Rename all_reduce_sum_gradients to experimental_aggregate_gradients

For some strategies we don't do all reduce, so all_reduce_sum_gradients can be
misleading. The parameter is also changed to experimental because of issues with
CentralStorageStrategy.

PiperOrigin-RevId: 302734837

official/nlp/optimization.py

@@ -140,19 +140,19 @@ class AdamWeightDecay(tf.keras.optimizers.Adam):
   def apply_gradients(self,
                       grads_and_vars,
                       name=None,
-                      all_reduce_sum_gradients=True):
+                      experimental_aggregate_gradients=True):
     grads, tvars = list(zip(*grads_and_vars))
-    if all_reduce_sum_gradients:
-      # when all_reduce_sum_gradients = False, apply_gradients() no longer
-      # implicitly allreduce gradients, users manually allreduce gradient and
-      # passed the allreduced grads_and_vars. For now, the clip_by_global_norm
-      # will be moved to before the explicit allreduce to keep the math
-      # the same as TF 1 and pre TF 2.2 implementation.
+    if experimental_aggregate_gradients:
+      # when experimental_aggregate_gradients = False, apply_gradients() no
+      # longer implicitly allreduce gradients, users manually allreduce gradient
+      # and passed the allreduced grads_and_vars. For now, the
+      # clip_by_global_norm will be moved to before the explicit allreduce to
+      # keep the math the same as TF 1 and pre TF 2.2 implementation.
       (grads, _) = tf.clip_by_global_norm(grads, clip_norm=1.0)
     return super(AdamWeightDecay, self).apply_gradients(
         zip(grads, tvars),
         name=name,
-        all_reduce_sum_gradients=all_reduce_sum_gradients)
+        experimental_aggregate_gradients=experimental_aggregate_gradients)
 
   def _get_lr(self, var_device, var_dtype, apply_state):
     """Retrieves the learning rate with the given state."""

official/staging/training/grad_utils.py

@@ -54,7 +54,7 @@ def _filter_and_allreduce_gradients(grads_and_vars,
   This utils function is used when users intent to explicitly allreduce
   gradients and customize gradients operations before and after allreduce.
   The allreduced gradients are then passed to optimizer.apply_gradients(
-  all_reduce_sum_gradients=False).
+  experimental_aggregate_gradients=False).
 
   Arguments:
       grads_and_vars: gradients and variables pairs.
@@ -139,4 +139,5 @@ def minimize_using_explicit_allreduce(tape,
     grads_and_vars = zip(allreduced_grads, filtered_training_vars)
   if post_allreduce_callbacks:
     grads_and_vars = _run_callbacks(post_allreduce_callbacks, grads_and_vars)
-  optimizer.apply_gradients(grads_and_vars, all_reduce_sum_gradients=False)
+  optimizer.apply_gradients(
+      grads_and_vars, experimental_aggregate_gradients=False)