resolve conflict

test=develop

python/paddle/fluid/tests/unittests/test_parallel_executor_fetch_feed.py

@@ -59,12 +59,13 @@ class TestFetchAndFeed(unittest.TestCase):
         exe = fluid.Executor(place)
         exe.run(startup)
 
-        pe = fluid.ParallelExecutor(
-            use_cuda=use_cuda, loss_name=loss.name, main_program=main_program)
-        run_parallel_exe(main_program, pe, use_cuda, data, label, loss)
+        train_cp = compiler.CompiledProgram(main_program).with_data_parallel(
+            loss_name=loss.name)
 
-    def run_parallel_exe_with_fetch(self, main, pe, use_cuda, data, label,
-                                    loss):
+        run_parallel_exe(train_cp, exe, use_cuda, data, label, loss)
+
+    def run_parallel_exe_with_fetch(self, compiled_program, exe, use_cuda, data,
+                                    label, loss):
         def get_data(batch_size=8):
             np.random.seed(5)
             while True:
@@ -79,7 +80,7 @@ class TestFetchAndFeed(unittest.TestCase):
         # conv2d_1.b_0@GRAD. Those variables should not be pruned.
         # fluid.memory_optimize(main)
         fetch_list = []
-        all_vars = main.global_block().vars
+        all_vars = compiled_program._program.global_block().vars
 
         for k, v in all_vars.items():
             if ('tmp' not in k) and (
@@ -90,14 +91,18 @@ class TestFetchAndFeed(unittest.TestCase):
         for batch_id, img_label in enumerate(get_data()):
             img, l = img_label
             train_inputs = {data.name: img, label.name: l}
-            ret = pe.run(fetch_list, feed=train_inputs, return_numpy=True)
+            ret = exe.run(compiled_program,
+                          fetch_list=fetch_list,
+                          feed=train_inputs,
+                          return_numpy=True)
             for i in range(len(fetch_list)):
                 assert not math.isnan(np.sum(ret[i])) and \
                        not math.isinf(np.sum(ret[i]))
             if batch_id == 2:
                 break
 
-    def run_parallel_exe_with_feed(self, main, pe, use_cuda, data, label, loss):
+    def run_parallel_exe_with_feed(self, compiled_program, exe, use_cuda, data,
+                                   label, loss):
         def get_data(batch_size=8):
             np.random.seed(5)
             while True:
@@ -115,7 +120,9 @@ class TestFetchAndFeed(unittest.TestCase):
         reader = feeder.decorate_reader(get_data, multi_devices=True)
 
         for batch_id, data in enumerate(reader()):
-            loss_np = exe.run(train_cp, feed=data, fetch_list=[loss.name])[0]
+            loss_np = exe.run(compiled_program,
+                              feed=data,
+                              fetch_list=[loss.name])[0]
             print(batch_id, loss_np)
             if batch_id == 2:
                 break