From: A. Unique TensorFlower Date: Tue, 27 Feb 2018 14:00:21 +0000 (-0800) Subject: Enable dynamic function calls. These are compiled just in time by inserting a call... X-Git-Tag: upstream/v1.7.0~76 X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=efa9a8ec649c72887cd286a78b3a2bf95e34f924;p=platform%2Fupstream%2Ftensorflow.git Enable dynamic function calls. These are compiled just in time by inserting a call to compile. PiperOrigin-RevId: 187165096 --- diff --git a/tensorflow/contrib/py2tf/__init__.py b/tensorflow/contrib/py2tf/__init__.py index 379fa7f..6531183 100644 --- a/tensorflow/contrib/py2tf/__init__.py +++ b/tensorflow/contrib/py2tf/__init__.py @@ -23,6 +23,7 @@ from __future__ import print_function from tensorflow.contrib.py2tf import utils from tensorflow.contrib.py2tf.impl.api import convert +from tensorflow.contrib.py2tf.impl.api import converted_call from tensorflow.contrib.py2tf.impl.api import graph_ready from tensorflow.contrib.py2tf.impl.api import to_code from tensorflow.contrib.py2tf.impl.api import to_graph @@ -30,7 +31,8 @@ from tensorflow.contrib.py2tf.pyct.transformer import PyFlowParseError from tensorflow.python.util.all_util import remove_undocumented _allowed_symbols = [ - 'to_graph', 'to_code', 'convert', 'graph_ready', 'utils', 'PyFlowParseError' + 'to_graph', 'to_code', 'convert', 'graph_ready', 'converted_call', 'utils', + 'PyFlowParseError' ] remove_undocumented(__name__, _allowed_symbols) diff --git a/tensorflow/contrib/py2tf/converters/BUILD b/tensorflow/contrib/py2tf/converters/BUILD index 42baaaa..78f46bc 100644 --- a/tensorflow/contrib/py2tf/converters/BUILD +++ b/tensorflow/contrib/py2tf/converters/BUILD @@ -46,6 +46,7 @@ py_library( visibility = ["//tensorflow:__subpackages__"], deps = [ ":converters", + "//tensorflow/contrib/py2tf/pyct", "//tensorflow/contrib/py2tf/pyct/static_analysis", "//tensorflow/contrib/py2tf/utils", "@gast_archive//:gast", @@ -59,7 +60,6 @@ py_test( srcs_version = "PY2AND3", deps = [ ":test_lib", - "//tensorflow/contrib/py2tf/pyct", "//tensorflow/python:client_testlib", ], ) @@ -70,7 +70,6 @@ py_test( srcs_version = "PY2AND3", deps = [ ":test_lib", - "//tensorflow/contrib/py2tf/pyct", "//tensorflow/python:client_testlib", ], ) @@ -81,7 +80,6 @@ py_test( srcs_version = "PY2AND3", deps = [ ":test_lib", - "//tensorflow/contrib/py2tf/pyct", "//tensorflow/python:client_testlib", ], ) @@ -92,7 +90,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":test_lib", - "//tensorflow/contrib/py2tf/pyct", + "//tensorflow/contrib/py2tf/impl", "//tensorflow/python:client_testlib", ], ) @@ -103,7 +101,6 @@ py_test( srcs_version = "PY2AND3", deps = [ ":test_lib", - "//tensorflow/contrib/py2tf/pyct", "//tensorflow/python:client_testlib", ], ) @@ -114,7 +111,6 @@ py_test( srcs_version = "PY2AND3", deps = [ ":test_lib", - "//tensorflow/contrib/py2tf/pyct", "//tensorflow/python:client_testlib", ], ) @@ -125,7 +121,6 @@ py_test( srcs_version = "PY2AND3", deps = [ ":test_lib", - "//tensorflow/contrib/py2tf/pyct", "//tensorflow/python:client_testlib", ], ) @@ -136,7 +131,6 @@ py_test( srcs_version = "PY2AND3", deps = [ ":test_lib", - "//tensorflow/contrib/py2tf/pyct", "//tensorflow/python:client_testlib", ], ) @@ -157,7 +151,6 @@ py_test( srcs_version = "PY2AND3", deps = [ ":test_lib", - "//tensorflow/contrib/py2tf/pyct", "//tensorflow/python:client_testlib", ], ) @@ -168,7 +161,6 @@ py_test( srcs_version = "PY2AND3", deps = [ ":test_lib", - "//tensorflow/contrib/py2tf/pyct", "//tensorflow/python:client_testlib", ], ) @@ -184,7 +176,6 @@ py_test( ], deps = [ ":test_lib", - "//tensorflow/contrib/py2tf/pyct", "//tensorflow/python:client_testlib", ], ) diff --git a/tensorflow/contrib/py2tf/converters/call_trees.py b/tensorflow/contrib/py2tf/converters/call_trees.py index 1050ba6..f18f9f6 100644 --- a/tensorflow/contrib/py2tf/converters/call_trees.py +++ b/tensorflow/contrib/py2tf/converters/call_trees.py @@ -27,6 +27,7 @@ import types import gast from tensorflow.contrib.py2tf.pyct import anno +from tensorflow.contrib.py2tf.pyct import inspect_utils from tensorflow.contrib.py2tf.pyct import parser from tensorflow.contrib.py2tf.pyct import templates from tensorflow.contrib.py2tf.pyct import transformer @@ -72,9 +73,8 @@ class CallTreeTransformer(transformer.Base): self.uncompiled_modules = uncompiled_modules self.nocompile_decorators = nocompile_decorators - # pylint:disable=invalid-name - def _resolve_name(self, node): + """Used to resolve decorator info.""" if isinstance(node, gast.Call): return self._resolve_name(node.func) if isinstance(node, gast.Name): @@ -99,7 +99,13 @@ class CallTreeTransformer(transformer.Base): (owner_type, node.attr)) return None + def _function_is_compilable(self, target_entity): + """Determines whether an entity can be compiled at all.""" + # TODO(mdan): This is just a placeholder. Implement. + return not isinstance(target_entity, types.BuiltinFunctionType) + def _should_compile(self, node, fqn): + """Determines whether an entity should be compiled in the context.""" for i in range(1, len(fqn)): if fqn[:i] in self.uncompiled_modules: return False @@ -141,33 +147,6 @@ class CallTreeTransformer(transformer.Base): return True - def _determine_function_owner(self, m): - # TODO(mdan): The parent type should be known at analysis. Use that instead. - if hasattr(m, 'im_class'): # Python 2 - return m.im_class - if hasattr(m, '__qualname__'): # Python 3 - # Object attributes: should be bound to "self". - if hasattr(m, '__self__'): - return type(m.__self__) - - # Class attributes: should have the owner name in their namespace. - qn = m.__qualname__.split('.') - if len(qn) < 2: - return None - owner_name, func_name = qn[-2:] - if func_name != m.__name__: - raise ValueError('Inconsistent names detected ' - '(__qualname__[1] = "%s", __name__ = "%s") for %s.' % - (func_name, m.__name__, m)) - if owner_name == '': - return None - if owner_name not in self.context.namespace: - raise ValueError( - 'Could not resolve name "%s" while analyzing %s. Namespace:\n%s' % - (owner_name, m, self.context.namespace)) - return self.context.namespace[owner_name] - return None - def _rename_compilable_function(self, node): assert anno.hasanno(node.func, 'live_val') assert anno.hasanno(node.func, 'fqn') @@ -182,7 +161,11 @@ class CallTreeTransformer(transformer.Base): target_fqn, live_entity=target_entity) do_rename = True else: - owner_type = self._determine_function_owner(target_entity) + if anno.hasanno(node.func, 'parent_type'): + owner_type = anno.getanno(node.func, 'parent_type') + else: + # Fallback - not reliable. + owner_type = inspect_utils.getmethodclass(target_entity) new_name, do_rename = self.context.namer.compiled_function_name( target_fqn, live_entity=target_entity, owner_type=owner_type) @@ -202,9 +185,32 @@ class CallTreeTransformer(transformer.Base): """ return templates.replace(template, func=node.func, original_args=node.args) - def _function_is_compilable(self, target_entity): - # TODO(mdan): This is just a placeholder. Implement. - return not isinstance(target_entity, types.BuiltinFunctionType) + def _converted_call(self, node): + """Inlines a dynamic conversion for a dynamic function.""" + # TODO(mdan): Pass information on the statically compiled functions. + # Having access to the statically compiled functions can help avoid + # unnecessary compilation. + # For example, this would lead to function `a` being compiled twice: + # + # def a(): + # v = b + # b() + # def b(): + # a() + # + # This is really a problem with recursive calls, which currently can + # only be gated by a static condition, and should be rare. + # TODO(mdan): It probably makes sense to use dynamic conversion every time. + # Before we could convert all the time though, we'd need a reasonable + # caching mechanism. + template = """ + py2tf_api.converted_call(func, True, False, {}, original_args) + """ + call_expr = templates.replace( + template, func=node.func, original_args=node.args) + return call_expr[0].value + + # pylint:disable=invalid-name def visit_Expr(self, node): if isinstance(node.value, gast.Call): @@ -245,9 +251,9 @@ class CallTreeTransformer(transformer.Base): raise NotImplementedError('py_func with return values') else: if self.context.recursive: - raise NotImplementedError('Could not resolve target function.') + node = self._converted_call(node) else: - # TODO(mdan): Double check. Is this reachable code? + # Unresolved functions are allowed in non-recursive mode. pass return node diff --git a/tensorflow/contrib/py2tf/converters/call_trees_test.py b/tensorflow/contrib/py2tf/converters/call_trees_test.py index 777648d..d482a9e 100644 --- a/tensorflow/contrib/py2tf/converters/call_trees_test.py +++ b/tensorflow/contrib/py2tf/converters/call_trees_test.py @@ -47,6 +47,21 @@ class CallTreesTest(converter_test_base.TestCase): result.renamed_test_fn_1 = renamed_test_fn_1 self.assertEquals(3, result.test_fn_2(1)) + def test_dynamic_function(self): + + def test_fn_1(): + raise ValueError('This should be masked by the mock.') + + def test_fn_2(f): + return f() + 3 + + node = self.parse_and_analyze(test_fn_2, {}) + node = call_trees.transform(node, self.ctx, (), ()) + + with self.compiled(node) as result: + # 10 = 7 (from the mock) + 3 (from test_fn_2) + self.assertEquals(10, result.test_fn_2(test_fn_1)) + def test_simple_methods(self): class TestClass(object): @@ -59,6 +74,7 @@ class CallTreesTest(converter_test_base.TestCase): node = self.parse_and_analyze( TestClass.test_fn_2, {'TestClass': TestClass}, + namer=converter_test_base.FakeNoRenameNamer(), arg_types={'self': (TestClass.__name__, TestClass)}) node = call_trees.transform(node, self.ctx, (), ()) diff --git a/tensorflow/contrib/py2tf/converters/converter_test_base.py b/tensorflow/contrib/py2tf/converters/converter_test_base.py index afa5c2f..1f98d84 100644 --- a/tensorflow/contrib/py2tf/converters/converter_test_base.py +++ b/tensorflow/contrib/py2tf/converters/converter_test_base.py @@ -25,6 +25,7 @@ from tensorflow.contrib.py2tf import utils from tensorflow.contrib.py2tf.pyct import compiler from tensorflow.contrib.py2tf.pyct import context from tensorflow.contrib.py2tf.pyct import parser +from tensorflow.contrib.py2tf.pyct import pretty_printer from tensorflow.contrib.py2tf.pyct import qual_names from tensorflow.contrib.py2tf.pyct.static_analysis import activity from tensorflow.contrib.py2tf.pyct.static_analysis import live_values @@ -52,26 +53,43 @@ class FakeNamer(object): return ('renamed_%s' % '_'.join(original_fqn)), True +class FakeNoRenameNamer(FakeNamer): + + def compiled_function_name(self, original_fqn, **_): + return str(original_fqn), False + + class TestCase(test.TestCase): """Base class for unit tests in this module. Contains relevant utilities.""" @contextlib.contextmanager def compiled(self, node, *symbols): - source = '' + source = None + + self.dynamic_calls = [] + def converted_call(*args): + """Mock version of api.converted_call.""" + self.dynamic_calls.append(args) + return 7 + try: result, source = compiler.ast_to_object(node) - result.tf = self.make_fake_tf(*symbols) + result.tf = self.make_fake_mod('fake_tf', *symbols) result.py2tf_utils = utils + result.py2tf_api = self.make_fake_mod('fake_api', converted_call) yield result except Exception: # pylint:disable=broad-except - print('Offending compiled code:\n%s' % source) + if source is None: + print('Offending AST:\n%s' % pretty_printer.fmt(node, color=False)) + else: + print('Offending compiled code:\n%s' % source) raise - def make_fake_tf(self, *symbols): - fake_tf = imp.new_module('fake_tf') + def make_fake_mod(self, name, *symbols): + fake_mod = imp.new_module(name) for s in symbols: - setattr(fake_tf, s.__name__, s) - return fake_tf + setattr(fake_mod, s.__name__, s) + return fake_mod def attach_namespace(self, module, **ns): for k, v in ns.items(): diff --git a/tensorflow/contrib/py2tf/impl/api.py b/tensorflow/contrib/py2tf/impl/api.py index 29d2e03..48100aa 100644 --- a/tensorflow/contrib/py2tf/impl/api.py +++ b/tensorflow/contrib/py2tf/impl/api.py @@ -26,7 +26,9 @@ import six from tensorflow.contrib.py2tf.impl import config from tensorflow.contrib.py2tf.impl import conversion from tensorflow.contrib.py2tf.pyct import compiler +from tensorflow.contrib.py2tf.pyct import inspect_utils from tensorflow.contrib.py2tf.pyct import parser +from tensorflow.contrib.py2tf.utils import builtins from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util import tf_inspect @@ -110,28 +112,7 @@ def convert(recursive=False, verbose=False, arg_types=None): @wraps(f) def wrapper(*args, **kwargs): - """Wrapper that calls the compiled version of the wrapped function.""" - partial_types = () - arg_values = {} - arg_names = tf_inspect.getargspec(f)[0] - for name, arg in zip(arg_names, args): - arg_values[name] = arg - arg_class = arg.__class__ - # If arg_value_hints specifies any name, use that instead. - if name not in arg_types: - arg_types[name] = (arg_class.__name__, arg_class) - if name == 'self' and tf_inspect.isclass(arg_class): - # Annotated methods need to specify that their owner type is partial, - # otherwise other members they call will not be converted. - partial_types = (arg_class,) - wrapped = to_graph( - f, - recursive=recursive, - verbose=verbose, - arg_values=arg_values, - arg_types=arg_types, - partial_types=partial_types) - return wrapped(*args, **kwargs) + return converted_call(f, recursive, verbose, arg_types, *args, **kwargs) # Sometimes the decorator is just desugared, making it impossible to detect. # This attribute makes detection easier. @@ -141,6 +122,78 @@ def convert(recursive=False, verbose=False, arg_types=None): return decorator +def converted_call(f, recursive, verbose, arg_types, *args, **kwargs): + """Compiles a function call inline.""" + # TODO(mdan): This needs cleanup. + # In particular, we may want to avoid renaming functions altogether. + + if conversion.is_whitelisted_for_graph(f): + return f(*args, **kwargs) + + unknown_arg_value = object() # Sentinel for arguments of unknown value + + if tf_inspect.isbuiltin(f): + return builtins.dynamic_builtin(f, *args, **kwargs) + + if tf_inspect.isfunction(f) or tf_inspect.ismethod(f): + # Regular functions + target_entity = f + arg_map_target = f + effective_args = args + f_class = inspect_utils.getmethodclass(f) + + if f_class is not None: + partial_types = (f_class,) + else: + partial_types = () + + elif tf_inspect.isclass(f): + # Constructors + target_entity = f + arg_map_target = f.__init__ + effective_args = (unknown_arg_value,) + args + partial_types = () + + elif hasattr(f, '__call__') and hasattr(f, '__class__'): + # Callable objects + target_entity = f.__call__ + arg_map_target = f.__call__ + effective_args = (f,) + args + partial_types = (f.__class__,) + + else: + NotImplementedError('unknown callable type "%s"' % type(f)) + + arg_values = tf_inspect.getcallargs(arg_map_target, *args, **kwargs) + for name, arg in arg_values.items(): + if arg is unknown_arg_value: + continue + arg_class = arg.__class__ + # If arg_value_hints specifies any name, use that instead. + if name not in arg_types: + arg_types[name] = (arg_class.__name__, arg_class) + + # When called from within a decorator, this is the only indication that + # the function is a method - it appears that the decorator is applied + # before the method is bound. + if not partial_types: + if 'self' in arg_values: + if tf_inspect.isclass(arg_values['self'].__class__): + partial_types = (arg_values['self'].__class__,) + elif 'cls' in arg_values: + if tf_inspect.isclass(arg_values['cls']): + partial_types = (arg_values['cls'],) + + converted_f = to_graph( + target_entity, + recursive=recursive, + verbose=verbose, + arg_values=arg_values, + arg_types=arg_types, + partial_types=partial_types) + return converted_f(*effective_args, **kwargs) + + def to_graph(e, recursive=True, verbose=False, @@ -189,7 +242,7 @@ def to_graph(e, # The compiled code should see everything the entry function saw. # TODO(mdan): This might not work well if the call tree spans modules? if tf_inspect.isfunction(e): - compiled_node.__dict__.update(six.get_function_globals(e)) + compiled_node.__dict__.update(inspect_utils.getnamespace(e)) compiled_fn = getattr(compiled_node, name) if verbose: