def function_to_graph(f, program_ctx, arg_values, arg_types, do_rename=True):
"""Specialization of `entity_to_graph` for callable functions."""
future_features = inspect_utils.getfutureimports(f)
node, source = parser.parse_entity(f, future_features=future_features)
logging.log(3, 'Source code of %s:\n\n%s\n', f, source)
# Parsed AST should contain future imports and one function def node.
# In general, the output of inspect.getsource is inexact for lambdas because
# it uses regex matching to adjust the exact location around the line number
# that CPython records. Then, the entire containing line is returned, which
# we may have trouble disambiguating. For example:
# x, y = lambda: 1, lambda: 2
if f.__name__ == '<lambda>':
nodes = ast_util.find_matching_definitions(node, f)
if len(nodes) != 1:
raise ValueError(
'Unable to identify source code of lambda function {}. It was'
' defined on this line: {}, which must contain a single lambda with'
' matching signature. To avoid ambiguity, define each lambda'
' in a separate expression.'.format(f, source))
node, = nodes
# TODO(znado): Place inside standard_analysis.
origin_info.resolve(node, source, f)
namespace = inspect_utils.getnamespace(f)
_add_self_references(namespace, program_ctx.autograph_module)
namer = naming.Namer(namespace)
entity_info = transformer.EntityInfo(source_code=source,
source_file='<fragment>',
future_features=future_features,
namespace=namespace,
arg_values=arg_values,
arg_types=arg_types)
context = converter.EntityContext(namer, entity_info, program_ctx)
try:
node = node_to_graph(node, context)
except (ValueError, AttributeError, KeyError, NotImplementedError) as e:
logging.error(1, 'Error converting %s', f, exc_info=True)
raise errors.InternalError('conversion', e)
# TODO(mdan): Catch and rethrow syntax errors.
if isinstance(node, gast.Lambda):
new_name = namer.new_symbol('tf__lambda', ())
node = gast.Assign(targets=[gast.Name(new_name, gast.Store(), None)],
value=node)
elif do_rename:
new_name = namer.function_name(f.__name__)
node.name = new_name
else:
new_name = f.__name__
assert node.name == new_name
return (node, ), new_name, entity_info
def function_to_graph(f, program_ctx, arg_values, arg_types, owner_type=None):
"""Specialization of `entity_to_graph` for callable functions."""
node, source = parser.parse_entity(f)
logging.log(3, 'Source code of %s:\n%s', f, source)
node = node.body[0]
# In general, the output of inspect.getsource is inexact because it uses
# regex matching to adjust the exact location around the line number that
# CPython records. This is particularly problematic for lambda functions,
# where the entire containing lines are returned.
nodes = ast_util.find_matching_definitions(node, f)
if len(nodes) != 1:
if f.__name__ == '<lambda>':
raise ValueError(
'Unable to identify source code of lambda function {}. It was'
' defined on this line: {}, which must contain a single lambda with'
' matching signature. To avoid ambiguity, define each lambda'
' in a separate expression.'.format(f, source))
else:
raise ValueError(
'Unable to identify source code of function {}({}). The source code'
' reported by Python did not include exactly one matching signature:'
'\n{}\n. This is an extremely rare occurrence. Please report it to'
' the TensorFlow team.'.format(f, tf_inspect.getfullargspec(f),
source))
node, = nodes
# TODO(znado): Place inside standard_analysis.
origin_info.resolve(node, source, f)
namespace = inspect_utils.getnamespace(f)
_add_self_references(namespace, program_ctx.autograph_module)
namer = program_ctx.new_namer(namespace)
entity_info = transformer.EntityInfo(source_code=source,
source_file='<fragment>',
namespace=namespace,
arg_values=arg_values,
arg_types=arg_types,
owner_type=owner_type)
context = converter.EntityContext(namer, entity_info, program_ctx)
try:
node = node_to_graph(node, context)
except (ValueError, AttributeError, KeyError, NotImplementedError) as e:
logging.error(1, 'Error converting %s', f, exc_info=True)
raise errors.InternalError('conversion', e)
# TODO(mdan): Catch and rethrow syntax errors.
if isinstance(node, gast.Lambda):
new_name = namer.new_symbol('tf__lambda', ())
node = gast.Assign(targets=[gast.Name(new_name, gast.Store(), None)],
value=node)
else:
# TODO(mdan): This somewhat duplicates the renaming logic in call_trees.py
new_name, did_rename = namer.compiled_function_name(
f.__name__, f, owner_type)
if did_rename:
node.name = new_name
else:
new_name = f.__name__
assert node.name == new_name
program_ctx.update_name_map(namer)
# TODO(mdan): Use this at compilation.
return [node], new_name, namespace