add dist transpiler test

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

@@ -875,5 +875,53 @@ class TestRemoteNce(TestDistLookupTableBase):
                 pass
 
 
+# test for remote prefetch
+class TestRemoteHsigmoid(TestDistLookupTableBase):
+    def network_with_table(self, is_sparse, is_distributed):
+
+        num_total_classes = 10
+
+        input = fluid.layers.data(name="input", shape=[10], dtype="float32")
+        label = fluid.layers.data(name="label", shape=[1], dtype="int64")
+        path_table = fluid.layers.data(
+            name='path_table', shape=[10], dtype='int64')
+        path_code = fluid.layers.data(
+            name='path_code', shape=[10], dtype='int64')
+        w_param = fluid.default_main_program().global_block().create_parameter(
+            shape=[num_total_classes, 10],
+            dtype='float32',
+            name='hs_w',
+            initializer=fluid.initializer.ConstantInitializer())
+        b_param = fluid.default_main_program().global_block().create_parameter(
+            shape=[num_total_classes, 1],
+            dtype='float32',
+            name='hs_b',
+            initializer=fluid.initializer.ConstantInitializer())
+
+        cost = fluid.layers.hsigmoid(
+            input=input,
+            label=label,
+            num_classes=non_leaf_num,
+            path_table=path_table,
+            path_code=path_code,
+            is_custom=True,
+            is_sparse=is_sparse)
+        avg_cost = fluid.layers.mean(cost)
+        # optimizer
+        optimizer = fluid.optimizer.SGD(learning_rate=0.003)
+        optimizer.minimize(avg_cost)
+
+    def net_conf(self):
+        import os
+        os.environ['PADDLE_ENABLE_REMOTE_PREFETCH'] = "1"
+        self.network_with_table(is_sparse=True, is_distributed=False)
+
+    def transpiler_test_impl(self):
+        trainer, _ = self.get_trainer()
+        for op in trainer.blocks[0].ops:
+            if op.type == "recv":
+                pass
+
+
 if __name__ == "__main__":
     unittest.main()