diff --git a/mindspore/ccsrc/parallel/step_parallel.cc b/mindspore/ccsrc/parallel/step_parallel.cc
index e2b4d55aada..28cddd61119 100644
--- a/mindspore/ccsrc/parallel/step_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_parallel.cc
@@ -49,6 +49,9 @@ namespace mindspore {
 namespace parallel {
 const std::set<std::string> COMMUNICATION_OPS = {ALL_REDUCE, ALL_GATHER, ALL_TO_ALL, REDUCE_SCATTER};
 const std::set<std::string> INVALID_LOSS_OPS = {GET_NEXT, VIRTUALLOSS};
+// g_RefMap, for CNode B input i is a RefKey[Parameter C],
+// it will be one item in map with key: C, and value: (B, i)
+static std::map<AnfNodePtr, std::pair<AnfNodePtr, int>> g_RefMap;
 
 void SetCommunicationOpGroupLabel(std::vector<AnfNodePtr> new_node_input) {
   if (new_node_input.empty()) {
@@ -1085,11 +1088,19 @@ std::vector<Shapes> ExtractShape(const CNodePtr& node) {
   std::vector<AnfNodePtr> all_inputs = node->inputs();
   std::vector<AnfNodePtr> node_inputs{all_inputs.begin() + 1, all_inputs.end()};
 
-  for (auto& input : node_inputs) {
+  size_t inputs_size = all_inputs.size();
+  for (size_t i = 1; i < inputs_size; ++i) {
     Shapes input_shapes;
+    AnfNodePtr input = all_inputs[i];
     if (IsValueNode<RefKey>(input)) {
       auto func_graph = node->func_graph();
       MS_EXCEPTION_IF_NULL(func_graph);
+      std::vector<AnfNodePtr> parameters = FindParameterByRefKeyNode(input, func_graph);
+      if (parameters.size() != 1) {
+        MS_LOG(EXCEPTION) << "Find parameter by ref key node failed";
+      }
+      std::pair<AnfNodePtr, int> node_pair = std::make_pair(node, SizeToInt(i));
+      g_RefMap[parameters[0]] = node_pair;
       input_shapes = GetRefKeyNodeShape(input, func_graph);
     } else if (IsValueNode<Tensor>(input) || input->isa<CNode>() || input->isa<Parameter>()) {
       input_shapes = GetNodeShape(input);
@@ -1205,14 +1216,20 @@ void CoverSliceShape(const FuncGraphPtr& root) {
   auto parameters = root->parameters();
   for (auto& parameter : parameters) {
     MS_EXCEPTION_IF_NULL(parameter->Shape());
+    auto iter = g_RefMap.find(parameter);
+    if (iter != g_RefMap.end()) {
+      SetParallelShape(parameter, g_RefMap[parameter]);
+      continue;
+    }
     std::pair<AnfNodePtr, int> res = FindSubGraph(root, parameter);
     if (res.first == nullptr) {
       MS_LOG(INFO) << "Parameter " << parameter->ToString() << " don't need to set parallel shape";
     } else {
       SetParallelShape(parameter, res);
-      MS_LOG(DEBUG) << "parameter " << parameter->ToString() << "  shape " << parameter->Shape()->ToString();
+      MS_LOG(DEBUG) << "Parameter " << parameter->ToString() << "  shape " << parameter->Shape()->ToString();
     }
   }
+  g_RefMap.clear();
 }
 
 bool ParameterIsCloned(const FuncGraphPtr& root, const AnfNodePtr& parameter_node) {
diff --git a/tests/ut/python/parallel/test_arithmetic.py b/tests/ut/python/parallel/test_arithmetic.py
index 2c7eabc8f2f..4c34c0371ef 100644
--- a/tests/ut/python/parallel/test_arithmetic.py
+++ b/tests/ut/python/parallel/test_arithmetic.py
@@ -13,14 +13,13 @@
 # limitations under the License.
 
 import numpy as np
-from mindspore import context
+import mindspore as ms
+from mindspore import Parameter, Tensor, context
 import mindspore.nn as nn
 from mindspore.ops import operations as P
-from mindspore import Tensor
-from tests.ut.python.ops.test_math_ops import VirtualLoss
-import mindspore as ms
-from mindspore.common.api import _executor
 from mindspore.ops import composite as C
+from mindspore.common.api import _executor
+from tests.ut.python.ops.test_math_ops import VirtualLoss
 
 
 class NetWithLoss(nn.Cell):
@@ -470,3 +469,30 @@ def test_matmul_floordiv_broadcast2():
     y = Tensor(np.ones([32, 1]), dtype=ms.float32)
     b = Tensor(np.ones([1, 64]), dtype=ms.float32)
     _executor.compile(net, x, y, b)
+
+
+def test_assign_sub():
+    class Net(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.assign_sub = P.AssignSub()
+            self.mul = P.Mul()
+            self.mul_weight = Parameter(Tensor(np.full([128, 32],
+                                        0.5, dtype=np.float32)),
+                                        name="mul_weight")
+            self.assignsub_weight = Parameter(Tensor(np.full([128, 32],
+                                              1.1, dtype=np.float32)),
+                                              name="assignsub_weight")
+
+        def construct(self, x, y, z):
+            out = self.mul(x, self.mul_weight)
+            out = self.assign_sub(self.assignsub_weight, out)
+            return out
+
+    context.set_auto_parallel_context(device_num=64, global_rank=15)
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+    net = GradWrap(NetWithLoss(Net()))
+    x = Tensor(np.ones([128, 32]), dtype=ms.float32)
+    y = Tensor(np.ones([128, 32]), dtype=ms.float32)
+    z = Tensor(np.ones([128, 32]), dtype=ms.float32)
+    _executor.compile(net, x, y, z)