Copybara import of the project:

--
a248f89 by Ruomei Yan <[email protected]>:

Enable differentiable training and update cluster indices

COPYBARA_INTEGRATE_REVIEW=#519 from Ruomei:toupstream/enable_differentiable_training a248f89
PiperOrigin-RevId: 333108062

Author: Ruomei

tensorflow_model_optimization/python/core/clustering/keras/cluster_wrapper.py

@@ -27,6 +27,7 @@
 k = keras.backend
 Layer = keras.layers.Layer
 Wrapper = keras.layers.Wrapper
+CentroidInitialization = cluster_config.CentroidInitialization
 
 
 class ClusterWeights(Wrapper):
@@ -105,7 +106,7 @@ def __init__(self,
     self.number_of_clusters = number_of_clusters
 
     # Stores the pairs of weight names and references to their tensors
-    self.clustered_vars = []
+    self.ori_weights_vars_tf = {}
 
     # Stores references to class instances that implement different clustering
     # behaviour for different shapes of objects
@@ -194,7 +195,7 @@ def build(self, input_shape):
       # initial value taken from a Tensor object. For each weight there is a
       # different set of cluster centroids
       self.cluster_centroids_tf[weight_name] = self.add_weight(
-          'cluster_centroids_tf',
+          '{}{}'.format('cluster_centroids_tf_', weight_name),
           shape=(self.number_of_clusters,),
           dtype=weight.dtype,
           trainable=True,
@@ -217,7 +218,7 @@ def build(self, input_shape):
       pulling_indices = self.clustering_impl[weight_name].\
           get_pulling_indices(weight)
       self.pulling_indices_tf[weight_name] = self.add_weight(
-          'pulling_indices_tf',
+          '{}{}'.format('pulling_indices_tf_', weight_name),
           shape=pulling_indices.shape,
           dtype=tf.int32,
           trainable=False,
@@ -227,21 +228,30 @@ def build(self, input_shape):
       )
 
       # We store these pairs to easily update this variables later on
-      self.clustered_vars.append((weight_name, weight))
+      self.ori_weights_vars_tf[weight_name] = self.add_weight(
+          '{}{}'.format('ori_weights_vars_tf_', weight_name),
+          shape=weight.shape,
+          dtype=weight.dtype,
+          trainable=True,
+          initializer=initializers.Constant(
+              value=k.batch_get_value([weight])[0]
+          )
+      )
 
     # We use currying here to get an updater which can be triggered at any time
     # in future and it would return the latest version of clustered weights
     def get_updater(for_weight_name):
       def fn():
-        return self.clustering_impl[for_weight_name].get_clustered_weight(
-            self.pulling_indices_tf[for_weight_name]
-        )
+        # Get the clustered weights
+        pulling_indices = self.pulling_indices_tf[for_weight_name]
+        clustered_weights = self.clustering_impl[for_weight_name].\
+            get_clustered_weight(pulling_indices)
+        return clustered_weights
 
       return fn
 
     # This will allow us to restore the order of weights later
     # This loop stores pairs of weight names and how to restore them
-
     for ct, weight in enumerate(self.layer.weights):
       name = self._weight_name(weight.name)
       full_name = self.layer.name + "/" + name
@@ -253,14 +263,26 @@ def fn():
         self.restore.append((name, full_name, weight))
 
   def call(self, inputs):
+    # In the forward pass, we need to update the cluster associations manually
+    # since they are integers and not differentiable. Gradients won't flow back
+    # through tf.argmin
     # Go through all tensors and replace them with their clustered copies.
-    for weight_name, _ in self.clustered_vars:
-      setattr(
-          self.layer, weight_name,
-          self.clustering_impl[weight_name].get_clustered_weight(
-              self.pulling_indices_tf[weight_name]
-          )
-      )
+    for weight_name in self.ori_weights_vars_tf:
+      pulling_indices = self.pulling_indices_tf[weight_name]
+
+      # Update cluster associations
+      pulling_indices.assign(tf.dtypes.cast(
+          self.clustering_impl[weight_name].\
+              get_pulling_indices(self.ori_weights_vars_tf[weight_name]),
+          pulling_indices.dtype
+      ))
+
+      clustered_weights = self.clustering_impl[weight_name].\
+          get_clustered_weight_forward(pulling_indices,\
+              self.ori_weights_vars_tf[weight_name])
+
+      # Replace the weights with their clustered counterparts
+      setattr(self.layer, weight_name, clustered_weights)
 
     return self.layer.call(inputs)
 
@@ -271,7 +293,7 @@ def get_config(self):
     base_config = super(ClusterWeights, self).get_config()
     config = {
         'number_of_clusters': self.number_of_clusters,
-        'cluster_centroids_init': self.cluster_centroids_init,
+        'cluster_centroids_init': self.cluster_centroids_init
     }
     return dict(list(base_config.items()) + list(config.items()))
 

tensorflow_model_optimization/python/core/clustering/keras/cluster_wrapper_test.py

@@ -15,7 +15,6 @@
 """Tests for keras ClusterWeights wrapper API."""
 
 import itertools
-import numpy as np
 import tensorflow as tf
 
 from absl.testing import parameterized
@@ -155,9 +154,9 @@ def testIfLayerHasBatchShapeClusterWeightsMustHaveIt(self):
       *itertools.product(
           range(2, 16, 4),
           (
-            CentroidInitialization.LINEAR,
-            CentroidInitialization.RANDOM,
-            CentroidInitialization.DENSITY_BASED
+              CentroidInitialization.LINEAR,
+              CentroidInitialization.RANDOM,
+              CentroidInitialization.DENSITY_BASED
           )
       )
   )
@@ -170,12 +169,8 @@ def testValuesAreClusteredAfterStripping(self,
     or equal to number_of_clusters.
     """
     original_model = tf.keras.Sequential([
-        layers.Dense(32, input_shape=(10,), name='abc'),
+        layers.Dense(32, input_shape=(10,)),
     ])
-
-    weights_name = original_model.layers[0].weights[0].name
-    bias_name = original_model.layers[0].weights[1].name
-
     clustered_model = cluster.cluster_weights(
         original_model,
         number_of_clusters=number_of_clusters,
@@ -190,9 +185,67 @@ def testValuesAreClusteredAfterStripping(self,
     # Make sure that the stripped layer is the Dense one
     self.assertIsInstance(stripped_model.layers[0], layers.Dense)
 
-    # Check that we keep names for weights/bias
-    self.assertEqual(stripped_model.layers[0].weights[0].name, weights_name)
-    self.assertEqual(stripped_model.layers[0].weights[1].name, bias_name)
+  def testClusterReassociation(self):
+    """
+    Verifies that the association of weights to cluster centroids are updated
+    every iteration.
+    """
+
+    # Create a dummy layer for this test
+    input_shape = (1, 2,)
+    l = cluster_wrapper.ClusterWeights(
+        keras.layers.Dense(8, input_shape=input_shape),
+        number_of_clusters=2,
+        cluster_centroids_init=CentroidInitialization.LINEAR
+    )
+    # Build a layer with the given shape
+    l.build(input_shape)
+
+    # Get name of the clusterable weights
+    clusterable_weights = l.layer.get_clusterable_weights()
+    self.assertEqual(len(clusterable_weights), 1)
+    weights_name = clusterable_weights[0][0]
+    self.assertEqual(weights_name, 'kernel')
+    # Get cluster centroids
+    centroids = l.cluster_centroids_tf[weights_name]
+
+    # Calculate some statistics of the weights to set the centroids later on
+    mean_weight = tf.reduce_mean(l.layer.kernel)
+    min_weight = tf.reduce_min(l.layer.kernel)
+    max_weight = tf.reduce_max(l.layer.kernel)
+    max_dist = max_weight - min_weight
+
+    def assert_all_weights_associated(weights, centroid_index):
+      """Helper function to make sure that all weights are associated with one
+      centroid."""
+      all_associated = tf.reduce_all(
+          tf.equal(
+              weights,
+              tf.constant(centroids[centroid_index], shape=weights.shape)
+          )
+      )
+      self.assertTrue(all_associated)
+
+    # Set centroids so that all weights should be re-associated with centroid 0
+    centroids[0].assign(mean_weight)
+    centroids[1].assign(mean_weight + 2.0 * max_dist)
+
+    # Update associations of weights to centroids
+    l.call(tf.ones(shape=input_shape))
+
+    # Weights should now be all clustered with the centroid 0
+    assert_all_weights_associated(l.layer.kernel, centroid_index=0)
+
+    # Set centroids so that all weights should be re-associated with centroid 1
+    centroids[0].assign(mean_weight - 2.0 * max_dist)
+    centroids[1].assign(mean_weight)
+
+    # Update associations of weights to centroids
+    l.call(tf.ones(shape=input_shape))
+
+    # Weights should now be all clustered with the centroid 1
+    assert_all_weights_associated(l.layer.kernel, centroid_index=1)
+
 
 if __name__ == '__main__':
   test.main()

tensorflow_model_optimization/python/core/clustering/keras/clustering_registry.py

@@ -64,6 +64,22 @@ def get_pulling_indices(self, weight):
     """
     pass
 
+  @tf.custom_gradient
+  def add_custom_gradients(self, clst_weights, weights):
+    """
+    This function overrides gradients in the backprop stage: original mul
+    becomes add, tf.sign becomes tf.identity. It is to update the original
+    weights with the gradients updates directly from the layer wrapped. We
+    assume the gradients updates on individual elements inside a cluster
+    will be different so that there is no point of mapping the gradient
+    updates back to original weight matrix using the LUT.
+    """
+    override_weights = tf.sign(tf.reshape(weights, shape=(-1,)) + 1e+6)
+    z = clst_weights*override_weights
+    def grad(dz):
+      return dz, dz
+    return z, grad
+
   def get_clustered_weight(self, pulling_indices):
     """
     Takes an array with integer number that represent lookup indices and forms a
@@ -75,9 +91,23 @@ def get_clustered_weight(self, pulling_indices):
     return tf.reshape(
         tf.gather(self.cluster_centroids,
                   tf.reshape(pulling_indices, shape=(-1,))),
-        pulling_indices.shape
+        shape=pulling_indices.shape
     )
 
+  def get_clustered_weight_forward(self, pulling_indices, weight):
+    """
+    Takes indices (pulling_indices) and original weights (weight) as inputs
+    and then forms a new array according to the given indices. The original
+    weights (weight) here are added to the graph since we want the backprop
+    to update their values via the new implementation using tf.custom_gradient
+    :param pulling_indices: an array of indices used for lookup.
+    :param weight: the original weights of the wrapped layer.
+    :return: array with the same shape as `pulling_indices`. Each array element
+      is a member of self.cluster_centroids
+    """
+    x = tf.reshape(self.get_clustered_weight(pulling_indices), shape=(-1,))
+    return tf.reshape(self.add_custom_gradients(
+        x, tf.reshape(weight, shape=(-1,))), pulling_indices.shape)
 
 class ConvolutionalWeightsCA(AbstractClusteringAlgorithm):
   """

tensorflow_model_optimization/python/core/clustering/keras/clustering_registry_test.py

@@ -45,6 +45,48 @@ def _pull_values(self, ca, pulling_indices, expected_output):
 
     self.assertSequenceEqual(res_np_list, expected_output)
 
+  def _check_gradients(self, ca, weight, pulling_indices, expected_output):
+    pulling_indices_tf = tf.convert_to_tensor(pulling_indices)
+    weight_tf = tf.convert_to_tensor(weight)
+    with tf.GradientTape(persistent=True) as t:
+      t.watch(pulling_indices_tf)
+      t.watch(weight_tf)
+      cls_weights_tf = tf.reshape(
+          ca.get_clustered_weight(pulling_indices_tf), shape=(-1,))
+      t.watch(cls_weights_tf)
+      out_forward = ca.add_custom_gradients(cls_weights_tf, weight_tf)
+      grad_cls_weight = t.gradient(out_forward, cls_weights_tf)
+      grad_weight = t.gradient(out_forward, weight_tf)
+
+      chk_output = tf.math.equal(grad_cls_weight, grad_weight)
+      chk_output_np = k.batch_get_value(chk_output)
+
+      self.assertSequenceEqual(chk_output_np, expected_output)
+
+  @parameterized.parameters(
+      ([-0.800450444, 0.864694357],
+       [[0.220442653, 0.854694366, 0.0328432359, 0.506857157],
+        [0.0527950861, -0.659555554, -0.849919915, -0.54047],
+        [-0.305815876, 0.0865516588, 0.659202456, -0.355699599],
+        [-0.348868281, -0.662001, 0.6171574, -0.296582848]],
+       [[1, 1, 1, 1],
+        [1, 0, 0, 0],
+        [0, 1, 1, 0],
+        [0, 0, 1, 0]],
+       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+      )
+  )
+  def testDenseWeightsCAGrad(self,
+                             clustering_centroids,
+                             weight,
+                             pulling_indices,
+                             expected_output):
+    """
+    Verifies that the gradients of DenseWeightsCA work as expected.
+    """
+    ca = clustering_registry.DenseWeightsCA(clustering_centroids)
+    self._check_gradients(ca, weight, pulling_indices, expected_output)
+
   @parameterized.parameters(
       ([-1, 1], [[0, 0, 1], [1, 1, 1]], [[-1, -1, 1], [1, 1, 1]]),
       ([-1, 0, 1], [[1, 1, 1], [1, 1, 1]], [[0, 0, 0], [0, 0, 0]]),
@@ -73,6 +115,29 @@ def testBiasWeightsCA(self,
     ca = clustering_registry.BiasWeightsCA(clustering_centroids)
     self._pull_values(ca, pulling_indices, expected_output)
 
+  @parameterized.parameters(
+      ([0.0, 3.0],
+       [[0.1, 0.1, 0.1],
+        [3.0, 3.0, 3.0],
+        [0.2, 0.2, 0.2]],
+       [[0, 0, 0],
+        [1, 1, 1],
+        [0, 0, 0]],
+       [1, 1, 1, 1, 1, 1, 1, 1, 1]
+      )
+  )
+  def testConvolutionalWeightsCAGrad(self,
+                                     clustering_centroids,
+                                     weight,
+                                     pulling_indices,
+                                     expected_output):
+    """
+    Verifies that the gradients of ConvolutionalWeightsCA work as expected.
+    """
+    ca = clustering_registry.DenseWeightsCA(clustering_centroids)
+    self._check_gradients(ca, weight, pulling_indices, expected_output)
+
+
   @parameterized.parameters(
       ([0, 3], [[[[0, 0, 0], [1, 1, 1], [0, 0, 0]]]],
        [[[[0, 0, 0], [3, 3, 3], [0, 0, 0]]]]),