concat 3 tensors in auto parallel mode

mindspore/ccsrc/frontend/parallel/ops_info/concat_info.cc

@@ -223,17 +223,32 @@ Status ConcatInfo::GenerateStrategies(int32_t stage_id) {
       input_split.push_back(1);
     }
   }
-  Shapes splittable_inputs;
-  for (size_t i = 0; i < inputs_shape_.size(); ++i) {
-    splittable_inputs.push_back(input_split);
-  }
+
+  // to generate the first input's strategy
+  Shapes splittable_input = {input_split};
+  Shapes tmp_inputs_shape = {inputs_shape_[0]};
 
   std::vector<StrategyPtr> sp_vector;
   is_auto_parallel_ = true;
-  if (GenerateStrategiesWithBroadcast(stage_id, inputs_shape_, splittable_inputs, &sp_vector) != SUCCESS) {
+  if (GenerateStrategiesForIndependentInputs(stage_id, tmp_inputs_shape, splittable_input, &sp_vector) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Generate strategies failed";
     return FAILED;
   }
 
+  // the others strategies are equal to the first input's strategy
+  for (auto &sp : sp_vector) {
+    if ((sp == nullptr) || sp->GetInputDim().empty()) {
+      MS_LOG(ERROR) << name_ << ": The strategy is null or empty";
+      return FAILED;
+    }
+    Strategys tmp_strategy;
+    Dimensions first_input_strategy = sp->GetInputDim()[0];
+    for (size_t i = 0; i < inputs_shape_.size(); ++i) {
+      tmp_strategy.push_back(first_input_strategy);
+    }
+    sp->ResetInputs(tmp_strategy);
+  }
+
   size_t success = 0;
   for (auto &sp : sp_vector) {
     PrintStrategy(sp);

mindspore/ccsrc/frontend/parallel/ops_info/strided_slice_info.cc

@@ -111,7 +111,6 @@ Status StridedSliceInfo::CheckStrategy(const StrategyPtr &strategy) {
 
   Dimensions strategy_value = stra[0];
   bool has_split = std::any_of(strategy_value.begin(), strategy_value.end(), [](int32_t v) { return v > 1; });
-
   if (has_split && has_mask_) {
     MS_LOG(ERROR) << name_ << ": When there is a mask, the input is not supported to be split";
     return FAILED;

tests/ut/python/parallel/test_concat.py

@@ -50,12 +50,34 @@ class Net2(Cell):
         return out
 
 
+class Net3(Cell):
+    def __init__(self, weight, weight2, weight3, strategy1=None, strategy2=None, is_parameter=True):
+        super().__init__()
+        self.concat = P.Concat(axis=0).set_strategy(strategy1)
+        if is_parameter:
+            self.weight = Parameter(weight, "w1")
+        else:
+            self.weight = weight
+        self.mul = P.Mul().set_strategy(strategy2)
+        self.weight2 = Parameter(weight2, "w2")
+        self.weight3 = Parameter(weight3, "w3")
+
+    def construct(self, x, b):
+        out = self.concat((self.weight, self.weight2, self.weight3))
+        out = self.mul(x, out)
+        return out
+
+
 _x = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
 _w1 = Tensor(np.ones([96, 64, 32]), dtype=ms.float32)
 _w2 = Tensor(np.ones([32, 64, 32]), dtype=ms.float32)
 _w3 = Tensor(np.ones([128, 16, 32]), dtype=ms.float32)
 _b = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
 
+w1 = Tensor(np.ones([48, 64, 32]), dtype=ms.float32)
+w2 = Tensor(np.ones([16, 64, 32]), dtype=ms.float32)
+w3 = Tensor(np.ones([64, 64, 32]), dtype=ms.float32)
+
 
 def compile_net(net):
     context.set_context(save_graphs=True)
@@ -126,3 +148,9 @@ def test_concat_auto_parallel2():
     strategy2 = None
     net = Net2(_w3, strategy1, strategy2, axis=1)
     compile_net(net)
+
+
+def test_concat_auto_parallel_3_tensor():
+    context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
+    net = Net3(w1, w2, w3)
+    compile_net(net)