Removed references to inlining in general-purpose places (#240)

Author: mtrofin

compiler_opt/rl/regalloc/regalloc_runner.py

@@ -34,7 +34,7 @@ class RegAllocRunner(compilation_runner.CompilationRunner):
   runner = RegAllocRunner(
                clang_path, launcher_path, moving_average_decay_rate)
   serialized_sequence_example, default_reward, moving_average_reward,
-  policy_reward = inliner.collect_data(
+  policy_reward = runner.collect_data(
       ir_path, tf_policy_path, default_reward, moving_average_reward)
   """
 
@@ -44,7 +44,7 @@ def compile_fn(
       self, command_line: corpus.FullyQualifiedCmdLine, tf_policy_path: str,
       reward_only: bool,
       workdir: str) -> Dict[str, Tuple[tf.train.SequenceExample, float]]:
-    """Run inlining for the given IR file under the given policy.
+    """Run the compiler for the given IR file under the given policy.
 
     Args:
       command_line: the fully qualified command line.

compiler_opt/rl/train_bc.py

@@ -12,7 +12,7 @@
 # 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.
-r"""Train behavioral cloning policy for LLVM Inliner decision rule."""
+r"""Train behavioral cloning policy."""
 
 import os
 
@@ -54,7 +54,7 @@ def train_eval(agent_config_type=agent_config.BCAgentConfig,
                num_iterations=100,
                batch_size=64,
                train_sequence_length=1):
-  """Train for LLVM inliner."""
+  """Train Behavioral Cloning."""
   root_dir = os.path.expanduser(_ROOT_DIR.value)
   root_dir = os.path.normpath(root_dir)
   problem_config = registry.get_configuration()

compiler_opt/rl/train_locally.py

@@ -12,7 +12,7 @@
 # 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.
-r"""Train and Eval LLVM Inliner decision rule with local_data_collector."""
+r"""Train and Eval with local_data_collector."""
 
 import collections
 import functools
@@ -72,7 +72,7 @@ def train_eval(worker_manager_class=LocalWorkerPoolManager,
                use_random_network_distillation=False,
                dump_best_trajectory=False,
                moving_average_decay_rate=1):
-  """Train for LLVM inliner."""
+  """Training coordinator."""
   root_dir = FLAGS.root_dir
   problem_config = registry.get_configuration()
   time_step_spec, action_spec = problem_config.get_signature_spec()

compiler_opt/tools/generate_vocab.py

@@ -156,15 +156,14 @@ def main(_) -> None:
       logging.info('Found valid sequence_features dict: %s', sequence_features)
       break
     except IndexError:
-      # modules with no inlining done have empty feature values and
-      # raise an IndexError.
-      # continue until an inlined module with non-empty feature values is found.
-      logging.warn('Found module that was not inlined and has empty feature '
-                   'values.')
+      # modules with no results have empty feature values and
+      # raise an IndexError. For example, in the inliner case, maybe there were
+      # no inlining opportunities (very small modules)
+      # continue until a module with non-empty feature values is found.
+      logging.warn('Found module with empty feature values.')
       continue
   if not sequence_features:
-    raise ValueError('No inlined module with non-empty sequence_features '
-                     'values found.')
+    raise ValueError('No module with non-empty sequence_features values found.')
 
   parser_fn = create_tfrecord_parser_fn(sequence_features)
   dataset = dataset.map(parser_fn, num_parallel_calls=tf.data.AUTOTUNE)