[Feature] bind delete_keys parameter on tf_client update_priority

reverb/cc/ops/client.cc

@@ -70,6 +70,7 @@ REGISTER_OP("ReverbClientUpdatePriorities")
     .Input("table: string")
     .Input("keys: uint64")
     .Input("priorities: double")
+    .Input("keys_to_delete: uint64")
     .Doc(R"doc(
 Blocking call to update the priorities of a collection of items. Keys that could
 not be found in table `table` on server are ignored and does not impact the rest
@@ -187,7 +188,9 @@ class UpdatePrioritiesOp : public tensorflow::OpKernel {
     const tensorflow::Tensor* keys;
     OP_REQUIRES_OK(context, context->input("keys", &keys));
     const tensorflow::Tensor* priorities;
-    OP_REQUIRES_OK(context, context->input("priorities", &priorities));
+    OP_REQUIRES_OK(context, context->input("priorities", &priorities));    
+    const tensorflow::Tensor* keys_to_delete;
+    OP_REQUIRES_OK(context, context->input("keys_to_delete", &keys_to_delete));
 
     OP_REQUIRES(
         context, keys->dims() == 1,
@@ -197,6 +200,9 @@ class UpdatePrioritiesOp : public tensorflow::OpKernel {
                     "Tensors `keys` and `priorities` do not match in shape (",
                     keys->shape().DebugString(), " vs. ",
                     priorities->shape().DebugString(), ")"));
+    OP_REQUIRES(
+        context, keys_to_delete->dims() == 1,
+        InvalidArgument("Tensors `keys_to_delete` must be of rank 1."));
 
     std::string table_str = table->scalar<tstring>()();
     std::vector<KeyWithPriority> updates;
@@ -207,14 +213,19 @@ class UpdatePrioritiesOp : public tensorflow::OpKernel {
       updates.push_back(std::move(update));
     }
 
+    std::vector<uint64_t> deletes;
+    for (int i = 0; i < keys_to_delete->dim_size(0); i++) {
+      deletes.push_back(keys_to_delete->flat<tensorflow::uint64>()(i));
+    }
+
     // The call will only fail if the Reverb-server is brought down during an
     // active call (e.g preempted). When this happens the request is retried and
     // since MutatePriorities sets `wait_for_ready` the request will no be sent
     // before the server is brought up again. It is therefore no problem to have
     // this retry in this tight loop.
     absl::Status status;
     do {
-      status = resource->client()->MutatePriorities(table_str, updates, {});
+      status = resource->client()->MutatePriorities(table_str, updates, deletes);
     } while (absl::IsUnavailable(status) || absl::IsDeadlineExceeded(status));
     OP_REQUIRES_OK(context, ToTensorflowStatus(status));
   }

reverb/tf_client.py

@@ -117,6 +117,7 @@ def update_priorities(self,
                         table: str,
                         keys: tf.Tensor,
                         priorities: tf.Tensor,
+                        keys_to_delete: Optional[tf.Tensor] = None,
                         name: Optional[str] = None):
     """Creates op for updating priorities of existing items in the replay.
 
@@ -126,16 +127,20 @@ def update_priorities(self,
       table: Probability table to update.
       keys: Keys of the items to update. Must be same length as `priorities`.
       priorities: New priorities for `keys`. Must be same length as `keys`.
+      keys_to_delete: Keys of the items to delete
       name: Optional name for the operation.
 
     Returns:
       A tf-op for performing the update.
     """
 
+    if keys_to_delete is None:
+      keys_to_delete = tf.constant([], dtype=tf.uint64)
+
     with tf.name_scope(name, f'{self._name}_update_priorities',
                        ['update_priorities']) as scope:
-      return gen_reverb_ops.reverb_client_update_priorities(
-          self._handle, table, keys, priorities, name=scope)
+      return gen_client_ops.reverb_client_update_priorities(
+          self._handle, table, keys, priorities, keys_to_delete, name=scope)
 
   def dataset(self,
               table: str,

reverb/tf_client_test.py

@@ -168,6 +168,39 @@ def test_priority_update_is_applied(self):
       self.fail('Updated item was not found')
 
 
+  def test_delete_key_is_applied(self):
+    # Start with 4 items
+    for i in range(4):
+      self._client.insert([np.array([i], dtype=np.uint32)], {'dist': 1})
+
+    # Until we have recieved all 4 items.
+    items = {}
+    while len(items) < 4:
+      item = next(self._client.sample('dist'))[0]
+      items[item.info.key] = item.info.probability
+
+    # remove 2 items
+    items_to_keep = [*items.keys()][:2]
+    items_to_remove = [*items.keys()][2:]
+    with self.session() as session:
+      client = tf_client.TFClient(self._client.server_address)
+      for key in items_to_remove:
+        update_op = client.update_priorities(
+          table=tf.constant('dist'),
+          keys=tf.constant([], dtype=tf.uint64),
+          priorities=tf.constant([], dtype=tf.float64),
+          keys_to_delete=tf.constant([key], dtype=tf.uint64))
+        self.assertIsNone(session.run(update_op))
+
+    # 2 remaining items must persist
+    final_items = {}
+    for _ in range(1000):
+      item = next(self._client.sample('dist'))[0]
+      self.assertTrue(item.info.key in items_to_keep)
+      final_items[item.info.key] = item.info.probability
+    self.assertEqual(len(final_items), 2)
+
+
 class InsertOpTest(tf.test.TestCase):
 
   @classmethod