chore: import upstream snapshot with attribution

tensorflow/lite/tools/optimize/python/BUILD

@@ -0,0 +1,77 @@
+load("@xla//third_party/rules_python/python:py_binary.bzl", "py_binary")
+load("@xla//third_party/rules_python/python:py_library.bzl", "py_library")
+load("//tensorflow:tensorflow.bzl", "py_test")
+load("//tensorflow:tensorflow.default.bzl", "pybind_extension")
+
+package(
+    # copybara:uncomment default_applicable_licenses = ["//tensorflow:LICENSE"],
+    default_visibility = [
+        "//visibility:public",
+    ],
+    licenses = ["notice"],
+)
+
+py_binary(
+    name = "modify_model_interface",
+    srcs = ["modify_model_interface.py"],
+    strict_deps = True,
+    deps = [
+        ":modify_model_interface_constants",
+        ":modify_model_interface_lib",
+        "@absl_py//absl:app",
+        "@absl_py//absl/flags",
+    ],
+)
+
+py_library(
+    name = "modify_model_interface_lib",
+    srcs = ["modify_model_interface_lib.py"],
+    strict_deps = True,
+    deps = [
+        ":_pywrap_modify_model_interface",
+        ":modify_model_interface_constants",
+        "//tensorflow/lite/python:schema_py",
+    ],
+)
+
+# Use --config=disable_tf_lite_py when running this test under github.
+py_test(
+    name = "modify_model_interface_lib_test",
+    srcs = ["modify_model_interface_lib_test.py"],
+    strict_deps = True,
+    deps = [
+        ":modify_model_interface_lib",
+        #internal proto upb dep
+        "//third_party/py/numpy",
+        "//tensorflow:tensorflow_py",
+        "//tensorflow/lite/python:lite",
+        "//tensorflow/python/framework:test_lib",
+        "//tensorflow/python/platform:client_testlib",
+    ],
+)
+
+py_library(
+    name = "modify_model_interface_constants",
+    srcs = ["modify_model_interface_constants.py"],
+    strict_deps = True,
+    deps = ["//tensorflow/python/framework:dtypes"],
+)
+
+pybind_extension(
+    name = "_pywrap_modify_model_interface",
+    srcs = ["modify_model_interface.cc"],
+    common_lib_packages = [
+        "litert/python",
+        "tensorflow/lite/python",
+    ],
+    enable_stub_generation = True,
+    pytype_srcs = [
+        "_pywrap_modify_model_interface.pyi",
+    ],
+    wrap_py_init = True,
+    deps = [
+        "//tensorflow/lite/schema:schema_fbs",
+        "//tensorflow/lite/tools/optimize:modify_model_interface",
+        "@pybind11",
+    ],
+)

tensorflow/lite/tools/optimize/python/_pywrap_modify_model_interface.pyi

@@ -0,0 +1,16 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+def modify_model_interface(arg0: str, arg1: str, arg2: int, arg3: int) -> int: ...

tensorflow/lite/tools/optimize/python/modify_model_interface.cc

@@ -0,0 +1,40 @@
+/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// Python wrapper to modify model interface.
+
+#include "tensorflow/lite/tools/optimize/modify_model_interface.h"
+
+#include <string>
+
+#include "pybind11/pybind11.h"  // from @pybind11
+#include "tensorflow/lite/schema/schema_generated.h"
+
+namespace pybind11 {
+
+PYBIND11_MODULE(_pywrap_modify_model_interface, m) {
+  // An anonymous function that invokes the C++ function
+  // after applying transformations to the python function arguments
+  m.def("modify_model_interface",
+        [](const std::string& input_file, const std::string& output_file,
+           const int input_type, const int output_type) -> int {
+          return tflite::optimize::ModifyModelInterface(
+              input_file, output_file,
+              static_cast<tflite::TensorType>(input_type),
+              static_cast<tflite::TensorType>(output_type));
+        });
+}
+
+}  // namespace pybind11

tensorflow/lite/tools/optimize/python/modify_model_interface.py

@@ -0,0 +1,54 @@
+# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+r"""Modify a quantized model's interface from float to integer."""
+
+from absl import app
+from absl import flags
+
+from tensorflow.lite.tools.optimize.python import modify_model_interface_constants as mmi_constants
+from tensorflow.lite.tools.optimize.python import modify_model_interface_lib as mmi_lib
+
+FLAGS = flags.FLAGS
+
+flags.DEFINE_string('input_tflite_file', None,
+                    'Full path name to the input TFLite file.')
+flags.DEFINE_string('output_tflite_file', None,
+                    'Full path name to the output TFLite file.')
+flags.DEFINE_enum('input_type', mmi_constants.DEFAULT_STR_TYPE,
+                  mmi_constants.STR_TYPES,
+                  'Modified input integer interface type.')
+flags.DEFINE_enum('output_type', mmi_constants.DEFAULT_STR_TYPE,
+                  mmi_constants.STR_TYPES,
+                  'Modified output integer interface type.')
+
+flags.mark_flag_as_required('input_tflite_file')
+flags.mark_flag_as_required('output_tflite_file')
+
+
+def main(_):
+  input_type = mmi_constants.STR_TO_TFLITE_TYPES[FLAGS.input_type]
+  output_type = mmi_constants.STR_TO_TFLITE_TYPES[FLAGS.output_type]
+
+  mmi_lib.modify_model_interface(FLAGS.input_tflite_file,
+                                 FLAGS.output_tflite_file, input_type,
+                                 output_type)
+
+  print('Successfully modified the model input type from FLOAT to '
+        '{input_type} and output type from FLOAT to {output_type}.'.format(
+            input_type=FLAGS.input_type, output_type=FLAGS.output_type))
+
+
+if __name__ == '__main__':
+  app.run(main)

tensorflow/lite/tools/optimize/python/modify_model_interface_constants.py

@@ -0,0 +1,30 @@
+# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Constants for modify_model_interface."""
+
+from tensorflow.python.framework import dtypes
+
+STR_TO_TFLITE_TYPES = {
+    'INT8': dtypes.int8,
+    'UINT8': dtypes.uint8,
+    'INT16': dtypes.int16,
+}
+TFLITE_TO_STR_TYPES = {v: k for k, v in STR_TO_TFLITE_TYPES.items()}
+
+STR_TYPES = STR_TO_TFLITE_TYPES.keys()
+TFLITE_TYPES = STR_TO_TFLITE_TYPES.values()
+
+DEFAULT_STR_TYPE = 'INT8'
+DEFAULT_TFLITE_TYPE = STR_TO_TFLITE_TYPES[DEFAULT_STR_TYPE]

tensorflow/lite/tools/optimize/python/modify_model_interface_lib.py

@@ -0,0 +1,74 @@
+# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Library to modify a quantized model's interface from float to integer."""
+
+from tensorflow.lite.python import schema_py_generated as schema_fb
+from tensorflow.lite.tools.optimize.python import _pywrap_modify_model_interface
+from tensorflow.lite.tools.optimize.python import modify_model_interface_constants as mmi_constants
+
+
+def _parse_type_to_int(dtype, flag):
+  """Converts a tflite type to it's integer representation.
+
+  Args:
+    dtype: tf.DType representing the inference type.
+    flag: str representing the flag name.
+
+  Returns:
+     integer, a tflite TensorType enum value.
+
+  Raises:
+    ValueError: Unsupported tflite type.
+  """
+  # Validate if dtype is supported in tflite and is a valid interface type.
+  if dtype not in mmi_constants.TFLITE_TYPES:
+    raise ValueError(
+        "Unsupported value '{0}' for {1}. Only {2} are supported.".format(
+            dtype, flag, mmi_constants.TFLITE_TYPES))
+
+  dtype_str = mmi_constants.TFLITE_TO_STR_TYPES[dtype]
+  dtype_int = schema_fb.TensorType.__dict__[dtype_str]
+
+  return dtype_int
+
+
+def modify_model_interface(input_file, output_file, input_type, output_type):
+  """Modify a quantized model's interface (input/output) from float to integer.
+
+  Args:
+    input_file: Full path name to the input tflite file.
+    output_file: Full path name to the output tflite file.
+    input_type: Final input interface type.
+    output_type: Final output interface type.
+
+  Raises:
+    RuntimeError: If the modification of the model interface was unsuccessful.
+    ValueError: If the input_type or output_type is unsupported.
+
+  """
+  # Map the interface types to integer values
+  input_type_int = _parse_type_to_int(input_type, 'input_type')
+  output_type_int = _parse_type_to_int(output_type, 'output_type')
+
+  # Invoke the function to modify the model interface
+  status = _pywrap_modify_model_interface.modify_model_interface(
+      input_file, output_file, input_type_int, output_type_int)
+
+  # Throw an exception if the return status is an error.
+  if status != 0:
+    raise RuntimeError(
+        'Error occurred when trying to modify the model input type from float '
+        'to {input_type} and output type from float to {output_type}.'.format(
+            input_type=input_type, output_type=output_type))

tensorflow/lite/tools/optimize/python/modify_model_interface_lib_test.py

@@ -0,0 +1,163 @@
+# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for modify_model_interface_lib.py."""
+
+import os
+import numpy as np
+import tensorflow as tf
+
+from tensorflow.lite.python import lite
+from tensorflow.lite.tools.optimize.python import modify_model_interface_lib
+from tensorflow.python.framework import test_util
+from tensorflow.python.platform import test
+
+
+def build_tflite_model_with_full_integer_quantization(
+    supported_ops=lite.OpsSet.TFLITE_BUILTINS_INT8):
+  # Define TF model
+  input_size = 3
+  model = tf.keras.Sequential([
+      tf.keras.layers.InputLayer(input_shape=(input_size,), dtype=tf.float32),
+      tf.keras.layers.Dense(units=5, activation=tf.nn.relu),
+      tf.keras.layers.Dense(units=2, activation=tf.nn.softmax)
+  ])
+
+  # Convert TF Model to a Quantized TFLite Model
+  converter = lite.TFLiteConverterV2.from_keras_model(model)
+  converter.optimizations = [lite.Optimize.DEFAULT]
+
+  def representative_dataset_gen():
+    for i in range(10):
+      yield [np.array([i] * input_size, dtype=np.float32)]
+
+  converter.representative_dataset = representative_dataset_gen
+  converter.target_spec.supported_ops = [supported_ops]
+  tflite_model = converter.convert()
+
+  return tflite_model
+
+
+class ModifyModelInterfaceTest(test_util.TensorFlowTestCase):
+
+  def testInt8Interface(self):
+    # 1. SETUP
+    # Define the temporary directory and files
+    temp_dir = self.get_temp_dir()
+    initial_file = os.path.join(temp_dir, 'initial_model.tflite')
+    final_file = os.path.join(temp_dir, 'final_model.tflite')
+    # Define initial model
+    initial_model = build_tflite_model_with_full_integer_quantization()
+    with open(initial_file, 'wb') as model_file:
+      model_file.write(initial_model)
+
+    # 2. INVOKE
+    # Invoke the modify_model_interface function
+    modify_model_interface_lib.modify_model_interface(initial_file, final_file,
+                                                      tf.int8, tf.int8)
+
+    # 3. VALIDATE
+    # Load TFLite model and allocate tensors.
+    initial_interpreter = lite.Interpreter(model_path=initial_file)
+    initial_interpreter.allocate_tensors()
+    final_interpreter = lite.Interpreter(model_path=final_file)
+    final_interpreter.allocate_tensors()
+
+    # Get input and output types.
+    initial_input_dtype = initial_interpreter.get_input_details()[0]['dtype']
+    initial_output_dtype = initial_interpreter.get_output_details()[0]['dtype']
+    final_input_dtype = final_interpreter.get_input_details()[0]['dtype']
+    final_output_dtype = final_interpreter.get_output_details()[0]['dtype']
+
+    # Validate the model interfaces
+    self.assertEqual(initial_input_dtype, np.float32)
+    self.assertEqual(initial_output_dtype, np.float32)
+    self.assertEqual(final_input_dtype, np.int8)
+    self.assertEqual(final_output_dtype, np.int8)
+
+  def testInt16Interface(self):
+    # 1. SETUP
+    # Define the temporary directory and files
+    temp_dir = self.get_temp_dir()
+    initial_file = os.path.join(temp_dir, 'initial_model.tflite')
+    final_file = os.path.join(temp_dir, 'final_model.tflite')
+    # Define initial model
+    initial_model = build_tflite_model_with_full_integer_quantization(
+        supported_ops=lite.OpsSet
+        .EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8)
+    with open(initial_file, 'wb') as model_file:
+      model_file.write(initial_model)
+
+    # 2. INVOKE
+    # Invoke the modify_model_interface function
+    modify_model_interface_lib.modify_model_interface(initial_file, final_file,
+                                                      tf.int16, tf.int16)
+
+    # 3. VALIDATE
+    # Load TFLite model and allocate tensors.
+    initial_interpreter = lite.Interpreter(model_path=initial_file)
+    initial_interpreter.allocate_tensors()
+    final_interpreter = lite.Interpreter(model_path=final_file)
+    final_interpreter.allocate_tensors()
+
+    # Get input and output types.
+    initial_input_dtype = initial_interpreter.get_input_details()[0]['dtype']
+    initial_output_dtype = initial_interpreter.get_output_details()[0]['dtype']
+    final_input_dtype = final_interpreter.get_input_details()[0]['dtype']
+    final_output_dtype = final_interpreter.get_output_details()[0]['dtype']
+
+    # Validate the model interfaces
+    self.assertEqual(initial_input_dtype, np.float32)
+    self.assertEqual(initial_output_dtype, np.float32)
+    self.assertEqual(final_input_dtype, np.int16)
+    self.assertEqual(final_output_dtype, np.int16)
+
+  def testUInt8Interface(self):
+    # 1. SETUP
+    # Define the temporary directory and files
+    temp_dir = self.get_temp_dir()
+    initial_file = os.path.join(temp_dir, 'initial_model.tflite')
+    final_file = os.path.join(temp_dir, 'final_model.tflite')
+    # Define initial model
+    initial_model = build_tflite_model_with_full_integer_quantization()
+    with open(initial_file, 'wb') as model_file:
+      model_file.write(initial_model)
+
+    # 2. INVOKE
+    # Invoke the modify_model_interface function
+    modify_model_interface_lib.modify_model_interface(initial_file, final_file,
+                                                      tf.uint8, tf.uint8)
+
+    # 3. VALIDATE
+    # Load TFLite model and allocate tensors.
+    initial_interpreter = lite.Interpreter(model_path=initial_file)
+    initial_interpreter.allocate_tensors()
+    final_interpreter = lite.Interpreter(model_path=final_file)
+    final_interpreter.allocate_tensors()
+
+    # Get input and output types.
+    initial_input_dtype = initial_interpreter.get_input_details()[0]['dtype']
+    initial_output_dtype = initial_interpreter.get_output_details()[0]['dtype']
+    final_input_dtype = final_interpreter.get_input_details()[0]['dtype']
+    final_output_dtype = final_interpreter.get_output_details()[0]['dtype']
+
+    # Validate the model interfaces
+    self.assertEqual(initial_input_dtype, np.float32)
+    self.assertEqual(initial_output_dtype, np.float32)
+    self.assertEqual(final_input_dtype, np.uint8)
+    self.assertEqual(final_output_dtype, np.uint8)
+
+
+if __name__ == '__main__':
+  test.main()