Make the individual call of Model.evaluate use no cached data, while Model.fit still uses cached data for evaluation.

PiperOrigin-RevId: 337194749
Change-Id: I1a99eadac1831918a21ab994a1d7a5e84b0623c6

RELEASE.md

@@ -225,6 +225,9 @@
         argument.
     *   Added `tf.metrics.log_cosh` and `tf.metrics.logcosh` API entrypoints
         with the same implementation as their `tf.losses` equivalent.
+    *   For Keras model, the individual call of `Model.evaluate` uses no cached
+        data for evaluation, while `Model.fit` uses cached data when
+        `validation_data` arg is provided for better performance.
 *   `tf.function` / AutoGraph:
     *   Added `experimental_follow_type_hints` argument for `tf.function`. When
         True, the function may use type annotations to optimize the tracing

tensorflow/python/keras/engine/training.py

@@ -58,6 +58,7 @@ from tensorflow.python.keras.saving.saved_model import json_utils
 from tensorflow.python.keras.saving.saved_model import model_serialization
 from tensorflow.python.keras.utils import generic_utils
 from tensorflow.python.keras.utils import layer_utils
+from tensorflow.python.keras.utils import tf_inspect
 from tensorflow.python.keras.utils import tf_utils
 from tensorflow.python.keras.utils import version_utils
 from tensorflow.python.keras.utils.io_utils import ask_to_proceed_with_overwrite
@@ -1099,6 +1100,7 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
         if validation_data and self._should_eval(epoch, validation_freq):
           # Create data_handler for evaluation and cache it.
           if getattr(self, '_eval_data_handler', None) is None:
+            self._fit_frame = tf_inspect.currentframe()
             self._eval_data_handler = data_adapter.DataHandler(
                 x=val_x,
                 y=val_y,
@@ -1134,6 +1136,7 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
       # If eval data_hanlder exists, delete it after all epochs are done.
       if getattr(self, '_eval_data_handler', None) is not None:
         del self._eval_data_handler
+        del self._fit_frame
       callbacks.on_train_end(logs=training_logs)
       return self.history
 
@@ -1327,7 +1330,10 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
     _disallow_inside_tf_function('evaluate')
 
     with self.distribute_strategy.scope():
-      if getattr(self, '_eval_data_handler', None) is not None:
+      # Use cached evaluation data only when it's called in `Model.fit`
+      if (getattr(self, '_fit_frame', None) is not None
+          and tf_inspect.currentframe().f_back is self._fit_frame
+          and getattr(self, '_eval_data_handler', None) is not None):
         data_handler = self._eval_data_handler
       else:
         # Creates a `tf.data.Dataset` and handles batch and epoch iteration.

tensorflow/python/keras/engine/training_test.py

@@ -3642,16 +3642,20 @@ class TestAutoUpdates(keras_parameterized.TestCase):
 
 class TestFunctionTracing(keras_parameterized.TestCase):
 
+  def _seq_model_and_data(self):
+    model = sequential.Sequential([layers_module.Dense(4, activation='relu')])
+    model.compile(loss='mse', optimizer='rmsprop')
+    x = np.random.random((10, 6))
+    y = np.random.random((10, 4))
+    return model, x, y
+
   @keras_parameterized.run_all_keras_modes(
       always_skip_v1=True, always_skip_eager=True)
   def test_no_tracing_between_epoch(self):
     if sys.version_info[0] < 3:
       self.skipTest('self.assertLogs() call is not available in Python 2.')
 
-    model = sequential.Sequential([layers_module.Dense(4, activation='relu')])
+    model, x, y = self._seq_model_and_data()
-    model.compile(loss='mse', optimizer='rmsprop')
-    x = np.random.random((10, 6))
-    y = np.random.random((10, 4))
 
     logging.set_verbosity(1)
     with self.assertLogs(level=1) as logs:
@@ -3660,6 +3664,21 @@ class TestFunctionTracing(keras_parameterized.TestCase):
     new_func_graph = 'INFO:absl:Creating new FuncGraph for Python function'
     self.assertEqual(sum(new_func_graph in log for log in logs.output), 9)
 
+  @keras_parameterized.run_all_keras_modes(
+      always_skip_v1=True, always_skip_eager=True)
+  def test_evaluate_no_cached_data(self):
+    if sys.version_info[0] < 3:
+      self.skipTest('self.assertLogs() call is not available in Python 2.')
+
+    model, x, y = self._seq_model_and_data()
+
+    new_func_graph = 'INFO:absl:Creating new FuncGraph for Python function'
+    logging.set_verbosity(1)
+    with self.assertLogs(level=1) as eval_logs:
+      for _ in range(6):
+        model.evaluate(x, y, batch_size=5)
+    self.assertEqual(sum(new_func_graph in log for log in eval_logs.output), 20)
+
 
 class TestBuildCustomModel(keras_parameterized.TestCase):
 

tensorflow/python/keras/utils/tf_inspect.py

@@ -90,6 +90,11 @@ else:
     return _convert_maybe_argspec_to_fullargspec(getargspec(target))
 
 
+def currentframe():
+  """TFDecorator-aware replacement for inspect.currentframe."""
+  return _inspect.stack()[1][0]
+
+
 def getargspec(obj):
   """TFDecorator-aware replacement for `inspect.getargspec`.