torch/jit/annotations.py - platform/external/pytorch - Git at Google

 import sys
 import ast
 import inspect
 import re
 import torch
 from .._jit_internal import List, BroadcastingList1, BroadcastingList2, \
     BroadcastingList3, Tuple, is_tuple, is_list, Dict, is_dict, Optional, \
     is_optional, _qualified_name, Any
 from torch._C import TensorType, TupleType, FloatType, IntType, \
     ListType, StringType, DictType, BoolType, OptionalType, ClassType, InterfaceType, AnyType, NoneType

 from textwrap import dedent
 from torch._six import builtins
 from torch._utils_internal import get_source_lines_and_file


 PY35 = sys.version_info >= (3, 5)


 class Module(object):
     def __init__(self, name, members):
         self.name = name
         self.members = members

     def __getattr__(self, name):
         try:
             return self.members[name]
         except KeyError:
             raise RuntimeError("Module {} has no member called {}".format(self.name, name))


 class EvalEnv(object):
     env = {
         'torch': Module('torch', {'Tensor': torch.Tensor}),
         'Tensor': torch.Tensor,
         'typing': Module('typing', {'Tuple': Tuple}),
         'Tuple': Tuple,
         'List': List,
         'Dict': Dict,
         'Optional': Optional,
     }

     def __init__(self, rcb):
         self.rcb = rcb

     def __getitem__(self, name):
         if name in self.env:
             return self.env[name]
         if self.rcb is not None:
             return self.rcb(name)
         return getattr(builtins, name, None)

 def get_signature(fn, rcb, loc):
     # Python 3.5 adds support for the nice annotation syntax, so try that first.
     if PY35:
         sig = try_real_annotations(fn)
         if sig is not None:
             return sig

     type_line, source = None, None
     try:
         source = dedent(''.join(get_source_lines_and_file(fn)[0]))
         type_line = get_type_line(source)
     except TypeError:
         pass
     # This might happen both because we failed to get the source of fn, or
     # because it didn't have any annotations.
     if type_line is None:
         return None

     return parse_type_line(type_line, rcb, loc)


 # This is essentially a weaker form of get_signature(), where we don't care if
 # we have the types, we just care that we can figure out how many parameters
 # a function takes.
 def get_num_params(fn, loc):
     try:
         source = dedent(''.join(get_source_lines_and_file(fn)[0]))
     except (TypeError, IOError):
         return None
     if source is None:
         return None
     py_ast = ast.parse(source)
     if len(py_ast.body) == 1 and isinstance(py_ast.body[0], ast.ClassDef):
         raise torch.jit.frontend.FrontendError(
             loc, "Cannot instantiate class '{}' in a script function".format(py_ast.body[0].name))
     if len(py_ast.body) != 1 or not isinstance(py_ast.body[0], ast.FunctionDef):
         raise torch.jit.frontend.FrontendError(loc, "Expected a single top-level function")
     py_def = py_ast.body[0]
     if py_def.args.vararg is not None:
         return None
     elif hasattr(py_def.args, 'kwonlyargs') and len(py_def.args.kwonlyargs) > 0:
         return None
     else:
         return len(py_def.args.args)


 def parse_type_line(type_line, rcb, loc):
     """Parses a type annotation specified as a comment.

     Example inputs:
         # type: (Tensor, torch.Tensor) -> Tuple[Tensor]
         # type: (Tensor, Tuple[Tensor, Tensor]) -> Tensor
     """
     arg_ann_str, ret_ann_str = split_type_line(type_line)

     try:
         arg_ann = eval(arg_ann_str, {}, EvalEnv(rcb))  # noqa: P204
     except (NameError, SyntaxError) as e:
         raise RuntimeError("Failed to parse the argument list of a type annotation: {}".format(str(e)))

     if not isinstance(arg_ann, tuple):
         arg_ann = (arg_ann,)

     try:
         ret_ann = eval(ret_ann_str, {}, EvalEnv(rcb))  # noqa: P204
     except (NameError, SyntaxError) as e:
         raise RuntimeError("Failed to parse the return type of a type annotation: {}".format(str(e)))

     resolver = (rcb, loc)
     arg_types = [ann_to_type(ann, resolver) for ann in arg_ann]
     return arg_types, ann_to_type(ret_ann, resolver)


 def get_type_line(source):
     """Tries to find the line containing a comment with the type annotation."""
     type_comment = '# type:'

     lines = source.split('\n')
     lines = [(line_num, line) for line_num, line in enumerate(lines)]
     type_lines = list(filter(lambda line: type_comment in line[1], lines))
     lines_with_type = list(filter(lambda line: 'type' in line[1], lines))

     if len(type_lines) == 0:
         type_pattern = re.compile('#[\t ]*type[\t ]*:')
         wrong_type_lines = list(filter(lambda line: type_pattern.search(line[1]), lines))
         if len(wrong_type_lines) > 0:
             raise RuntimeError("The annotation prefix in line " + str(wrong_type_lines[0][0])
                                + " is probably invalid.\nIt must be '# type:'"
                                + "\nSee PEP 484 (https://www.python.org/dev/peps/pep-0484/#suggested-syntax-for-python-2-7-and-straddling-code)" # noqa
                                + "\nfor examples")
         return None
     elif len(type_lines) == 1:
         # Only 1 type line, quit now
         return type_lines[0][1].strip()

     # Parse split up argument types according to PEP 484
     # https://www.python.org/dev/peps/pep-0484/#suggested-syntax-for-python-2-7-and-straddling-code
     return_line = None
     parameter_type_lines = []
     for line_num, line in reversed(type_lines):
         if '# type: (...) -> ' in line:
             return_line = (line_num, line)
         elif type_comment in line:
             if return_line is None:
                 raise RuntimeError("Return type line '# type: (...) -> ...' not found on multiline "
                                    "type annotation\n(See PEP 484 https://www.python.org/dev/peps/pep-0484/#suggested-syntax-for-python-2-7-and-straddling-code)")  # noqa
             if line_num < return_line[0]:
                 parameter_type_lines.insert(0, line)

     def get_parameter_type(line):
         item_type = line[line.find(type_comment) + len(type_comment):]
         return item_type.strip()

     types = map(get_parameter_type, parameter_type_lines)
     parameter_types = ", ".join(types)

     return return_line[1].replace("...", parameter_types)


 def split_type_line(type_line):
     """Splits the comment with the type annotation into parts for argument and return types.

     For example, for an input of:
         # type: (Tensor, torch.Tensor) -> Tuple[Tensor, Tensor]

     This function will return:
         ("(Tensor, torch.Tensor)", "Tuple[Tensor, Tensor]")

     """
     start_offset = len('# type:')
     try:
         arrow_pos = type_line.index('->')
     except ValueError:
         raise RuntimeError("Syntax error in type annotation (cound't find `->`)")
     return type_line[start_offset:arrow_pos].strip(), type_line[arrow_pos + 2:].strip()


 def try_real_annotations(fn):
     """Tries to use the Py3.5+ annotation syntax to get the type."""
     try:
         sig = inspect.signature(fn)
     except ValueError:
         return None

     all_annots = [sig.return_annotation] + [p.annotation for p in sig.parameters.values()]
     if all(ann is sig.empty for ann in all_annots):
         return None

     def as_ann(ann):
         # sig.empty is really annoying so convert it to None
         return ann if ann is not sig.empty else None

     arg_types = [ann_to_type(as_ann(p.annotation))
                  for p in sig.parameters.values()]
     return_type = ann_to_type(as_ann(sig.return_annotation))
     return arg_types, return_type


 def ann_to_type(ann, resolver=None):
     # resolver should be a Tuple[Callable, SourceRange] where the Callable
     # is a resolutionCallback
     if ann is None:
         return TensorType.get()
     elif ann is torch.Tensor:
         return TensorType.get()
     elif is_tuple(ann):
         return TupleType([ann_to_type(a) for a in ann.__args__])
     elif is_list(ann):
         return ListType(ann_to_type(ann.__args__[0]))
     elif is_dict(ann):
         key = ann_to_type(ann.__args__[0])
         value = ann_to_type(ann.__args__[1])
         return DictType(key, value)
     elif is_optional(ann):
         if issubclass(ann.__args__[1], type(None)):
             return OptionalType(ann_to_type(ann.__args__[0]))
         else:
             return OptionalType(ann_to_type(ann.__args__[1]))
     elif ann is float:
         return FloatType.get()
     elif ann is int:
         return IntType.get()
     elif ann is str:
         return StringType.get()
     elif ann is bool:
         return BoolType.get()
     elif ann is Any:
         return AnyType.get()
     elif ann is type(None):
         return NoneType.get()
     elif hasattr(ann, "__torch_script_class__"):
         return ClassType(_qualified_name(ann))
     elif hasattr(ann, "__torch_script_interface__"):
         return InterfaceType(_qualified_name(ann))
     elif resolver is not None:
         # Maybe resolve a NamedTuple to a Tuple Type
         rcb, loc = resolver
         the_type = torch._C._resolve_type(ann.__name__, loc, rcb)
         if the_type is not None:
             return the_type
     raise ValueError("Unknown type annotation: '{}'".format(ann))


 __all__ = [
     'Any',
     'List',
     'BroadcastingList1',
     'BroadcastingList2',
     'BroadcastingList3',
     'Tuple',
     'is_tuple',
     'is_list',
     'Dict',
     'is_dict',
     'TensorType',
     'TupleType',
     'FloatType',
     'IntType',
     'ListType',
     'StringType',
     'DictType',
     'AnyType',
     'Module',
     # TODO: Consider not exporting these during wildcard import (reserve
     # that for the types; for idiomatic typing code.)
     'get_signature',
     'get_num_params',
     'parse_type_line',
     'get_type_line',
     'split_type_line',
     'try_real_annotations',
     'ann_to_type',
 ]
	import sys
	import ast
	import inspect
	import re
	import torch
	from .._jit_internal import List, BroadcastingList1, BroadcastingList2, \
	BroadcastingList3, Tuple, is_tuple, is_list, Dict, is_dict, Optional, \
	is_optional, _qualified_name, Any
	from torch._C import TensorType, TupleType, FloatType, IntType, \
	ListType, StringType, DictType, BoolType, OptionalType, ClassType, InterfaceType, AnyType, NoneType

	from textwrap import dedent
	from torch._six import builtins
	from torch._utils_internal import get_source_lines_and_file


	PY35 = sys.version_info >= (3, 5)


	class Module(object):
	def __init__(self, name, members):
	self.name = name
	self.members = members

	def __getattr__(self, name):
	try:
	return self.members[name]
	except KeyError:
	raise RuntimeError("Module {} has no member called {}".format(self.name, name))


	class EvalEnv(object):
	env = {
	'torch': Module('torch', {'Tensor': torch.Tensor}),
	'Tensor': torch.Tensor,
	'typing': Module('typing', {'Tuple': Tuple}),
	'Tuple': Tuple,
	'List': List,
	'Dict': Dict,
	'Optional': Optional,
	}

	def __init__(self, rcb):
	self.rcb = rcb

	def __getitem__(self, name):
	if name in self.env:
	return self.env[name]
	if self.rcb is not None:
	return self.rcb(name)
	return getattr(builtins, name, None)

	def get_signature(fn, rcb, loc):
	# Python 3.5 adds support for the nice annotation syntax, so try that first.
	if PY35:
	sig = try_real_annotations(fn)
	if sig is not None:
	return sig

	type_line, source = None, None
	try:
	source = dedent(''.join(get_source_lines_and_file(fn)[0]))
	type_line = get_type_line(source)
	except TypeError:
	pass
	# This might happen both because we failed to get the source of fn, or
	# because it didn't have any annotations.
	if type_line is None:
	return None

	return parse_type_line(type_line, rcb, loc)


	# This is essentially a weaker form of get_signature(), where we don't care if
	# we have the types, we just care that we can figure out how many parameters
	# a function takes.
	def get_num_params(fn, loc):
	try:
	source = dedent(''.join(get_source_lines_and_file(fn)[0]))
	except (TypeError, IOError):
	return None
	if source is None:
	return None
	py_ast = ast.parse(source)
	if len(py_ast.body) == 1 and isinstance(py_ast.body[0], ast.ClassDef):
	raise torch.jit.frontend.FrontendError(
	loc, "Cannot instantiate class '{}' in a script function".format(py_ast.body[0].name))
	if len(py_ast.body) != 1 or not isinstance(py_ast.body[0], ast.FunctionDef):
	raise torch.jit.frontend.FrontendError(loc, "Expected a single top-level function")
	py_def = py_ast.body[0]
	if py_def.args.vararg is not None:
	return None
	elif hasattr(py_def.args, 'kwonlyargs') and len(py_def.args.kwonlyargs) > 0:
	return None
	else:
	return len(py_def.args.args)


	def parse_type_line(type_line, rcb, loc):
	"""Parses a type annotation specified as a comment.

	Example inputs:
	# type: (Tensor, torch.Tensor) -> Tuple[Tensor]
	# type: (Tensor, Tuple[Tensor, Tensor]) -> Tensor
	"""
	arg_ann_str, ret_ann_str = split_type_line(type_line)

	try:
	arg_ann = eval(arg_ann_str, {}, EvalEnv(rcb)) # noqa: P204
	except (NameError, SyntaxError) as e:
	raise RuntimeError("Failed to parse the argument list of a type annotation: {}".format(str(e)))

	if not isinstance(arg_ann, tuple):
	arg_ann = (arg_ann,)

	try:
	ret_ann = eval(ret_ann_str, {}, EvalEnv(rcb)) # noqa: P204
	except (NameError, SyntaxError) as e:
	raise RuntimeError("Failed to parse the return type of a type annotation: {}".format(str(e)))

	resolver = (rcb, loc)
	arg_types = [ann_to_type(ann, resolver) for ann in arg_ann]
	return arg_types, ann_to_type(ret_ann, resolver)


	def get_type_line(source):
	"""Tries to find the line containing a comment with the type annotation."""
	type_comment = '# type:'

	lines = source.split('\n')
	lines = [(line_num, line) for line_num, line in enumerate(lines)]
	type_lines = list(filter(lambda line: type_comment in line[1], lines))
	lines_with_type = list(filter(lambda line: 'type' in line[1], lines))

	if len(type_lines) == 0:
	type_pattern = re.compile('#[\t ]type[\t ]:')
	wrong_type_lines = list(filter(lambda line: type_pattern.search(line[1]), lines))
	if len(wrong_type_lines) > 0:
	raise RuntimeError("The annotation prefix in line " + str(wrong_type_lines[0][0])
	+ " is probably invalid.\nIt must be '# type:'"
	+ "\nSee PEP 484 (https://www.python.org/dev/peps/pep-0484/#suggested-syntax-for-python-2-7-and-straddling-code)" # noqa
	+ "\nfor examples")
	return None
	elif len(type_lines) == 1:
	# Only 1 type line, quit now
	return type_lines[0][1].strip()

	# Parse split up argument types according to PEP 484
	# https://www.python.org/dev/peps/pep-0484/#suggested-syntax-for-python-2-7-and-straddling-code
	return_line = None
	parameter_type_lines = []
	for line_num, line in reversed(type_lines):
	if '# type: (...) -> ' in line:
	return_line = (line_num, line)
	elif type_comment in line:
	if return_line is None:
	raise RuntimeError("Return type line '# type: (...) -> ...' not found on multiline "
	"type annotation\n(See PEP 484 https://www.python.org/dev/peps/pep-0484/#suggested-syntax-for-python-2-7-and-straddling-code)") # noqa
	if line_num < return_line[0]:
	parameter_type_lines.insert(0, line)

	def get_parameter_type(line):
	item_type = line[line.find(type_comment) + len(type_comment):]
	return item_type.strip()

	types = map(get_parameter_type, parameter_type_lines)
	parameter_types = ", ".join(types)

	return return_line[1].replace("...", parameter_types)


	def split_type_line(type_line):
	"""Splits the comment with the type annotation into parts for argument and return types.

	For example, for an input of:
	# type: (Tensor, torch.Tensor) -> Tuple[Tensor, Tensor]

	This function will return:
	("(Tensor, torch.Tensor)", "Tuple[Tensor, Tensor]")

	"""
	start_offset = len('# type:')
	try:
	arrow_pos = type_line.index('->')
	except ValueError:
	raise RuntimeError("Syntax error in type annotation (cound't find `->`)")
	return type_line[start_offset:arrow_pos].strip(), type_line[arrow_pos + 2:].strip()


	def try_real_annotations(fn):
	"""Tries to use the Py3.5+ annotation syntax to get the type."""
	try:
	sig = inspect.signature(fn)
	except ValueError:
	return None

	all_annots = [sig.return_annotation] + [p.annotation for p in sig.parameters.values()]
	if all(ann is sig.empty for ann in all_annots):
	return None

	def as_ann(ann):
	# sig.empty is really annoying so convert it to None
	return ann if ann is not sig.empty else None

	arg_types = [ann_to_type(as_ann(p.annotation))
	for p in sig.parameters.values()]
	return_type = ann_to_type(as_ann(sig.return_annotation))
	return arg_types, return_type


	def ann_to_type(ann, resolver=None):
	# resolver should be a Tuple[Callable, SourceRange] where the Callable
	# is a resolutionCallback
	if ann is None:
	return TensorType.get()
	elif ann is torch.Tensor:
	return TensorType.get()
	elif is_tuple(ann):
	return TupleType([ann_to_type(a) for a in ann.__args__])
	elif is_list(ann):
	return ListType(ann_to_type(ann.__args__[0]))
	elif is_dict(ann):
	key = ann_to_type(ann.__args__[0])
	value = ann_to_type(ann.__args__[1])
	return DictType(key, value)
	elif is_optional(ann):
	if issubclass(ann.__args__[1], type(None)):
	return OptionalType(ann_to_type(ann.__args__[0]))
	else:
	return OptionalType(ann_to_type(ann.__args__[1]))
	elif ann is float:
	return FloatType.get()
	elif ann is int:
	return IntType.get()
	elif ann is str:
	return StringType.get()
	elif ann is bool:
	return BoolType.get()
	elif ann is Any:
	return AnyType.get()
	elif ann is type(None):
	return NoneType.get()
	elif hasattr(ann, "__torch_script_class__"):
	return ClassType(_qualified_name(ann))
	elif hasattr(ann, "__torch_script_interface__"):
	return InterfaceType(_qualified_name(ann))
	elif resolver is not None:
	# Maybe resolve a NamedTuple to a Tuple Type
	rcb, loc = resolver
	the_type = torch._C._resolve_type(ann.__name__, loc, rcb)
	if the_type is not None:
	return the_type
	raise ValueError("Unknown type annotation: '{}'".format(ann))


	__all__ = [
	'Any',
	'List',
	'BroadcastingList1',
	'BroadcastingList2',
	'BroadcastingList3',
	'Tuple',
	'is_tuple',
	'is_list',
	'Dict',
	'is_dict',
	'TensorType',
	'TupleType',
	'FloatType',
	'IntType',
	'ListType',
	'StringType',
	'DictType',
	'AnyType',
	'Module',
	# TODO: Consider not exporting these during wildcard import (reserve
	# that for the types; for idiomatic typing code.)
	'get_signature',
	'get_num_params',
	'parse_type_line',
	'get_type_line',
	'split_type_line',
	'try_real_annotations',
	'ann_to_type',
	]