# Copyright 2018 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Contains private utilities used mainly by the base Layer class.""" import functools import threading import tensorflow.compat.v1 as tf1 import tensorflow.compat.v2 as tf from keras import backend from keras.dtensor import dtensor_api as dtensor from keras.utils import control_flow_util from keras.utils import tf_inspect from keras.utils import tf_utils # isort: off from tensorflow.python.util.tf_export import keras_export _call_context = threading.local() def create_mean_metric(value, name=None): # import keras will import base_layer and then this module, and metric # relies on base_layer, which result into a cyclic dependency. from keras import metrics as metrics_module metric_obj = metrics_module.Mean(name=name, dtype=value.dtype) return metric_obj, metric_obj(value) def make_variable( name, shape=None, dtype=tf.float32, initializer=None, trainable=None, caching_device=None, validate_shape=True, constraint=None, use_resource=None, collections=None, synchronization=tf.VariableSynchronization.AUTO, aggregation=tf.VariableAggregation.NONE, partitioner=None, layout=None, ): """Temporary util to create a variable (relies on `variable_scope.variable`). Some reuse-related technicalities prevent us from using `variable_scope.get_variable()` directly, so we use a subcomponent that has fewer constraints (`variable_scope.variable()`). In the longer term, it seems like a similar "default variable creator" method should exist in `Trackable` instead. When this happens, we can get rid of this temporary solution. TODO(fchollet): remove this method when no longer needed. Args: name: Variable name. shape: Variable shape. dtype: The type of the variable. Defaults to `self.dtype` or `float32`. initializer: Initializer instance (callable). trainable: Whether the variable should be part of the layer's "trainable_variables" (e.g. variables, biases) or "non_trainable_variables" (e.g. BatchNorm mean, stddev). Note, if the current variable scope is marked as non-trainable then this parameter is ignored and any added variables are also marked as non-trainable. `trainable` defaults to `True` unless `synchronization` is set to `ON_READ`. caching_device: Passed to `tf.Variable`. validate_shape: Passed to `tf.Variable`. constraint: Constraint instance (callable). use_resource: Whether to use a `ResourceVariable`. collections: List of graph collections keys. The new variable is added to these collections. Defaults to `[GraphKeys.GLOBAL_VARIABLES]`. 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. If `synchronization` is set to `ON_READ`, `trainable` must not be set to `True`. aggregation: Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class `tf.VariableAggregation`. partitioner: Not handled at this time. layout: the optional DTensor layout, used for creating DVariable. Returns: Variable instance. """ initializing_from_value = False if initializer is not None and not callable(initializer): initializing_from_value = True if initializing_from_value: init_val = initializer variable_dtype = None else: # Instantiate initializer if provided initializer is a type object. if tf_inspect.isclass(initializer): initializer = initializer() if layout: init_val = functools.partial( initializer, shape, dtype=dtype, layout=layout ) else: init_val = functools.partial(initializer, shape, dtype=dtype) variable_dtype = dtype.base_dtype variable_shape = tf.TensorShape(shape) if use_resource is None: use_resource = True if layout is None: # In theory, in `use_resource` is True and `collections` is empty # (that is to say, in TF2), we can use tf.Variable. # However, this breaks legacy (Estimator) checkpoints because # it changes variable names. Remove this when V1 is fully deprecated. return tf1.Variable( initial_value=init_val, name=name, trainable=trainable, caching_device=caching_device, dtype=variable_dtype, validate_shape=validate_shape, constraint=constraint, use_resource=use_resource, collections=collections, synchronization=synchronization, aggregation=aggregation, shape=variable_shape if variable_shape else None, ) else: return dtensor.DVariable( initial_value=init_val, name=name, trainable=trainable, caching_device=caching_device, dtype=variable_dtype, validate_shape=validate_shape, constraint=constraint, collections=collections, synchronization=synchronization, aggregation=aggregation, shape=variable_shape if variable_shape else None, ) def collect_previous_mask(input_tensors): """Retrieves the output mask(s) of the previous node. Args: input_tensors: An arbitrary structure of Tensors. Returns: A mask tensor or list of mask tensors. """ def _collect_previous_mask(x): return getattr(x, "_keras_mask", None) return tf.nest.map_structure(_collect_previous_mask, input_tensors) def have_all_keras_metadata(tensors): return all(hasattr(x, "_keras_history") for x in tf.nest.flatten(tensors)) def generate_placeholders_from_shape(shape): return tf1.placeholder(shape=shape, dtype=backend.floatx()) def create_keras_history(tensors): """Wraps TensorFlow Operations for compatibility with the Functional API. This method checks to see if a Tensor in `tensors` is missing Keras metadata and has its origin in a Keras `Input` Layer. If so, this method will replace the raw TensorFlow Operations that created this tensor with `TensorFlowOpLayer` instances that create identical operations. Any Tensors not originating from a Keras `Input` Layer will be treated as constants when constructing `TensorFlowOpLayer` instances. Args: tensors: A structure of Tensors, some of which come from raw TensorFlow operations and need to have Keras metadata assigned to them. Returns: created_layers: List. The `TensorFlowOpLayer` instances created to wrap the raw Tensorflow operations. """ _, created_layers = _create_keras_history_helper(tensors, set(), []) return created_layers # Unsafe Internal attribute. # If True, Keras will not evaluate the constant-foldable inputs to tf op # layers in TF1 graphs. This *might* speed up model construction time in # certain settings, but it means # the models will not be serializable/deserializable via get_config # (Only via Savedmodels). It may also change the semantics of whether # generated random numbers are generated once and re-used, or recomputed # each time. # Note: This path triggers for TPUEstimators / xla compiled graphs regardless # of this setting. _UNSAFE_GRAPH_OP_LAYER_CREATION = False def _create_keras_history_helper(tensors, processed_ops, created_layers): """Helper method for `create_keras_history`. Args: tensors: A structure of Tensors for which to create Keras metadata. processed_ops: Set. TensorFlow operations that have already been wrapped in `TensorFlowOpLayer` instances. created_layers: List. The `TensorFlowOpLayer` instances created. Returns: Tuple. First element is the updated set of TensorFlow Operations that have been wrapped in `TensorFlowOpLayer` instances. Second element is a list of the `TensorFlowOpLayer` instances created. """ if tf1.executing_eagerly_outside_functions(): raise ValueError( "`create_keras_history` should only be called if eager is disabled!" ) # Import of `base_layer` needed in order to create `TensorFlowOpLayer`. # Cannot be imported at top because of circular dependencies. # TODO(omalleyt): Resolve circular dependency. from keras.engine import base_layer tensor_list = tf.nest.flatten(tensors) sparse_ops = [] ragged_tensors = [] for tensor in tensor_list: if getattr(tensor, "_keras_history", None) is not None: continue if isinstance(tensor, (tf.SparseTensor, tf1.SparseTensorValue)): sparse_ops.append(tensor.op) continue if tf_utils.is_ragged(tensor): # Ragged tensors don't have an op property ragged_tensors.append(tensor) continue op = tensor.op # The Op that created this Tensor. if op not in processed_ops: # Recursively set `_keras_history`. op_inputs = list(op.inputs) constants = {} layer_inputs = [] for i, op_input in enumerate(op_inputs): if uses_keras_history(op_input): layer_inputs.append(op_input) else: # Treat any value not originating from a `keras.Input` as # a constant. Variables cannot be supported. ds_with_session = ( tf.distribute.in_cross_replica_context() and not tf1.executing_eagerly_outside_functions() ) using_xla = control_flow_util.GraphOrParentsInXlaContext( tf1.get_default_graph() ) if ( ds_with_session or using_xla or _UNSAFE_GRAPH_OP_LAYER_CREATION ): # In Legacy Graph mode, evaluating here makes Session be # configured improperly. The downside of this is that # saving via `get_config` breaks, but SavedModel still # works. constants[i] = op_input else: with tf.init_scope(): constants[i] = backend.function([], op_input)([]) layer_inputs = unnest_if_single_tensor(layer_inputs) processed_ops, created_layers = _create_keras_history_helper( layer_inputs, processed_ops, created_layers ) name = op.name node_def = op.node_def.SerializeToString() op_layer = base_layer.TensorFlowOpLayer( node_def, constants=constants, name=name ) created_layers.append(op_layer) op_layer._set_connectivity_metadata( args=(layer_inputs,), kwargs={}, outputs=op.outputs ) processed_ops.update([op]) if sparse_ops or ragged_tensors: lambda_example = """ weights_mult = lambda x: tf.sparse.sparse_dense_matmul(x, weights) output = tf.keras.layers.Lambda(weights_mult)(input) """ raise ValueError( "Tensorflow ops that generate ragged or sparse tensor " "outputs are currently not supported by Keras automatic " "op wrapping. Please wrap these ops in a Lambda layer: " "\n\n```\n{example}\n```\n" "Sparse ops encountered: {sparse_ops}\n" "Ragged tensors encountered: {ragged_tensors}\n".format( example=lambda_example, sparse_ops=str(sparse_ops), ragged_tensors=str(ragged_tensors), ) ) return processed_ops, created_layers def unnest_if_single_tensor(input_tensors): # Preserve compatibility with older configs flat_input_tensors = tf.nest.flatten(input_tensors) # If this is a single element but not a dict, unwrap. If this is a dict, # assume the first layer expects a dict (as is the case with a # DenseFeatures layer); pass through. if not isinstance(input_tensors, dict) and len(flat_input_tensors) == 1: input_tensors = flat_input_tensors[0] return input_tensors def needs_keras_history(tensors, ignore_call_context=False): """Check if any Tensors need to be wrapped in TensorFlowOpLayers. This will never return True inside a sublayer, because sublayers do not need to create Keras History. Otherwise, this returns True if one or more of `tensors` originates from a `keras.Input` and does not have `_keras_history` set. Args: tensors: An arbitrary nested structure of Tensors. ignore_call_context: Whether to ignore the check of if currently outside of a `call` context. This is `True` when creating KerasHistory inside `Node`, where we always know that Tensors are being used with the Functional API. Returns: Bool, whether at least one Tensor needs to be wrapped. """ input_tensors = tf.nest.flatten(tensors) if call_context().in_call and not ignore_call_context: return False if all( getattr(tensor, "_keras_history", None) is not None for tensor in input_tensors ): # KerasHistory already set. return False return uses_keras_history(tensors) def is_in_keras_graph(): """Returns if currently executing inside of a Keras graph.""" return call_context().in_keras_graph def is_in_eager_or_tf_function(): """Returns if in eager mode or inside of a tf.function.""" return tf.executing_eagerly() or is_in_tf_function() def is_in_tf_function(): """Returns if inside of a tf.function.""" # Check if running in V1 graph mode. if not tf1.executing_eagerly_outside_functions(): return False if not tf.inside_function(): return False # Check if inside Keras FuncGraph. if is_in_keras_graph(): return False # Check for a v1 `wrap_function` FuncGraph. graph = tf1.get_default_graph() if getattr(graph, "name", False) and graph.name.startswith( "wrapped_function" ): return False return True def uses_keras_history(tensors): """Check if at least one Tensor originates from a `keras.Input`. This is `True` if at least one Tensor has its origin in a `keras.Input`. Any Tensor that originates from a `keras.Input` will have a dependency Tensor with a `_keras_history` attribute attached. Tensors that have already been checked to not originate from a `keras.Input` are marked as `_keras_history_checked`. Args: tensors: An arbitrary nested structure of Tensors. Returns: Bool, whether at least one Tensor originates from a `keras.Input`. """ checked_tensors = set() tensors_to_check = tf.nest.flatten(tensors) while tensors_to_check: new_tensors_to_check = [] for tensor in tensors_to_check: if id(tensor) in checked_tensors: continue checked_tensors.add(id(tensor)) if getattr(tensor, "_keras_history_checked", None) is not None: continue if getattr(tensor, "_keras_history", None) is not None: return True try: new_tensors_to_check.extend(tensor.op.inputs) except AttributeError: # In case `tensor` is a Variable created in an Eager context. pass tensors_to_check = new_tensors_to_check # Mark that these Tensors have been checked once for `_keras_history`, # and should not be checked again for performance reasons. mark_checked(tensors) return False def mark_checked(tensors): """Marks that these Tensors should not be tracked. This prevents Layers from attempting to create TensorFlowOpLayers for these Tensors. Args: tensors: An arbitrary structure of Tensors. """ def _mark_checked(tensor): tensor._keras_history_checked = True tf.nest.map_structure(_mark_checked, tensors) def call_context(): """Returns currently active `CallContext`.""" call_ctx = getattr(_call_context, "call_context", None) if call_ctx is None: call_ctx = CallContext() _call_context.call_context = call_ctx return call_ctx # Inject the call_context function to keras_deps to remove the dependency # from TFLite to Keras. tf.__internal__.register_call_context_function(call_context) class CallContext: """Keeps track of properties currently inside a Layer/Model's `call`. Attributes: in_call: Whether currently inside the `call` of a Layer. layer: The `Layer` whose `call` is currently active. inputs: The inputs to the currently active `Layer`. build_graph: Whether currently inside a Graph or FuncGraph. training: Whether currently executing in training or inference mode. saving: Whether currently saving to SavedModel. frozen: Whether currently executing inside a `Layer` with `trainable` set to `False`. in_keras_graph: Whether executing inside the Keras Graph. """ def __init__(self): # Handle `in_call` separately as it is the most-read attr and reading it # is on the hot path. self.in_call = False self._state = { "layer": None, "inputs": None, "build_graph": False, "training": None, "saving": None, } # TODO(b/150169018): This logic can be replaced after the Functional API # refactor. self._in_keras_graph = False def enter(self, layer, inputs, build_graph, training, saving=None): """Push a Layer and its inputs and state onto the current call context. Args: layer: The `Layer` whose `call` is currently active. inputs: The inputs to the currently active `Layer`. build_graph: Whether currently inside a Graph or FuncGraph. training: Whether currently executing in training or inference mode. saving: Whether currently saving to SavedModel. Returns: Context manager. """ state = { "layer": layer, "inputs": inputs, "build_graph": build_graph, "training": training, "saving": saving, } return CallContextManager(self, state) @property def layer(self): return self._state["layer"] @property def inputs(self): return self._state["inputs"] @property def build_graph(self): return self._state["build_graph"] @property def training(self): return self._state["training"] @property def saving(self): return self._state["saving"] @property def frozen(self): layer = self._state["layer"] if not layer: return False return not layer.trainable @property def in_keras_graph(self): # Returns True even if in a subgraph of the Keras graph, such as those # created by control flow ops. if tf.executing_eagerly(): return False return ( self._in_keras_graph or getattr(backend.get_graph(), "name", None) == "keras_graph" ) class CallContextManager: """Context manager for `CallContext`.""" def __init__(self, call_ctx, state): self._call_ctx = call_ctx self._state = state self._build_graph = state["build_graph"] def __enter__(self): call_ctx = self._call_ctx self._prev_in_call = call_ctx.in_call self._prev_state = call_ctx._state call_ctx.in_call = True call_ctx._state = self._state # TODO(b/150169018): This logic can be removed after the Functional API # refactor. if self._build_graph: self._prev_in_keras_graph = call_ctx._in_keras_graph call_ctx._in_keras_graph = ( call_ctx._in_keras_graph or getattr(backend.get_graph(), "name", None) == "keras_graph" ) def __exit__(self, *exc_info): call_ctx = self._call_ctx call_ctx.in_call = self._prev_in_call call_ctx._state = self._prev_state if self._build_graph: call_ctx._in_keras_graph = self._prev_in_keras_graph def training_arg_passed_to_call(argspec, args, kwargs): """Returns whether a user passed the `training` argument in `__call__`.""" # `argspec.args` starts with ['self', 'inputs'] full_args = dict(zip(argspec.args[2:], args)) full_args.update(kwargs) return "training" in full_args and full_args["training"] is not None def is_subclassed(layer): """Returns True if the object is a subclassed layer or subclassed model.""" return ( layer.__module__.find("keras.engine") == -1 and layer.__module__.find("keras.layers") == -1 ) def from_saved_model(layer): """Returns whether the layer is loaded from a SavedModel.""" return layer.__module__.find("keras.saving.saved_model") != -1 def check_graph_consistency(tensor=None, method="add_loss", force_raise=False): """Checks that tensors passed to `add_*` method match the Keras graph. When one of the `add_*` method is called inside a V2 conditional branch, the underlying tensor gets created in a FuncGraph managed by control_flow_v2. We need to raise clear error messages in such cases. Args: tensor: Tensor to check, or `False` if it is known that an error should be raised. method: Caller method, one of {'add_metric', 'add_loss', 'add_update'}. force_raise: If an error should be raised regardless of `tensor`. Raises: RuntimeError: In case of an out-of-graph tensor. """ if force_raise or ( tf1.executing_eagerly_outside_functions() and hasattr(tensor, "graph") and tensor.graph.is_control_flow_graph ): if method == "activity_regularizer": bad_example = """ class TestModel(tf.keras.Model): def __init__(self): super(TestModel, self).__init__(name='test_model') self.dense = tf.keras.layers.Dense(2, activity_regularizer='l2') def call(self, x, training=None): if training: return self.dense(x) else: return self.dense(x) """ correct_example = """ class TestModel(tf.keras.Model): def __init__(self): super(TestModel, self).__init__(name='test_model') self.dense = tf.keras.layers.Dense(2, activity_regularizer='l2') def call(self, x, training=None): return self.dense(x) """ raise RuntimeError( "You are using a layer with `activity_regularizer` in a " f"control flow branch, e.g.:\n{bad_example}\nThis is currently " "not supported. Please move your call to the layer with " "`activity_regularizer` out of the control flow branch, " f"e.g.:\n{correct_example}\nYou can also resolve this by " "marking your outer model/layer dynamic (eager-only) by " "passing `dynamic=True` to the layer constructor. Any kind of " "control flow is supported with dynamic layers. Note that " "using `dynamic=True` requires you to implement static shape " "inference in the `compute_output_shape(input_shape)` " "method." ) if method == "add_metric": bad_example = """ def call(self, inputs, training=None): if training: metric = compute_metric(inputs) self.add_metric(metric, name='my_metric', aggregation='mean') return inputs """ correct_example = """ def call(self, inputs, training=None): if training: metric = compute_metric(inputs) else: metric = 0. self.add_metric(metric, name='my_metric', aggregation='mean') return inputs """ elif method == "add_loss": bad_example = """ def call(self, inputs, training=None): if training: loss = compute_loss(inputs) self.add_loss(loss) return inputs """ correct_example = """ def call(self, inputs, training=None): if training: loss = compute_loss(inputs) else: loss = 0. self.add_loss(loss) return inputs """ else: bad_example = """ def call(self, inputs, training=None): if training: self.add_update(self.w.assign_add(1)) return inputs """ correct_example = """ def call(self, inputs, training=None): if training: increment = 1 else: increment = 0 self.add_update(self.w.assign_add(increment)) return inputs """ raise RuntimeError( "You are using the method `{method}` in a control flow branch " "in your layer, e.g.:\n{bad_example}\n" "This is not currently supported. " "Please move your call to {method} out of the control flow branch, " "e.g.:\n{correct_example}\n" "You can also resolve this by marking your layer " "as dynamic (eager-only) by passing " "`dynamic=True` to the layer constructor. " "Any kind of control flow is supported with dynamic layers. " "Note that using `dynamic=True` requires you " "to implement static shape inference " "in the `compute_output_shape(input_shape)` method.".format( method=method, bad_example=bad_example, correct_example=correct_example, ) ) def mark_as_return(outputs, acd): """Marks `outputs` as the return values for automatic control deps.""" def _mark_as_return(tensor): """Marks `tensor` as the return value for automatic control deps.""" if not tf.is_tensor(tensor): return tensor return_tensor = acd.mark_as_return(tensor) if getattr(tensor, "_keras_mask", None) is not None: return_tensor._keras_mask = acd.mark_as_return(tensor._keras_mask) else: return_tensor._keras_mask = None # Handle TensorFlow Probability attached metadata. # TODO(b/132076537): Remove this once TFP uses `CompositeTensor`. if getattr(tensor, "_tfp_distribution", None) is not None: return_tensor._tfp_distribution = tensor._tfp_distribution return return_tensor return tf.nest.map_structure(_mark_as_return, outputs) V2_DTYPE_BEHAVIOR = None @keras_export(v1=["keras.layers.enable_v2_dtype_behavior"]) def enable_v2_dtype_behavior(): """Enable the V2 dtype behavior for Keras layers. By default, the V2 dtype behavior is enabled in TensorFlow 2, so this function is only useful if `tf.compat.v1.disable_v2_behavior` has been called. Since mixed precision requires V2 dtype behavior to be enabled, this function allows you to use mixed precision in Keras layers if `disable_v2_behavior` has been called. When enabled, the dtype of Keras layers defaults to floatx (which is typically float32) instead of None. In addition, layers will automatically cast floating-point inputs to the layer's dtype. >>> x = tf.ones((4, 4, 4, 4), dtype='float64') >>> layer = tf.keras.layers.Conv2D(filters=4, kernel_size=2) >>> print(layer.dtype) # float32 since V2 dtype behavior is enabled float32 >>> y = layer(x) # Layer casts inputs since V2 dtype behavior is enabled >>> print(y.dtype.name) float32 A layer author can opt-out their layer from the automatic input casting by passing `autocast=False` to the base Layer's constructor. This disables the autocasting part of the V2 behavior for that layer, but not the defaulting to floatx part of the V2 behavior. When a global `tf.keras.mixed_precision.Policy` is set, a Keras layer's dtype will default to the global policy instead of floatx. Layers will automatically cast inputs to the policy's compute_dtype. """ global V2_DTYPE_BEHAVIOR V2_DTYPE_BEHAVIOR = True @keras_export(v1=["keras.layers.disable_v2_dtype_behavior"]) def disable_v2_dtype_behavior(): """Disables the V2 dtype behavior for Keras layers. See `tf.compat.v1.keras.layers.enable_v2_dtype_behavior`. """ global V2_DTYPE_BEHAVIOR V2_DTYPE_BEHAVIOR = False def v2_dtype_behavior_enabled(): """Returns True if the V2 dtype behavior is enabled.""" if V2_DTYPE_BEHAVIOR is None: return tf.__internal__.tf2.enabled() return V2_DTYPE_BEHAVIOR class TrackableWeightHandler: """Keras wrapper for handling tracking.Trackable object saving and restoring. This class handles Trackables in both V1 and V2 modes, ensuring that they can be saved and restored with the correct data and without adding additional ops on every save. Attributes: trackable: The trackable to wrap. num_tensors: The number of tensors that this trackable requires for saving. """ def __init__(self, trackable): if not isinstance(trackable, tf.__internal__.tracking.Trackable): raise ValueError(f"{trackable} is not a Trackable object.") self._trackable = trackable self._distribute_strategy = tf.distribute.get_strategy() saveables = tf.__internal__.tracking.saveable_objects_from_trackable( trackable ).values() # 'Saveables' won't exist when we're passed a legacy TF1 table like # a StaticHashTable. if not saveables: self._num_tensors = 0 self._setter = lambda weights: None self._getter = lambda: [] elif len(saveables) == 1: saveable = list(saveables)[0] if tf1.executing_eagerly_outside_functions(): # If we're in eager mode, we need to defer calling the # Trackable's saveable() callable until data export time. # However, it is safe to call the saveable as many times as we # want, so we will call it now to figure out how many tensors # this Trackable will produce. self._saveable = saveable self._num_tensors = len(self._saveable().specs) self._setter = lambda weights: self._saveable().restore( weights, None ) self._getter = lambda: [ spec.tensor for spec in self._saveable().specs ] else: # If we're in Graph mode, we need to evaluate the Saveable only # once and cache the resulting restore graph. Failing to do this # will result in new assignment ops being added to the graph # each time set_weights() is called. self._placeholder_tensors = [] self._saveable = saveable() self._num_tensors = len(self._saveable.specs) for spec in self._saveable.specs: tensor = spec.tensor self._placeholder_tensors.append( tf1.placeholder(tensor.dtype, tensor.shape) ) self._assign_op = self._saveable.restore( self._placeholder_tensors, None ) self._setter = self._set_weights_v1 self._getter = lambda: [ spec.tensor for spec in self._saveable.specs ] else: raise ValueError( "Only Trackables with one Saveable are supported. " f"The Trackable {trackable} has {len(saveables)} Saveables." ) @property def num_tensors(self): return self._num_tensors def set_weights(self, weights): if len(weights) != self._num_tensors: raise ValueError( f"Weight handler for trackable {self._trackable} received " "an incorrect number of weights: " f"expected {self._num_tensors} weights, " f"got {len(weights)} weights." ) self._setter(weights) def get_tensors(self): return self._getter() def _set_weights_v1(self, weights): feed_dict = {} for idx, tensor in enumerate(weights): feed_dict[self._placeholder_tensors[idx]] = tensor backend.get_session().run(self._assign_op, feed_dict) def no_ragged_support(inputs, layer_name): input_list = tf.nest.flatten(inputs) if any(isinstance(x, tf.RaggedTensor) for x in input_list): raise ValueError( f"Layer {layer_name} does not support RaggedTensors as input. " f"Inputs received: {inputs}. You can try converting your " "input to a dense (uniform) tensor." ) def is_split_variable(v): """Returns True if `v` is a PartionedVariable or a ShardedVariable.""" return hasattr(v, "_variable_list") or hasattr(v, "_variables") def has_weights(obj): obj_type = type(obj) return ( hasattr(obj_type, "trainable_weights") and hasattr(obj_type, "non_trainable_weights") and not isinstance(obj, type) ) # TODO(kathywu): This is a temporary hack. When a network of layers is revived # from SavedModel, only the top-level layer will have losses. This causes issues # in eager mode because the child layers may have graph losses # (thus model.losses returns a mix of Eager and graph tensors). To fix this, # whenever eager losses are added to one layer, add eager losses to all # child layers. This causes `.losses` to only return eager losses. REVIVED_LOSS_PLACEHOLDER = ( "This layer's losses have been added to the parent layer." )