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review.mlplatform.org / ml / ethos-u / ml-embedded-evaluation-kit / f32a86a6969508d7a156decbed0bfc9466ad92fa / . / model_conditioning_examples / weight_pruning.py

# SPDX-FileCopyrightText: Copyright 2021 Arm Limited and/or its affiliates <open-source-office@arm.com> | |

# 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, | |

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# 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() |