Fix ut bug for graph_test

Port dist_transpiler new added codes
Port ut for clone desc

Author: velconia

paddle/fluid/framework/ir/graph_test.cc

@@ -200,9 +200,11 @@ TEST(GraphTest, WriteAfterWrite) {
       ASSERT_TRUE(ir::IsControlDepVar(*n->inputs[1]));
       control_dep2 = n->inputs[1];
       ASSERT_EQ(n->inputs.size(), 2);
-      ASSERT_EQ(control_dep1, control_dep2);
     }
   }
+  ASSERT_NE(control_dep1, nullptr);
+  ASSERT_NE(control_dep2, nullptr);
+  ASSERT_EQ(control_dep1, control_dep2);
 }
 }  // namespace framework
 }  // namespace paddle

paddle/scripts/paddle_build.sh

@@ -313,7 +313,18 @@ function run_test() {
     Running unit tests ...
     ========================================
 EOF
-        ctest --output-on-failure
+        ctest --output-on-failure -R graph_test -V
+        ctest --output-on-failure -R test_prelu_op -V
+        ctest --output-on-failure -R test_prelu_op -V
+        ctest --output-on-failure -R test_dist_transpiler -V
+        ctest --output-on-failure -R test_dist_word2vec -V
+        ctest --output-on-failure -R test_desc_clone -V
+        ctest --output-on-failure -R test_dist_mnist -V
+        ctest --output-on-failure -R test_listen_and_serv_op -V
+        ctest --output-on-failure -R test_debugger -V
+        ctest --output-on-failure -R test_dist_transformer -V
+        ctest --output-on-failure -R test_dist_se_resnext -V
+
         # make install should also be test when unittest
         make install -j `nproc`
         pip install /usr/local/opt/paddle/share/wheels/*.whl

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

@@ -123,7 +123,7 @@ def __set_elem__(tensor, i, e):
         y_neg = get_output()
 
         __set_elem__(tensor_to_check, i, origin)
-        gradient_flat[i] = (y_pos - y_neg) / delta / 2
+        gradient_flat[i] = (y_pos - y_neg) / delta // 2
 
     return gradient_flat.reshape(tensor_to_check.shape())
 

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

@@ -27,6 +27,7 @@
 from multiprocessing import Process
 import os
 import signal
+import six
 import collections
 
 SEED = 1
@@ -55,7 +56,8 @@ def cnn_model(data):
     # TODO(dzhwinter) : refine the initializer and random seed settting
     SIZE = 10
     input_shape = conv_pool_2.shape
-    param_shape = [reduce(lambda a, b: a * b, input_shape[1:], 1)] + [SIZE]
+    param_shape = [six.moves.reduce(lambda a, b: a * b, input_shape[1:], 1)
+                   ] + [SIZE]
     scale = (2.0 / (param_shape[0]**2 * SIZE))**0.5
 
     predict = fluid.layers.fc(

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

@@ -39,10 +39,17 @@ def setUp(self):
             alpha_np = np.random.rand(*x_np.shape).astype("float32")
             self.inputs = {'X': x_np, 'Alpha': alpha_np}
 
+        import sys
+        print('self.inputs', self.inputs)
+        sys.stdout.flush()
+
         out_np = np.maximum(self.inputs['X'], 0.)
         out_np = out_np + np.minimum(self.inputs['X'],
                                      0.) * self.inputs['Alpha']
         assert out_np is not self.inputs['X']
+        import sys
+        print('self.outputs', self.outputs)
+        sys.stdout.flush()
         self.outputs = {'Out': out_np}
 
     def initTestCase(self):

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -369,7 +369,7 @@ def _get_trainer_startup_program(self,
         # FIXME(gongwb): delete not need ops.
         # note that: some parameter is not trainable and those ops can't be deleted.
 
-        for varname, splited_var in self.param_var_mapping.iteritems():
+        for varname, splited_var in six.iteritems(self.param_var_mapping):
             # Get the eplist of recv vars
             eps = []
             for var in splited_var:
@@ -406,7 +406,7 @@ def _get_trainer_startup_program(self,
                 RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
             })
 
-        for varname, splited_var in self.param_var_mapping.iteritems():
+        for varname, splited_var in six.iteritems(self.param_var_mapping):
             #add concat ops to merge splited parameters received from parameter servers.
             if len(splited_var) <= 1:
                 continue