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Pruning comprehensive guide

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Welcome to the comprehensive guide for Keras weight pruning.

This page documents various use cases and shows how to use the API for each one. Once you know which APIs you need, find the parameters and the low-level details in the API docs.

• If you want to see the benefits of pruning and what's supported, see the overview.

• For a single end-to-end example, see the pruning example.

The following use cases are covered:

• Define and train a pruned model.

  • Sequential and Functional.

  • Keras model.fit and custom training loops

• Checkpoint and deserialize a pruned model.

• Deploy a pruned model and see compression benefits.

For configuration of the pruning algorithm, refer to the tfmot.sparsity.keras.prune_low_magnitude API docs.

Setup

For finding the APIs you need and understanding purposes, you can run but skip reading this section.

! pip install -q tensorflow-model-optimization

import tensorflow as tf
import numpy as np
import tensorflow_model_optimization as tfmot

%load_ext tensorboard

import tempfile

input_shape = [20]
x_train = np.random.randn(1, 20).astype(np.float32)
y_train = tf.keras.utils.to_categorical(np.random.randn(1), num_classes=20)

def setup_model():
  model = tf.keras.Sequential([
      tf.keras.layers.Dense(20, input_shape=input_shape),
      tf.keras.layers.Flatten()
  ])
  return model

def setup_pretrained_weights():
  model = setup_model()

  model.compile(
      loss=tf.keras.losses.categorical_crossentropy,
      optimizer='adam',
      metrics=['accuracy']
  )

  model.fit(x_train, y_train)

  _, pretrained_weights = tempfile.mkstemp('.tf')

  model.save_weights(pretrained_weights)

  return pretrained_weights

def get_gzipped_model_size(model):
  # Returns size of gzipped model, in bytes.
  import os
  import zipfile

  _, keras_file = tempfile.mkstemp('.h5')
  model.save(keras_file, include_optimizer=False)

  _, zipped_file = tempfile.mkstemp('.zip')
  with zipfile.ZipFile(zipped_file, 'w', compression=zipfile.ZIP_DEFLATED) as f:
    f.write(keras_file)

  return os.path.getsize(zipped_file)

setup_model()
pretrained_weights = setup_pretrained_weights()

Define model

Prune whole model (Sequential and Functional)

Tips for better model accuracy:

• Try "Prune some layers" to skip pruning the layers that reduce accuracy the most.

• It's generally better to finetune with pruning as opposed to training from scratch.

To make the whole model train with pruning, apply tfmot.sparsity.keras.prune_low_magnitude to the model.

base_model = setup_model()
base_model.load_weights(pretrained_weights) # optional but recommended.

model_for_pruning = tfmot.sparsity.keras.prune_low_magnitude(base_model)

model_for_pruning.summary()

Prune some layers (Sequential and Functional)

Pruning a model can have a negative effect on accuracy. You can selectively prune layers of a model to explore the trade-off between accuracy, speed, and model size.

Tips for better model accuracy:

• It's generally better to finetune with pruning as opposed to training from scratch.

• Try pruning the later layers instead of the first layers.

• Avoid pruning critical layers (e.g. attention mechanism).

More:

• The tfmot.sparsity.keras.prune_low_magnitude API docs provide details on how to vary the pruning configuration per layer.

In the example below, prune only the Dense layers.

# Create a base model
base_model = setup_model()
base_model.load_weights(pretrained_weights) # optional but recommended for model accuracy

# Helper function uses `prune_low_magnitude` to make only the 
# Dense layers train with pruning.
def apply_pruning_to_dense(layer):
  if isinstance(layer, tf.keras.layers.Dense):
    return tfmot.sparsity.keras.prune_low_magnitude(layer)
  return layer

# Use `tf.keras.models.clone_model` to apply `apply_pruning_to_dense` 
# to the layers of the model.
model_for_pruning = tf.keras.models.clone_model(
    base_model,
    clone_function=apply_pruning_to_dense,
)

model_for_pruning.summary()

While this example used the type of the layer to decide what to prune, the easiest way to prune a particular layer is to set its name property, and look for that name in the clone_function.

print(base_model.layers[0].name)

More readable but potentially lower model accuracy

This is not compatible with fine-tuning with pruning, which is why it may be less accurate than the above examples which support fine-tuning.

While prune_low_magnitude can be applied while defining the initial model, loading the weights after does not work in the below examples.

Functional example

# Use `prune_low_magnitude` to make the `Dense` layer train with pruning.
i = tf.keras.Input(shape=(20,))
x = tfmot.sparsity.keras.prune_low_magnitude(tf.keras.layers.Dense(10))(i)
o = tf.keras.layers.Flatten()(x)
model_for_pruning = tf.keras.Model(inputs=i, outputs=o)

model_for_pruning.summary()

Sequential example

# Use `prune_low_magnitude` to make the `Dense` layer train with pruning.
model_for_pruning = tf.keras.Sequential([
  tfmot.sparsity.keras.prune_low_magnitude(tf.keras.layers.Dense(20, input_shape=input_shape)),
  tf.keras.layers.Flatten()
])

model_for_pruning.summary()

Prune custom Keras layer or modify parts of layer to prune

Common mistake: pruning the bias usually harms model accuracy too much.

tfmot.sparsity.keras.PrunableLayer serves two use cases:

1. Prune a custom Keras layer

2. Modify parts of a built-in Keras layer to prune.

For an example, the API defaults to only pruning the kernel of the Dense layer. The example below prunes the bias also.

class MyDenseLayer(tf.keras.layers.Dense, tfmot.sparsity.keras.PrunableLayer):

  def get_prunable_weights(self):
    # Prune bias also, though that usually harms model accuracy too much.
    return [self.kernel, self.bias]

# Use `prune_low_magnitude` to make the `MyDenseLayer` layer train with pruning.
model_for_pruning = tf.keras.Sequential([
  tfmot.sparsity.keras.prune_low_magnitude(MyDenseLayer(20, input_shape=input_shape)),
  tf.keras.layers.Flatten()
])

model_for_pruning.summary()

Train model

Model.fit

Call the tfmot.sparsity.keras.UpdatePruningStep callback during training.

To help debug training, use the tfmot.sparsity.keras.PruningSummaries callback.

# Define the model.
base_model = setup_model()
base_model.load_weights(pretrained_weights) # optional but recommended for model accuracy
model_for_pruning = tfmot.sparsity.keras.prune_low_magnitude(base_model)

log_dir = tempfile.mkdtemp()
callbacks = [
    tfmot.sparsity.keras.UpdatePruningStep(),
    # Log sparsity and other metrics in Tensorboard.
    tfmot.sparsity.keras.PruningSummaries(log_dir=log_dir)
]

model_for_pruning.compile(
      loss=tf.keras.losses.categorical_crossentropy,
      optimizer='adam',
      metrics=['accuracy']
)

model_for_pruning.fit(
    x_train,
    y_train,
    callbacks=callbacks,
    epochs=2,
)

#docs_infra: no_execute
%tensorboard --logdir={log_dir}

For non-Colab users, you can see the results of a previous run of this code block on TensorBoard.dev.

Custom training loop

Call the tfmot.sparsity.keras.UpdatePruningStep callback during training.

To help debug training, use the tfmot.sparsity.keras.PruningSummaries callback.

# Define the model.
base_model = setup_model()
base_model.load_weights(pretrained_weights) # optional but recommended for model accuracy
model_for_pruning = tfmot.sparsity.keras.prune_low_magnitude(base_model)

# Boilerplate
loss = tf.keras.losses.categorical_crossentropy
optimizer = tf.keras.optimizers.Adam()
log_dir = tempfile.mkdtemp()
unused_arg = -1
epochs = 2
batches = 1 # example is hardcoded so that the number of batches cannot change.

# Non-boilerplate.
model_for_pruning.optimizer = optimizer
step_callback = tfmot.sparsity.keras.UpdatePruningStep()
step_callback.set_model(model_for_pruning)
log_callback = tfmot.sparsity.keras.PruningSummaries(log_dir=log_dir) # Log sparsity and other metrics in Tensorboard.
log_callback.set_model(model_for_pruning)

step_callback.on_train_begin() # run pruning callback
for _ in range(epochs):
  log_callback.on_epoch_begin(epoch=unused_arg) # run pruning callback
  for _ in range(batches):
    step_callback.on_train_batch_begin(batch=unused_arg) # run pruning callback

    with tf.GradientTape() as tape:
      logits = model_for_pruning(x_train, training=True)
      loss_value = loss(y_train, logits)
      grads = tape.gradient(loss_value, model_for_pruning.trainable_variables)
      optimizer.apply_gradients(zip(grads, model_for_pruning.trainable_variables))

  step_callback.on_epoch_end(batch=unused_arg) # run pruning callback

#docs_infra: no_execute
%tensorboard --logdir={log_dir}

For non-Colab users, you can see the results of a previous run of this code block on TensorBoard.dev.

Improve pruned model accuracy

First, look at the tfmot.sparsity.keras.prune_low_magnitude API docs to understand what a pruning schedule is and the math of each type of pruning schedule.

Tips:

• Have a learning rate that's not too high or too low when the model is pruning. Consider the pruning schedule to be a hyperparameter.

• As a quick test, try experimenting with pruning a model to the final sparsity at the begining of training by setting begin_step to 0 with a tfmot.sparsity.keras.ConstantSparsity schedule. You might get lucky with good results.

• Do not prune very frequently to give the model time to recover. The pruning schedule provides a decent default frequency.

• For general ideas to improve model accuracy, look for tips for your use case(s) under "Define model".

Checkpoint and deserialize

You must preserve the optimizer step during checkpointing. This means while you can use Keras HDF5 models for checkpointing, you cannot use Keras HDF5 weights.

# Define the model.
base_model = setup_model()
base_model.load_weights(pretrained_weights) # optional but recommended for model accuracy
model_for_pruning = tfmot.sparsity.keras.prune_low_magnitude(base_model)

_, keras_model_file = tempfile.mkstemp('.h5')

# Checkpoint: saving the optimizer is necessary (include_optimizer=True is the default).
model_for_pruning.save(keras_model_file, include_optimizer=True)

The above applies generally. The code below is only needed for the HDF5 model format (not HDF5 weights and other formats).

# Deserialize model.
with tfmot.sparsity.keras.prune_scope():
  loaded_model = tf.keras.models.load_model(keras_model_file)

loaded_model.summary()

Deploy pruned model

Export model with size compression

Common mistake: both strip_pruning and applying a standard compression algorithm (e.g. via gzip) are necessary to see the compression benefits of pruning.

# Define the model.
base_model = setup_model()
base_model.load_weights(pretrained_weights) # optional but recommended for model accuracy
model_for_pruning = tfmot.sparsity.keras.prune_low_magnitude(base_model)

# Typically you train the model here.

model_for_export = tfmot.sparsity.keras.strip_pruning(model_for_pruning)

print("final model")
model_for_export.summary()

print("\n")
print("Size of gzipped pruned model without stripping: %.2f bytes" % (get_gzipped_model_size(model_for_pruning)))
print("Size of gzipped pruned model with stripping: %.2f bytes" % (get_gzipped_model_size(model_for_export)))

Hardware-specific optimizations

Once different backends enable pruning to improve latency, using block sparsity can improve latency for certain hardware.

Increasing the block size will decrease the peak sparsity that's achievable for a target model accuracy. Despite this, latency can still improve.

For details on what's supported for block sparsity, see the tfmot.sparsity.keras.prune_low_magnitude API docs.

base_model = setup_model()

# For using intrinsics on a CPU with 128-bit registers, together with 8-bit
# quantized weights, a 1x16 block size is nice because the block perfectly
# fits into the register.
pruning_params = {'block_size': [1, 16]}
model_for_pruning = tfmot.sparsity.keras.prune_low_magnitude(base_model, **pruning_params)

model_for_pruning.summary()