python: rename package tflm_runtime to runtime (#2030)

Rename the Python package `tflm_runtime` to simply `runtime` in preparation for adding it under the new namespace package `tflite_micro`. Its full name will then be `tflite_micro.runtime`.

We have kept the `tflm_runtime` target as an alias in order to stage this change. More details in http://b/286456378

BUG=part of #1484

tensorflow/lite/micro/examples/hello_world/BUILD

@@ -54,7 +54,7 @@ py_binary(
         "@absl_py//absl/logging",
         requirement("numpy"),
         requirement("tensorflow-cpu"),
-        "//tensorflow/lite/micro/python/interpreter/src:tflm_runtime",
+        "//tensorflow/lite/micro/python/interpreter/src:runtime",
     ],
 )
 

tensorflow/lite/micro/examples/hello_world/evaluate.py

@@ -19,7 +19,7 @@ from absl import flags
 import numpy as np
 import matplotlib.pyplot as plt
 from tensorflow.python.platform import resource_loader
-from tflite_micro.tensorflow.lite.micro.python.interpreter.src import tflm_runtime
+from tflite_micro.tensorflow.lite.micro.python.interpreter.src import runtime
 
 _USE_TFLITE_INTERPRETER = flags.DEFINE_bool(
     'use_tflite',
@@ -73,7 +73,7 @@ def generate_random_float_input(sample_count=1000):
 # returns the prediction of the interpreter.
 def get_tflm_prediction(model_path, x_values):
   # Create the tflm interpreter
-  tflm_interpreter = tflm_runtime.Interpreter.from_file(model_path)
+  tflm_interpreter = runtime.Interpreter.from_file(model_path)
 
   input_shape = np.array(tflm_interpreter.get_input_details(0).get('shape'))
 

tensorflow/lite/micro/examples/hello_world/evaluate_test.py

@@ -18,8 +18,7 @@ import numpy as np
 from tensorflow.python.framework import test_util
 from tensorflow.python.platform import resource_loader
 from tensorflow.python.platform import test
-from tflite_micro.tensorflow.lite.micro.python.interpreter.src import \
-  tflm_runtime
+from tflite_micro.tensorflow.lite.micro.python.interpreter.src import runtime
 from tflite_micro.tensorflow.lite.micro.examples.hello_world import evaluate
 
 PREFIX_PATH = resource_loader.get_path_to_datafile('')
@@ -29,7 +28,7 @@ class HelloWorldFloatModelTest(test_util.TensorFlowTestCase):
   model_path = os.path.join(PREFIX_PATH, 'models/hello_world_float.tflite')
   input_shape = (1, 1)
   output_shape = (1, 1)
-  tflm_interpreter = tflm_runtime.Interpreter.from_file(model_path)
+  tflm_interpreter = runtime.Interpreter.from_file(model_path)
 
   def test_compare_with_tflite(self):
     x_values = evaluate.generate_random_float_input()
@@ -47,7 +46,7 @@ class HelloWorldQuantModelTest(test_util.TensorFlowTestCase):
   model_path = os.path.join(PREFIX_PATH, 'models/hello_world_int8.tflite')
   input_shape = (1, 1)
   output_shape = (1, 1)
-  tflm_interpreter = tflm_runtime.Interpreter.from_file(model_path)
+  tflm_interpreter = runtime.Interpreter.from_file(model_path)
 
   def test_compare_with_tflite(self):
     x_values = evaluate.generate_random_int8_input()

tensorflow/lite/micro/examples/hello_world/quantization/BUILD

@@ -12,6 +12,6 @@ py_binary(
         "@absl_py//absl/logging",
         requirement("numpy"),
         requirement("tensorflow-cpu"),
-        "//tensorflow/lite/micro/python/interpreter/src:tflm_runtime",
+        "//tensorflow/lite/micro/python/interpreter/src:runtime",
     ],
 )

tensorflow/lite/micro/examples/mnist_lstm/BUILD

@@ -15,7 +15,7 @@ py_binary(
     srcs = ["evaluate.py"],
     srcs_version = "PY3",
     deps = [
-        "//tensorflow/lite/micro/python/interpreter/src:tflm_runtime",
+        "//tensorflow/lite/micro/python/interpreter/src:runtime",
         "@absl_py//absl:app",
     ],
 )

tensorflow/lite/micro/examples/mnist_lstm/evaluate.py

@@ -28,7 +28,7 @@ from absl import logging
 import numpy as np
 from PIL import Image
 
-from tflite_micro.tensorflow.lite.micro.python.interpreter.src import tflm_runtime
+from tflite_micro.tensorflow.lite.micro.python.interpreter.src import runtime
 
 FLAGS = flags.FLAGS
 
@@ -113,7 +113,7 @@ def predict(interpreter, data):
   """Use TFLM interpreter to predict a MNIST image
 
   Args:
-      interpreter (tflm_runtime.Interpreter): the TFLM python interpreter
+      interpreter (runtime.Interpreter): the TFLM python interpreter
       data (np.array): data to be predicted
 
   Returns:
@@ -141,7 +141,7 @@ def predict_image(interpreter, image_path):
   """Use TFLM interpreter to predict a MNIST image
 
   Args:
-      interpreter (tflm_runtime.Interpreter): the TFLM python interpreter
+      interpreter (runtime.Interpreter): the TFLM python interpreter
       image_path (str): path for the image that need to be tested
 
   Returns:
@@ -158,7 +158,7 @@ def main(_):
   if not os.path.exists(FLAGS.img_path):
     raise ValueError("Image file does not exist. Please check the image path.")
 
-  tflm_interpreter = tflm_runtime.Interpreter.from_file(FLAGS.model_path)
+  tflm_interpreter = runtime.Interpreter.from_file(FLAGS.model_path)
   category_probabilities = predict_image(tflm_interpreter, FLAGS.img_path)
   predicted_category = np.argmax(category_probabilities)
   logging.info("Model predicts the image as %i with probability %.2f",

tensorflow/lite/micro/examples/mnist_lstm/evaluate_test.py

@@ -20,7 +20,7 @@ import tensorflow as tf
 from tensorflow.python.framework import test_util
 from tensorflow.python.platform import resource_loader
 from tensorflow.python.platform import test
-from tflite_micro.tensorflow.lite.micro.python.interpreter.src import tflm_runtime
+from tflite_micro.tensorflow.lite.micro.python.interpreter.src import runtime
 from tflite_micro.tensorflow.lite.micro.examples.mnist_lstm import evaluate
 from tflite_micro.tensorflow.lite.micro.tools import requantize_flatbuffer
 
@@ -33,7 +33,7 @@ class LSTMFloatModelTest(test_util.TensorFlowTestCase):
     self.model_path = os.path.join(PREFIX_PATH, "trained_lstm.tflite")
     self.input_shape = (1, 28, 28)
     self.output_shape = (1, 10)
-    self.tflm_interpreter = tflm_runtime.Interpreter.from_file(self.model_path)
+    self.tflm_interpreter = runtime.Interpreter.from_file(self.model_path)
     np.random.seed(42)  #Seed the random number generator
 
   def testInputErrHandling(self):
@@ -95,7 +95,7 @@ class LSTMInt8ModelTest(test_util.TensorFlowTestCase):
                                         "trained_lstm_int8.tflite")
     self.input_shape = (1, 28, 28)
     self.output_shape = (1, 10)
-    self.tflm_interpreter_quant = tflm_runtime.Interpreter.from_file(
+    self.tflm_interpreter_quant = runtime.Interpreter.from_file(
         self.int8_model_path)
     np.random.seed(42)  #Seed the random number generator
 
@@ -106,8 +106,7 @@ class LSTMInt8ModelTest(test_util.TensorFlowTestCase):
 
     # Create a float model for results comparison
     float_model_path = os.path.join(PREFIX_PATH, "trained_lstm.tflite")
-    tflm_interpreter_float = tflm_runtime.Interpreter.from_file(
-        float_model_path)
+    tflm_interpreter_float = runtime.Interpreter.from_file(float_model_path)
 
     num_test = 10
     for _ in range(num_test):
@@ -163,7 +162,7 @@ class LSTMInt16ModelTest(test_util.TensorFlowTestCase):
     self.int16_model = self.requantizer.model_bytearray()
     self.input_shape = (1, 28, 28)
     self.output_shape = (1, 10)
-    self.tflm_interpreter_quant = tflm_runtime.Interpreter.from_bytes(
+    self.tflm_interpreter_quant = runtime.Interpreter.from_bytes(
         self.int16_model)
     np.random.seed(42)  #Seed the random number generator
 
@@ -174,8 +173,7 @@ class LSTMInt16ModelTest(test_util.TensorFlowTestCase):
 
     # Create a float model for results comparison
     float_model_path = os.path.join(PREFIX_PATH, "trained_lstm.tflite")
-    tflm_interpreter_float = tflm_runtime.Interpreter.from_file(
-        float_model_path)
+    tflm_interpreter_float = runtime.Interpreter.from_file(float_model_path)
 
     num_test = 10
     for _ in range(num_test):

tensorflow/lite/micro/examples/recipes/BUILD

@@ -26,6 +26,8 @@ py_test(
     ],
     deps = [
         ":resource_variables_lib",
+        # TODO(b/286456378): update tflm_runtime to runtime when we are ready to
+        # remove the alias.
         "//tensorflow/lite/micro/python/interpreter/src:tflm_runtime",
     ],
 )

tensorflow/lite/micro/examples/recipes/resource_variables_test.py

@@ -17,7 +17,7 @@ import numpy as np
 from tensorflow.python.framework import test_util
 from tensorflow.python.platform import test
 from tflite_micro.tensorflow.lite.micro.examples.recipes import resource_variables_lib
-from tflite_micro.tensorflow.lite.micro.python.interpreter.src import tflm_runtime
+from tflite_micro.tensorflow.lite.micro.python.interpreter.src import runtime
 
 
 class ResourceVariablesTest(test_util.TensorFlowTestCase):
@@ -27,7 +27,7 @@ class ResourceVariablesTest(test_util.TensorFlowTestCase):
   # (variable value), to be accumulated by 5.0 each invoke.
   def test_resource_variables_model(self):
     model_keras = resource_variables_lib.get_model_from_keras()
-    tflm_interpreter = tflm_runtime.Interpreter.from_bytes(model_keras)
+    tflm_interpreter = runtime.Interpreter.from_bytes(model_keras)
 
     tflm_interpreter.set_input([[True]], 0)
     tflm_interpreter.set_input([np.full((100,), 15.0, dtype=np.float32)], 1)

tensorflow/lite/micro/python/interpreter/README.md

@@ -13,7 +13,7 @@ in near future by installing a PyPi package.
 #### Build
 
 The only package that needs to be included in the `BUILD` file is
-`//tensorflow/lite/micro/python/interpreter/src:tflm_runtime`. It contains all
+`//tensorflow/lite/micro/python/interpreter/src:runtime`. It contains all
 the correct dependencies to build the Python interpreter.
 
 ### PyPi
@@ -34,13 +34,13 @@ bytearray format or file format.
 
 ```
 # For the Bazel workflow
-from tflite_micro.tensorflow.lite.micro.python.interpreter.src import tflm_runtime
+from tflite_micro.tensorflow.lite.micro.python.interpreter.src import runtime
 
 
 # If model is a bytearray
-tflm_interpreter = tflm_runtime.Interpreter.from_bytes(model_data)
+tflm_interpreter = runtime.Interpreter.from_bytes(model_data)
 # If model is a file
-tflm_interpreter = tflm_runtime.Interpreter.from_file(model_filepath)
+tflm_interpreter = runtime.Interpreter.from_file(model_filepath)
 
 # Run inference on TFLM using an ndarray `data_x`
 tflm_interpreter.set_input(data_x, 0)
@@ -62,7 +62,7 @@ expose an evolving set of C++ APIs. The Bazel build leverages the
 [pybind11_bazel extension](https://github.com/pybind/pybind11_bazel).
 
 The most updated Python APIs can be found in
-`tensorflow/lite/micro/python/interpreter/src/tflm_runtime.py`.
+`tensorflow/lite/micro/python/interpreter/src/runtime.py`.
 
 ## Custom Ops
 
@@ -116,7 +116,7 @@ in the target that calls the Python interpreter with custom ops.
 For example,
 
 ```
-interpreter = tflm_runtime.Interpreter.from_file(
+interpreter = runtime.Interpreter.from_file(
     model_path=model_path,
     custom_op_registerers=['SomeCustomRegisterer'])
 ```
@@ -152,7 +152,7 @@ will print
 
 10016 bytes is the actual memory arena size.
 
-During instantiation via the class methods `tflm_runtime.Interpreter.from_file`
-or `tflm_runtime.Interpreter.from_bytes`, if `arena_size` is not explicitly
+During instantiation via the class methods `runtime.Interpreter.from_file`
+or `runtime.Interpreter.from_bytes`, if `arena_size` is not explicitly
 specified, the interpreter will default to a heuristic which is 10x the model
 size. This can be adjusted manually if desired.

tensorflow/lite/micro/python/interpreter/src/BUILD

@@ -63,10 +63,23 @@ pybind_extension(
     ],
 )
 
-py_library(
+# tflm_runtime is deprecated, please use runtime instead.
+# TODO(b/286456378): remove the alias once all usage is change to the runtime
+# target.
+alias(
     name = "tflm_runtime",
-    srcs = ["tflm_runtime.py"],
-    data = [":interpreter_wrapper_pybind.so"],
+    actual = ":runtime",
+    visibility = ["//visibility:public"],
+)
+
+py_library(
+    name = "runtime",
+    srcs = [
+        "runtime.py",
+    ],
+    data = [
+        ":interpreter_wrapper_pybind.so",
+    ],
     srcs_version = "PY3",
     visibility = ["//visibility:public"],
     deps = [

tensorflow/lite/micro/python/interpreter/tests/BUILD

@@ -15,7 +15,7 @@ py_test(
     deps = [
         requirement("numpy"),
         requirement("tensorflow-cpu"),
-        "//tensorflow/lite/micro/python/interpreter/src:tflm_runtime",
+        "//tensorflow/lite/micro/python/interpreter/src:runtime",
         "//tensorflow/lite/micro/testing:generate_test_models_lib",
     ],
 )

tensorflow/lite/micro/python/interpreter/tests/interpreter_test.py

@@ -30,7 +30,7 @@ import tensorflow as tf
 from tensorflow.python.framework import test_util
 from tensorflow.python.platform import test
 from tflite_micro.tensorflow.lite.micro.testing import generate_test_models
-from tflite_micro.tensorflow.lite.micro.python.interpreter.src import tflm_runtime
+from tflite_micro.tensorflow.lite.micro.python.interpreter.src import runtime
 
 
 class ConvModelTests(test_util.TensorFlowTestCase):
@@ -41,11 +41,11 @@ class ConvModelTests(test_util.TensorFlowTestCase):
   def testInitErrorHandling(self):
     with self.assertRaisesWithPredicateMatch(ValueError,
                                              "Invalid model file path"):
-      tflm_runtime.Interpreter.from_file("wrong.tflite")
+      runtime.Interpreter.from_file("wrong.tflite")
 
   def testInput(self):
     model_data = generate_test_models.generate_conv_model(False)
-    tflm_interpreter = tflm_runtime.Interpreter.from_bytes(model_data)
+    tflm_interpreter = runtime.Interpreter.from_bytes(model_data)
 
     data_x = np.random.randint(-127, 127, self.input_shape, dtype=np.int8)
     tflm_interpreter.set_input(data_x, 0)
@@ -68,7 +68,7 @@ class ConvModelTests(test_util.TensorFlowTestCase):
 
   def testInputErrorHandling(self):
     model_data = generate_test_models.generate_conv_model(True, self.filename)
-    tflm_interpreter = tflm_runtime.Interpreter.from_bytes(model_data)
+    tflm_interpreter = runtime.Interpreter.from_bytes(model_data)
 
     data_x = np.random.randint(-127, 127, self.input_shape, dtype=np.int8)
     # Try to access out of bound data
@@ -96,7 +96,7 @@ class ConvModelTests(test_util.TensorFlowTestCase):
 
   def testOutput(self):
     model_data = generate_test_models.generate_conv_model(True, self.filename)
-    tflm_interpreter = tflm_runtime.Interpreter.from_bytes(model_data)
+    tflm_interpreter = runtime.Interpreter.from_bytes(model_data)
 
     # Initial output values are all 0
     output = tflm_interpreter.get_output(0)
@@ -121,7 +121,7 @@ class ConvModelTests(test_util.TensorFlowTestCase):
 
   def testOutputErrorHandling(self):
     model_data = generate_test_models.generate_conv_model(True, self.filename)
-    tflm_interpreter = tflm_runtime.Interpreter.from_bytes(model_data)
+    tflm_interpreter = runtime.Interpreter.from_bytes(model_data)
     # Try to access out of bound data
     with self.assertRaisesWithPredicateMatch(IndexError,
                                              "Tensor is out of bound"):
@@ -134,7 +134,7 @@ class ConvModelTests(test_util.TensorFlowTestCase):
     model_data = generate_test_models.generate_conv_model(True, self.filename)
 
     # TFLM interpreter
-    tflm_interpreter = tflm_runtime.Interpreter.from_bytes(model_data)
+    tflm_interpreter = runtime.Interpreter.from_bytes(model_data)
 
     # TFLite interpreter
     tflite_interpreter = tf.lite.Interpreter(
@@ -169,8 +169,8 @@ class ConvModelTests(test_util.TensorFlowTestCase):
   def _helperModelFromFileAndBufferEqual(self):
     model_data = generate_test_models.generate_conv_model(True, self.filename)
 
-    file_interpreter = tflm_runtime.Interpreter.from_file(self.filename)
-    bytes_interpreter = tflm_runtime.Interpreter.from_bytes(model_data)
+    file_interpreter = runtime.Interpreter.from_file(self.filename)
+    bytes_interpreter = runtime.Interpreter.from_bytes(model_data)
 
     num_steps = 100
     for i in range(0, num_steps):
@@ -198,7 +198,7 @@ class ConvModelTests(test_util.TensorFlowTestCase):
     model_data = generate_test_models.generate_conv_model(False)
 
     interpreters = [
-        tflm_runtime.Interpreter.from_bytes(model_data) for i in range(10)
+        runtime.Interpreter.from_bytes(model_data) for i in range(10)
     ]
 
     num_steps = 100
@@ -221,7 +221,7 @@ class ConvModelTests(test_util.TensorFlowTestCase):
     pass
 
   def _helperOutputTensorMemoryLeak(self):
-    interpreter = tflm_runtime.Interpreter.from_file(self.filename)
+    interpreter = runtime.Interpreter.from_file(self.filename)
     int_ref = weakref.finalize(interpreter, self._helperNoop)
     some_output = interpreter.get_output(0)
     output_ref = weakref.finalize(some_output, self._helperNoop)
@@ -250,22 +250,22 @@ class ConvModelTests(test_util.TensorFlowTestCase):
     custom_op_registerers = [("wrong", "format")]
     with self.assertRaisesWithPredicateMatch(ValueError,
                                              "must be a list of strings"):
-      interpreter = tflm_runtime.Interpreter.from_bytes(
-          model_data, custom_op_registerers)
+      interpreter = runtime.Interpreter.from_bytes(model_data,
+                                                   custom_op_registerers)
 
     custom_op_registerers = "WrongFormat"
     with self.assertRaisesWithPredicateMatch(ValueError,
                                              "must be a list of strings"):
-      interpreter = tflm_runtime.Interpreter.from_bytes(
-          model_data, custom_op_registerers)
+      interpreter = runtime.Interpreter.from_bytes(model_data,
+                                                   custom_op_registerers)
 
   def testNonExistentCustomOps(self):
     model_data = generate_test_models.generate_conv_model(False)
     custom_op_registerers = ["SomeRandomOp"]
     with self.assertRaisesWithPredicateMatch(
         RuntimeError, "TFLM could not register custom op via SomeRandomOp"):
-      interpreter = tflm_runtime.Interpreter.from_bytes(
-          model_data, custom_op_registerers)
+      interpreter = runtime.Interpreter.from_bytes(model_data,
+                                                   custom_op_registerers)
 
 
 if __name__ == "__main__":

tensorflow/lite/micro/tools/BUILD

@@ -59,7 +59,7 @@ py_test(
     ],
     deps = [
         ":requantize_flatbuffer",
-        "//tensorflow/lite/micro/python/interpreter/src:tflm_runtime",
+        "//tensorflow/lite/micro/python/interpreter/src:runtime",
         requirement("numpy"),
         requirement("tensorflow-cpu"),
     ],

tensorflow/lite/micro/tools/requantize_flatbuffer_test.py

@@ -20,7 +20,7 @@ import tensorflow as tf
 from tensorflow.python.framework import test_util
 from tensorflow.python.platform import test
 from tflite_micro.tensorflow.lite.micro.tools import requantize_flatbuffer
-from tflite_micro.tensorflow.lite.micro.python.interpreter.src import tflm_runtime
+from tflite_micro.tensorflow.lite.micro.python.interpreter.src import runtime
 from tflite_micro.tensorflow.lite.tools import flatbuffer_utils
 
 
@@ -92,9 +92,9 @@ class SimpleFCModelTest(test_util.TensorFlowTestCase):
     int8_converted_int16_model = convert_8to16_requantizer(
         keras_model, representative_dataset_gen)
 
-    interpreter_tfl_converted = tflm_runtime.Interpreter.from_bytes(
+    interpreter_tfl_converted = runtime.Interpreter.from_bytes(
         tfl_converted_int16_model)
-    interpreter_tool_converted = tflm_runtime.Interpreter.from_bytes(
+    interpreter_tool_converted = runtime.Interpreter.from_bytes(
         int8_converted_int16_model)
 
     num_steps = 10