Automated rollback of commit cff4768

PiperOrigin-RevId: 470893869

Author: tensorflower-gardener

tensorflow_privacy/__init__.py

@@ -61,14 +61,9 @@
   from tensorflow_privacy.privacy.keras_models.dp_keras_model import make_dp_model_class
 
   # Optimizers
-  from tensorflow_privacy.privacy.optimizers.dp_optimizer_keras import GenericDPAdagradOptimizer
-  from tensorflow_privacy.privacy.optimizers.dp_optimizer_keras import GenericDPAdamOptimizer
-  from tensorflow_privacy.privacy.optimizers.dp_optimizer_keras import GenericDPSGDOptimizer
   from tensorflow_privacy.privacy.optimizers.dp_optimizer_keras import DPKerasAdagradOptimizer
   from tensorflow_privacy.privacy.optimizers.dp_optimizer_keras import DPKerasAdamOptimizer
   from tensorflow_privacy.privacy.optimizers.dp_optimizer_keras import DPKerasSGDOptimizer
-  from tensorflow_privacy.privacy.optimizers.dp_optimizer_keras import make_gaussian_query_optimizer_class
-  from tensorflow_privacy.privacy.optimizers.dp_optimizer_keras import make_keras_generic_optimizer_class
   from tensorflow_privacy.privacy.optimizers.dp_optimizer_keras import make_keras_optimizer_class
 
   from tensorflow_privacy.privacy.optimizers.dp_optimizer_keras_vectorized import VectorizedDPKerasAdagradOptimizer

tensorflow_privacy/privacy/optimizers/BUILD

@@ -18,18 +18,6 @@ py_library(
     deps = ["//tensorflow_privacy/privacy/dp_query:gaussian_query"],
 )
 
-py_library(
-    name = "dp_optimizer_factory",
-    srcs = [
-        "dp_optimizer_keras.py",
-    ],
-    srcs_version = "PY3",
-    deps = [
-        "//tensorflow_privacy/privacy/dp_query",
-        "//tensorflow_privacy/privacy/dp_query:gaussian_query",
-    ],
-)
-
 py_library(
     name = "dp_optimizer_vectorized",
     srcs = [
@@ -44,10 +32,7 @@ py_library(
         "dp_optimizer_keras.py",
     ],
     srcs_version = "PY3",
-    deps = [
-        "//tensorflow_privacy/privacy/dp_query",
-        "//tensorflow_privacy/privacy/dp_query:gaussian_query",
-    ],
+    deps = ["//tensorflow_privacy/privacy/dp_query:gaussian_query"],
 )
 
 py_library(
@@ -99,7 +84,7 @@ py_test(
     python_version = "PY3",
     srcs_version = "PY3",
     deps = [
-        ":dp_optimizer_keras",
-        ":dp_optimizer_keras_vectorized",
+        "//tensorflow_privacy/privacy/optimizers:dp_optimizer_keras",
+        "//tensorflow_privacy/privacy/optimizers:dp_optimizer_keras_vectorized",
     ],
 )

tensorflow_privacy/privacy/optimizers/dp_optimizer_keras.py

@@ -13,28 +13,21 @@
 # limitations under the License.
 # ==============================================================================
 """Differentially private version of Keras optimizer v2."""
-from typing import Optional, Type
-import warnings
 
 import tensorflow as tf
-from tensorflow_privacy.privacy.dp_query import dp_query
-from tensorflow_privacy.privacy.dp_query import gaussian_query
-
 
-def _normalize(microbatch_gradient: tf.Tensor,
-               num_microbatches: float) -> tf.Tensor:
-  """Normalizes `microbatch_gradient` by `num_microbatches`."""
-  return tf.truediv(microbatch_gradient,
-                    tf.cast(num_microbatches, microbatch_gradient.dtype))
+from tensorflow_privacy.privacy.dp_query import gaussian_query
 
 
-def make_keras_generic_optimizer_class(
-    cls: Type[tf.keras.optimizers.Optimizer]):
-  """Returns a differentially private (DP) subclass of `cls`.
+def make_keras_optimizer_class(cls):
+  """Given a subclass of `tf.keras.optimizers.Optimizer`, returns a DP-SGD subclass of it.
 
   Args:
     cls: Class from which to derive a DP subclass. Should be a subclass of
       `tf.keras.optimizers.Optimizer`.
+
+  Returns:
+    A DP-SGD subclass of `cls`.
   """
 
   class DPOptimizerClass(cls):  # pylint: disable=empty-docstring
@@ -145,23 +138,24 @@ class DPOptimizerClass(cls):  # pylint: disable=empty-docstring
 
     def __init__(
         self,
-        dp_sum_query: dp_query.DPQuery,
-        num_microbatches: Optional[int] = None,
-        gradient_accumulation_steps: int = 1,
+        l2_norm_clip,
+        noise_multiplier,
+        num_microbatches=None,
+        gradient_accumulation_steps=1,
         *args,  # pylint: disable=keyword-arg-before-vararg, g-doc-args
         **kwargs):
-      """Initializes the DPOptimizerClass.
+      """Initialize the DPOptimizerClass.
 
       Args:
-        dp_sum_query: `DPQuery` object, specifying differential privacy
-          mechanism to use.
+        l2_norm_clip: Clipping norm (max L2 norm of per microbatch gradients).
+        noise_multiplier: Ratio of the standard deviation to the clipping norm.
         num_microbatches: Number of microbatches into which each minibatch is
-          split. Default is `None` which means that number of microbatches is
-          equal to batch size (i.e. each microbatch contains exactly one
+          split. Default is `None` which means that number of microbatches
+          is equal to batch size (i.e. each microbatch contains exactly one
           example). If `gradient_accumulation_steps` is greater than 1 and
           `num_microbatches` is not `None` then the effective number of
-          microbatches is equal to `num_microbatches *
-          gradient_accumulation_steps`.
+          microbatches is equal to
+          `num_microbatches * gradient_accumulation_steps`.
         gradient_accumulation_steps: If greater than 1 then optimizer will be
           accumulating gradients for this number of optimizer steps before
           applying them to update model weights. If this argument is set to 1
@@ -171,13 +165,13 @@ def __init__(
       """
       super().__init__(*args, **kwargs)
       self.gradient_accumulation_steps = gradient_accumulation_steps
+      self._l2_norm_clip = l2_norm_clip
+      self._noise_multiplier = noise_multiplier
       self._num_microbatches = num_microbatches
-      self._dp_sum_query = dp_sum_query
-      self._was_dp_gradients_called = False
-      # We initialize the self.`_global_state` within the gradient functions
-      # (and not here) because tensors must be initialized within the graph.
-
+      self._dp_sum_query = gaussian_query.GaussianSumQuery(
+          l2_norm_clip, l2_norm_clip * noise_multiplier)
       self._global_state = None
+      self._was_dp_gradients_called = False
 
     def _create_slots(self, var_list):
       super()._create_slots(var_list)  # pytype: disable=attribute-error
@@ -241,62 +235,66 @@ def _compute_gradients(self, loss, var_list, grad_loss=None, tape=None):
       """DP-SGD version of base class method."""
 
       self._was_dp_gradients_called = True
-      if self._global_state is None:
-        self._global_state = self._dp_sum_query.initial_global_state()
-
       # Compute loss.
       if not callable(loss) and tape is None:
         raise ValueError('`tape` is required when a `Tensor` loss is passed.')
-
       tape = tape if tape is not None else tf.GradientTape()
 
-      with tape:
-        if callable(loss):
+      if callable(loss):
+        with tape:
           if not callable(var_list):
             tape.watch(var_list)
 
           loss = loss()
-        if self._num_microbatches is None:
-          num_microbatches = tf.shape(input=loss)[0]
-        else:
-          num_microbatches = self._num_microbatches
-        microbatch_losses = tf.reduce_mean(
-            tf.reshape(loss, [num_microbatches, -1]), axis=1)
-
-        if callable(var_list):
-          var_list = var_list()
+          if self._num_microbatches is None:
+            num_microbatches = tf.shape(input=loss)[0]
+          else:
+            num_microbatches = self._num_microbatches
+          microbatch_losses = tf.reduce_mean(
+              tf.reshape(loss, [num_microbatches, -1]), axis=1)
+
+          if callable(var_list):
+            var_list = var_list()
+      else:
+        with tape:
+          if self._num_microbatches is None:
+            num_microbatches = tf.shape(input=loss)[0]
+          else:
+            num_microbatches = self._num_microbatches
+          microbatch_losses = tf.reduce_mean(
+              tf.reshape(loss, [num_microbatches, -1]), axis=1)
 
       var_list = tf.nest.flatten(var_list)
 
-      sample_params = (
-          self._dp_sum_query.derive_sample_params(self._global_state))
-
       # Compute the per-microbatch losses using helpful jacobian method.
       with tf.keras.backend.name_scope(self._name + '/gradients'):
-        jacobian_per_var = tape.jacobian(
+        jacobian = tape.jacobian(
             microbatch_losses, var_list, unconnected_gradients='zero')
 
-        def process_microbatch(sample_state, microbatch_jacobians):
-          """Process one microbatch (record) with privacy helper."""
-          sample_state = self._dp_sum_query.accumulate_record(
-              sample_params, sample_state, microbatch_jacobians)
-          return sample_state
+        # Clip gradients to given l2_norm_clip.
+        def clip_gradients(g):
+          return tf.clip_by_global_norm(g, self._l2_norm_clip)[0]
 
-        sample_state = self._dp_sum_query.initial_sample_state(var_list)
-        for idx in range(num_microbatches):
-          microbatch_jacobians_per_var = [
-              jacobian[idx] for jacobian in jacobian_per_var
-          ]
-          sample_state = process_microbatch(sample_state,
-                                            microbatch_jacobians_per_var)
+        clipped_gradients = tf.map_fn(clip_gradients, jacobian)
 
-        grad_sums, self._global_state, _ = (
-            self._dp_sum_query.get_noised_result(sample_state,
-                                                 self._global_state))
-        final_grads = tf.nest.map_structure(_normalize, grad_sums,
-                                            [num_microbatches] * len(grad_sums))
+        def reduce_noise_normalize_batch(g):
+          # Sum gradients over all microbatches.
+          summed_gradient = tf.reduce_sum(g, axis=0)
 
-      return list(zip(final_grads, var_list))
+          # Add noise to summed gradients.
+          noise_stddev = self._l2_norm_clip * self._noise_multiplier
+          noise = tf.random.normal(
+              tf.shape(input=summed_gradient), stddev=noise_stddev)
+          noised_gradient = tf.add(summed_gradient, noise)
+
+          # Normalize by number of microbatches and return.
+          return tf.truediv(noised_gradient,
+                            tf.cast(num_microbatches, tf.float32))
+
+        final_gradients = tf.nest.map_structure(reduce_noise_normalize_batch,
+                                                clipped_gradients)
+
+      return list(zip(final_gradients, var_list))
 
     def get_gradients(self, loss, params):
       """DP-SGD version of base class method."""
@@ -324,13 +322,17 @@ def process_microbatch(i, sample_state):
       sample_state = self._dp_sum_query.initial_sample_state(params)
       for idx in range(self._num_microbatches):
         sample_state = process_microbatch(idx, sample_state)
-
       grad_sums, self._global_state, _ = (
           self._dp_sum_query.get_noised_result(sample_state,
                                                self._global_state))
 
-      final_grads = tf.nest.map_structure(
-          _normalize, grad_sums, [self._num_microbatches] * len(grad_sums))
+      def normalize(v):
+        try:
+          return tf.truediv(v, tf.cast(self._num_microbatches, tf.float32))
+        except TypeError:
+          return None
+
+      final_grads = tf.nest.map_structure(normalize, grad_sums)
 
       return final_grads
 
@@ -366,87 +368,7 @@ def apply_gradients(self, *args, **kwargs):
   return DPOptimizerClass
 
 
-def make_gaussian_query_optimizer_class(cls):
-  """Returns a differentially private optimizer using the `GaussianSumQuery`.
-
-  Args:
-    cls: `DPOptimizerClass`, the output of `make_keras_optimizer_class`.
-  """
-
-  def return_gaussian_query_optimizer(
-      l2_norm_clip: float,
-      noise_multiplier: float,
-      num_microbatches: Optional[int] = None,
-      gradient_accumulation_steps: int = 1,
-      *args,  # pylint: disable=keyword-arg-before-vararg, g-doc-args
-      **kwargs):
-    """Returns a `DPOptimizerClass` `cls` using the `GaussianSumQuery`.
-
-    This function is a thin wrapper around
-    `make_keras_optimizer_class.<locals>.DPOptimizerClass` which can be used to
-    apply a `GaussianSumQuery` to any `DPOptimizerClass`.
-
-    When combined with stochastic gradient descent, this creates the canonical
-    DP-SGD algorithm of "Deep Learning with Differential Privacy"
-    (see https://arxiv.org/abs/1607.00133).
-
-    Args:
-      l2_norm_clip: Clipping norm (max L2 norm of per microbatch gradients).
-      noise_multiplier: Ratio of the standard deviation to the clipping norm.
-      num_microbatches: Number of microbatches into which each minibatch is
-        split. Default is `None` which means that number of microbatches is
-        equal to batch size (i.e. each microbatch contains exactly one example).
-        If `gradient_accumulation_steps` is greater than 1 and
-        `num_microbatches` is not `None` then the effective number of
-        microbatches is equal to `num_microbatches *
-        gradient_accumulation_steps`.
-      gradient_accumulation_steps: If greater than 1 then optimizer will be
-        accumulating gradients for this number of optimizer steps before
-        applying them to update model weights. If this argument is set to 1 then
-        updates will be applied on each optimizer step.
-      *args: These will be passed on to the base class `__init__` method.
-      **kwargs: These will be passed on to the base class `__init__` method.
-    """
-    dp_sum_query = gaussian_query.GaussianSumQuery(
-        l2_norm_clip, l2_norm_clip * noise_multiplier)
-    return cls(
-        dp_sum_query=dp_sum_query,
-        num_microbatches=num_microbatches,
-        gradient_accumulation_steps=gradient_accumulation_steps,
-        *args,
-        **kwargs)
-
-  return return_gaussian_query_optimizer
-
-
-def make_keras_optimizer_class(cls: Type[tf.keras.optimizers.Optimizer]):
-  """Returns a differentially private optimizer using the `GaussianSumQuery`.
-
-  For backwards compatibility, we create this symbol to match the previous
-  output of `make_keras_optimizer_class` but using the new logic.
-
-  Args:
-    cls: Class from which to derive a DP subclass. Should be a subclass of
-      `tf.keras.optimizers.Optimizer`.
-  """
-  warnings.warn(
-      '`make_keras_optimizer_class` will be depracated on 2023-02-23. '
-      'Please switch to `make_gaussian_query_optimizer_class` and the '
-      'generic optimizers (`make_keras_generic_optimizer_class`).')
-  return make_gaussian_query_optimizer_class(
-      make_keras_generic_optimizer_class(cls))
-
-
-GenericDPAdagradOptimizer = make_keras_generic_optimizer_class(
+DPKerasAdagradOptimizer = make_keras_optimizer_class(
     tf.keras.optimizers.Adagrad)
-GenericDPAdamOptimizer = make_keras_generic_optimizer_class(
-    tf.keras.optimizers.Adam)
-GenericDPSGDOptimizer = make_keras_generic_optimizer_class(
-    tf.keras.optimizers.SGD)
-
-# We keep the same names for backwards compatibility.
-DPKerasAdagradOptimizer = make_gaussian_query_optimizer_class(
-    GenericDPAdagradOptimizer)
-DPKerasAdamOptimizer = make_gaussian_query_optimizer_class(
-    GenericDPAdamOptimizer)
-DPKerasSGDOptimizer = make_gaussian_query_optimizer_class(GenericDPSGDOptimizer)
+DPKerasAdamOptimizer = make_keras_optimizer_class(tf.keras.optimizers.Adam)
+DPKerasSGDOptimizer = make_keras_optimizer_class(tf.keras.optimizers.SGD)