fix_cast_bug

mindspore/ccsrc/parallel/step_auto_parallel.cc

@@ -346,6 +346,8 @@ 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()) {
@@ -381,8 +383,8 @@ 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
+  // auto-strategy searching, if this primitive is Cast, we ignore the user-specified strategy
-  if (!StrategyFound(attrs)) {
+  if (!StrategyFound(attrs) || prim->name() == CAST) {
     // Compute split_flag_list_, indicating which input has batch dimension. This is ONLY used for preparation for
     // BatchParallelInfo operator
     operator_info->ComputeBatchSplitFlagList();

mindspore/ccsrc/parallel/step_parallel.cc

@@ -370,15 +370,12 @@ 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)) {
+  if ((prim->name() == CAST) && (cnode->operator_info() == nullptr)) {
-    if ((!attrs.count(STRATEGY)) && (cnode->operator_info() == nullptr)) {
+    return false;
-      return false;
-    }
   }
 
   return cnode->in_forward_flag();
@@ -648,6 +645,13 @@ 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) {
@@ -1943,6 +1947,13 @@ 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();

tests/ut/python/parallel/test_element_wise_function.py

@@ -272,3 +272,32 @@ 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)