fix two cast bug in auto parallel

mindspore/ccsrc/parallel/step_auto_parallel.cc

@@ -350,6 +350,8 @@ 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()) {

mindspore/ccsrc/parallel/step_parallel.cc

@@ -374,7 +374,6 @@ 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;
@@ -1971,11 +1970,7 @@ 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);
-<<<<<<< HEAD
-<<<<<<< HEAD
 
-=======
->>>>>>> fix_cast_bug
   // return -> cast
   if (current_prim->name() == CAST && pre_cnode->operator_info() == nullptr) {
     pre_cnode = pre_cnode->input(1)->cast<CNodePtr>();
@@ -1983,8 +1978,7 @@ CNodePtr FindLossCNode(const FuncGraphPtr& func_graph) {
     current_prim = GetValueNode<PrimitivePtr>(pre_cnode->input(0));
   }
 
-=======
->>>>>>> 回退 'Pull Request !17 : [AutoParallel]Fix bug in the case of two cast'
+
   // 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();

tests/ut/python/parallel/test_element_wise_function.py

@@ -268,3 +268,32 @@ 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)