Internal change.

PiperOrigin-RevId: 212864677

4 files changed, 82 insertions, 5 deletions

diff --git a/tensorflow/contrib/lite/python/convert.py b/tensorflow/contrib/lite/python/convert.py
index 1c5516ae7c..1f48a826d4 100644
--- a/tensorflow/contrib/lite/python/convert.py
+++ b/tensorflow/contrib/lite/python/convert.py
@@ -18,6 +18,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import enum  # pylint: disable=g-bad-import-order
+
 import os as _os
 import platform as _platform
 import subprocess as _subprocess
@@ -30,7 +32,6 @@ from tensorflow.python.platform import resource_loader as _resource_loader
 from tensorflow.python.util import deprecation
 from tensorflow.python.util.lazy_loader import LazyLoader
 
-
 # Lazy load since some of the performance benchmark skylark rules
 # break dependencies.
 _toco_python = LazyLoader(
@@ -52,6 +53,31 @@ if _toco_from_proto_bin and not _os.path.exists(_toco_from_proto_bin):
   _toco_from_proto_bin = "toco_from_protos"
 
 
+class ConverterMode(enum.Enum):
+  """Enum class defining the converters available to generate TFLite models.
+
+  WARNING: Experimental interface, subject to change.
+  """
+  # Convert model using TOCO such that all ops are TensorFlow Lite native ops.
+  #
+  # This is the only supported mode for any models that contain operations that
+  # cannot be resolved in TensorFlow.
+  DEFAULT = "DEFAULT"
+
+  # Convert model using TOCO such that only unsupported operations are
+  # represented as TensorFlow ops.
+  # WARNING: Experimental interface, subject to change.
+  TOCO_EXTENDED = "TOCO_EXTENDED"
+
+  # Convert model using TOCO such that all operations are represented as
+  # TensorFlow ops.
+  # WARNING: Experimental interface, subject to change.
+  TOCO_EXTENDED_ALL = "TOCO_EXTENDED_ALL"
+
+  def __str__(self):
+    return self.value
+
+
 def toco_convert_protos(model_flags_str, toco_flags_str, input_data_str):
   """Convert `input_data_str` according to model and toco parameters.
 
@@ -128,7 +154,8 @@ def build_toco_convert_protos(input_tensors,
                               change_concat_input_ranges=False,
                               post_training_quantize=False,
                               dump_graphviz_dir=None,
-                              dump_graphviz_video=False):
+                              dump_graphviz_video=False,
+                              converter_mode=ConverterMode.DEFAULT):
   """Builds protocol buffers describing a conversion of a model using TOCO.
 
   Typically this is to convert from TensorFlow GraphDef to TFLite, in which
@@ -183,6 +210,8 @@ def build_toco_convert_protos(input_tensors,
       output file. (default None)
     dump_graphviz_video: Boolean indicating whether to dump the graph after
       every graph transformation. (default False)
+    converter_mode: Experimental flag, subject to change. ConverterMode
+      indicating which converter to use. (default ConverterMode.DEFAULT)
 
   Returns:
     model_flags, toco_flags: two protocol buffers describing the conversion
@@ -211,6 +240,11 @@ def build_toco_convert_protos(input_tensors,
   if dump_graphviz_dir:
     toco.dump_graphviz_dir = dump_graphviz_dir
   toco.dump_graphviz_include_video = dump_graphviz_video
+  if converter_mode == ConverterMode.TOCO_EXTENDED:
+    toco.allow_eager_ops = True
+  elif converter_mode == ConverterMode.TOCO_EXTENDED_ALL:
+    toco.allow_eager_ops = True
+    toco.force_eager_ops = True
 
   model = _model_flags_pb2.ModelFlags()
   model.change_concat_input_ranges = change_concat_input_ranges
@@ -301,9 +335,8 @@ def toco_convert_impl(input_data, input_tensors, output_tensors, *args,
   Raises:
     Defined in `build_toco_convert_protos`.
   """
-  model_flags, toco_flags = build_toco_convert_protos(input_tensors,
-                                                      output_tensors,
-                                                      *args, **kwargs)
+  model_flags, toco_flags = build_toco_convert_protos(
+      input_tensors, output_tensors, *args, **kwargs)
   data = toco_convert_protos(model_flags.SerializeToString(),
                              toco_flags.SerializeToString(),
                              input_data.SerializeToString())
diff --git a/tensorflow/contrib/lite/python/lite.py b/tensorflow/contrib/lite/python/lite.py
index 44dfb97b84..2be24455d8 100644
--- a/tensorflow/contrib/lite/python/lite.py
+++ b/tensorflow/contrib/lite/python/lite.py
@@ -40,6 +40,7 @@ from google.protobuf import text_format as _text_format
 from google.protobuf.message import DecodeError
 from tensorflow.contrib.lite.python import lite_constants as constants
 from tensorflow.contrib.lite.python.convert import build_toco_convert_protos  # pylint: disable=unused-import
+from tensorflow.contrib.lite.python.convert import ConverterMode
 from tensorflow.contrib.lite.python.convert import tensor_name as _tensor_name
 from tensorflow.contrib.lite.python.convert import toco_convert  # pylint: disable=unused-import
 from tensorflow.contrib.lite.python.convert import toco_convert_graph_def as _toco_convert_graph_def
@@ -113,6 +114,8 @@ class TocoConverter(object):
       output file. (default None)
     dump_graphviz_video: Boolean indicating whether to dump the graph after
       every graph transformation. (default False)
+    converter_mode: Experimental flag, subject to change. ConverterMode
+      indicating which converter to use. (default ConverterMode.DEFAULT)
 
   Example usage:
 
@@ -179,6 +182,7 @@ class TocoConverter(object):
     self.post_training_quantize = False
     self.dump_graphviz_dir = None
     self.dump_graphviz_video = False
+    self.converter_mode = ConverterMode.DEFAULT
 
     # Attributes are used by models that cannot be loaded into TensorFlow.
     if not self._has_valid_tensors():
@@ -389,6 +393,7 @@ class TocoConverter(object):
       ValueError:
         Input shape is not specified.
         None value for dimension in input_tensor.
+        ConverterMode option is unsupported for the model.
     """
     # Checks dimensions in input tensor.
     if self._has_valid_tensors():
@@ -439,12 +444,18 @@ class TocoConverter(object):
 
     # Converts model.
     if self._has_valid_tensors():
+      converter_kwargs["converter_mode"] = self.converter_mode
       result = _toco_convert_impl(
           input_data=self._graph_def,
           input_tensors=self._input_tensors,
           output_tensors=self._output_tensors,
           **converter_kwargs)
     else:
+      # Graphs without valid tensors cannot be loaded into tf.Session since they
+      # contain TFLite operation(s) that cannot be resolved in TensorFlow.
+      if self.converter_mode != ConverterMode.DEFAULT:
+        raise ValueError("This model can only be converted with the default "
+                         "converter.")
       result = _toco_convert_graph_def(
           input_data=self._graph_def,
           input_arrays_with_shape=self._input_arrays_with_shape,
diff --git a/tensorflow/contrib/lite/python/lite_test.py b/tensorflow/contrib/lite/python/lite_test.py
index 3f8ea433ff..f112ed5cdd 100644
--- a/tensorflow/contrib/lite/python/lite_test.py
+++ b/tensorflow/contrib/lite/python/lite_test.py
@@ -402,6 +402,28 @@ class FromSessionTest(test_util.TensorFlowTestCase):
     # Ensure that the quantized weights tflite model is smaller.
     self.assertTrue(len(quantized_tflite) < len(float_tflite))
 
+  def testExtendedMode(self):
+    in_tensor = array_ops.placeholder(
+        shape=[1, 16, 16, 3], dtype=dtypes.float32)
+    out_tensor = in_tensor + in_tensor
+    sess = session.Session()
+
+    # Convert model and ensure model is not None.
+    converter = lite.TocoConverter.from_session(sess, [in_tensor], [out_tensor])
+    converter.converter_mode = lite.ConverterMode.TOCO_EXTENDED_ALL
+    tflite_model = converter.convert()
+    self.assertTrue(tflite_model)
+
+    # Ensures the model contains TensorFlow ops.
+    # TODO(nupurgarg): Check values once there is a Python delegate interface.
+    interpreter = Interpreter(model_content=tflite_model)
+    with self.assertRaises(RuntimeError) as error:
+      interpreter.allocate_tensors()
+    self.assertIn(
+        'Regular TensorFlow ops are not supported by this interpreter. Make '
+        'sure you invoke the Eager delegate before inference.',
+        str(error.exception))
+
 
 class FromFrozenGraphFile(test_util.TensorFlowTestCase):
 
diff --git a/tensorflow/contrib/lite/python/tflite_convert.py b/tensorflow/contrib/lite/python/tflite_convert.py
index cc08ed3fe9..c0ff7f37f9 100644
--- a/tensorflow/contrib/lite/python/tflite_convert.py
+++ b/tensorflow/contrib/lite/python/tflite_convert.py
@@ -140,8 +140,11 @@ def _convert_model(flags):
   if flags.change_concat_input_ranges:
     converter.change_concat_input_ranges = (
         flags.change_concat_input_ranges == "TRUE")
+
   if flags.allow_custom_ops:
     converter.allow_custom_ops = flags.allow_custom_ops
+  if flags.converter_mode:
+    converter.converter_mode = flags.converter_mode
 
   if flags.post_training_quantize:
     converter.post_training_quantize = flags.post_training_quantize
@@ -363,6 +366,8 @@ def run_main(_):
       help=("Boolean to change behavior of min/max ranges for inputs and "
             "outputs of the concat operator for quantized models. Changes the "
             "ranges of concat operator overlap when true. (default False)"))
+
+  # Permitted ops flags.
   parser.add_argument(
       "--allow_custom_ops",
       action="store_true",
@@ -371,6 +376,12 @@ def run_main(_):
             "created for any op that is unknown. The developer will need to "
             "provide these to the TensorFlow Lite runtime with a custom "
             "resolver. (default False)"))
+  parser.add_argument(
+      "--converter_mode",
+      type=lite.ConverterMode,
+      choices=list(lite.ConverterMode),
+      help=("Experimental flag, subject to change. ConverterMode indicating "
+            "which converter to use. (default ConverterMode.DEFAULT)"))
 
   # Logging flags.
   parser.add_argument(