add unit test in dist transpiler

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

@@ -393,13 +393,14 @@ class TestDistLookupTableBase(TranspilerTest):
     def network_with_table(self, is_sparse, is_distributed):
         self.table_size = 1000
         self.emb_size = 64
+        self.lookup_table_name = 'shared_w'
 
         def emb_pool(ids):
             emb = fluid.layers.embedding(
                 input=ids,
                 size=[self.table_size, self.emb_size],
                 dtype='float32',
-                param_attr='shared_w',  # share parameter
+                param_attr=self.lookup_table_name,  # share parameter
                 is_sparse=is_sparse,
                 is_distributed=is_distributed)
             pool = fluid.layers.sequence_pool(input=emb, pool_type='average')
@@ -572,7 +573,7 @@ class TestDistLookupTableSliceSize(TestDistLookupTableBase):
 
     def transpiler_test_impl(self):
         config = fluid.DistributeTranspilerConfig()
-        pserver1, startup1 = self.get_pserver(self.pserver1_ep, config)
+        pserver1, _ = self.get_pserver(self.pserver1_ep, config)
 
         self.assertTrue(self.transpiler.has_distributed_lookup_table)
         lookup_table_var = pserver1.global_block().vars[
@@ -582,6 +583,23 @@ class TestDistLookupTableSliceSize(TestDistLookupTableBase):
         self.assertEqual(row_size, calc_row_size)
 
 
+class TestDistArgsInProgram(TestDistLookupTableBase):
+    def net_conf(self):
+        self.network_with_table(is_sparse=True, is_distributed=True)
+
+    def transpiler_test_impl(self):
+        config = fluid.DistributeTranspilerConfig()
+
+        trainer, _ = self.get_trainer()
+
+        self.assertTrue(trainer._is_distributed)
+        self.assertTrue(trainer._is_chief)
+        self.assertEqual(trainer._distributed_lookup_table,
+                         self.lookup_table_name)
+        self.assertEqual(trainer._endpoints,
+                         [self.pserver1_ep, self.pserver2_ep])
+
+
 class TestRMSPropOptimizer(TranspilerTest):
     def net_conf(self):
         x = fluid.layers.data(name='x', shape=[1000], dtype='float32')