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tensorflow
GitHub Repository: tensorflow/docs-l10n
Path: blob/master/site/en-snapshot/model_optimization/guide/combine/pqat_example.ipynb
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Kernel: Python 3

Copyright 2021 The TensorFlow Authors.

#@title 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 # # https://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.

Pruning preserving quantization aware training (PQAT) Keras example

Overview

This is an end to end example showing the usage of the pruning preserving quantization aware training (PQAT) API, part of the TensorFlow Model Optimization Toolkit's collaborative optimization pipeline.

Other pages

For an introduction to the pipeline and other available techniques, see the collaborative optimization overview page.

Contents

In the tutorial, you will:

  1. Train a tf.keras model for the MNIST dataset from scratch.

  2. Fine-tune the model with pruning, using the sparsity API, and see the accuracy.

  3. Apply QAT and observe the loss of sparsity.

  4. Apply PQAT and observe that the sparsity applied earlier has been preserved.

  5. Generate a TFLite model and observe the effects of applying PQAT on it.

  6. Compare the achieved PQAT model accuracy with a model quantized using post-training quantization.

Setup

You can run this Jupyter Notebook in your local virtualenv or colab. For details of setting up dependencies, please refer to the installation guide.

! pip install -q tensorflow-model-optimization
import tensorflow as tf import numpy as np import tempfile import zipfile import os

Train a tf.keras model for MNIST without pruning

# Load MNIST dataset mnist = tf.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 model = tf.keras.Sequential([ tf.keras.layers.InputLayer(input_shape=(28, 28)), tf.keras.layers.Reshape(target_shape=(28, 28, 1)), tf.keras.layers.Conv2D(filters=12, kernel_size=(3, 3), activation=tf.nn.relu), tf.keras.layers.MaxPooling2D(pool_size=(2, 2)), tf.keras.layers.Flatten(), tf.keras.layers.Dense(10) ]) # Train the digit classification model model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy']) model.fit( train_images, train_labels, validation_split=0.1, epochs=10 )

Evaluate the baseline model and save it for later usage

_, baseline_model_accuracy = model.evaluate( test_images, test_labels, verbose=0) print('Baseline test accuracy:', baseline_model_accuracy) _, keras_file = tempfile.mkstemp('.h5') print('Saving model to: ', keras_file) tf.keras.models.save_model(model, keras_file, include_optimizer=False)

Prune and fine-tune the model to 50% sparsity

Apply the prune_low_magnitude() API to prune the whole pre-trained model to demonstrate and observe its effectiveness in reducing the model size when applying zip, while maintaining accuracy. For how best to use the API to achieve the best compression rate while maintaining your target accuracy, refer to the pruning comprehensive guide.

Define the model and apply the sparsity API

The model needs to be pre-trained before using the sparsity API.

import tensorflow_model_optimization as tfmot prune_low_magnitude = tfmot.sparsity.keras.prune_low_magnitude pruning_params = { 'pruning_schedule': tfmot.sparsity.keras.ConstantSparsity(0.5, begin_step=0, frequency=100) } callbacks = [ tfmot.sparsity.keras.UpdatePruningStep() ] pruned_model = prune_low_magnitude(model, **pruning_params) # Use smaller learning rate for fine-tuning opt = tf.keras.optimizers.Adam(learning_rate=1e-5) pruned_model.compile( loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), optimizer=opt, metrics=['accuracy']) pruned_model.summary()

Fine-tune the model and evaluate the accuracy against baseline

Fine-tune the model with pruning for 3 epochs.

# Fine-tune model pruned_model.fit( train_images, train_labels, epochs=3, validation_split=0.1, callbacks=callbacks)

Define helper functions to calculate and print the sparsity of the model.

def print_model_weights_sparsity(model): for layer in model.layers: if isinstance(layer, tf.keras.layers.Wrapper): weights = layer.trainable_weights else: weights = layer.weights for weight in weights: # ignore auxiliary quantization weights if "quantize_layer" in weight.name: continue weight_size = weight.numpy().size zero_num = np.count_nonzero(weight == 0) print( f"{weight.name}: {zero_num/weight_size:.2%} sparsity ", f"({zero_num}/{weight_size})", )

Check that the model was correctly pruned. We need to strip the pruning wrapper first.

stripped_pruned_model = tfmot.sparsity.keras.strip_pruning(pruned_model) print_model_weights_sparsity(stripped_pruned_model)

For this example, there is minimal loss in test accuracy after pruning, compared to the baseline.

_, pruned_model_accuracy = pruned_model.evaluate( test_images, test_labels, verbose=0) print('Baseline test accuracy:', baseline_model_accuracy) print('Pruned test accuracy:', pruned_model_accuracy)

Apply QAT and PQAT and check effect on model sparsity in both cases

Next, we apply both QAT and pruning-preserving QAT (PQAT) on the pruned model and observe that PQAT preserves sparsity on your pruned model. Note that we stripped pruning wrappers from your pruned model with tfmot.sparsity.keras.strip_pruning before applying PQAT API.

# QAT qat_model = tfmot.quantization.keras.quantize_model(stripped_pruned_model) qat_model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy']) print('Train qat model:') qat_model.fit(train_images, train_labels, batch_size=128, epochs=1, validation_split=0.1) # PQAT quant_aware_annotate_model = tfmot.quantization.keras.quantize_annotate_model( stripped_pruned_model) pqat_model = tfmot.quantization.keras.quantize_apply( quant_aware_annotate_model, tfmot.experimental.combine.Default8BitPrunePreserveQuantizeScheme()) pqat_model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy']) print('Train pqat model:') pqat_model.fit(train_images, train_labels, batch_size=128, epochs=1, validation_split=0.1)
print("QAT Model sparsity:") print_model_weights_sparsity(qat_model) print("PQAT Model sparsity:") print_model_weights_sparsity(pqat_model)

See compression benefits of PQAT model

Define helper function to get zipped model file.

def get_gzipped_model_size(file): # It returns the size of the gzipped model in kilobytes. _, zipped_file = tempfile.mkstemp('.zip') with zipfile.ZipFile(zipped_file, 'w', compression=zipfile.ZIP_DEFLATED) as f: f.write(file) return os.path.getsize(zipped_file)/1000

Since this is a small model, the difference between the two models isn't very noticeable. Applying pruning and PQAT to a bigger production model would yield a more significant compression.

# QAT model converter = tf.lite.TFLiteConverter.from_keras_model(qat_model) converter.optimizations = [tf.lite.Optimize.DEFAULT] qat_tflite_model = converter.convert() qat_model_file = 'qat_model.tflite' # Save the model. with open(qat_model_file, 'wb') as f: f.write(qat_tflite_model) # PQAT model converter = tf.lite.TFLiteConverter.from_keras_model(pqat_model) converter.optimizations = [tf.lite.Optimize.DEFAULT] pqat_tflite_model = converter.convert() pqat_model_file = 'pqat_model.tflite' # Save the model. with open(pqat_model_file, 'wb') as f: f.write(pqat_tflite_model) print("QAT model size: ", get_gzipped_model_size(qat_model_file), ' KB') print("PQAT model size: ", get_gzipped_model_size(pqat_model_file), ' KB')

See the persistence of accuracy from TF to TFLite

Define a helper function to evaluate the TFLite model on the test dataset.

def eval_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 i, test_image in enumerate(test_images): if i % 1000 == 0: print(f"Evaluated on {i} results so far.") # 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) print('\n') # Compare prediction results with ground truth labels to calculate accuracy. prediction_digits = np.array(prediction_digits) accuracy = (prediction_digits == test_labels).mean() return accuracy

You evaluate the model, which has been pruned and quantized, and then see the accuracy from TensorFlow persists in the TFLite backend.

interpreter = tf.lite.Interpreter(pqat_model_file) interpreter.allocate_tensors() pqat_test_accuracy = eval_model(interpreter) print('Pruned and quantized TFLite test_accuracy:', pqat_test_accuracy) print('Pruned TF test accuracy:', pruned_model_accuracy)

Apply post-training quantization and compare to PQAT model

Next, we use normal post-training quantization (no fine-tuning) on the pruned model and check its accuracy against the PQAT model. This demonstrates why you would need to use PQAT to improve the quantized model's accuracy.

First, define a generator for the callibration dataset from the first 1000 training images.

def mnist_representative_data_gen(): for image in train_images[:1000]: image = np.expand_dims(image, axis=0).astype(np.float32) yield [image]

Quantize the model and compare accuracy to the previously acquired PQAT model. Note that the model quantized with fine-tuning achieves higher accuracy.

converter = tf.lite.TFLiteConverter.from_keras_model(stripped_pruned_model) converter.optimizations = [tf.lite.Optimize.DEFAULT] converter.representative_dataset = mnist_representative_data_gen post_training_tflite_model = converter.convert() post_training_model_file = 'post_training_model.tflite' # Save the model. with open(post_training_model_file, 'wb') as f: f.write(post_training_tflite_model) # Compare accuracy interpreter = tf.lite.Interpreter(post_training_model_file) interpreter.allocate_tensors() post_training_test_accuracy = eval_model(interpreter) print('PQAT TFLite test_accuracy:', pqat_test_accuracy) print('Post-training (no fine-tuning) TF test accuracy:', post_training_test_accuracy)

Conclusion

In this tutorial, you learned how to create a model, prune it using the sparsity API, and apply the sparsity-preserving quantization aware training (PQAT) to preserve sparsity while using QAT. The final PQAT model was compared to the QAT one to show that the sparsity is preserved in the former and lost in the latter. Next, the models were converted to TFLite to show the compression benefits of chaining pruning and PQAT model optimization techniques and the TFLite model was evaluated to ensure that the accuracy persists in the TFLite backend. Finally, the PQAT model was compared to a quantized pruned model achieved using the post-training quantization API to demonstrate the advantage of PQAT in recovering the accuracy loss from normal quantization.