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Overview
TensorFlow Lite now supports converting weights to 16-bit floating point values during model conversion from TensorFlow to TensorFlow Lite's flat buffer format. This results in a 2x reduction in model size. Some hardware, like GPUs, can compute natively in this reduced precision arithmetic, realizing a speedup over traditional floating point execution. The Tensorflow Lite GPU delegate can be configured to run in this way. However, a model converted to float16 weights can still run on the CPU without additional modification: the float16 weights are upsampled to float32 prior to the first inference. This permits a significant reduction in model size in exchange for a minimal impacts to latency and accuracy.
In this tutorial, you train an MNIST model from scratch, check its accuracy in TensorFlow, and then convert the model into a Tensorflow Lite flatbuffer with float16 quantization. Finally, check the accuracy of the converted model and compare it to the original float32 model.
Build an MNIST model
Setup
import logging
logging.getLogger("tensorflow").setLevel(logging.DEBUG)
import tensorflow as tf
from tensorflow import keras
import numpy as np
import pathlib
2022-10-20 13:38:51.975649: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory 2022-10-20 13:38:51.975745: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory 2022-10-20 13:38:51.975755: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
Train and export the model
# Load MNIST dataset
mnist = keras.datasets.mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
# Normalize the input image so that each pixel value is between 0 to 1.
train_images = train_images / 255.0
test_images = test_images / 255.0
# Define the model architecture
model = keras.Sequential([
keras.layers.InputLayer(input_shape=(28, 28)),
keras.layers.Reshape(target_shape=(28, 28, 1)),
keras.layers.Conv2D(filters=12, kernel_size=(3, 3), activation=tf.nn.relu),
keras.layers.MaxPooling2D(pool_size=(2, 2)),
keras.layers.Flatten(),
keras.layers.Dense(10)
])
# Train the digit classification model
model.compile(optimizer='adam',
loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
model.fit(
train_images,
train_labels,
epochs=1,
validation_data=(test_images, test_labels)
)
1875/1875 [==============================] - 8s 3ms/step - loss: 0.2797 - accuracy: 0.9210 - val_loss: 0.1306 - val_accuracy: 0.9639 <keras.callbacks.History at 0x7f185cfe1550>
For the example, you trained the model for just a single epoch, so it only trains to ~96% accuracy.
Convert to a TensorFlow Lite model
Using the TensorFlow Lite Converter, you can now convert the trained model into a TensorFlow Lite model.
Now load the model using the TFLiteConverter:
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op while saving (showing 1 of 1). These functions will not be directly callable after loading. INFO:tensorflow:Assets written to: /tmpfs/tmp/tmprx55x79h/assets INFO:tensorflow:Assets written to: /tmpfs/tmp/tmprx55x79h/assets 2022-10-20 13:39:05.936852: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:362] Ignored output_format. 2022-10-20 13:39:05.936889: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:365] Ignored drop_control_dependency.
Write it out to a .tflite file:
tflite_models_dir = pathlib.Path("/tmp/mnist_tflite_models/")
tflite_models_dir.mkdir(exist_ok=True, parents=True)
tflite_model_file = tflite_models_dir/"mnist_model.tflite"
tflite_model_file.write_bytes(tflite_model)
84824
To instead quantize the model to float16 on export, first set the optimizations flag to use default optimizations. Then specify that float16 is the supported type on the target platform:
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_types = [tf.float16]
Finally, convert the model like usual. Note, by default the converted model will still use float input and outputs for invocation convenience.
tflite_fp16_model = converter.convert()
tflite_model_fp16_file = tflite_models_dir/"mnist_model_quant_f16.tflite"
tflite_model_fp16_file.write_bytes(tflite_fp16_model)
WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op while saving (showing 1 of 1). These functions will not be directly callable after loading. INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpqqd0ynac/assets INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpqqd0ynac/assets 2022-10-20 13:39:06.852583: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:362] Ignored output_format. 2022-10-20 13:39:06.852619: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:365] Ignored drop_control_dependency. 44628
Note how the resulting file is approximately 1/2 the size.
ls -lh {tflite_models_dir}
total 214M -rw-rw-r-- 1 kbuilder kbuilder 83K Oct 20 13:39 mnist_model.tflite -rw-rw-r-- 1 kbuilder kbuilder 24K Oct 20 13:36 mnist_model_quant.tflite -rw-rw-r-- 1 kbuilder kbuilder 25K Oct 20 13:27 mnist_model_quant_16x8.tflite -rw-rw-r-- 1 kbuilder kbuilder 44K Oct 20 13:39 mnist_model_quant_f16.tflite -rw-rw-r-- 1 kbuilder kbuilder 171M Oct 20 13:36 resnet_v2_101.tflite -rw-rw-r-- 1 kbuilder kbuilder 44M Oct 20 13:37 resnet_v2_101_quantized.tflite
Run the TensorFlow Lite models
Run the TensorFlow Lite model using the Python TensorFlow Lite Interpreter.
Load the model into the interpreters
interpreter = tf.lite.Interpreter(model_path=str(tflite_model_file))
interpreter.allocate_tensors()
INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
interpreter_fp16 = tf.lite.Interpreter(model_path=str(tflite_model_fp16_file))
interpreter_fp16.allocate_tensors()
Test the models on one image
test_image = np.expand_dims(test_images[0], axis=0).astype(np.float32)
input_index = interpreter.get_input_details()[0]["index"]
output_index = interpreter.get_output_details()[0]["index"]
interpreter.set_tensor(input_index, test_image)
interpreter.invoke()
predictions = interpreter.get_tensor(output_index)
import matplotlib.pylab as plt
plt.imshow(test_images[0])
template = "True:{true}, predicted:{predict}"
_ = plt.title(template.format(true= str(test_labels[0]),
predict=str(np.argmax(predictions[0]))))
plt.grid(False)
test_image = np.expand_dims(test_images[0], axis=0).astype(np.float32)
input_index = interpreter_fp16.get_input_details()[0]["index"]
output_index = interpreter_fp16.get_output_details()[0]["index"]
interpreter_fp16.set_tensor(input_index, test_image)
interpreter_fp16.invoke()
predictions = interpreter_fp16.get_tensor(output_index)
plt.imshow(test_images[0])
template = "True:{true}, predicted:{predict}"
_ = plt.title(template.format(true= str(test_labels[0]),
predict=str(np.argmax(predictions[0]))))
plt.grid(False)
Evaluate the models
# A helper function to evaluate the TF Lite model using "test" dataset.
def evaluate_model(interpreter):
input_index = interpreter.get_input_details()[0]["index"]
output_index = interpreter.get_output_details()[0]["index"]
# Run predictions on every image in the "test" dataset.
prediction_digits = []
for test_image in test_images:
# Pre-processing: add batch dimension and convert to float32 to match with
# the model's input data format.
test_image = np.expand_dims(test_image, axis=0).astype(np.float32)
interpreter.set_tensor(input_index, test_image)
# Run inference.
interpreter.invoke()
# Post-processing: remove batch dimension and find the digit with highest
# probability.
output = interpreter.tensor(output_index)
digit = np.argmax(output()[0])
prediction_digits.append(digit)
# Compare prediction results with ground truth labels to calculate accuracy.
accurate_count = 0
for index in range(len(prediction_digits)):
if prediction_digits[index] == test_labels[index]:
accurate_count += 1
accuracy = accurate_count * 1.0 / len(prediction_digits)
return accuracy
print(evaluate_model(interpreter))
0.9639
Repeat the evaluation on the float16 quantized model to obtain:
# NOTE: Colab runs on server CPUs. At the time of writing this, TensorFlow Lite
# doesn't have super optimized server CPU kernels. For this reason this may be
# slower than the above float interpreter. But for mobile CPUs, considerable
# speedup can be observed.
print(evaluate_model(interpreter_fp16))
0.964
In this example, you have quantized a model to float16 with no difference in the accuracy.
It's also possible to evaluate the fp16 quantized model on the GPU. To perform all arithmetic with the reduced precision values, be sure to create the TfLiteGPUDelegateOptions struct in your app and set precision_loss_allowed to 1, like this:
//Prepare GPU delegate.
const TfLiteGpuDelegateOptions options = {
.metadata = NULL,
.compile_options = {
.precision_loss_allowed = 1, // FP16
.preferred_gl_object_type = TFLITE_GL_OBJECT_TYPE_FASTEST,
.dynamic_batch_enabled = 0, // Not fully functional yet
},
};
Detailed documentation on the TFLite GPU delegate and how to use it in your application can be found here