Make the experimental_new_quantizer flag to be public

PiperOrigin-RevId: 351421198
Change-Id: I90a1df2a7d6ad074cc7f0d1d04a005a16580fb4c

tensorflow/lite/g3doc/tutorials/model_maker_question_answer.ipynb

@@ -601,7 +601,7 @@
       "outputs": [],
       "source": [
         "config = configs.QuantizationConfig.create_dynamic_range_quantization(optimizations=[tf.lite.Optimize.OPTIMIZE_FOR_LATENCY])\n",
-        "config._experimental_new_quantizer = True"
+        "config.experimental_new_quantizer = True"
       ]
     },
     {

tensorflow/lite/g3doc/tutorials/model_maker_text_classification.ipynb

@@ -476,7 +476,7 @@
       "outputs": [],
       "source": [
         "config = configs.QuantizationConfig.create_dynamic_range_quantization(optimizations=[tf.lite.Optimize.OPTIMIZE_FOR_LATENCY])\n",
-        "config._experimental_new_quantizer = True"
+        "config.experimental_new_quantizer = True"
       ]
     },
     {

tensorflow/lite/python/lite.py

@@ -429,7 +429,8 @@ class TFLiteConverterBase(object):
     self.target_spec = TargetSpec()
     self.allow_custom_ops = False
     self.experimental_new_converter = True
-    self._experimental_new_quantizer = False
+    self.experimental_new_quantizer = False
+    self._experimental_new_quantizer = None
     self._experimental_calibrate_only = False
     self._experimental_sparsify_model = False
     self._debug_info = None  # contains the stack traces of all the original
@@ -474,13 +475,13 @@ class TFLiteConverterBase(object):
     # Add intermediate tensors to the model if needed.
     result = _calibrator.add_intermediate_tensors(result)
     calibrate_quantize = _calibrator.Calibrator(result)
-    if self._experimental_calibrate_only or self._experimental_new_quantizer:
+    if self._experimental_calibrate_only or self.experimental_new_quantizer:
       calibrated = calibrate_quantize.calibrate(
           self.representative_dataset.input_gen)
 
     if self._experimental_calibrate_only:
       return calibrated
-    elif self._experimental_new_quantizer and (
+    elif self.experimental_new_quantizer and (
         activations_type != _dtypes.int16):
       # TODO(b/175659372): remove the activations_type restriction and enable
       # it for all the activation types.
@@ -565,6 +566,10 @@ class TFLiteConverterBase(object):
   def _sparsify_model(self):
     return Optimize.EXPERIMENTAL_SPARSITY in self.optimizations
 
+  def _validate_experimental_new_quantizer_flag(self):
+    if self._experimental_new_quantizer is not None:
+      raise ValueError("Please use 'experimental_new_quantizer' instead.")
+
 
 class TFLiteConverterBaseV2(TFLiteConverterBase):
   """Converter subclass to share functionality between V2 converters."""
@@ -617,6 +622,7 @@ class TFLiteConverterBaseV2(TFLiteConverterBase):
                                   self.representative_dataset, graph_def)
 
     self._validate_inference_input_output_types(quant_mode)
+    self._validate_experimental_new_quantizer_flag()
 
     if not self._is_unknown_shapes_allowed():
       # Checks dimensions in input tensor.
@@ -1011,6 +1017,9 @@ class TFLiteConverterV2(TFLiteFrozenGraphConverterV2):
       to the TensorFlow Lite runtime with a custom resolver. (default False)
     experimental_new_converter: Experimental flag, subject to change. Enables
       MLIR-based conversion instead of TOCO conversion. (default True)
+    experimental_new_quantizer: Experimental flag, subject to change. Enables
+      MLIR-based quantization conversion instead of Flatbuffer-based conversion.
+      (default False)
 
   Example usage:
 
@@ -1340,6 +1349,7 @@ class TFLiteConverterBaseV1(TFLiteConverterBase):
       calibrate_quantize, flags = quant_mode.quantizer_flags()
 
     self._validate_quantized_input_stats(converter_kwargs, calibrate_quantize)
+    self._validate_experimental_new_quantizer_flag()
 
     # Converts model.
     if self._has_valid_tensors():
@@ -1358,7 +1368,7 @@ class TFLiteConverterBaseV1(TFLiteConverterBase):
     if calibrate_quantize:
       result = self._calibrate_quantize_model(result, **flags)
 
-    if self.experimental_new_converter or self._experimental_new_quantizer:
+    if self.experimental_new_converter or self.experimental_new_quantizer:
       flags_modify_model_io_type = quant_mode.flags_modify_model_io_type(
           self.inference_input_type, self.inference_output_type)
       if flags_modify_model_io_type:
@@ -1731,6 +1741,9 @@ class TFLiteConverter(TFLiteFrozenGraphConverter):
       set it to `{tf.lite.Optimize.DEFAULT}`. (default False)
     experimental_new_converter: Experimental flag, subject to change. Enables
       MLIR-based conversion instead of TOCO conversion. (default True)
+    experimental_new_quantizer: Experimental flag, subject to change. Enables
+      MLIR-based quantization conversion instead of Flatbuffer-based conversion.
+      (default False)
 
   Example usage:
 

tensorflow/lite/python/lite_test.py

@@ -367,7 +367,7 @@ class FromSessionTest(TestModels, parameterized.TestCase):
 
     quantized_converter.inference_input_type = inference_input_output_type
     quantized_converter.inference_output_type = inference_input_output_type
-    quantized_converter._experimental_new_quantizer = enable_mlir_quantizer
+    quantized_converter.experimental_new_quantizer = enable_mlir_quantizer
     quantized_tflite_model = quantized_converter.convert()
     self.assertIsNotNone(quantized_tflite_model)
 
@@ -1163,7 +1163,7 @@ class FromSessionTest(TestModels, parameterized.TestCase):
     quantized_converter = lite.TFLiteConverter.from_session(
         sess, [inp], [output])
     quantized_converter.experimental_new_converter = enable_mlir_converter
-    quantized_converter._experimental_new_quantizer = enable_mlir_quantizer
+    quantized_converter.experimental_new_quantizer = enable_mlir_quantizer
     quantized_converter.optimizations = [lite.Optimize.DEFAULT]
     quantized_converter.target_spec.supported_types = [dtypes.float16]
     if include_int8:
@@ -1310,7 +1310,7 @@ class FromSessionTest(TestModels, parameterized.TestCase):
     # trigger post-training quantization
     converter.optimizations = [lite.Optimize.DEFAULT]
     converter.representative_dataset = calibration_gen
-    converter._experimental_new_quantizer = True
+    converter.experimental_new_quantizer = True
     quantized_tflite_model = converter.convert()
     self.assertIsNotNone(quantized_tflite_model)
     self.assertLess(len(quantized_tflite_model), len(float_tflite_model))

tensorflow/lite/python/lite_v2_test.py

@@ -178,7 +178,7 @@ class FromConcreteFunctionTest(lite_v2_test_util.ModelTest):
     quantized_converter = lite.TFLiteConverterV2.from_concrete_functions([func])
     quantized_converter.optimizations = [lite.Optimize.DEFAULT]
     quantized_converter.representative_dataset = calibration_gen
-    quantized_converter._experimental_new_quantizer = mlir_quantizer
+    quantized_converter.experimental_new_quantizer = mlir_quantizer
     quantized_tflite_model = quantized_converter.convert()
     self.assertIsNotNone(quantized_tflite_model)
 
@@ -418,11 +418,11 @@ class FromConcreteFunctionTest(lite_v2_test_util.ModelTest):
     quantized_converter.representative_dataset = calibration_gen
 
     # default quantizer
-    quantized_converter._experimental_new_quantizer = False
+    quantized_converter.experimental_new_quantizer = False
     old_tflite = quantized_converter.convert()
 
     # new quantizer
-    quantized_converter._experimental_new_quantizer = True
+    quantized_converter.experimental_new_quantizer = True
     new_tflite = quantized_converter.convert()
 
     for _ in range(5):
@@ -648,7 +648,7 @@ class FromConcreteFunctionTest(lite_v2_test_util.ModelTest):
 
     quantized_converter.inference_input_type = inference_input_output_type
     quantized_converter.inference_output_type = inference_input_output_type
-    quantized_converter._experimental_new_quantizer = enable_mlir_quantizer
+    quantized_converter.experimental_new_quantizer = enable_mlir_quantizer
     quantized_tflite_model = quantized_converter.convert()
     self.assertIsNotNone(quantized_tflite_model)
 
@@ -681,7 +681,7 @@ class FromConcreteFunctionTest(lite_v2_test_util.ModelTest):
 
     # Create a TFLite model with new quantizer.
     quantized_converter.optimizations = [lite.Optimize.DEFAULT]
-    quantized_converter._experimental_new_quantizer = True
+    quantized_converter.experimental_new_quantizer = True
     production_tflite = quantized_converter.convert()
     # Create a TFLite model with new quantizer and numeric verify ops.
     quantized_converter._experimental_calibrate_only = True

tensorflow/lite/testing/model_coverage/model_coverage_lib.py

@@ -148,7 +148,7 @@ def _convert(converter, **kwargs):
     converter.target_spec.supported_ops = [_lite.OpsSet.TFLITE_BUILTINS_INT8]
     converter.representative_dataset = _get_calib_data_func(input_size)
     # Note that the full integer quantization is by the mlir quantizer
-    converter._experimental_new_quantizer = True  # pylint: disable=protected-access
+    converter.experimental_new_quantizer = True
   if kwargs.get("post_training_quantize_16x8", False):
     input_size = kwargs.get("model_input_size")
     converter.optimizations = [_lite.Optimize.DEFAULT]

tensorflow/lite/testing/toco_convert.py

@@ -115,7 +115,7 @@ def toco_convert(options, graph_def, input_tensors, output_tensors, **kwargs):
           graphdef_file.name, input_arrays, output_tensors, input_shapes)
 
       converter.experimental_new_converter = options.use_experimental_converter
-      converter._experimental_new_quantizer = options.mlir_quantizer  # pylint: disable=protected-access
+      converter.experimental_new_quantizer = options.mlir_quantizer
       converter.optimizations = [tf.lite.Optimize.DEFAULT]
 
       if fully_quantize: