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Diffstat (limited to 'model_conditioning_examples/weight_pruning.py')
-rw-r--r-- | model_conditioning_examples/weight_pruning.py | 106 |
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diff --git a/model_conditioning_examples/weight_pruning.py b/model_conditioning_examples/weight_pruning.py new file mode 100644 index 0000000..bf26f1f --- /dev/null +++ b/model_conditioning_examples/weight_pruning.py @@ -0,0 +1,106 @@ +# Copyright (c) 2021 Arm Limited. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# 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. +""" +This script will provide you with a short example of how to perform magnitude-based weight pruning in TensorFlow +using the TensorFlow Model Optimization Toolkit. + +The output from this example will be a TensorFlow Lite model file where ~75% percent of the weights have been 'pruned' to the +value 0 during training - quantization has then been applied on top of this. + +By pruning the model we can improve compression of the model file. This can be essential for deploying certain models +on systems with limited resources - such as embedded systems using Arm Ethos NPU. Also, if the pruned model is run +on an Arm Ethos NPU then this pruning can improve the execution time of the model. + +After pruning is complete we do post-training quantization to quantize the model and then generate a TensorFlow Lite file. + +If you are targetting an Arm Ethos-U55 NPU then the output TensorFlow Lite file will also need to be passed through the Vela +compiler for further optimizations before it can be used. + +For more information on using Vela see: https://git.mlplatform.org/ml/ethos-u/ethos-u-vela.git/about/ +For more information on weight pruning see: https://www.tensorflow.org/model_optimization/guide/pruning +""" +import pathlib + +import tensorflow as tf +import tensorflow_model_optimization as tfmot + +from training_utils import get_data, create_model +from post_training_quantization import post_training_quantize, evaluate_tflite_model + + +def prepare_for_pruning(keras_model): + """Prepares a Keras model for pruning.""" + + # We use a constant sparsity schedule so the amount of sparsity in the model is kept at the same percent throughout + # training. An alternative is PolynomialDecay where sparsity can be gradually increased during training. + pruning_schedule = tfmot.sparsity.keras.ConstantSparsity(target_sparsity=0.75, begin_step=0) + + # Apply the pruning wrapper to the whole model so weights in every layer will get pruned. You may find that to avoid + # too much accuracy loss only certain non-critical layers in your model should be pruned. + pruning_ready_model = tfmot.sparsity.keras.prune_low_magnitude(keras_model, pruning_schedule=pruning_schedule) + + # We must recompile the model after making it ready for pruning. + pruning_ready_model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), + loss=tf.keras.losses.sparse_categorical_crossentropy, + metrics=['accuracy']) + + return pruning_ready_model + + +def main(): + x_train, y_train, x_test, y_test = get_data() + model = create_model() + + # Compile and train the model first. + # In general it is easier to do pruning as a fine-tuning step after the model is fully trained. + model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), + loss=tf.keras.losses.sparse_categorical_crossentropy, + metrics=['accuracy']) + + model.fit(x=x_train, y=y_train, batch_size=128, epochs=5, verbose=1, shuffle=True) + + # Test the trained model accuracy. + test_loss, test_acc = model.evaluate(x_test, y_test) + print(f"Test accuracy before pruning: {test_acc:.3f}") + + # Prepare the model for pruning and add the pruning update callback needed in training. + pruned_model = prepare_for_pruning(model) + callbacks = [tfmot.sparsity.keras.UpdatePruningStep()] + + # Continue training the model but now with pruning applied - remember to pass in the callbacks! + pruned_model.fit(x=x_train, y=y_train, batch_size=128, epochs=1, verbose=1, shuffle=True, callbacks=callbacks) + test_loss, test_acc = pruned_model.evaluate(x_test, y_test) + print(f"Test accuracy after pruning: {test_acc:.3f}") + + # Remove all variables that pruning only needed in the training phase. + model_for_export = tfmot.sparsity.keras.strip_pruning(pruned_model) + + # Apply post-training quantization on top of the pruning and save the resulting TensorFlow Lite model to file. + tflite_model = post_training_quantize(model_for_export, x_train) + + tflite_models_dir = pathlib.Path('./conditioned_models/') + tflite_models_dir.mkdir(exist_ok=True, parents=True) + + pruned_quant_model_save_path = tflite_models_dir / 'pruned_post_training_quant_model.tflite' + with open(pruned_quant_model_save_path, 'wb') as f: + f.write(tflite_model) + + # Test the pruned quantized model accuracy. Save time by only testing a subset of the whole data. + num_test_samples = 1000 + evaluate_tflite_model(pruned_quant_model_save_path, x_test[0:num_test_samples], y_test[0:num_test_samples]) + + +if __name__ == "__main__": + main() |