fix auto parallel reshape strategy set when it is first operator

mindspore/ccsrc/frontend/parallel/auto_parallel/graph_costmodel.cc

@@ -1565,24 +1565,33 @@ Status CostGraph::InitSelectedStrategy() {
       auto next_iter = std::find_if(out_edges.begin(), out_edges.end(), [&](std::shared_ptr<Edge> edge) {
         return edge->next_operator()->name() == reshape_info->next_operator_name();
       });
-      if (pre_iter != in_edges.end()) {
+      bool reshape_is_first_op = reshape_info->pre_operator_name() == reshape_info->name();
+      if (reshape_is_first_op) {
+        reshape_info->InitSelectedStrategy(reshape_info->selected_strategy());
+      }
+      if (pre_iter != in_edges.end() || reshape_is_first_op) {
         MS_LOG(DEBUG) << "Set reshape input layout by " << reshape_info->pre_operator_name();
         int32_t pre_index = reshape_info->pre_operator_index();
         TensorInfo pre_info;
-        if (ops_[i]->name() == (*pre_iter)->prev_operator()->name()) {
-          pre_info = (*pre_iter)->prev_operator()->inputs_tensor_info()[pre_index];
+        std::shared_ptr<OperatorInfo> pre_op_info;
+        if (reshape_is_first_op) {
+          pre_op_info = reshape_info;
+          pre_info = pre_op_info->inputs_tensor_info()[pre_index];
         } else {
-          pre_info = (*pre_iter)->prev_operator()->outputs_tensor_info()[pre_index];
+          pre_op_info = (*pre_iter)->prev_operator();
+          pre_info = pre_op_info->outputs_tensor_info()[pre_index];
         }
         reshape_info->SetInputLayout(pre_info.tensor_layout());
-        Dimensions stra = pre_info.InferStrategy();
-        if (stra.empty()) {
-          MS_LOG(EXCEPTION) << "Infer strategy by tensor_info failed";
+        if (pre_iter != in_edges.end()) {
+          Dimensions stra = pre_info.InferStrategy();
+          if (stra.empty()) {
+            MS_LOG(EXCEPTION) << "Infer strategy by tensor_info failed";
+          }
+          Strategys stra_inputs = {stra};
+          StrategyPtr reshape_stra =
+            std::make_shared<Strategy>((*pre_iter)->prev_operator()->strategy()->GetInputStage(), stra_inputs);
+          reshape_info->set_strategy(reshape_stra);
         }
-        Strategys stra_inputs = {stra};
-        StrategyPtr reshape_stra =
-          std::make_shared<Strategy>((*pre_iter)->prev_operator()->strategy()->GetInputStage(), stra_inputs);
-        reshape_info->set_strategy(reshape_stra);
       }
       if (next_iter != out_edges.end()) {
         MS_LOG(DEBUG) << "Set reshape output layout by " << reshape_info->next_operator_name();

tests/ut/python/parallel/test_auto_parallel_reshape.py

@@ -245,3 +245,50 @@ def test_reshape_auto_5():
     context.set_auto_parallel_context(parallel_mode="auto_parallel")
     net.set_auto_parallel()
     _executor.compile(net, x, y)
+
+def test_reshape_auto_6():
+    class NetWithLoss6(nn.Cell):
+        def __init__(self, network):
+            super(NetWithLoss6, self).__init__()
+            self.loss = VirtualLoss()
+            self.network = network
+
+        def construct(self, x, y):
+            predict = self.network(x, y)
+            return self.loss(predict)
+
+    class GradWrap6(nn.Cell):
+        def __init__(self, network):
+            super(GradWrap6, self).__init__()
+            self.network = network
+
+        def construct(self, x, y):
+            return grad_all(self.network)(x, y)
+
+    class Net(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.relu = P.ReLU()
+            self.mul = P.Mul()
+            self.reshape = P.Reshape()
+            self.reduce_mean = P.ReduceMean()
+            self.wide_w = Parameter(Tensor(np.ones([4, 1024, 1]), dtype=ms.float32), name="weight")
+
+        def construct(self, x, y):
+            out1 = x + self.wide_w
+            w = self.reshape(self.wide_w, (4, 1024))
+            out1 = self.reduce_mean(out1, 1)
+            out1 = out1 - w
+            out2 = self.mul(y, w)
+            out = out1 + out2
+            return out
+
+    size = 8
+    context.set_auto_parallel_context(device_num=size, global_rank=0)
+    x = Tensor(np.ones([4, 1024, 1]), dtype=ms.float32)
+    y = Tensor(np.ones([4, 1024,]), dtype=ms.float32)
+
+    net = GradWrap6(NetWithLoss6(Net()))
+    context.set_auto_parallel_context(parallel_mode="auto_parallel")
+    net.set_auto_parallel()
+    _executor.compile(net, x, y)