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

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PiperOrigin-RevId: 314625466
Change-Id: I0000e7f3fe91f0024d2871a2d1e8be3af0c0cad5
This commit is contained in:
Gregory Clark 2020-06-03 16:20:39 -07:00 committed by TensorFlower Gardener
parent 911c0ec205
commit 778ed688df
4 changed files with 27 additions and 26 deletions
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20
tensorflow/lite/python/lite.py
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@ -579,10 +579,9 @@ class TFLiteConverterBaseV2(TFLiteConverterBase):
converter_kwargs.update(quant_mode.converter_flags()) converter_kwargs.update(quant_mode.converter_flags())
if not self.experimental_new_converter: if not self.experimental_new_converter:
logging.warning( logging.warning(
"Please consider switching to use new converter by setting " "Please consider switching to the new converter by setting "
"experimental_new_converter to true. " "experimental_new_converter=True. "
"Old converter (TOCO) is deprecated and flow will be switched on " "The old converter (TOCO) is deprecated.")
"by default to use new converter soon.")
else: else:
logging.info("Using experimental converter: If you encountered a problem " logging.info("Using experimental converter: If you encountered a problem "
"please file a bug. You can opt-out " "please file a bug. You can opt-out "
@ -875,7 +874,7 @@ class TFLiteConverterV2(TFLiteFrozenGraphConverterV2):
training integer quantization. (default tf.float32, must be in training integer quantization. (default tf.float32, must be in
{tf.float32, tf.int8, tf.uint8}) {tf.float32, tf.int8, tf.uint8})
experimental_new_converter: Experimental flag, subject to change. Enables 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: Example usage:
@ -1095,7 +1094,7 @@ class TFLiteConverterBaseV1(TFLiteConverterBase):
generate input and output samples for the model. The converter can use the generate input and output samples for the model. The converter can use the
dataset to evaluate different optimizations. dataset to evaluate different optimizations.
experimental_new_converter: Experimental flag, subject to change. Enables 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): def __init__(self, experimental_debug_info_func):
@ -1256,10 +1255,9 @@ class TFLiteConverterBaseV1(TFLiteConverterBase):
if not self.experimental_new_converter: if not self.experimental_new_converter:
logging.warning( logging.warning(
"Please consider switching to use new converter by setting " "Please consider switching to the new converter by setting "
"experimental_new_converter to true. " "experimental_new_converter=True. "
"Old converter (TOCO) is deprecated and flow will be switched on " "The old converter (TOCO) is deprecated.")
"by default to use new converter soon.")
else: else:
logging.info("Using experimental converter: If you encountered a problem " logging.info("Using experimental converter: If you encountered a problem "
"please file a bug. You can opt-out " "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 generate input and output samples for the model. The converter can use
the dataset to evaluate different optimizations. the dataset to evaluate different optimizations.
experimental_new_converter: Experimental flag, subject to change. 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: Example usage:

2
tensorflow/lite/python/lite_test.py
View File

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

8
tensorflow/lite/python/tflite_convert.py
View File

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

23
tensorflow/lite/python/tflite_convert_test.py
View File

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