Fix model average on multi-GPUs. (#11814) (#11826)

* Fix average_accumulate_op for parallel executor.

* Fix model average on multi-GPUs.

python/paddle/fluid/optimizer.py

@@ -1113,7 +1113,6 @@ class ModelAverage(Optimizer):
 
     Args:
         average_window_rate: The rate of average window.
-        params_grads: A list of parameter-grad variable pairs.
         min_average_window: The minimum size of average window.
         max_average_window: The maximum size of average window.
 
@@ -1122,8 +1121,8 @@ class ModelAverage(Optimizer):
       .. code-block:: python
 
         optimizer = fluid.optimizer.Momentum()
-        _, params_grads = optimizer.minimize(cost)
-        model_average = fluid.optimizer.ModelAverage(params_grads, 0.15,
+        optimizer.minimize(cost)
+        model_average = fluid.optimizer.ModelAverage(0.15,
                                                 min_average_window=10000,
                                                 max_average_window=20000)
         for pass_id in range(args.pass_num):
@@ -1137,7 +1136,6 @@ class ModelAverage(Optimizer):
 
     def __init__(self,
                  average_window_rate,
-                 params_grads=None,
                  min_average_window=10000,
                  max_average_window=10000,
                  **kwargs):
@@ -1146,21 +1144,16 @@ class ModelAverage(Optimizer):
         self.min_average_window = min_average_window
         self.max_average_window = max_average_window
 
-        self.params_grads = [] if params_grads is None else params_grads
-        params = {}
-        for param, grad in self.params_grads:
-            if param.do_model_average != False:
-                params[param.name] = (param, grad)
+        self.params_grads = []
         for param in framework.default_main_program().global_block(
         ).all_parameters():
-            if param.name not in params and param.do_model_average != False:
+            if param.do_model_average != False:
                 grad = param.block.create_var(
                     name=unique_name.generate(".".join([param.name, 'tmp'])),
                     dtype=param.dtype,
                     persistable=False,
                     stop_gradient=True)
-                params[param.name] = (param, grad)
-        self.params_grads = params.values()
+                self.params_grads.append((param, grad))
 
         for param, grad in self.params_grads:
             self._append_average_accumulate_op(param)