回退 'Pull Request !17 : [AutoParallel]Fix bug in the case of two cast'

mindspore/ccsrc/parallel/step_auto_parallel.cc

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

mindspore/ccsrc/parallel/step_parallel.cc

@@ -374,6 +374,7 @@ 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;
@@ -653,13 +654,6 @@ 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 -> cast
   auto pre_cnode = pre_node->cast<CNodePtr>();
@@ -1978,6 +1972,7 @@ CNodePtr FindLossCNode(const FuncGraphPtr& func_graph) {
   PrimitivePtr current_prim = current_value->value()->cast<PrimitivePtr>();
   MS_EXCEPTION_IF_NULL(current_prim);
 <<<<<<< HEAD
+<<<<<<< HEAD
 
 =======
 >>>>>>> fix_cast_bug
@@ -1988,6 +1983,8 @@ 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

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