diff --git a/mindspore/ccsrc/parallel/step_auto_parallel.cc b/mindspore/ccsrc/parallel/step_auto_parallel.cc
index aee4247755..c3e3f5893e 100644
--- a/mindspore/ccsrc/parallel/step_auto_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_auto_parallel.cc
@@ -346,8 +346,6 @@ bool IsAutoParallelCareNode(const CNodePtr &cnode) {
 }
 
 OperatorInfoPtr CreateTheOperatorInfo(const PrimitivePtr &prim, const CNodePtr &cnode) {
-  MS_EXCEPTION_IF_NULL(prim);
-  MS_EXCEPTION_IF_NULL(cnode);
   auto attrs = prim->attrs();
   std::vector<Shapes> shape_list = ExtractShape(cnode);
   if (shape_list.empty()) {
@@ -383,8 +381,8 @@ OperatorInfoPtr CreateTheOperatorInfo(const PrimitivePtr &prim, const CNodePtr &
   operator_info->set_outputs_dtype(cnode->Type());
   operator_info->set_cnode(cnode);
   // If no strategy has been configured for this operator, then candidate strategies are generated for
-  // auto-strategy searching, if this primitive is Cast, we ignore the user-specified strategy
-  if (!StrategyFound(attrs) || prim->name() == CAST) {
+  // auto-strategy searching
+  if (!StrategyFound(attrs)) {
     // Compute split_flag_list_, indicating which input has batch dimension. This is ONLY used for preparation for
     // BatchParallelInfo operator
     operator_info->ComputeBatchSplitFlagList();
diff --git a/mindspore/ccsrc/parallel/step_parallel.cc b/mindspore/ccsrc/parallel/step_parallel.cc
index 2d948679d7..927acea705 100644
--- a/mindspore/ccsrc/parallel/step_parallel.cc
+++ b/mindspore/ccsrc/parallel/step_parallel.cc
@@ -370,12 +370,15 @@ bool IsParallelCareNode(const CNodePtr& cnode) {
   if (prim == nullptr) {
     return false;
   }
+  auto attrs = prim->attrs();
   if (IsInBlackList(prim)) {
     MS_LOG(INFO) << "Parallel don't care node: " << prim->name();
     return false;
   }
-  if ((prim->name() == CAST) && (cnode->operator_info() == nullptr)) {
-    return false;
+  if ((prim->name() == CAST)) {
+    if ((!attrs.count(STRATEGY)) && (cnode->operator_info() == nullptr)) {
+      return false;
+    }
   }
 
   return cnode->in_forward_flag();
@@ -645,13 +648,6 @@ LossNodeInfo GetLossNodeInfo(const AnfNodePtr& loss_node) {
   MS_EXCEPTION_IF_NULL(pre_node);
 
   LossNodeInfo node_info;
-  // return -> cast
-  auto pre_cnode = pre_node->cast<CNodePtr>();
-  MS_EXCEPTION_IF_NULL(pre_cnode);
-  auto pre_prim = GetValueNode<PrimitivePtr>(pre_cnode->input(0));
-  if (pre_prim->name() == CAST && pre_cnode->operator_info() == nullptr) {
-    pre_node = pre_cnode->input(1);
-  }
 
   // return -> loss
   if (pre_node == loss_node) {
@@ -1947,13 +1943,6 @@ CNodePtr FindLossCNode(const FuncGraphPtr& func_graph) {
   MS_EXCEPTION_IF_NULL(current_value);
   PrimitivePtr current_prim = current_value->value()->cast<PrimitivePtr>();
   MS_EXCEPTION_IF_NULL(current_prim);
-  // return -> cast
-  if (current_prim->name() == CAST && pre_cnode->operator_info() == nullptr) {
-    pre_cnode = pre_cnode->input(1)->cast<CNodePtr>();
-    MS_EXCEPTION_IF_NULL(pre_cnode);
-    current_prim = GetValueNode<PrimitivePtr>(pre_cnode->input(0));
-  }
-
   // notice: the GetNext op has not input
   if (INVALID_LOSS_OPS.find(current_prim->name()) != INVALID_LOSS_OPS.end()) {
     MS_LOG(INFO) << "The loss is: " << current_prim->name();
diff --git a/tests/ut/python/parallel/test_element_wise_function.py b/tests/ut/python/parallel/test_element_wise_function.py
index a917dce9b6..dfcebdc5ab 100644
--- a/tests/ut/python/parallel/test_element_wise_function.py
+++ b/tests/ut/python/parallel/test_element_wise_function.py
@@ -272,32 +272,3 @@ def test_cast_before_mirror3():
     y = Tensor(np.ones([32, 64]), dtype=ms.float16)
     b = Tensor(np.ones([64, 64]), dtype=ms.float32)
     _executor.compile(net, x, y, b)
-
-
-def test_mul_two_cast():
-    class Net(nn.Cell):
-        def __init__(self, strategy1, strategy2, strategy3):
-            super().__init__()
-            self.mul = P.Mul().set_strategy(strategy1)
-            self.mul2 = P.Mul().set_strategy(strategy2)
-            self.cast = P.Cast().set_strategy(strategy3)
-            self.cast2 = P.Cast().set_strategy(strategy3)
-
-        def construct(self, x, y, b):
-            out = self.mul(x, y)
-            out = self.mul2(out, b)
-            out = self.cast(out, ms.int32)
-            out = self.cast2(out, ms.bool_)
-            return out
-
-    context.set_auto_parallel_context(device_num=8, global_rank=0)
-    strategy1 = ((2, 2), (2, 2))
-    strategy2 = ((8, 1), (8, 1))
-    strategy3 = ((8, 1), )
-    net = GradWrap(Net(strategy1, strategy2, strategy3))
-    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
-
-    x = Tensor(np.ones([128, 32]), dtype=ms.float32)
-    y = Tensor(np.ones([128, 32]), dtype=ms.float32)
-    b = Tensor(np.ones([128, 32]), dtype=ms.float32)
-    _executor.compile(net, x, y, b)