def assert_same_ast(self, expected_node, node, msg=None):
expected_source = compiler.ast_to_source(expected_node,
indentation=' ')
expected_str = textwrap.dedent(expected_source).strip()
got_source = compiler.ast_to_source(node, indentation=' ')
got_str = textwrap.dedent(got_source).strip()
self.assertEqual(expected_str, got_str, msg=msg)
def __repr__(self):
if isinstance(self.ast_node, gast.FunctionDef):
return 'def %s' % self.ast_node.name
elif isinstance(self.ast_node, gast.ClassDef):
return 'class %s' % self.ast_node.name
elif isinstance(self.ast_node, gast.withitem):
return compiler.ast_to_source(self.ast_node.context_expr).strip()
return compiler.ast_to_source(self.ast_node).strip()
def get_definition_directive(self, node, directive, arg, default):
"""Returns the unique directive argument for a symbol.
See lang/directives.py for details on directives.
Example:
# Given a directive in the code:
ag.foo_directive(bar, baz=1)
# One can write for an AST node Name(id='bar'):
get_definition_directive(node, ag.foo_directive, 'baz')
Args:
node: ast.AST, the node representing the symbol for which the directive
argument is needed.
directive: Callable[..., Any], the directive to search.
arg: str, the directive argument to return.
default: Any
Raises:
ValueError: if conflicting annotations have been found
"""
defs = anno.getanno(node, anno.Static.ORIG_DEFINITIONS, ())
if not defs:
return default
arg_values_found = []
for def_ in defs:
if (directive in def_.directives
and arg in def_.directives[directive]):
arg_values_found.append(def_.directives[directive][arg])
if not arg_values_found:
return default
if len(arg_values_found) == 1:
return arg_values_found[0]
# If multiple annotations reach the symbol, they must all match. If they do,
# return any of them.
first_value = arg_values_found[0]
for other_value in arg_values_found[1:]:
if not ast_util.matches(first_value, other_value):
qn = anno.getanno(node, anno.Basic.QN)
raise ValueError(
'%s has ambiguous annotations for %s(%s): %s, %s' %
(qn, directive.__name__, arg,
compiler.ast_to_source(other_value).strip(),
compiler.ast_to_source(first_value).strip()))
return first_value
def assert_body_anfs_as_expected(self, expected_fn, test_fn):
# Testing the code bodies only. Wrapping them in functions so the
# syntax highlights nicely, but Python doesn't try to execute the
# statements.
node, _ = parser.parse_entity(test_fn, future_features=())
orig_source = compiler.ast_to_source(node, indentation=' ')
orig_str = textwrap.dedent(orig_source).strip()
config = [(anf.ANY, anf.LEAVE)] # Configuration to trasform nothing
node = anf.transform(
node, self._simple_context(),
config=config, gensym_source=DummyGensym)
new_source = compiler.ast_to_source(node, indentation=' ')
new_str = textwrap.dedent(new_source).strip()
self.assertEqual(orig_str, new_str)
def get_definition_directive(self, node, directive, arg, default):
"""Returns the unique directive argument for a symbol.
See lang/directives.py for details on directives.
Example:
# Given a directive in the code:
ag.foo_directive(bar, baz=1)
# One can write for an AST node Name(id='bar'):
get_definition_directive(node, ag.foo_directive, 'baz')
Args:
node: ast.AST, the node representing the symbol for which the directive
argument is needed.
directive: Callable[..., Any], the directive to search.
arg: str, the directive argument to return.
default: Any
Raises:
ValueError: if conflicting annotations have been found
"""
defs = anno.getanno(node, anno.Static.ORIG_DEFINITIONS, ())
if not defs:
return default
arg_values_found = []
for def_ in defs:
if (directive in def_.directives and arg in def_.directives[directive]):
arg_values_found.append(def_.directives[directive][arg])
if not arg_values_found:
return default
if len(arg_values_found) == 1:
return arg_values_found[0]
# If multiple annotations reach the symbol, they must all match. If they do,
# return any of them.
first_value = arg_values_found[0]
for other_value in arg_values_found[1:]:
if not ast_util.matches(first_value, other_value):
qn = anno.getanno(node, anno.Basic.QN)
raise ValueError('%s has ambiguous annotations for %s(%s): %s, %s' %
(qn, directive.__name__, arg,
compiler.ast_to_source(other_value).strip(),
compiler.ast_to_source(first_value).strip()))
return first_value
def assertLambdaNodes(self, matching_nodes, expected_bodies):
self.assertEqual(len(matching_nodes), len(expected_bodies))
for node in matching_nodes:
self.assertIsInstance(node, gast.Lambda)
self.assertIn(
compiler.ast_to_source(node.body,
include_encoding_marker=False).strip(),
expected_bodies)
def test_rename_symbols_attributes(self):
node = parser.parse_str('b.c = b.c.d')
node = qual_names.resolve(node)
node = ast_util.rename_symbols(
node, {qual_names.from_str('b.c'): qual_names.QN('renamed_b_c')})
source = compiler.ast_to_source(node)
self.assertEqual(source.strip(), 'renamed_b_c = renamed_b_c.d')
def test_rename_symbols_attributes(self):
node = parser.parse_str('b.c = b.c.d')
node = qual_names.resolve(node)
node = ast_util.rename_symbols(
node, {qual_names.from_str('b.c'): qual_names.QN('renamed_b_c')})
source = compiler.ast_to_source(node)
self.assertEqual(source.strip(), 'renamed_b_c = renamed_b_c.d')
def entity_to_graph(o, program_ctx, arg_values, arg_types):
"""Compile a Python entity into equivalent TensorFlow.
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.
"""
logging.log(1, 'Converting %s', o)
if tf_inspect.isclass(o):
nodes, name, entity_info = class_to_graph(o, program_ctx)
elif tf_inspect.isfunction(o):
nodes, name, entity_info = function_to_graph(o, program_ctx,
arg_values, arg_types)
elif tf_inspect.ismethod(o):
nodes, name, entity_info = 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/'
'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 _get_source(self, node):
try:
source, _ = compiler.ast_to_source(node)
return source
# pylint: disable=broad-except
# This function is used for error reporting. If an exception occurs here,
# it should be suppressed, in favor of emitting as informative a message
# about the original error as possible.
except Exception:
return '<could not convert AST to source>'
def test_rename_symbols_basic(self):
node = parser.parse_str('a + b')
node = qual_names.resolve(node)
node = ast_util.rename_symbols(
node, {qual_names.QN('a'): qual_names.QN('renamed_a')})
self.assertIsInstance(node.body[0].value.left.id, str)
source = compiler.ast_to_source(node)
self.assertEqual(source.strip(), 'renamed_a + b')
def test_rename_symbols_basic(self):
node = parser.parse_str('a + b')
node = qual_names.resolve(node)
node = ast_util.rename_symbols(
node, {qual_names.QN('a'): qual_names.QN('renamed_a')})
self.assertIsInstance(node.body[0].value.left.id, str)
source = compiler.ast_to_source(node)
self.assertEqual(source.strip(), 'renamed_a + b')
def _get_source(self, node):
try:
source, _ = compiler.ast_to_source(node)
return source
# pylint: disable=broad-except
# This function is used for error reporting. If an exception occurs here,
# it should be suppressed, in favor of emitting as informative a message
# about the original error as possible.
except Exception:
return '<could not convert AST to source>'
def test_source_map_no_origin(self):
def test_fn(x):
return x + 1
node, _, _ = parser.parse_entity(test_fn)
converted_code = compiler.ast_to_source(node)
source_map = origin_info.create_source_map(node, converted_code,
'test_filename', [0])
self.assertEqual(len(source_map), 0)
def get_definition_directive(self, node, directive, arg, default):
"""Returns the unique directive for a symbol, or a default if none exist.
See lang/directives.py for details on directives.
Args:
node: ast.AST
directive: Callable[..., Any]
arg: str
default: Any
Raises:
ValueError: if conflicting annotations have been found
"""
defs = anno.getanno(node, anno.Static.ORIG_DEFINITIONS, ())
if not defs:
return default
# TODO(mdan): Simplify this.
arg_values = []
for def_ in defs:
if (directive not in def_.directives
or arg not in def_.directives[directive]):
continue
arg_value = def_.directives[directive][arg]
for prev_value in arg_values:
if not ast_util.matches(arg_value, prev_value):
qn = anno.getanno(node, anno.Basic.QN)
raise ValueError(
'%s has ambiguous annotations for %s(%s): %s, %s' %
(qn, directive.__name__, arg,
compiler.ast_to_source(arg_value).strip(),
compiler.ast_to_source(prev_value).strip()))
arg_values.append(arg_value)
if not arg_values:
return default
arg_value, = arg_values
return arg_value
def test_entity_to_graph_function_with_defaults(self):
b = 2
c = 1
def f(a, d=c + 1):
return a + b + d
program_ctx = self._simple_program_ctx()
nodes, name, _ = conversion.entity_to_graph(f, program_ctx, None, None)
fn_node, _ = nodes
self.assertIsInstance(fn_node, gast.FunctionDef)
self.assertEqual('tf__f', name)
self.assertEqual(
compiler.ast_to_source(fn_node.args.defaults[0]).strip(), 'None')
def test_source_map_no_origin(self):
def test_fn(x):
return x + 1
node, _ = parser.parse_entity(test_fn)
fn_node = node.body[0]
converted_code = compiler.ast_to_source(fn_node)
source_map = origin_info.create_source_map(
fn_node, converted_code, 'test_filename', [0])
self.assertEqual(len(source_map), 0)
def test_entity_to_graph_function_with_defaults(self):
b = 2
c = 1
def f(a, d=c + 1):
return a + b + d
program_ctx = self._simple_program_ctx()
nodes, name, _ = conversion.entity_to_graph(f, program_ctx, None, None)
fn_node, _ = nodes
self.assertIsInstance(fn_node, gast.FunctionDef)
self.assertEqual('tf__f', name)
self.assertEqual(
compiler.ast_to_source(fn_node.args.defaults[0]).strip(), 'None')
def get_definition_directive(self, node, directive, arg, default):
"""Returns the unique directive for a symbol, or a default if none exist.
See lang/directives.py for details on directives.
Args:
node: ast.AST
directive: Callable[..., Any]
arg: str
default: Any
Raises:
ValueError: if conflicting annotations have been found
"""
defs = anno.getanno(node, anno.Static.ORIG_DEFINITIONS, ())
if not defs:
return default
# TODO(mdan): Simplify this.
arg_values = []
for def_ in defs:
if (directive not in def_.directives or
arg not in def_.directives[directive]):
continue
arg_value = def_.directives[directive][arg]
for prev_value in arg_values:
if not ast_util.matches(arg_value, prev_value):
qn = anno.getanno(node, anno.Basic.QN)
raise ValueError('%s has ambiguous annotations for %s(%s): %s, %s' %
(qn, directive.__name__, arg,
compiler.ast_to_source(arg_value).strip(),
compiler.ast_to_source(prev_value).strip()))
arg_values.append(arg_value)
if not arg_values:
return default
arg_value, = arg_values
return arg_value
def to_code(entity,
recursive=True,
arg_values=None,
arg_types=None,
indentation=' ',
experimental_optional_features=converter.Feature.ALL,
experimental_partial_types=None):
"""Similar to `to_graph`, but returns Python source code as a string.
Also see: `tf.autograph.to_graph`.
`to_graph` returns the Python source code that can be used to generate a
TensorFlow graph that is functionally identical to the input Python code.
Args:
entity: Python callable or class to convert.
recursive: Whether to recursively convert any functions that the
converted function may call.
arg_values: Optional dict of value hints for symbols including
function arguments mapping string names to actual values. For example,
`arg_values={'a': 1}` will map the variable `a` to the value `1`.
arg_types: Optional dict of type hints for symbols including function
arguments. Type hints allow specifying just the type of a variable, rather
than a specific value.
indentation: The string to use for indenting. Typically two or four spaces,
or just the tab character.
experimental_optional_features: `None`, a tuple of, or a single
`tf.autograph.experimental.Feature` value. Controls the use of
optional features in the conversion process.
experimental_partial_types: A `set` of `type` values, reserved for internal
use.
Returns:
The converted code as string.
"""
program_ctx = converter.ProgramContext(
options=converter.ConversionOptions(
recursive=recursive,
verbose=converter.Verbosity.BRIEF,
strip_decorators=(convert, do_not_convert, converted_call),
optional_features=experimental_optional_features),
partial_types=experimental_partial_types,
autograph_module=tf_inspect.getmodule(to_graph),
uncompiled_modules=config.DEFAULT_UNCOMPILED_MODULES)
conversion.entity_to_graph(entity, program_ctx, arg_values, arg_types)
code = '\n'.join(
compiler.ast_to_source(program_ctx.dependency_cache[dep], indentation)
for dep in reversed(program_ctx.conversion_order))
return program_ctx.required_imports + '\n\n' + code
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 to_code(entity,
recursive=True,
arg_values=None,
arg_types=None,
indentation=' ',
experimental_optional_features=converter.Feature.ALL,
experimental_partial_types=None):
"""Similar to `to_graph`, but returns Python source code as a string.
Also see: `tf.autograph.to_graph`.
`to_graph` returns the Python source code that can be used to generate a
TensorFlow graph that is functionally identical to the input Python code.
Args:
entity: Python callable or class to convert.
recursive: Whether to recursively convert any functions that the
converted function may call.
arg_values: Optional dict of value hints for symbols including
function arguments mapping string names to actual values. For example,
`arg_values={'a': 1}` will map the variable `a` to the value `1`.
arg_types: Optional dict of type hints for symbols including function
arguments. Type hints allow specifying just the type of a variable, rather
than a specific value.
indentation: The string to use for indenting. Typically two or four spaces,
or just the tab character.
experimental_optional_features: `None`, a tuple of, or a single
`tf.autograph.experimental.Feature` value. Controls the use of
optional features in the conversion process.
experimental_partial_types: A `set` of `type` values, reserved for internal
use.
Returns:
The converted code as string.
"""
program_ctx = converter.ProgramContext(
options=converter.ConversionOptions(
recursive=recursive,
verbose=converter.Verbosity.BRIEF,
strip_decorators=(convert, do_not_convert, converted_call),
optional_features=experimental_optional_features),
partial_types=experimental_partial_types,
autograph_module=tf_inspect.getmodule(to_graph),
uncompiled_modules=config.DEFAULT_UNCOMPILED_MODULES)
conversion.entity_to_graph(entity, program_ctx, arg_values, arg_types)
code = '\n'.join(
compiler.ast_to_source(program_ctx.dependency_cache[dep], indentation)
for dep in reversed(program_ctx.conversion_order))
return program_ctx.required_imports + '\n\n' + code
def test_convert_entity_to_ast_function_with_defaults(self):
b = 2
c = 1
def f(a, d=c + 1):
return a + b + d
program_ctx = self._simple_program_ctx()
nodes, name, _ = conversion.convert_entity_to_ast(f, program_ctx)
fn_node, = nodes
self.assertIsInstance(fn_node, gast.FunctionDef)
self.assertEqual('tf__f', name)
self.assertEqual(
compiler.ast_to_source(fn_node.args.defaults[0],
include_encoding_marker=False).strip(),
'None')
def to_code(entity,
recursive=True,
arg_values=None,
arg_types=None,
indentation=' ',
experimental_optional_features=converter.Feature.ALL,
experimental_partial_types=None):
"""Similar to `to_graph`, but returns Python source code as a string.
Also see: `tf.autograph.to_graph`.
`to_graph` returns the Python source code that can be used to generate a
TensorFlow graph that is functionally identical to the input Python code.
Args:
entity: Python callable or class to convert.
recursive: Whether to recursively convert any functions that the
converted function may call.
arg_values: Optional dict of value hints for symbols including
function arguments mapping string names to actual values. For example,
`arg_values={'a': 1}` will map the variable `a` to the value `1`.
arg_types: Optional dict of type hints for symbols including function
arguments. Type hints allow specifying just the type of a variable, rather
than a specific value.
indentation: The string to use for indenting. Typically two or four spaces,
or just the tab character.
experimental_optional_features: `None`, a tuple of, or a single
`tf.autograph.experimental.Feature` value. Controls the use of
optional features in the conversion process.
experimental_partial_types: Deprecated, unused.
Returns:
The converted code as string.
"""
del experimental_partial_types
program_ctx = converter.ProgramContext(
options=converter.ConversionOptions(
recursive=recursive,
optional_features=experimental_optional_features),
autograph_module=tf_inspect.getmodule(to_graph))
nodes, _, _ = conversion.entity_to_graph(entity, program_ctx, arg_values,
arg_types)
code = compiler.ast_to_source(nodes, indentation)
return program_ctx.required_imports + '\n\n' + code
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 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 test_create_source_map(self):
def test_fn(x):
return x + 1
node, _, _ = parser.parse_entity(test_fn)
fake_origin = origin_info.OriginInfo(
loc=origin_info.Location('fake_filename', 3, 7),
function_name='fake_function_name',
source_code_line='fake source line',
comment=None)
anno.setanno(node.body[0], anno.Basic.ORIGIN, fake_origin)
converted_code = compiler.ast_to_source(node)
source_map = origin_info.create_source_map(node, converted_code,
'test_filename', [0])
loc = origin_info.LineLocation('test_filename', 2)
self.assertIn(loc, source_map)
self.assertIs(source_map[loc], fake_origin)
def test_source_map(self):
def test_fn(x):
if x > 0:
x += 1
return x
node, source = parser.parse_entity(test_fn)
fn_node = node.body[0]
origin_info.resolve(fn_node, source)
# Insert a traced line.
new_node = parser.parse_str('x = abs(x)').body[0]
anno.copyanno(fn_node.body[0], new_node, anno.Basic.ORIGIN)
fn_node.body.insert(0, new_node)
# Insert an untraced line.
fn_node.body.insert(0, parser.parse_str('x = 0').body[0])
modified_source = compiler.ast_to_source(fn_node)
source_map = origin_info.source_map(fn_node, modified_source,
'test_filename', [0])
loc = origin_info.LineLocation('test_filename', 1)
origin = source_map[loc]
self.assertEqual(origin.source_code_line, 'def test_fn(x):')
self.assertEqual(origin.loc.lineno, 1)
# The untraced line, inserted second.
loc = origin_info.LineLocation('test_filename', 2)
self.assertFalse(loc in source_map)
# The traced line, inserted first.
loc = origin_info.LineLocation('test_filename', 3)
origin = source_map[loc]
self.assertEqual(origin.source_code_line, ' if x > 0:')
self.assertEqual(origin.loc.lineno, 2)
loc = origin_info.LineLocation('test_filename', 4)
origin = source_map[loc]
self.assertEqual(origin.source_code_line, ' if x > 0:')
self.assertEqual(origin.loc.lineno, 2)
def test_ast_to_source(self):
node = gast.If(
test=gast.Num(1),
body=[
gast.Assign(targets=[gast.Name('a', gast.Store(), None)],
value=gast.Name('b', gast.Load(), None))
],
orelse=[
gast.Assign(targets=[gast.Name('a', gast.Store(), None)],
value=gast.Str('c'))
])
source = compiler.ast_to_source(node, indentation=' ')
self.assertEqual(
textwrap.dedent("""
if 1:
a = b
else:
a = 'c'
""").strip(), source.strip())
def test_create_source_map(self):
def test_fn(x):
return x + 1
node, _ = parser.parse_entity(test_fn)
fake_origin = origin_info.OriginInfo(
loc=origin_info.Location('fake_filename', 3, 7),
function_name='fake_function_name',
source_code_line='fake source line',
comment=None)
fn_node = node.body[0]
anno.setanno(fn_node.body[0], anno.Basic.ORIGIN, fake_origin)
converted_code = compiler.ast_to_source(fn_node)
source_map = origin_info.create_source_map(
fn_node, converted_code, 'test_filename', [0])
loc = origin_info.LineLocation('test_filename', 2)
self.assertIn(loc, source_map)
self.assertIs(source_map[loc], fake_origin)
def to_code(e,
recursive=True,
arg_values=None,
arg_types=None,
partial_types=None,
indentation=' '):
"""Returns the equivalent code that uses TensorFlow ops.
Also see: `to_graph`, `convert`
Args:
e: Union[Callable, Type], the Python entity to convert.
recursive: bool, whether to recursively convert any functions that the
converted function may call.
arg_values: Optional[Dict[Text, Any]], value hints for symbols including
function arguments.
arg_types: Optional[Dict[Text, Type]], type hints for symbols including
function arguments.
partial_types: Set[Type], reserved for internal use.
indentation: Text, when to use for each level of indentation.
Returns:
Text, the converted code.
"""
program_ctx = converter.ProgramContext(
options=converter.ConversionOptions(recursive=recursive,
strip_decorators=(convert,
do_not_convert,
converted_call)),
partial_types=partial_types,
autograph_module=tf_inspect.getmodule(to_graph),
uncompiled_modules=config.DEFAULT_UNCOMPILED_MODULES)
conversion.entity_to_graph(e, program_ctx, arg_values, arg_types)
code = '\n'.join(
compiler.ast_to_source(program_ctx.dependency_cache[dep], indentation)
for dep in reversed(program_ctx.conversion_order))
return program_ctx.required_imports + '\n\n' + code
def test_ast_to_source(self):
node = gast.If(
test=gast.Num(1),
body=[
gast.Assign(
targets=[gast.Name('a', gast.Store(), None)],
value=gast.Name('b', gast.Load(), None))
],
orelse=[
gast.Assign(
targets=[gast.Name('a', gast.Store(), None)],
value=gast.Str('c'))
])
source = compiler.ast_to_source(node, indentation=' ')
self.assertEqual(
textwrap.dedent("""
if 1:
a = b
else:
a = 'c'
""").strip(), source.strip())
def to_code(e,
recursive=True,
arg_values=None,
arg_types=None,
partial_types=None,
indentation=' '):
"""Returns the equivalent code that uses TensorFlow ops.
Also see: `to_graph`, `convert`
Args:
e: Union[Callable, Type], the Python entity to convert.
recursive: bool, whether to recursively convert any functions that the
converted function may call.
arg_values: Optional[Dict[Text, Any]], value hints for symbols including
function arguments.
arg_types: Optional[Dict[Text, Type]], type hints for symbols including
function arguments.
partial_types: Set[Type], reserved for internal use.
indentation: Text, when to use for each level of indentation.
Returns:
Text, the converted code.
"""
program_ctx = converter.ProgramContext(
options=converter.ConversionOptions(
recursive=recursive,
strip_decorators=(convert, do_not_convert, converted_call)),
partial_types=partial_types,
autograph_module=tf_inspect.getmodule(to_graph),
uncompiled_modules=config.DEFAULT_UNCOMPILED_MODULES)
conversion.entity_to_graph(e, program_ctx, arg_values, arg_types)
code = '\n'.join(
compiler.ast_to_source(program_ctx.dependency_cache[dep], indentation)
for dep in reversed(program_ctx.conversion_order))
return program_ctx.required_imports + '\n\n' + code
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.
"""
logging.log(1, 'Converting %s', 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 logging.has_verbosity(2):
logging.log(2, 'Compiled output of %s:\n\n%s\n', 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 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 assert_same_ast(self, expected_node, node, msg=None): expected_source = compiler.ast_to_source(expected_node, indentation=' ') expected_str = textwrap.dedent(expected_source).strip() got_source = compiler.ast_to_source(node, indentation=' ') got_str = textwrap.dedent(got_source).strip() self.assertEqual(expected_str, got_str, msg=msg)
def debug_print_src(self, node):
"""Helper method useful for debugging. Prints the AST as code."""
if __debug__:
print(compiler.ast_to_source(node))
return node
def debug_print_src(self, node):
"""Helper method useful for debugging. Prints the AST as code."""
if __debug__:
print(compiler.ast_to_source(node))
return node
def _mock_apply_fn(self, target, source): target = compiler.ast_to_source(target, include_encoding_marker=False) source = compiler.ast_to_source(source, include_encoding_marker=False) self._invocation_counts[(target.strip(), source.strip())] += 1
def assertFunctionDefNodes(self, matching_nodes, expected_bodies):
self.assertEqual(len(matching_nodes), len(expected_bodies))
for node in matching_nodes:
self.assertIsInstance(node, gast.FunctionDef)
self.assertIn(
compiler.ast_to_source(node.body).strip(), expected_bodies)
def assertFunctionDefNodes(self, matching_nodes, expected_bodies):
self.assertEqual(len(matching_nodes), len(expected_bodies))
for node in matching_nodes:
self.assertIsInstance(node, gast.FunctionDef)
self.assertIn(compiler.ast_to_source(node.body).strip(), expected_bodies)
def _mock_apply_fn(self, target, source): target = compiler.ast_to_source(target) source = compiler.ast_to_source(source) self._invocation_counts[(target.strip(), source.strip())] += 1
def _mock_apply_fn(self, target, source):
target = compiler.ast_to_source(target)
source = compiler.ast_to_source(source)
self._invocation_counts[(target.strip(), source.strip())] += 1
def __repr__(self):
if isinstance(self.ast_node, gast.FunctionDef):
return 'def %s' % self.ast_node.name
elif isinstance(self.ast_node, gast.withitem):
return compiler.ast_to_source(self.ast_node.context_expr).strip()
return compiler.ast_to_source(self.ast_node).strip()
def assertTransformedFirstLineIs(self, node, expected):
self.assertEqual(
compiler.ast_to_source(
node, include_encoding_marker=False).split('\n')[0], expected)
def assertTransformedFirstLineIs(self, node, expected):
self.assertEqual(compiler.ast_to_source(node).split('\n')[0], expected)
