Reduce 1-Layer Functional.__call__ overhead by ~10%.

Moves Model._set_save_spec to Layer. This allows Layer.__call__ to avoid a
hasattr check and also Model.__call__ to avoid an expensive call to a method
wrapped in trackable.no_automatic_dependency_tracking.

This should also allow SavedModel to use this spec in place of
build_input_shape in the future.

PiperOrigin-RevId: 313795786
Change-Id: Id7b23f98911468ed3e11261ac60989685de47aa1

tensorflow/python/keras/engine/base_layer.py

@@ -316,6 +316,7 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
     # TODO(kathywu): Move this to Layer._set_save_spec once cl/290121460 is
     # submitted.
     self._build_input_shape = None
+    self._saved_model_inputs_spec = None
     # Provides information about which inputs are compatible with the layer.
     self._input_spec = None
     self.supports_masking = False
@@ -1002,7 +1003,7 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
             outputs = self.call(cast_inputs, *args, **kwargs)
           self._handle_activity_regularization(inputs, outputs)
           self._set_mask_metadata(inputs, outputs, input_masks, build_graph)
-          if hasattr(self, '_set_save_spec'):
+          if self._saved_model_inputs_spec is None:
             self._set_save_spec(cast_inputs)
 
     return outputs
@@ -2809,6 +2810,22 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
 
   # SavedModel properties. Please see keras/saving/saved_model for details.
 
+  @trackable.no_automatic_dependency_tracking
+  def _set_save_spec(self, inputs):
+    if self._saved_model_inputs_spec is not None:
+      return  # Already set.
+
+    self._saved_model_inputs_spec = nest.map_structure(tf_utils.get_tensor_spec,
+                                                       inputs)
+
+  def _get_save_spec(self, dynamic_batch=True):
+    if self._saved_model_inputs_spec is None:
+      return None
+
+    return nest.map_structure(
+        lambda t: tf_utils.get_tensor_spec(t, dynamic_batch=dynamic_batch),
+        self._saved_model_inputs_spec)
+
   @property
   def _trackable_saved_model_saver(self):
     return layer_serialization.LayerSavedModelSaver(self)

tensorflow/python/keras/engine/training.py

@@ -2366,14 +2366,6 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
 
     self._saved_model_inputs_spec = specs
 
-  def _get_save_spec(self, dynamic_batch=True):
-    if self._saved_model_inputs_spec is None:
-      return None
-
-    return nest.map_structure(
-        lambda t: tf_utils.get_tensor_spec(t, dynamic_batch=dynamic_batch),
-        self._saved_model_inputs_spec)
-
   def _assert_weights_created(self):
     """Asserts that all the weights for the model have been created.