Implement fast gradient clipping for loss functions that use inputs that are fed into shared weights.

PiperOrigin-RevId: 625395017

tensorflow_privacy/privacy/fast_gradient_clipping/BUILD

@@ -87,6 +87,7 @@ py_test(
     name = "clip_grads_test",
     srcs = ["clip_grads_test.py"],
     python_version = "PY3",
+    shard_count = 8,
     srcs_version = "PY3",
     deps = [
         ":clip_grads",

tensorflow_privacy/privacy/fast_gradient_clipping/clip_grads.py

@@ -21,6 +21,7 @@ of the approach given in https://arxiv.org/pdf/2009.03106.pdf (see the
 `compute_gradient_norms()` function).
 """
 
+import collections
 from collections.abc import Sequence
 from typing import Optional
 
@@ -56,6 +57,7 @@ def get_registry_generator_fn(
             layer_instance, args, kwargs, tape, num_microbatches
         )
         return layer_outputs, (
+            str(id(layer_instance)),
             layer_vars,
             layer_sqr_norm_fn,
             layer_instance.trainable_weights,
@@ -156,32 +158,47 @@ def compute_gradient_norms(
   # Unwrap the generator outputs so that the next loop avoids duplicating
   # backprop ops.
   filtered_outputs = [t for t in generator_outputs_list if t is not None]
-  vars_list = []
-  sqr_norm_fns_list = []
   if trainable_vars is not None:
     # Create a set using `ref()` for fast set membership check. tf.Variable
     # itself is not hashable.
     trainable_vars = set([v.ref() for v in trainable_vars])
-  for v, f, weights_list in filtered_outputs:
+  layer_vars = collections.defaultdict(list)
+  layer_sqr_norm_fns = collections.defaultdict(list)
+  # The case of shared weights:
+  #   If a layer is called k times, it will appear k times in filtered_outputs,
+  #   with the same id, but potentially with different v and f. The code below
+  #   groups filtered_outputs by layer_id, so we can correctly compute gradient
+  #   norms. The gradient norm of a layer that occurs k times is computed as
+  #   $sqrt(k * \sum_i c_i^2)$ where $c_i$ is the norm estimate of its i-th
+  #   occurrence. This is an over-estimate of the actual norm. For more details,
+  #   see the explanation in go/dp-sgd-shared-weights.
+  for layer_id, v, f, weights_list in filtered_outputs:
     if trainable_vars is None or any(
         w.ref() in trainable_vars for w in weights_list
     ):
-      # Include only those variables in trainable_vars.
+      layer_vars[layer_id].append(v)
-      vars_list.append(v)
+      layer_sqr_norm_fns[layer_id].append(f)
-      sqr_norm_fns_list.append(f)
   # Second loop evaluates the squared L2 norm functions and appends the results.
-  grads_list = tape.gradient(
+  layer_grad_vars = tape.gradient(
       summed_loss,
-      vars_list,
+      layer_vars,
       unconnected_gradients=tf.UnconnectedGradients.ZERO,
   )
-  if not grads_list:
+  if not layer_grad_vars:
     raise ValueError('The gradient list cannot be empty.')
-  if len(grads_list) != len(sqr_norm_fns_list):
-    raise ValueError('There must be as many norms as gradients.')
   sqr_norm_list = []
-  for grads, f in zip(grads_list, sqr_norm_fns_list):
+  for layer_id in layer_sqr_norm_fns.keys():
-    sqr_norm_list.append(f(grads))
+    fns = layer_sqr_norm_fns[layer_id]
+    grads = layer_grad_vars[layer_id]
+    # Number of duplicates for this layer in `filtered_outputs`.
+    num_passes = len(fns)
+    if len(fns) != len(grads):
+      raise ValueError(
+          'There must be as many gradients as squared norm functions.'
+      )
+    # See go/dp-sgd-shared-weights for more details.
+    for fn, grad in zip(fns, grads):
+      sqr_norm_list.append(num_passes * fn(grad))
   sqr_norm_tsr = tf.stack(sqr_norm_list, axis=1)
   return tf.sqrt(tf.reduce_sum(sqr_norm_tsr, axis=1))
 

tensorflow_privacy/privacy/fast_gradient_clipping/clip_grads_test.py

@@ -197,5 +197,108 @@ class ComputeClippedGradsAndOutputsTest(
       self.assertAlmostEqual(computed_norm, true_norm)
 
 
+class SharedLayerTest(tf.test.TestCase, parameterized.TestCase):
+
+  def _make_shared_model(self, num_inputs, input_dim):
+    base_model = tf.keras.Sequential([tf.keras.layers.Dense(1, use_bias=False)])
+    inputs = []
+    outputs = []
+    for _ in range(num_inputs):
+      input_tensor = tf.keras.Input(shape=[input_dim])
+      inputs.append(input_tensor)
+      output_tensor = base_model(input_tensor)
+      outputs.append(output_tensor)
+    return tf.keras.Model(inputs=inputs, outputs=tf.add_n(outputs))
+
+  def _get_computed_and_true_norms(self, model, x_batch, y_batch, is_eager):
+    model.compile(
+        loss=tf.keras.losses.MeanSquaredError(reduction='none'),
+        run_eagerly=is_eager,
+    )
+    computed_norms = clip_grads.compute_gradient_norms(
+        model, layer_registry.make_default_layer_registry(), x_batch, y_batch
+    )
+    with tf.GradientTape() as tape:
+      y_pred = model(x_batch)
+      loss_value = model.loss(y_pred, y_batch)
+    true_grads = tape.jacobian(loss_value, model.trainable_variables)
+    true_norms = tf.sqrt(
+        tf.add_n([tf.reduce_sum(tf.square(g), axis=[1, 2]) for g in true_grads])
+    )
+    return computed_norms, true_norms
+
+  @parameterized.product(
+      num_inputs=[1, 2, 10],
+      batch_size=[1, 2],
+      input_dim=[1, 3],
+      is_eager=[True, False],
+  )
+  def test_gradient_norms_on_multiple_inputs_are_upper_bounded(
+      self, num_inputs, batch_size, input_dim, is_eager
+  ):
+    model = self._make_shared_model(num_inputs, input_dim)
+    model.compile(
+        loss=tf.keras.losses.MeanSquaredError(reduction='none'),
+        run_eagerly=is_eager,
+    )
+    x_batch = [
+        float(k + 1) * tf.ones([batch_size, input_dim], dtype=tf.float64)
+        for k in range(num_inputs)
+    ]
+    y_batch = tf.reshape(
+        1.0 + tf.range(batch_size, dtype=tf.float32), [batch_size, -1]
+    )
+    computed_norms, true_norms = self._get_computed_and_true_norms(
+        model, x_batch, y_batch, is_eager
+    )
+    self.assertAllLessEqual(true_norms - computed_norms, 1e-3)
+
+  @parameterized.product(
+      num_repeats=[1, 2, 10],
+      batch_size=[1, 2],
+      input_dim=[1, 3],
+      is_eager=[True, False],
+  )
+  def test_gradient_norms_on_single_repeated_input_are_upper_bounded(
+      self, num_repeats, batch_size, input_dim, is_eager
+  ):
+    base_model = tf.keras.Sequential([tf.keras.layers.Dense(1, use_bias=False)])
+    inputs = tf.keras.layers.Input([input_dim])
+    outputs = tf.add_n([base_model(inputs) for _ in range(num_repeats)])
+    model = tf.keras.Model(inputs=inputs, outputs=outputs)
+    x_batch = tf.ones([batch_size, input_dim], dtype=tf.float64)
+    y_batch = tf.reshape(
+        1.0 + tf.range(batch_size, dtype=tf.float32), [batch_size, -1]
+    )
+    computed_norms, true_norms = self._get_computed_and_true_norms(
+        model, x_batch, y_batch, is_eager
+    )
+    self.assertAllLessEqual(true_norms - computed_norms, 1e-3)
+
+  @parameterized.product(
+      batch_size=[1, 2],
+      input_dim=[1, 3],
+      is_eager=[True, False],
+  )
+  def test_gradient_norms_on_input_slices_are_upper_bounded(
+      self, batch_size, input_dim, is_eager
+  ):
+    base_model = tf.keras.Sequential([tf.keras.layers.Dense(1, use_bias=False)])
+    inputs = tf.keras.layers.Input([input_dim, 2])
+    outputs = base_model(inputs[:, :, 0]) + base_model(inputs[:, :, 1])
+    model = tf.keras.Model(inputs=inputs, outputs=outputs)
+    x_batch = tf.reshape(
+        tf.range(batch_size * input_dim * 2, dtype=tf.float64),
+        [batch_size, input_dim, -1],
+    )
+    y_batch = tf.reshape(
+        1.0 + tf.range(batch_size, dtype=tf.float32), [batch_size, -1]
+    )
+    computed_norms, true_norms = self._get_computed_and_true_norms(
+        model, x_batch, y_batch, is_eager
+    )
+    self.assertAllLessEqual(true_norms - computed_norms, 1e-3)
+
+
 if __name__ == '__main__':
   tf.test.main()