Clarify that MLIR is now the default for the TFLite converter.

PiperOrigin-RevId: 314625466
Change-Id: I0000e7f3fe91f0024d2871a2d1e8be3af0c0cad5

tensorflow/lite/python/lite.py

@@ -579,10 +579,9 @@ class TFLiteConverterBaseV2(TFLiteConverterBase):
     converter_kwargs.update(quant_mode.converter_flags())
     if not self.experimental_new_converter:
       logging.warning(
-          "Please consider switching to use new converter by setting "
-          "experimental_new_converter to true. "
-          "Old converter (TOCO) is deprecated and flow will be switched on "
-          "by default to use new converter soon.")
+          "Please consider switching to the new converter by setting "
+          "experimental_new_converter=True. "
+          "The old converter (TOCO) is deprecated.")
     else:
       logging.info("Using experimental converter: If you encountered a problem "
                    "please file a bug. You can opt-out "
@@ -875,7 +874,7 @@ class TFLiteConverterV2(TFLiteFrozenGraphConverterV2):
       training integer quantization. (default tf.float32, must be in
       {tf.float32, tf.int8, tf.uint8})
     experimental_new_converter: Experimental flag, subject to change. Enables
-      MLIR-based conversion instead of TOCO conversion.
+      MLIR-based conversion instead of TOCO conversion. (default True)
 
   Example usage:
 
@@ -1095,7 +1094,7 @@ class TFLiteConverterBaseV1(TFLiteConverterBase):
       generate input and output samples for the model. The converter can use the
       dataset to evaluate different optimizations.
     experimental_new_converter: Experimental flag, subject to change. Enables
-      MLIR-based conversion instead of TOCO conversion.
+      MLIR-based conversion instead of TOCO conversion. (default True)
   """
 
   def __init__(self, experimental_debug_info_func):
@@ -1256,10 +1255,9 @@ class TFLiteConverterBaseV1(TFLiteConverterBase):
 
     if not self.experimental_new_converter:
       logging.warning(
-          "Please consider switching to use new converter by setting "
-          "experimental_new_converter to true. "
-          "Old converter (TOCO) is deprecated and flow will be switched on "
-          "by default to use new converter soon.")
+          "Please consider switching to the new converter by setting "
+          "experimental_new_converter=True. "
+          "The old converter (TOCO) is deprecated.")
     else:
       logging.info("Using experimental converter: If you encountered a problem "
                    "please file a bug. You can opt-out "
@@ -1637,7 +1635,7 @@ class TFLiteConverter(TFLiteFrozenGraphConverter):
       generate input and output samples for the model. The converter can use
       the dataset to evaluate different optimizations.
     experimental_new_converter: Experimental flag, subject to change.
-      Enables MLIR-based conversion instead of TOCO conversion.
+      Enables MLIR-based conversion instead of TOCO conversion. (default True)
 
   Example usage:
 

tensorflow/lite/python/lite_test.py

@@ -1767,7 +1767,7 @@ class FromSavedModelTest(TestModels):
     log = io.BytesIO() if six.PY2 else io.StringIO()
     handler = logging.StreamHandler(log)
     logging.root.addHandler(handler)
-    warning_message = 'Please consider switching to use new converter'
+    warning_message = 'Please consider switching to the new converter'
     # Convert model and ensure model is not None.
     converter = lite.TFLiteConverter.from_saved_model(saved_model_dir)
     converter.experimental_new_converter = False

tensorflow/lite/python/tflite_convert.py

@@ -13,7 +13,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Python command line interface for running TOCO."""
+"""Python command line interface for converting TF models to TFLite models."""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -71,7 +71,7 @@ def _parse_inference_type(value, flag):
                    "QUANTIZED_UINT8 are supported.".format(flag))
 
 
-def _get_toco_converter(flags):
+def _get_tflite_converter(flags):
   """Makes a TFLiteConverter object based on the flags provided.
 
   Args:
@@ -129,7 +129,7 @@ def _convert_tf1_model(flags):
     ValueError: Invalid flags.
   """
   # Create converter.
-  converter = _get_toco_converter(flags)
+  converter = _get_tflite_converter(flags)
   if flags.inference_type:
     converter.inference_type = _parse_inference_type(flags.inference_type,
                                                      "inference_type")
@@ -589,7 +589,7 @@ def _get_parser(use_v2_converter):
       action=_ParseExperimentalNewConverter,
       nargs="?",
       help=("Experimental flag, subject to change. Enables MLIR-based "
-            "conversion instead of TOCO conversion."))
+            "conversion instead of TOCO conversion. (default True)"))
   return parser
 
 

tensorflow/lite/python/tflite_convert_test.py

@@ -281,11 +281,11 @@ class TfLiteConvertV1Test(TestModels):
                                                 self._input_shapes,
                                                 custom_opdefs_str))
 
-    # Ensure --experimental_new_converter.
+    # Ensure --allow_custom_ops.
     flags_str_final = ('{} --allow_custom_ops').format(flags_str)
     self._run(flags_str_final, should_succeed=False)
 
-    # Ensure --allow_custom_ops.
+    # Ensure --experimental_new_converter.
     flags_str_final = ('{} --experimental_new_converter').format(flags_str)
     self._run(flags_str_final, should_succeed=False)
 
@@ -344,15 +344,18 @@ class ArgParserTest(test_util.TensorFlowTestCase):
         '--output_file=/tmp/output.tflite',
     ]
 
+    # Note that when the flag parses to None, the converter uses the default
+    # value, which is True.
+
     # V1 parser.
-    parser = tflite_convert._get_parser(False)
+    parser = tflite_convert._get_parser(use_v2_converter=False)
     parsed_args = parser.parse_args(args)
-    self.assertFalse(parsed_args.experimental_new_converter)
+    self.assertIsNone(parsed_args.experimental_new_converter)
 
     # V2 parser.
-    parser = tflite_convert._get_parser(True)
+    parser = tflite_convert._get_parser(use_v2_converter=True)
     parsed_args = parser.parse_args(args)
-    self.assertFalse(parsed_args.experimental_new_converter)
+    self.assertIsNone(parsed_args.experimental_new_converter)
 
   def test_experimental_new_converter(self):
     args = [
@@ -362,12 +365,12 @@ class ArgParserTest(test_util.TensorFlowTestCase):
     ]
 
     # V1 parser.
-    parser = tflite_convert._get_parser(False)
+    parser = tflite_convert._get_parser(use_v2_converter=False)
     parsed_args = parser.parse_args(args)
     self.assertTrue(parsed_args.experimental_new_converter)
 
     # V2 parser.
-    parser = tflite_convert._get_parser(True)
+    parser = tflite_convert._get_parser(use_v2_converter=True)
     parsed_args = parser.parse_args(args)
     self.assertTrue(parsed_args.experimental_new_converter)
 
@@ -396,12 +399,12 @@ class ArgParserTest(test_util.TensorFlowTestCase):
     ]
 
     # V1 parser.
-    parser = tflite_convert._get_parser(False)
+    parser = tflite_convert._get_parser(use_v2_converter=False)
     parsed_args = parser.parse_args(args)
     self.assertFalse(parsed_args.experimental_new_converter)
 
     # V2 parser.
-    parser = tflite_convert._get_parser(True)
+    parser = tflite_convert._get_parser(use_v2_converter=True)
     parsed_args = parser.parse_args(args)
     self.assertFalse(parsed_args.experimental_new_converter)