] = ['v1', 'v2']
traverse.traverse(tf, visitor)
renames_file_text = '%srenames = {\n%s\n}\n' % (_FILE_HEADER, ',\n'.join(
sorted(rename_line_set)))
file_io.write_string_to_file(output_file_path, renames_file_text)
def testV2KeywordArgNames(self):
# This test converts a call of the form:
# tf.foo(arg1=0, arg2=1, ...)
# to 2.0. Then, checks that converted function has valid argument names.
if not hasattr(tf.compat, "v2"):
return
v2_arg_exceptions = {
"verify_shape_is_now_always_true",
# These arguments should not be used, they just specify
# that a function takes named arguments.
"keyword_required",
"_sentinel",
}
v1_name_exceptions = {
"tf.print", # requires print_function import
}
function_warnings = (tf_upgrade_v2.TFAPIChangeSpec().function_warnings)
function_handles = (tf_upgrade_v2.TFAPIChangeSpec().function_handle)
keyword_renames = (
tf_upgrade_v2.TFAPIChangeSpec().function_keyword_renames)
# Visitor that converts to V2 and checks V2 argument names.
def conversion_visitor(unused_path, unused_parent, children):
for child in children:
_, attr = tf_decorator.unwrap(child[1])
if not tf_inspect.isfunction(attr):
continue
names_v1 = tf_export.get_v1_names(attr)
arg_names_v1 = get_args(attr)
for name in names_v1:
tf_name = "tf.%s" % name
if tf_name in function_warnings or tf_name in function_handles:
continue # These require manual change
if tf_name in v1_name_exceptions:
continue
# Assert that arg names after converting to v2 are present in
# v2 function.
# 1. First, create an input of the form:
# tf.foo(arg1=val1, arg2=val2, ...)
args = ",".join([
"%s=%d" % (from_name, from_index)
for from_index, from_name in enumerate(arg_names_v1)
])
text_input = "%s(%s)" % (tf_name, args)
# 2. Convert the input to V2.
_, _, _, text = self._upgrade(text_input)
new_function_name, new_args = get_func_and_args_from_str(
text)
if new_function_name == "tf.compat.v1.%s" % name:
if tf_name in keyword_renames:
# If we rename arguments, new function must be available in 2.0.
# We should not be using compat.v1 in this case.
self.assertFalse(
"Function '%s' is not in 2.0 when converting\n%s\nto\n%s"
% (new_function_name, text_input, text))
continue
# 3. Verify V2 function and arguments.
args_v2 = get_args(self.v2_symbols[new_function_name])
args_v2.extend(v2_arg_exceptions)
for new_arg in new_args:
self.assertIn(
new_arg, args_v2,
"Invalid argument '%s' in 2.0 when converting\n%s\nto\n%s.\n"
"Supported arguments: %s" %
(new_arg, text_input, text, str(args_v2)))
visitor = public_api.PublicAPIVisitor(conversion_visitor)
visitor.do_not_descend_map["tf"].append("contrib")
visitor.private_map["tf.compat"] = ["v1", "v2"]
traverse.traverse(tf.compat.v1, visitor)
