Additional tests on delayed update

opennmt/tests/optim_test.py

@@ -56,6 +56,39 @@ def _check_step(grad, expected_variable, expected_step):
       _check_step([0.0, -3.0], [-5.0, -2.0], 1)  # accum_grad = [-3.0, -2.0]
       _check_step([2.0, -1.0], [-4.0, 1.0], 2)   # accum_grad = [-1.0, -3.0], apply
 
+  def testDelayedUpdateSparseGradients(self):
+    # Test that delayed update does not crash on sparse gradients.
+    global_step = tf.Variable(0, trainable=False, dtype=tf.int64)
+    optimizer = tf.train.GradientDescentOptimizer(1.0)
+    embeddings = tf.Variable([[1.0, 2.0], [3.0, 4.0]])
+    x = tf.nn.embedding_lookup(embeddings, [0])
+    loss = tf.losses.mean_squared_error([[1.1, 2.1]], x)
+    gradients = optimizer.compute_gradients(loss)
+    _ = optim.delayed_update(
+        optimizer,
+        gradients,
+        global_step,
+        accum_count=3)
+
+  def testDelayedUpdateOptimizerSlots(self):
+    # Test that delayed update does not change any variable names, in particular
+    # optimizer variables.
+    def _create_variables(accum_count):
+      global_step = tf.Variable(0, trainable=False, dtype=tf.int64)
+      optimizer = tf.train.AdamOptimizer(1.0)
+      gradient = tf.placeholder(tf.float32, shape=[2])
+      variable = tf.Variable([1.0, 2.0])
+      optim.delayed_update(
+          optimizer,
+          [(gradient, variable)],
+          global_step,
+          accum_count=accum_count)
+      return list(sorted(var.name for var in tf.global_variables()))
+
+    vars_no_accum = _create_variables(accum_count=1)
+    tf.reset_default_graph()
+    vars_accum = _create_variables(accum_count=3)
+    self.assertListEqual(vars_accum, vars_no_accum)
 
 if __name__ == "__main__":
   tf.test.main()