Make default Keras ConfigProto use tf.config

PiperOrigin-RevId: 251659257

Author: jaingaurav

tensorflow/python/keras/backend.py

@@ -69,6 +69,7 @@
 from tensorflow.python.ops import tensor_array_grad  # pylint: disable=unused-import
 from tensorflow.python.ops import tensor_array_ops
 from tensorflow.python.ops import variables as variables_module
+from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.training import server_lib
 from tensorflow.python.util import nest
 from tensorflow.python.util import tf_contextlib
@@ -521,14 +522,14 @@ def set_session(session):
 
 
 def get_default_session_config():
-  if not os.environ.get('OMP_NUM_THREADS'):
-    config = config_pb2.ConfigProto(allow_soft_placement=True)
-  else:
-    num_thread = int(os.environ.get('OMP_NUM_THREADS'))
-    config = config_pb2.ConfigProto(
-        intra_op_parallelism_threads=num_thread,
-        inter_op_parallelism_threads=num_thread,
-        allow_soft_placement=True)
+  if os.environ.get('OMP_NUM_THREADS'):
+    logging.warning(
+        'OMP_NUM_THREADS is no longer used by the default Keras config. '
+        'To configure the number of threads, use tf.config.threading APIs.')
+
+  config = context.context().config
+  config.allow_soft_placement = True
+
   return config
 
 

tensorflow/python/keras/backend_test.py

@@ -24,6 +24,7 @@
 from tensorflow.core.protobuf import config_pb2
 from tensorflow.python import keras
 from tensorflow.python.eager import context
+from tensorflow.python.framework import config
 from tensorflow.python.framework import errors_impl
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import sparse_tensor
@@ -94,6 +95,34 @@ def compare_two_inputs_op_to_numpy(keras_op,
 
 class BackendResetTest(test.TestCase, parameterized.TestCase):
 
+  @test_util.run_all_in_graph_and_eager_modes
+  def test_new_config(self):
+    # User defined jit setting
+    config.set_optimizer_jit(False)
+    sess = keras.backend.get_session()
+    default_config = context.context().config
+    self.assertEqual(
+        sess._config.graph_options.optimizer_options.global_jit_level,
+        default_config.graph_options.optimizer_options.global_jit_level)
+    keras.backend.clear_session()
+
+    # New session has the same jit setting
+    sess = keras.backend.get_session()
+    default_config = context.context().config
+    self.assertEqual(
+        sess._config.graph_options.optimizer_options.global_jit_level,
+        default_config.graph_options.optimizer_options.global_jit_level)
+    keras.backend.clear_session()
+
+    # Change respected
+    config.set_optimizer_jit(True)
+    sess = keras.backend.get_session()
+    default_config = context.context().config
+    self.assertEqual(
+        sess._config.graph_options.optimizer_options.global_jit_level,
+        default_config.graph_options.optimizer_options.global_jit_level)
+    keras.backend.clear_session()
+
   # We can't use the normal parameterized decorator because the test session
   # will block graph clearing.
   @parameterized.named_parameters(('_v1', context.graph_mode),