Generalizes the first argument in keras layers further. Now, functional models get constructed if *any* tensor in the arguments or keyword arguments has a keras history, rather than if *all* of the elements in the first argument to the layer do.
PiperOrigin-RevId: 313718130 Change-Id: I77f65f49decf45f6a2b53ab0519d6d2ac38232d3
This commit is contained in:
Tomer Kaftan
TensorFlower Gardener
parent
618ff4c618
commit
a1ae008076
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@ -830,14 +830,13 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
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in_call = call_context.in_call
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input_list = nest.flatten(inputs)
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# We will attempt to build a TF graph if & only if all inputs are symbolic.
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# This is always the case in graph mode. It can also be the case in eager
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# mode when all inputs can be traced back to `keras.Input()` (when building
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# models using the functional API).
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# TODO(kaftan): make this not special case inputs. Instead
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# build a functional api model if *any* *arg or **kwarg is symbolic,
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# even if part of the data structure in that arg is not symbolic.
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build_graph = tf_utils.are_all_symbolic_tensors(input_list)
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# We will attempt to trace in a graph if & only if inputs are symbolic.
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# This is always the case when tracing a function. It can also be the case
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# when running eagerly if any input can be traced back to `keras.Input()`
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# (when building models using the functional API).
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build_graph = tf_utils.are_all_symbolic_tensors(input_list) or (
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any(map(tf_utils.is_symbolic_tensor, nest.flatten(
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[input_list, args, kwargs]))) and context.executing_eagerly())
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# Accept NumPy and scalar inputs by converting to Tensors.
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if any(isinstance(x, (np.ndarray, float, int)) for x in input_list):
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@ -890,11 +889,14 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
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'training', training_value, args, kwargs)
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training_arg_passed_by_framework = True
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# Only create Keras history if at least one tensor originates from a
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# `keras.Input`. Otherwise this Layer may be being used outside the Keras
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# framework.
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# TODO(kaftan): make this not special case inputs
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if build_graph and base_layer_utils.needs_keras_history(inputs):
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# Turn inputs into TF op layers if necessary.
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# This process is fragile and prone to bad interactions with inputs
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# when calling nested layers with tf.functions floating around,
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# and with nonsymbolic tensors.
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# So, we limit it to the
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# case where *all* inputs in the first arg are symbolic.
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if (tf_utils.are_all_symbolic_tensors(input_list)
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and base_layer_utils.needs_keras_history(inputs)):
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base_layer_utils.create_keras_history(inputs)
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with call_context.enter(self, inputs, build_graph, training_value):
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@ -968,8 +970,12 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
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raise ValueError('A layer\'s `call` method should return a '
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'Tensor or a list of Tensors, not None '
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'(layer: ' + self.name + ').')
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# TODO(kaftan): This should be 'any' and check all args
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if base_layer_utils.have_all_keras_metadata(inputs):
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# We configure connectivity metadata if all inputs in the first
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# arg have keras history, or if we're actively building the
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# functional api outside of any outer keras model.
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if base_layer_utils.have_all_keras_metadata(inputs) or (
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context.executing_eagerly() and
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base_layer_utils.have_any_keras_metadata(inputs, args, kwargs)):
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if training_arg_passed_by_framework:
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args, kwargs = self._set_call_arg_value(
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'training', None, args, kwargs, pop_kwarg_if_none=True)
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@ -165,6 +165,10 @@ def have_all_keras_metadata(tensors):
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return all(hasattr(x, '_keras_history') for x in nest.flatten(tensors))
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def have_any_keras_metadata(*tensors):
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return any(hasattr(x, '_keras_history') for x in nest.flatten(tensors))
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def generate_placeholders_from_shape(shape):
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return array_ops.placeholder(shape=shape, dtype=backend.floatx())
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@ -214,7 +218,10 @@ def _create_keras_history_helper(tensors, processed_ops, created_layers):
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for tensor in tensor_list:
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if getattr(tensor, '_keras_history', None) is not None:
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continue
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op = tensor.op # The Op that created this Tensor.
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try:
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op = tensor.op # The Op that created this Tensor.
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except AttributeError:
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continue
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if op not in processed_ops:
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if op.type.startswith('Sparse'):
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lambda_example = """
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@ -392,7 +399,10 @@ def mark_checked(tensors):
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"""
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def _mark_checked(tensor):
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tensor._keras_history_checked = True # pylint: disable=protected-access
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try:
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tensor._keras_history_checked = True # pylint: disable=protected-access
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except AttributeError:
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pass
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nest.map_structure(_mark_checked, tensors)
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@ -32,6 +32,7 @@ from tensorflow.python.keras import backend
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from tensorflow.python.keras.engine import base_layer
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from tensorflow.python.keras.engine import base_layer_utils
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from tensorflow.python.keras.engine import input_layer as input_layer_module
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from tensorflow.python.keras.engine import node as node_module
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from tensorflow.python.keras.engine import training as training_lib
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from tensorflow.python.keras.engine import training_utils
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from tensorflow.python.keras.saving.saved_model import network_serialization
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@ -1111,19 +1112,28 @@ def reconstruct_from_config(config, custom_objects=None, created_layers=None):
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kwargs = {}
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elif len(input_data) == 4:
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kwargs = input_data[3]
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kwargs = _deserialize_keras_tensors(kwargs, created_layers)
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try:
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kwargs = _deserialize_keras_tensors(kwargs, created_layers)
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except IndexError:
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# Happens if keras tensors in kwargs are still unprocessed
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add_unprocessed_node(layer, node_data)
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return
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else:
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raise ValueError('Improperly formatted model config.')
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inbound_layer = created_layers[inbound_layer_name]
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inbound_node_index = get_node_index(inbound_layer, inbound_node_index)
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if inbound_layer_name != node_module._CONSTANT_VALUE:
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inbound_layer = created_layers[inbound_layer_name]
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inbound_node_index = get_node_index(inbound_layer, inbound_node_index)
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if inbound_node_index is None:
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add_unprocessed_node(layer, node_data)
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return
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inbound_node = inbound_layer._inbound_nodes[inbound_node_index]
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input_tensors.append(
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nest.flatten(inbound_node.outputs)[inbound_tensor_index])
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if inbound_node_index is None:
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add_unprocessed_node(layer, node_data)
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return
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inbound_node = inbound_layer._inbound_nodes[inbound_node_index]
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input_tensors.append(
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nest.flatten(inbound_node.outputs)[inbound_tensor_index])
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else:
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# We received a constant w/ no Keras history attached
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input_tensors.append(inbound_tensor_index)
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input_tensors = nest.pack_sequence_as(node_data, input_tensors)
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# Call layer on its inputs, thus creating the node
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# and building the layer if needed.
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@ -964,6 +964,43 @@ class NetworkConstructionTest(keras_parameterized.TestCase):
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# Check that second input was correctly added to first.
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self.assertEqual(history.history['loss'][0], 0.0)
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@combinations.generate(combinations.keras_mode_combinations())
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def test_call_kwarg_derived_from_keras_layer_and_first_arg_is_constant(self):
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class MaybeAdd(layers.Layer):
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def call(self, x1, x2=None):
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if x2 is not None:
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return x1 + x2
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return x1
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input2 = input_layer_lib.Input(10)
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outputs = MaybeAdd()(3., x2=input2)
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model = training_lib.Model([input2], outputs)
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model.compile(
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'sgd',
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'mse',
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run_eagerly=testing_utils.should_run_eagerly())
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history = model.fit(
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x=7 * np.ones((10, 10)),
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y=10 * np.ones((10, 10)),
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batch_size=2)
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# Check that second input was correctly added to first.
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self.assertEqual(history.history['loss'][0], 0.0)
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model = training_lib.Model.from_config(
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model.get_config(), custom_objects={'MaybeAdd': MaybeAdd})
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model.compile(
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'sgd',
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'mse',
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run_eagerly=testing_utils.should_run_eagerly())
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history = model.fit(
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x=7 * np.ones((10, 10)),
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y=10 * np.ones((10, 10)),
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batch_size=2)
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# Check that second input was correctly added to first.
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self.assertEqual(history.history['loss'][0], 0.0)
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@combinations.generate(combinations.keras_mode_combinations())
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def test_composite_call_kwarg_derived_from_keras_layer(self):
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@ -1005,6 +1042,56 @@ class NetworkConstructionTest(keras_parameterized.TestCase):
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# Check that second input was correctly added to first.
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self.assertEqual(history.history['loss'][0], 0.0)
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@combinations.generate(combinations.keras_mode_combinations(mode='eager'))
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def test_call_some_not_all_nested_in_first_arg_derived_from_keras_layer(self):
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# This functionality is unsupported in v1 graphs
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class AddAll(layers.Layer):
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def call(self, x1_x2, x3):
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x1, x2 = x1_x2
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out = x1 + x2
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if x3 is not None:
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for t in x3.values():
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out += t
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return out
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input1 = input_layer_lib.Input(10)
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input2 = input_layer_lib.Input(10)
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input3 = input_layer_lib.Input(10)
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outputs = AddAll()(
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[input1, 4 * array_ops.ones((1, 10))],
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x3={
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'a': input2,
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'b': input3,
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'c': 5 * array_ops.ones((1, 10))
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})
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model = training_lib.Model([input1, input2, input3], outputs)
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model.compile(
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'sgd',
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'mse',
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run_eagerly=testing_utils.should_run_eagerly())
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history = model.fit(
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x=[np.ones((10, 10)), 2 * np.ones((10, 10)), 3 * np.ones((10, 10))],
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y=15 * np.ones((10, 10)),
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batch_size=2)
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# Check that all inputs were correctly added.
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self.assertEqual(history.history['loss'][0], 0.0)
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model = training_lib.Model.from_config(
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model.get_config(), custom_objects={'AddAll': AddAll})
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model.compile(
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'sgd',
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'mse',
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run_eagerly=testing_utils.should_run_eagerly())
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history = model.fit(
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x=[np.ones((10, 10)), 2 * np.ones((10, 10)), 3 * np.ones((10, 10))],
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y=15 * np.ones((10, 10)),
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batch_size=2)
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# Check that all inputs were correctly added.
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self.assertEqual(history.history['loss'][0], 0.0)
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@combinations.generate(combinations.keras_mode_combinations())
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def test_call_nested_arg_derived_from_keras_layer(self):
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@ -32,6 +32,8 @@ from tensorflow.python.platform import tf_logging as logging
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from tensorflow.python.util import nest
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from tensorflow.python.util import serialization
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_CONSTANT_VALUE = '_CONSTANT_VALUE'
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class Node(object):
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"""A `Node` describes the connectivity between two layers.
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@ -181,11 +183,14 @@ class Node(object):
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# `kwargs` is added to each Tensor in the first arg. This should be
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# changed in a future version of the serialization format.
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def serialize_first_arg_tensor(t):
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kh = t._keras_history
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node_index = kh.node_index
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node_key = make_node_key(kh.layer.name, node_index)
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new_node_index = node_conversion_map.get(node_key, 0)
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data = [kh.layer.name, new_node_index, kh.tensor_index, kwargs]
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if is_keras_tensor(t):
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kh = t._keras_history
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node_index = kh.node_index
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node_key = make_node_key(kh.layer.name, node_index)
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new_node_index = node_conversion_map.get(node_key, 0)
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data = [kh.layer.name, new_node_index, kh.tensor_index, kwargs]
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else:
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data = [_CONSTANT_VALUE, -1, _serialize_keras_tensor(t), kwargs]
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return tf_utils.ListWrapper(data)
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data = nest.map_structure(serialize_first_arg_tensor, inputs)
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