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tensorflow
GitHub Repository: tensorflow/docs-l10n
Path: blob/master/site/en-snapshot/model_optimization/guide/combine/cqat_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.

Cluster preserving quantization aware training (CQAT) Keras example

Overview

This is an end to end example showing the usage of the cluster preserving quantization aware training (CQAT) 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 clustering and see the accuracy.

  3. Apply QAT and observe the loss of clusters.

  4. Apply CQAT and observe that the clustering applied earlier has been preserved.

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

  6. Compare the achieved CQAT 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 clustering

# 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)

Cluster and fine-tune the model with 8 clusters

Apply the cluster_weights() API to cluster 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 clustering comprehensive guide.

Define the model and apply the clustering API

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

import tensorflow_model_optimization as tfmot cluster_weights = tfmot.clustering.keras.cluster_weights CentroidInitialization = tfmot.clustering.keras.CentroidInitialization clustering_params = { 'number_of_clusters': 8, 'cluster_centroids_init': CentroidInitialization.KMEANS_PLUS_PLUS, 'cluster_per_channel': True, } clustered_model = cluster_weights(model, **clustering_params) # Use smaller learning rate for fine-tuning opt = tf.keras.optimizers.Adam(learning_rate=1e-5) clustered_model.compile( loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), optimizer=opt, metrics=['accuracy']) clustered_model.summary()

Fine-tune the model and evaluate the accuracy against baseline

Fine-tune the model with clustering for 3 epochs.

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

Define helper functions to calculate and print the number of clustering in each kernel of the model.

def print_model_weight_clusters(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 if "kernel" in weight.name: unique_count = len(np.unique(weight)) print( f"{layer.name}/{weight.name}: {unique_count} clusters " )

Check that the model kernels were correctly clustered. We need to strip the clustering wrapper first.

stripped_clustered_model = tfmot.clustering.keras.strip_clustering(clustered_model) print_model_weight_clusters(stripped_clustered_model)

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

_, clustered_model_accuracy = clustered_model.evaluate( test_images, test_labels, verbose=0) print('Baseline test accuracy:', baseline_model_accuracy) print('Clustered test accuracy:', clustered_model_accuracy)

Apply QAT and CQAT and check effect on model clusters in both cases

Next, we apply both QAT and cluster preserving QAT (CQAT) on the clustered model and observe that CQAT preserves weight clusters in your clustered model. Note that we stripped clustering wrappers from your model with tfmot.clustering.keras.strip_clustering before applying CQAT API.

# QAT qat_model = tfmot.quantization.keras.quantize_model(stripped_clustered_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) # CQAT quant_aware_annotate_model = tfmot.quantization.keras.quantize_annotate_model( stripped_clustered_model) cqat_model = tfmot.quantization.keras.quantize_apply( quant_aware_annotate_model, tfmot.experimental.combine.Default8BitClusterPreserveQuantizeScheme()) cqat_model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy']) print('Train cqat model:') cqat_model.fit(train_images, train_labels, batch_size=128, epochs=1, validation_split=0.1)
print("QAT Model clusters:") print_model_weight_clusters(qat_model) print("CQAT Model clusters:") print_model_weight_clusters(cqat_model)

See compression benefits of CQAT 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

Note that this is a small model. Applying clustering and CQAT 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) # CQAT model converter = tf.lite.TFLiteConverter.from_keras_model(cqat_model) converter.optimizations = [tf.lite.Optimize.DEFAULT] cqat_tflite_model = converter.convert() cqat_model_file = 'cqat_model.tflite' # Save the model. with open(cqat_model_file, 'wb') as f: f.write(cqat_tflite_model) print("QAT model size: ", get_gzipped_model_size(qat_model_file), ' KB') print("CQAT model size: ", get_gzipped_model_size(cqat_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 clustered and quantized, and then see the accuracy from TensorFlow persists in the TFLite backend.

interpreter = tf.lite.Interpreter(cqat_model_file) interpreter.allocate_tensors() cqat_test_accuracy = eval_model(interpreter) print('Clustered and quantized TFLite test_accuracy:', cqat_test_accuracy) print('Clustered TF test accuracy:', clustered_model_accuracy)

Apply post-training quantization and compare to CQAT model

Next, we use post-training quantization (no fine-tuning) on the clustered model and check its accuracy against the CQAT model. This demonstrates why you would need to use CQAT to improve the quantized model's accuracy. The difference may not be very visible, because the MNIST model is quite small and overparametrized.

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 CQAT model. Note that the model quantized with fine-tuning achieves higher accuracy.

converter = tf.lite.TFLiteConverter.from_keras_model(stripped_clustered_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('CQAT TFLite test_accuracy:', cqat_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, cluster it using the cluster_weights() API, and apply the cluster preserving quantization aware training (CQAT) to preserve clusters while using QAT. The final CQAT model was compared to the QAT one to show that the clusters are preserved in the former and lost in the latter. Next, the models were converted to TFLite to show the compression benefits of chaining clustering and CQAT model optimization techniques and the TFLite model was evaluated to ensure that the accuracy persists in the TFLite backend. Finally, the CQAT model was compared to a quantized clustered model achieved using the post-training quantization API to demonstrate the advantage of CQAT in recovering the accuracy loss from normal quantization.