In MovingAverageOptimizer, delegate compute_gradients() to the wrapped optimizer,

which is a bug fix in case the wrapper optimizer (or any other optimizer in the stack)
does something non-standard in its compute_gradients() method.

PiperOrigin-RevId: 178447959

tensorflow/contrib/opt/python/training/moving_average_optimizer.py

@@ -86,6 +86,9 @@ class MovingAverageOptimizer(optimizer.Optimizer):
     self._variable_map = None
     self._sequential_update = sequential_update
 
+  def compute_gradients(self, *args, **kwargs):
+    return self._optimizer.compute_gradients(*args, **kwargs)
+
   def apply_gradients(self, grads_and_vars, global_step=None, name=None):
     train_op = self._optimizer.apply_gradients(
         grads_and_vars, global_step=global_step, name=name)

tensorflow/contrib/opt/python/training/moving_average_optimizer_test.py

@@ -116,6 +116,37 @@ class MovingAverageOptimizerTest(test.TestCase):
       with self.assertRaises(RuntimeError):
         _ = opt.swapping_saver([var])
 
+  def testCorrectOverride(self):
+
+    class WrapperOptimizer(gradient_descent.GradientDescentOptimizer):
+
+      def compute_gradients(self, *args, **kwargs):
+        self.compute_gradients_called = True
+        return super(WrapperOptimizer, self).compute_gradients(
+            *args, **kwargs)
+
+      def apply_gradients(self, *args, **kwargs):
+        self.apply_gradients_called = True
+        return super(WrapperOptimizer, self).apply_gradients(*args, **kwargs)
+
+    with self.test_session() as sess:
+      var = variables.Variable([1.2], name='var', dtype=dtypes.float32)
+      loss = var ** 2
+      wrapper_opt = WrapperOptimizer(learning_rate=2.0)
+      opt = moving_average_optimizer.MovingAverageOptimizer(wrapper_opt)
+      train_op = opt.minimize(loss)
+
+      # Check that both methods are called on the underlying optimizer.
+      self.assertTrue(wrapper_opt.compute_gradients_called)
+      self.assertTrue(wrapper_opt.apply_gradients_called)
+
+      # Run train_op once, and verify that we've updated the variable.
+      variables.global_variables_initializer().run()
+      sess.run(train_op)
+      var_value = sess.run(var)
+      # Started at 1.2, gradient is 2*1.2=2.4, lr=2, so should now be -3.6.
+      self.assertNear(-3.6, var_value, 1e-6)
+
 
 if __name__ == '__main__':
   test.main()