diff --git a/paddle/fluid/imperative/partial_grad_engine.cc b/paddle/fluid/imperative/partial_grad_engine.cc
index c9081ddd8a969a0a0c00061b3c5855c09e09eac8..0b45c189dd714adedc1fb1600e2b350c3dedb62b 100644
--- a/paddle/fluid/imperative/partial_grad_engine.cc
+++ b/paddle/fluid/imperative/partial_grad_engine.cc
@@ -36,6 +36,15 @@
 namespace paddle {
 namespace imperative {
 
+struct HashPair {
+  template <class T1, class T2>
+  size_t operator()(const std::pair<T1, T2> &p) const noexcept {
+    auto hash1 = std::hash<T1>{}(p.first);
+    auto hash2 = std::hash<T2>{}(p.second);
+    return hash1 ^ hash2;
+  }
+};
+
 /**
  * This function prunes the graph to get the ops between `output_targets`
  * and `input_target_grads`.
@@ -152,8 +161,10 @@ static void GetGraphInfoBetweenTargets(
   target_vars = *input_target_grads;
 
   std::queue<std::pair<OpBase * /*op*/, OpBase * /*pending op*/>> op_queue;
+  std::unordered_set<std::pair<OpBase *, OpBase *>, HashPair> op_base_visited;
   for (auto &endpoint_op : endpoint_ops) {
     op_queue.emplace(endpoint_op, nullptr);
+    op_base_visited.emplace(endpoint_op, nullptr);
   }
 
   while (!op_queue.empty()) {
@@ -207,6 +218,7 @@ static void GetGraphInfoBetweenTargets(
     if (pending_op) {
       VLOG(10) << "Pending op of " << op->Type() << " is "
                << pending_op->Type();
+
       pending_ops[op].insert(pending_op);
       ++op_deps[pending_op];
     } else {
@@ -216,7 +228,10 @@ static void GetGraphInfoBetweenTargets(
     auto iter = preceding_ops.find(op);
     if (iter != preceding_ops.end()) {
       for (auto &preceding_op : iter->second) {
-        op_queue.emplace(preceding_op, op);
+        if (op_base_visited.count(std::make_pair(preceding_op, op)) == 0) {
+          op_queue.emplace(preceding_op, op);
+          op_base_visited.emplace(preceding_op, op);
+        }
       }
     }
   }
diff --git a/python/paddle/fluid/tests/unittests/test_imperative_double_grad.py b/python/paddle/fluid/tests/unittests/test_imperative_double_grad.py
index 5f1d020bff89c12f81f413d2eb803771ed782598..d7d0af009034bcd26cdb3e2f7dedb81a90233b71 100644
--- a/python/paddle/fluid/tests/unittests/test_imperative_double_grad.py
+++ b/python/paddle/fluid/tests/unittests/test_imperative_double_grad.py
@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import paddle.fluid as fluid
+import paddle
 from paddle.fluid.wrapped_decorator import wrap_decorator
 import unittest
 from unittest import TestCase
@@ -295,5 +296,48 @@ class TestDygraphDoubleGradSortGradient(TestDygraphDoubleGrad):
         self.shape = [5, 10]
 
 
+class TestDygraphDoubleGradVisitedUniq(TestCase):
+    def test_compare(self):
+        value = np.random.uniform(-0.5, 0.5, 100).reshape(10, 2,
+                                                          5).astype("float32")
+
+        def model_f(input):
+            linear = fluid.dygraph.Linear(5, 3, bias_attr=False)
+            for i in range(10):
+                if i == 0:
+                    out = linear(input)
+                else:
+                    out = out + linear(input)
+            return out
+
+        backward_strategy = fluid.dygraph.BackwardStrategy()
+        backward_strategy.sort_sum_gradient = True
+        with fluid.dygraph.guard():
+            fluid.default_startup_program().random_seed = 123
+            fluid.default_main_program().random_seed = 123
+            a = fluid.dygraph.to_variable(value)
+            a.stop_gradient = False
+
+            out = model_f(a)
+
+            dx=fluid.dygraph.grad(outputs=[out],inputs=[a],create_graph=False,retain_graph=False,  \
+                        only_inputs=True,allow_unused=False, backward_strategy=backward_strategy)
+
+            grad_1 = dx[0].numpy()
+
+        with fluid.dygraph.guard():
+            fluid.default_startup_program().random_seed = 123
+            fluid.default_main_program().random_seed = 123
+            a = fluid.dygraph.to_variable(value)
+            a.stop_gradient = False
+
+            out = model_f(a)
+            out.backward(backward_strategy)
+
+            grad_2 = a.gradient()
+
+        self.assertTrue(np.array_equal(grad_1, grad_2))
+
+
 if __name__ == '__main__':
     unittest.main()