Drop support for Python <=3.8 and TensorFlow <=2.4 (#787)

* Drop support for Python <=3.8 and TensorFlow <=2.2

* Use Python 3.9 typing syntax

* Remove unused import

Author: lgeiger

.github/workflows/lint.yml

@@ -24,7 +24,7 @@ jobs:
       - name: Run PyFlakes
         run: pyflakes larq_compute_engine
       - name: Black code style
-        run: black larq_compute_engine --check --target-version py36
+        run: black larq_compute_engine --check --target-version py39
       - name: clang-format lint
         uses: DoozyX/[email protected]
         with:

.github/workflows/release.yml

@@ -94,7 +94,7 @@ jobs:
           fetch-depth: 0
       - uses: actions/setup-python@v4
         with:
-          python-version: 3.8
+          python-version: 3.9
       - uses: actions/cache@v3
         id: cache
         with:
@@ -137,7 +137,7 @@ jobs:
     runs-on: macos-latest
     strategy:
       matrix:
-        python-version: [3.7, 3.8, 3.9, "3.10", 3.11]
+        python-version: [3.9, "3.10", 3.11]
       fail-fast: false
     steps:
       - uses: actions/checkout@v3
@@ -180,7 +180,7 @@ jobs:
     runs-on: macos-11
     strategy:
       matrix:
-        python-version: [3.8, 3.9, "3.10", 3.11]
+        python-version: [3.9, "3.10", 3.11]
       fail-fast: false
     steps:
       - uses: actions/checkout@v3
@@ -223,7 +223,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.7, 3.8, 3.9, "3.10", 3.11]
+        python-version: [3.9, "3.10", 3.11]
       fail-fast: false
     steps:
       - uses: actions/checkout@v3
@@ -266,7 +266,7 @@ jobs:
     runs-on: windows-2019
     strategy:
       matrix:
-        python-version: [3.7, 3.8, 3.9, "3.10", 3.11]
+        python-version: [3.9, "3.10", 3.11]
       fail-fast: false
     steps:
       - name: Configure Pagefile

.github/workflows/unittests.yml

@@ -100,8 +100,8 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        tf-version: [1.14.0, 1.15.5, 2.0.4, 2.1.4, 2.2.3, 2.3.3, 2.4.4, 2.5.3, 2.6.5, 2.7.4, 2.8.4, 2.9.3, 2.10.1, 2.11.0]
-        python-version: [3.7]
+        tf-version: [2.5.3, 2.6.5, 2.7.4, 2.8.4, 2.9.3, 2.10.1, 2.11.0]
+        python-version: [3.9]
         flatbuffers-version: [2.0]
         protobuf-version: [3.19.6]
         include:

larq_compute_engine/mlir/python/converter.py

@@ -1,7 +1,6 @@
 import os
-from packaging import version
 import warnings
-from typing import Optional, Tuple, Union
+from typing import Optional, Union
 import tensorflow as tf
 import tempfile
 
@@ -23,18 +22,17 @@
 
 def concrete_function_from_keras_model(model):
     input_signature = None
-    if version.parse(tf.__version__) >= version.parse("2.1"):
-        # If the model's call is not a `tf.function`, then we need to first get its
-        # input signature from `model_input_signature` method. We can't directly
-        # call `trace_model_call` because otherwise the batch dimension is set
-        # to None.
-        # Once we have better support for dynamic shapes, we can remove this.
-        if not isinstance(model.call, def_function.Function):
-            # Pass `keep_original_batch_size=True` will ensure that we get an input
-            # signature including the batch dimension specified by the user.
-            input_signature = saving_utils.model_input_signature(
-                model, keep_original_batch_size=True
-            )
+    # If the model's call is not a `tf.function`, then we need to first get its
+    # input signature from `model_input_signature` method. We can't directly
+    # call `trace_model_call` because otherwise the batch dimension is set
+    # to None.
+    # Once we have better support for dynamic shapes, we can remove this.
+    if not isinstance(model.call, def_function.Function):
+        # Pass `keep_original_batch_size=True` will ensure that we get an input
+        # signature including the batch dimension specified by the user.
+        input_signature = saving_utils.model_input_signature(
+            model, keep_original_batch_size=True
+        )
 
     func = saving_utils.trace_model_call(model, input_signature)
     return func.get_concrete_function()
@@ -99,7 +97,7 @@ def convert_saved_model(
     inference_input_type: tf.DType = tf.float32,
     inference_output_type: tf.DType = tf.float32,
     target: str = "arm",
-    experimental_default_int8_range: Optional[Tuple[float, float]] = None,
+    experimental_default_int8_range: Optional[tuple[float, float]] = None,
 ) -> bytes:
     """Converts a SavedModel to TFLite flatbuffer.
 
@@ -125,10 +123,6 @@ def convert_saved_model(
     # Returns
         The converted data in serialized format.
     """
-    if version.parse(tf.__version__) < version.parse("2.2"):
-        raise RuntimeError(
-            "TensorFlow 2.2 or newer is required for saved model conversion."
-        )
     _validate_options(
         inference_input_type=inference_input_type,
         inference_output_type=inference_output_type,
@@ -175,7 +169,7 @@ def convert_keras_model(
     inference_input_type: tf.DType = tf.float32,
     inference_output_type: tf.DType = tf.float32,
     target: str = "arm",
-    experimental_default_int8_range: Optional[Tuple[float, float]] = None,
+    experimental_default_int8_range: Optional[tuple[float, float]] = None,
 ) -> bytes:
     """Converts a Keras model to TFLite flatbuffer.
 
@@ -236,10 +230,7 @@ def convert_keras_model(
         )
 
     func = concrete_function_from_keras_model(model)
-    if version.parse(tf.__version__) >= version.parse("1.15"):
-        frozen_func = convert_variables_to_constants_v2(func, lower_control_flow=False)
-    else:
-        frozen_func = convert_variables_to_constants_v2(func)
+    frozen_func = convert_variables_to_constants_v2(func, lower_control_flow=False)
     input_tensors = [
         tensor for tensor in frozen_func.inputs if tensor.dtype != tf.dtypes.resource
     ]

larq_compute_engine/mlir/python/converter_test.py

@@ -1,6 +1,5 @@
 import sys
 import unittest
-from packaging import version
 from unittest import mock
 
 import tensorflow as tf
@@ -15,7 +14,6 @@
 
 from larq_compute_engine.mlir.python.converter import convert_keras_model
 from larq_compute_engine.mlir._tf_tfl_flatbuffer import (
-    convert_graphdef_to_tflite_flatbuffer as mocked_graphdef_converter,
     convert_saved_model_to_tflite_flatbuffer as mocked_saved_model_converter,
 )
 
@@ -67,21 +65,9 @@ def test_model(self):
         with context.eager_mode():
             model = get_test_model()
             convert_keras_model(model)
-        if version.parse(tf.__version__) < version.parse("2.2"):
-            mocked_graphdef_converter.assert_called_once_with(
-                mock.ANY,
-                ["quant_conv2d_input"],
-                ["DT_FLOAT"],
-                [[1, 28, 28, 1]],
-                ["Identity"],
-                False,
-                "arm",
-                None,
-            )
-        else:
-            mocked_saved_model_converter.assert_called_once_with(
-                mock.ANY, ["serve"], ["serving_default"], 1, "arm", None
-            )
+        mocked_saved_model_converter.assert_called_once_with(
+            mock.ANY, ["serve"], ["serving_default"], 1, "arm", None
+        )
 
     def test_wrong_arg(self):
         with self.assertRaises(ValueError):

larq_compute_engine/tests/end2end_test.py

@@ -2,7 +2,6 @@
 import os
 import sys
 import tempfile
-from packaging import version
 
 import larq as lq
 import numpy as np
@@ -238,9 +237,6 @@ def test_simple_model(dataset, conversion_function, model_cls):
 def test_int8_input_output(
     conversion_function, model_cls, inference_input_type, inference_output_type
 ):
-    if version.parse(tf.__version__) < version.parse("2.2"):
-        pytest.skip("TensorFlow 2.2 or newer is required for saved model conversion.")
-
     model_lce = conversion_function(
         model_cls(),
         inference_input_type=inference_input_type,

larq_compute_engine/tflite/python/interpreter_base.py

@@ -1,12 +1,13 @@
-from typing import Iterator, List, Tuple, Union, Optional
+from collections.abc import Iterator
+from typing import Union, Optional
 
 import numpy as np
 from tqdm import tqdm
 
-Data = Union[np.ndarray, List[np.ndarray]]
+Data = Union[np.ndarray, list[np.ndarray]]
 
 
-def data_generator(x: Union[Data, Iterator[Data]]) -> Iterator[List[np.ndarray]]:
+def data_generator(x: Union[Data, Iterator[Data]]) -> Iterator[list[np.ndarray]]:
     if isinstance(x, np.ndarray):
         for inputs in x:
             yield [np.expand_dims(inputs, axis=0)]
@@ -36,17 +37,17 @@ def input_types(self) -> list:
         return self.interpreter.input_types
 
     @property
-    def input_shapes(self) -> List[Tuple[int]]:
+    def input_shapes(self) -> list[tuple[int]]:
         """Returns a list of input shapes."""
         return self.interpreter.input_shapes
 
     @property
-    def input_scales(self) -> List[Optional[Union[float, List[float]]]]:
+    def input_scales(self) -> list[Optional[Union[float, list[float]]]]:
         """Returns a list of input scales."""
         return self.interpreter.input_scales
 
     @property
-    def input_zero_points(self) -> List[Optional[int]]:
+    def input_zero_points(self) -> list[Optional[int]]:
         """Returns a list of input zero points."""
         return self.interpreter.input_zero_points
 
@@ -56,17 +57,17 @@ def output_types(self) -> list:
         return self.interpreter.output_types
 
     @property
-    def output_shapes(self) -> List[Tuple[int]]:
+    def output_shapes(self) -> list[tuple[int]]:
         """Returns a list of output shapes."""
         return self.interpreter.output_shapes
 
     @property
-    def output_scales(self) -> List[Optional[Union[float, List[float]]]]:
+    def output_scales(self) -> list[Optional[Union[float, list[float]]]]:
         """Returns a list of input scales."""
         return self.interpreter.output_scales
 
     @property
-    def output_zero_points(self) -> List[Optional[int]]:
+    def output_zero_points(self) -> list[Optional[int]]:
         """Returns a list of input zero points."""
         return self.interpreter.output_zero_points
 

setup.py

@@ -30,7 +30,7 @@ def get_version_number(default):
 setup(
     name="larq-compute-engine",
     version=get_version_number(default="0.11.1"),
-    python_requires=">=3.7",
+    python_requires=">=3.9",
     description="Highly optimized inference engine for binarized neural networks.",
     long_description=readme(),
     long_description_content_type="text/markdown",
@@ -39,12 +39,7 @@ def get_version_number(default):
     packages=find_packages(),
     ext_modules=ext_modules,
     url="https://larq.dev/",
-    install_requires=[
-        "flatbuffers>=2.0",
-        "packaging>=19",
-        "tqdm>=4",
-        "importlib-metadata ~= 2.0 ; python_version<'3.8'",
-    ],
+    install_requires=["flatbuffers>=2.0", "tqdm>=4"],
     extras_require={
         "tensorflow": ["tensorflow>=1.14"],
         "tensorflow_gpu": ["tensorflow-gpu>=1.14"],
@@ -60,10 +55,9 @@ def get_version_number(default):
         "License :: OSI Approved :: Apache Software License",
         "Programming Language :: Python :: 3",
         "Programming Language :: Python :: 3 :: Only",
-        "Programming Language :: Python :: 3.7",
-        "Programming Language :: Python :: 3.8",
         "Programming Language :: Python :: 3.9",
         "Programming Language :: Python :: 3.10",
+        "Programming Language :: Python :: 3.11",
         "Topic :: Scientific/Engineering",
         "Topic :: Scientific/Engineering :: Mathematics",
         "Topic :: Scientific/Engineering :: Artificial Intelligence",