Remove inlining_default as it's not used by anything (#344)

The compiler side change will follow, but training can continue with this
repository at HEAD because LLVM will patch features it can't find by
allocating appropriate buffers and just never passing them to a model.

compiler_opt/es/blackbox_learner_test.py

@@ -109,7 +109,7 @@ def setUp(self):
         actor_network=actor_network)
 
     # make the policy all zeros to be deterministic
-    expected_policy_length = 17218
+    expected_policy_length = 17154
     policy_utils.set_vectorized_parameters_for_policy(policy, [0.0] *
                                                       expected_policy_length)
     init_params = policy_utils.get_vectorized_parameters_from_policy(policy)
@@ -183,7 +183,7 @@ def test_run_step(self):
         ESWorker, count=3, arg='', kwarg='') as pool:
       self._learner.run_step(pool)  # pylint: disable=protected-access
       # expected length calculated from expected shapes of variables
-      self.assertEqual(len(self._learner.get_model_weights()), 17218)
+      self.assertEqual(len(self._learner.get_model_weights()), 17154)
       # check that first 5 weights are not all zero
       # this will indicate general validity of all the values
       for value in self._learner.get_model_weights()[:5]:

compiler_opt/es/policy_utils_test.py

@@ -100,7 +100,7 @@ def test_regalloc_config(self):
 
 class VectorTest(absltest.TestCase):
 
-  expected_variable_shapes = [(71, 64), (64), (64, 64), (64), (64, 64), (64),
+  expected_variable_shapes = [(70, 64), (64), (64, 64), (64), (64, 64), (64),
                               (64, 64), (64), (64, 2), (2)]
   expected_length_of_a_perturbation = sum(
       np.prod(shape) for shape in expected_variable_shapes)

compiler_opt/rl/inlining/config.py

@@ -64,10 +64,7 @@ def get_inlining_signature_spec():
           'is_multiple_blocks',
           'nested_inlines',
           'nested_inline_cost_estimate',
-          'threshold',
-
-          # inlining_default is not used as feature in training.
-          'inlining_default'))
+          'threshold'))
   reward_spec = tf.TensorSpec(dtype=tf.float32, shape=(), name='reward')
   time_step_spec = time_step.time_step_spec(observation_spec, reward_spec)
   action_spec = tensor_spec.BoundedTensorSpec(
@@ -86,9 +83,6 @@ def get_observation_processing_layer_creator(quantile_file_dir=None,
 
   def observation_processing_layer(obs_spec):
     """Creates the layer to process observation given obs_spec."""
-    if obs_spec.name == 'inlining_default':
-      return tf.keras.layers.Lambda(feature_ops.discard_fn)
-
     quantile = quantile_map[obs_spec.name]
     return tf.keras.layers.Lambda(
         feature_ops.get_normalize_fn(quantile, with_sqrt,
@@ -98,4 +92,4 @@ def observation_processing_layer(obs_spec):
 
 
 def get_nonnormalized_features():
-  return ['reward', 'inlining_default', 'inlining_decision']
+  return ['reward', 'inlining_decision']

compiler_opt/rl/trainer_test.py

@@ -31,7 +31,7 @@ def _create_test_data(batch_size, sequence_length):
   test_trajectory = trajectory.Trajectory(
       step_type=tf.fill([batch_size, sequence_length], 1),
       observation={
-          'inlining_default':
+          'callee_users':
               tf.fill([batch_size, sequence_length],
                       tf.constant(10, dtype=tf.int64))
       },
@@ -54,8 +54,8 @@ class TrainerTest(tf.test.TestCase):
 
   def setUp(self):
     observation_spec = {
-        'inlining_default':
-            tf.TensorSpec(dtype=tf.int64, shape=(), name='inlining_default')
+        'callee_users':
+            tf.TensorSpec(dtype=tf.int64, shape=(), name='callee_users')
     }
     self._time_step_spec = time_step.time_step_spec(observation_spec)
     self._action_spec = tensor_spec.BoundedTensorSpec(
@@ -69,7 +69,7 @@ def setUp(self):
         action_spec=self._action_spec,
         lstm_size=(40,),
         preprocessing_layers={
-            'inlining_default': tf.keras.layers.Lambda(lambda x: x)
+            'callee_users': tf.keras.layers.Lambda(lambda x: x)
         })
     super().setUp()