def testCyclicInitializer(self): with self.cached_session(): cyclic = control_flow_ops.while_loop( cond=lambda i: i < 10, body=lambda i: i + 1, loop_vars=(constant_op.constant(0), )) initial_value = variables._try_guard_against_uninitialized_dependencies( "test", cyclic) self.assertIs(initial_value, cyclic)
def _init_from_args(self, embedding_dim, initializer=None, trainable=True, collections=None, caching_device=None, name=None, ktype=None, vtype=None, constraint=None, synchronization=None, aggregation=None, distribute_strategy=None, invalid_key=-1): """Creates a variable. Args: initial_value: A `Tensor`, or Python object convertible to a `Tensor`, which is the initial value for the EmbeddingVariable. Can also be a callable with no argument that returns the initial value when called. (Note that initializer functions from init_ops.py must first be bound to a shape before being used here.) trainable: If `True`, the default, also adds the variable to the graph collection `GraphKeys.TRAINABLE_VARIABLES`. This collection is used as the default list of variables to use by the `Optimizer` classes. collections: List of graph collections keys. The new variable is added to these collections. Defaults to `[GraphKeys.GLOBAL_VARIABLES]`. name: Optional name for the variable. Defaults to `'EmbeddingVariable'` and gets uniquified automatically. ktype: If set, EV's key will be converted to the given type. If None, int32 will be used. vtype: If set, initial_value will be converted to the given type. If None, either the datatype will be kept (if initial_value is a Tensor) or float32 will be used (if it is a Python object convertible to a Tensor). constraint: An optional projection function to be applied to the variable after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected Tensor representing the value of the variable and return the Tensor for the projected value (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. @compatibility(eager) When Eager Execution is enabled, variables are never added to collections. It is not implicitly added to the GLOBAL_VARIABLES or TRAINABLE_VARIABLES collections, and the `collections` argument is ignored. @end_compatibility """ if isinstance(embedding_dim, tensor_shape.TensorShape): embedding_shape = embedding_dim elif isinstance(embedding_dim, six.integer_types): embedding_shape = [embedding_dim] initial_value = initializer(shape=embedding_shape) init_from_fn = callable(initial_value) if ktype is None: ktype = dtypes.int32 if collections is None: collections = [ops.GraphKeys.GLOBAL_VARIABLES] if not isinstance(collections, (list, tuple, set)): raise ValueError( "collections argument to EmbeddingVariable constructor must be a list, tuple, " "or set. Got %s of type %s" % (collections, type(collections))) if constraint is not None and not callable(constraint): raise ValueError("The `constraint` argument must be a callable.") self._initializer = initializer if trainable and ops.GraphKeys.TRAINABLE_VARIABLES not in collections: collections = list(collections) + [ops.GraphKeys.TRAINABLE_VARIABLES] with ops.init_scope(): self._in_graph_mode = not context.executing_eagerly() with ops.name_scope(name, "EmbeddingVariable", [] if init_from_fn else [initial_value], skip_on_eager=False) as name: # pylint: disable=protected-access self._invalid_key = invalid_key self._ktype = ktype handle_name = ops.name_from_scope_name(name) if self._in_graph_mode: shared_name = handle_name unique_id = shared_name else: # When in eager mode use a uid for the shared_name, to prevent # accidental sharing. unique_id = "%s_%d" % (handle_name, ops.uid()) shared_name = unique_id # Use attr_scope and device(None) to simulate the behavior of # colocate_with when the variable we want to colocate with doesn't # yet exist. device_context_manager = (ops.device if self._in_graph_mode else ops.NullContextmanager) attr = attr_value_pb2.AttrValue(list=attr_value_pb2.AttrValue.ListValue( s=[compat.as_bytes("loc:@%s" % handle_name)])) with ops.get_default_graph()._attr_scope({"_class": attr}): with ops.name_scope("Initializer"), device_context_manager(None): if init_from_fn: initial_value = initial_value() if isinstance(initial_value, trackable.CheckpointInitialValue): self._maybe_initialize_trackable() self._update_uid = initial_value.checkpoint_position.restore_uid initial_value = initial_value.wrapped_value initial_value = ops.convert_to_tensor(initial_value, name="initial_value", dtype=vtype) shape = initial_value.shape handle = self._embedding_variable_handle( shape=initial_value.get_shape(), dtype=initial_value.dtype.base_dtype, shared_name=shared_name, name=name, graph_mode=self._in_graph_mode) # pylint: disable=protected-access if (self._in_graph_mode and initial_value is not None and initial_value.op._get_control_flow_context() is not None): raise ValueError( "Initializer for variable %s is from inside a control-flow " "construct, such as a loop or conditional. When creating a " "variable inside a loop or conditional, use a lambda as the " "initializer." % name) # pylint: enable=protected-access vtype = initial_value.dtype.base_dtype if self._in_graph_mode: with ops.name_scope("IsInitialized"): self._ev_is_initialized_op = (gen_ev_ops.ev_is_initialized_op( handle, Tkey=self._ktype, Tvalue=vtype)) if initial_value is not None: # pylint: disable=g-backslash-continuation with ops.name_scope("Initialize") as n, \ ops.colocate_with(None, ignore_existing=True), \ ops.device(handle.device): # pylint: disable=protected-access initializer_op = (gen_ev_ops.initialize_ev_op( handle, variables._try_guard_against_uninitialized_dependencies( name, initial_value), ops.convert_to_tensor(invalid_key, dtype=self._ktype), shape=initial_value.get_shape(), name=n)) cached_value = None graph_element = None else: gen_ev_ops.initialize_ev_op(handle, initial_value, ops.convert_to_tensor(invalid_key, dtype=self._ktype), shape=initial_value.get_shape()) self._ev_is_initialized_op = None initializer_op = None graph_element = None cached_value = None if not context.executing_eagerly(): # Eager variables are only added to collections if they are part of an # eager variable store (otherwise in an interactive session they would # hog memory and cause OOM). This is done in ops/variable_scope.py. ops.add_to_collections(collections, self) elif ops.GraphKeys.GLOBAL_STEP in collections: ops.add_to_collections(ops.GraphKeys.GLOBAL_STEP, self) initial_value = initial_value if self._in_graph_mode else None new_dim = shape.as_list() new_dim.insert(0, 0) new_shape = tensor_shape.TensorShape(new_dim) super(resource_variable_ops.ResourceVariable, self).__init__(trainable=trainable, shape=new_shape, dtype=vtype, handle=handle, synchronization=synchronization, constraint=constraint, aggregation=aggregation, distribute_strategy=distribute_strategy, name=name, unique_id=unique_id, handle_name=handle_name, graph_element=graph_element, initial_value=initial_value, initializer_op=initializer_op, is_initialized_op=self._ev_is_initialized_op, cached_value=cached_value, caching_device=caching_device) tensors = gen_ev_ops.ev_export(self.handle, Tkey=self._ktype, Tvalue=vtype) self.specs = [ BaseSaverBuilder.SaveSpec(tensors[0], "", name + "-keys"), BaseSaverBuilder.SaveSpec(tensors[1], "", name + "-values"), ]
def _init_from_args( self, initial_value=None, trainable=None, collections=None, caching_device=None, name=None, dtype=None, constraint=None, synchronization=None, aggregation=None, distribute_strategy=None, shape=None, ): """Creates a variable. Args: initial_value: A `Tensor`, or Python object convertible to a `Tensor`, which is the initial value for the Variable. The initial value must have a shape specified unless `validate_shape` is set to False. Can also be a callable with no argument that returns the initial value when called. (Note that initializer functions from init_ops.py must first be bound to a shape before being used here.) trainable: If `True`, the default, also adds the variable to the graph collection `GraphKeys.TRAINABLE_VARIABLES`. This collection is used as the default list of variables to use by the `Optimizer` classes. Defaults to `True`, unless `synchronization` is set to `ON_READ`, in which case it defaults to `False`. collections: List of graph collections keys. The new variable is added to these collections. Defaults to `[GraphKeys.GLOBAL_VARIABLES]`. caching_device: Optional device string or function describing where the Variable should be cached for reading. Defaults to the Variable's device. If not `None`, caches on another device. Typical use is to cache on the device where the Ops using the Variable reside, to deduplicate copying through `Switch` and other conditional statements. name: Optional name for the variable. Defaults to `'Variable'` and gets uniquified automatically. dtype: If set, initial_value will be converted to the given type. If None, either the datatype will be kept (if initial_value is a Tensor) or float32 will be used (if it is a Python object convertible to a Tensor). constraint: An optional projection function to be applied to the variable after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected Tensor representing the value of the variable and return the Tensor for the projected value (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. synchronization: Indicates when a distributed a variable will be aggregated. Accepted values are constants defined in the class `tf.VariableSynchronization`. By default the synchronization is set to `AUTO` and the current `DistributionStrategy` chooses when to synchronize. aggregation: Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class `tf.VariableAggregation`. distribute_strategy: DistributionStrategy under which this variable was created. shape: (optional) The shape of this variable. If None, the shape of `initial_value` will be used. When setting this argument to `tf.TensorShape(None)` (representing an unspecified shape), the variable can be assigned with values of different shapes. Raises: ValueError: If the initial value is not specified, or does not have a shape and `validate_shape` is `True`. @compatibility(eager) When Eager Execution is enabled, variables are never added to collections. It is not implicitly added to the `GLOBAL_VARIABLES` or `TRAINABLE_VARIABLES` collections, and the `collections` argument is ignored. @end_compatibility """ ( synchronization, aggregation, trainable, ) = variables.validate_synchronization_aggregation_trainable( synchronization, aggregation, trainable, name) if initial_value is None: raise ValueError("initial_value must be specified.") init_from_fn = callable(initial_value) if (isinstance(initial_value, ops.Tensor) and hasattr(initial_value, "graph") and initial_value.graph.building_function): raise ValueError("Tensor-typed variable initializers must either be " "wrapped in an init_scope or callable " "(e.g., `tf.Variable(lambda : " "tf.truncated_normal([10, 40]))`) when building " "functions. Please file a feature request if this " "restriction inconveniences you.") if collections is None: collections = [ops.GraphKeys.GLOBAL_VARIABLES] if not isinstance(collections, (list, tuple, set)): raise ValueError( "collections argument to Variable constructor must be a list, tuple, " "or set. Got %s of type %s" % (collections, type(collections))) if constraint is not None and not callable(constraint): raise ValueError("The `constraint` argument must be a callable.") if isinstance(initial_value, trackable.CheckpointInitialValue): self._maybe_initialize_trackable() self._update_uid = initial_value.checkpoint_position.restore_uid initial_value = initial_value.wrapped_value if trainable and ops.GraphKeys.TRAINABLE_VARIABLES not in collections: collections = list(collections) + [ops.GraphKeys.TRAINABLE_VARIABLES] with ops.init_scope(): self._in_graph_mode = not context.executing_eagerly() with ops.name_scope(name, "TrainableWrapper", [] if init_from_fn else [initial_value]) as name: # pylint: disable=protected-access handle_name = ops.name_from_scope_name(name) handle_name = handle_name or "TrainableWrapperHandle" if self._in_graph_mode: shared_name = handle_name unique_id = shared_name else: # When in eager mode use a uid for the shared_name, to prevent # accidental sharing. unique_id = "%s_%d" % (handle_name, ops.uid()) shared_name = None # Never shared # Use attr_scope and device(None) to simulate the behavior of # colocate_with when the variable we want to colocate with doesn't # yet exist. device_context_manager = (ops.device if self._in_graph_mode else ops.NullContextmanager) attr = attr_value_pb2.AttrValue(list=attr_value_pb2.AttrValue.ListValue( s=[compat.as_bytes("loc:@%s" % handle_name)])) with ops.get_default_graph()._attr_scope({"_class": attr}): with ops.name_scope("Initializer"), device_context_manager(None): initial_value = ops.convert_to_tensor( initial_value() if init_from_fn else initial_value, name="initial_value", dtype=dtype, ) if shape is None: shape = initial_value.shape handle = resource_variable_ops.eager_safe_variable_handle( initial_value=initial_value, shape=None, # shape, shared_name=shared_name, name=name, graph_mode=self._in_graph_mode, ) # pylint: disable=protected-access if (self._in_graph_mode and initial_value is not None and initial_value.op._get_control_flow_context() is not None): raise ValueError( "Initializer for variable %s is from inside a control-flow " "construct, such as a loop or conditional. When creating a " "variable inside a loop or conditional, use a lambda as the " "initializer." % name) # pylint: enable=protected-access dtype = initial_value.dtype.base_dtype if self._in_graph_mode: with ops.name_scope("IsInitialized"): is_initialized_op = ( gen_resource_variable_ops.var_is_initialized_op(handle)) if initial_value is not None: # pylint: disable=g-backslash-continuation with ops.name_scope("Assign") as n, ops.colocate_with( None, ignore_existing=True), ops.device(handle.device): # pylint: disable=protected-access initializer_op = gen_resource_variable_ops.assign_variable_op( handle, variables._try_guard_against_uninitialized_dependencies( name, initial_value), name=n, ) # pylint: enable=protected-access # pylint: enable=g-backslash-continuation with ops.name_scope("Read"): # Manually assign reads to the handle's device to avoid log # messages. with ops.device(handle.device): with ops.control_dependencies([ gen_resource_variable_ops.assign_variable_op( handle, self.prefetch_values(), name="AssignBeforeInitRead", ) ]): value = gen_resource_variable_ops.read_variable_op( handle, dtype) graph_element = value if caching_device is not None: # Variables may be created in a tf.device() or ops.colocate_with() # context. At the same time, users would expect caching device to # be independent of this context, and/or would not expect the # current device context to be merged with the caching device # spec. Therefore we reset the colocation stack before creating # the cached value. Note that resetting the colocation stack will # also reset the device stack. with ops.colocate_with(None, ignore_existing=True): with ops.device(caching_device): cached_value = array_ops.identity(value) else: cached_value = None else: gen_resource_variable_ops.assign_variable_op(handle, initial_value) is_initialized_op = None initializer_op = None graph_element = None if caching_device: with ops.device(caching_device): with ops.control_dependencies([ gen_resource_variable_ops.assign_variable_op( handle, self.prefetch_values(), name="AssignBeforeInitRead", ) ]): cached_value = (gen_resource_variable_ops.read_variable_op( handle, dtype)) else: cached_value = None if not context.executing_eagerly(): # Eager variables are only added to collections if they are part of an # eager variable store (otherwise in an interactive session they would # hog memory and cause OOM). This is done in ops/variable_scope.py. ops.add_to_collections(collections, self) elif ops.GraphKeys.GLOBAL_STEP in collections: ops.add_to_collections(ops.GraphKeys.GLOBAL_STEP, self) initial_value = initial_value if self._in_graph_mode else None super(resource_variable_ops.ResourceVariable, self).__init__( trainable=trainable, shape=shape, dtype=dtype, handle=handle, synchronization=synchronization, constraint=constraint, aggregation=aggregation, distribute_strategy=distribute_strategy, name=name, unique_id=unique_id, handle_name=handle_name, graph_element=graph_element, initial_value=initial_value, initializer_op=initializer_op, is_initialized_op=is_initialized_op, cached_value=cached_value, )