Automated g4 rollback of changelist 304482662.

PiperOrigin-RevId: 304649602
Change-Id: Iede54c48e16f5575d780a9d79d13b989aebabd9c

tensorflow/lite/python/convert.py

@@ -108,20 +108,6 @@ class ConverterError(Exception):
   pass
 
 
-def mlir_quantize(input_data_str):
-  """Quantize `input_data_str` with calibration results.
-
-  Args:
-    input_data_str: Input data in serialized form (e.g. a TFLITE model with
-                    calibration results).
-
-  Returns:
-    Quantized model in serialized form (e.g. a TFLITE model) with floating-point
-    inputs and outputs.
-  """
-  return wrap_toco.wrapped_experimental_mlir_quantize(input_data_str)
-
-
 def toco_convert_protos(model_flags_str,
                         toco_flags_str,
                         input_data_str,

tensorflow/lite/python/lite.py

@@ -37,7 +37,6 @@ from tensorflow.lite.experimental.tensorboard.ops_util import get_potentially_su
 from tensorflow.lite.python import lite_constants as constants
 from tensorflow.lite.python.convert import build_toco_convert_protos  # pylint: disable=unused-import
 from tensorflow.lite.python.convert import ConverterError  # pylint: disable=unused-import
-from tensorflow.lite.python.convert import mlir_quantize as _mlir_quantize
 from tensorflow.lite.python.convert import OpsSet
 from tensorflow.lite.python.convert import toco_convert  # pylint: disable=unused-import
 from tensorflow.lite.python.convert import toco_convert_graph_def as _toco_convert_graph_def
@@ -311,19 +310,12 @@ class TFLiteConverterBase(object):
 
   def _calibrate_quantize_model(self, result, inference_input_type,
                                 inference_output_type, allow_float):
-    """Calibrate and quantize the model."""
     if not isinstance(self.representative_dataset, RepresentativeDataset):
       self.representative_dataset = RepresentativeDataset(
           self.representative_dataset)
     calibrate_quantize = _calibrator.Calibrator(result)
-    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:
-      return _mlir_quantize(calibrated)
+      return calibrate_quantize.calibrate(self.representative_dataset.input_gen)
     else:
       return calibrate_quantize.calibrate_and_quantize(
           self.representative_dataset.input_gen, inference_input_type,

tensorflow/lite/python/lite_v2_test.py

@@ -137,10 +137,7 @@ class FromConcreteFunctionTest(lite_v2_test_util.ModelTest):
     to_save = root.f.get_concrete_function()
     return (to_save, calibration_gen)
 
-  @parameterized.named_parameters(
-      ('EnableMlirQuantizer', True),  # enable mlir quantizer
-      ('DisableMlirQuantizer', False))  # disable mlir quantizer
-  def testPostTrainingCalibrateAndQuantize(self, mlir_quantizer):
+  def testPostTrainingCalibrateAndQuantize(self):
     func, calibration_gen = self._getCalibrationQuantizeModel()
 
     # Convert float model.
@@ -152,7 +149,6 @@ 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_tflite = quantized_converter.convert()
     self.assertTrue(quantized_tflite)
 
@@ -169,10 +165,7 @@ class FromConcreteFunctionTest(lite_v2_test_util.ModelTest):
     # Ensure that the quantized weights tflite model is smaller.
     self.assertLess(len(quantized_tflite), len(float_tflite))
 
-  @parameterized.named_parameters(
-      ('EnableMlirQuantizer', True),  # enable mlir quantizer
-      ('DisableMlirQuantizer', False))  # disable mlir quantizer
-  def testCalibrateAndQuantizeBuiltinInt8(self, mlir_quantizer):
+  def testCalibrateAndQuantizeBuiltinInt8(self):
     func, calibration_gen = self._getCalibrationQuantizeModel()
 
     # Convert float model.
@@ -187,7 +180,6 @@ class FromConcreteFunctionTest(lite_v2_test_util.ModelTest):
         lite.OpsSet.TFLITE_BUILTINS_INT8
     ]
     quantized_converter.representative_dataset = calibration_gen
-    quantized_converter._experimental_new_quantizer = mlir_quantizer
     quantized_tflite = quantized_converter.convert()
     self.assertTrue(quantized_tflite)
 

tensorflow/lite/python/wrap_toco.py

@@ -41,8 +41,3 @@ def wrapped_toco_convert(model_flags_str, toco_flags_str, input_data_str,
 def wrapped_get_potentially_supported_ops():
   """Wraps TocoGetPotentiallySupportedOps with lazy loader."""
   return _pywrap_toco_api.TocoGetPotentiallySupportedOps()
-
-
-def wrapped_experimental_mlir_quantize(input_data_str):
-  """Wraps experimental mlir quantize model."""
-  return _pywrap_toco_api.ExperimentalMlirQuantizeModel(input_data_str)

tensorflow/lite/toco/python/BUILD

@@ -35,12 +35,9 @@ cc_library(
     ],
     deps = [
         "@com_google_protobuf//:protobuf_headers",
+        "//third_party/python_runtime:headers",
         "//tensorflow/core:lib",
-        "//tensorflow/lite/c:common",
-        "//tensorflow/lite/core/api",
         "//tensorflow/lite/python/interpreter_wrapper:python_utils",
-        "//tensorflow/lite/python/interpreter_wrapper:python_error_reporter",
-        "//tensorflow/lite/schema:schema_fbs",
         "//tensorflow/lite/toco/logging:conversion_log_util",
         "//tensorflow/lite/toco:model_flags_proto_cc",
         "//tensorflow/lite/toco:toco_convert",
@@ -50,8 +47,8 @@ cc_library(
         "//tensorflow/lite/toco:toco_port",
         "//tensorflow/lite/toco:toco_tooling",
         "//tensorflow/lite/toco:tooling_util",
+        "//tensorflow/core:protos_all_cc",
         "//tensorflow/compiler/mlir/lite/python:graphdef_to_tfl_flatbuffer",
-        "//tensorflow/compiler/mlir/lite/quantization/lite:quantize_model",
     ] + select({
         # This is required when running `tflite_convert` from `bazel`.
         # It requires to link with TensorFlow Ops to get the op definitions.

tensorflow/lite/toco/python/toco_python_api.cc

@@ -21,13 +21,8 @@ limitations under the License.
 
 #include "google/protobuf/text_format.h"
 #include "tensorflow/compiler/mlir/lite/python/graphdef_to_tfl_flatbuffer.h"
-#include "tensorflow/compiler/mlir/lite/quantization/lite/quantize_model.h"
 #include "tensorflow/core/platform/logging.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/core/api/error_reporter.h"
-#include "tensorflow/lite/python/interpreter_wrapper/python_error_reporter.h"
 #include "tensorflow/lite/python/interpreter_wrapper/python_utils.h"
-#include "tensorflow/lite/schema/schema_generated.h"
 #include "tensorflow/lite/toco/import_tensorflow.h"
 #include "tensorflow/lite/toco/logging/conversion_log_util.h"
 #include "tensorflow/lite/toco/logging/toco_conversion_log.pb.h"
@@ -214,39 +209,4 @@ PyObject* TocoGetPotentiallySupportedOps() {
   return list;
 }
 
-PyObject* MlirQuantizeModel(PyObject* data, bool fully_quantize) {
-  using tflite::interpreter_wrapper::PythonErrorReporter;
-  char* buf = nullptr;
-  Py_ssize_t length;
-  std::unique_ptr<PythonErrorReporter> error_reporter(new PythonErrorReporter);
-
-  if (tflite::python_utils::ConvertFromPyString(data, &buf, &length) == -1) {
-    PyErr_Format(PyExc_ValueError, "Failed to convert input PyObject");
-    return nullptr;
-  }
-  std::unique_ptr<tflite::FlatBufferModel> model =
-      tflite::FlatBufferModel::BuildFromBuffer(buf, length,
-                                               error_reporter.get());
-  if (!model) {
-    PyErr_Format(PyExc_ValueError, "Invalid model");
-    return nullptr;
-  }
-  auto tflite_model = absl::make_unique<tflite::ModelT>();
-  model->GetModel()->UnPackTo(tflite_model.get(), nullptr);
-
-  flatbuffers::FlatBufferBuilder builder;
-  auto status = mlir::lite::QuantizeModel(
-      *tflite_model, tflite::TensorType::TensorType_FLOAT32,
-      tflite::TensorType::TensorType_FLOAT32, {}, fully_quantize, &builder,
-      error_reporter.get());
-
-  if (status != kTfLiteOk) {
-    error_reporter->exception();
-    return nullptr;
-  }
-  return tflite::python_utils::ConvertToPyString(
-      reinterpret_cast<const char*>(builder.GetCurrentBufferPointer()),
-      builder.GetSize());
-}
-
 }  // namespace toco

tensorflow/lite/toco/python/toco_python_api.h

@@ -16,7 +16,6 @@ limitations under the License.
 #define TENSORFLOW_LITE_TOCO_PYTHON_TOCO_PYTHON_API_H_
 
 #include <Python.h>
-
 #include <string>
 
 namespace toco {
@@ -40,10 +39,6 @@ PyObject* TocoConvert(PyObject* model_flags_proto_txt_raw,
 // Returns a list of names of all ops potentially supported by tflite.
 PyObject* TocoGetPotentiallySupportedOps();
 
-// Quantize the model with calibration data. Throw errors if `fully_quantize`
-// is specified by the calibration data are not sufficient to quantize the
-// model.
-PyObject* MlirQuantizeModel(PyObject* data, bool fully_quantize);
 }  // namespace toco
 
 #endif  // TENSORFLOW_LITE_TOCO_PYTHON_TOCO_PYTHON_API_H_

tensorflow/python/lite/toco_python_api_wrapper.cc

@@ -54,14 +54,4 @@ PYBIND11_MODULE(_pywrap_toco_api, m) {
       R"pbdoc(
       Returns a list of names of all ops potentially supported by tflite.
     )pbdoc");
-  m.def(
-      "ExperimentalMlirQuantizeModel",
-      [](py::object input_contents_txt_raw, bool fully_quantize) {
-        return tensorflow::pyo_or_throw(toco::MlirQuantizeModel(
-            input_contents_txt_raw.ptr(), fully_quantize));
-      },
-      py::arg("input_contents_txt_raw"), py::arg("fully_quantize") = true,
-      R"pbdoc(
-      Returns a quantized model.
-    )pbdoc");
 }