fix auto parallel prelu

mindspore/ccsrc/parallel/ops_info/prelu_info.cc

@@ -52,7 +52,7 @@ Status PReLUInfo::CheckStrategy(const StrategyPtr& strategy) {
     }
     return FAILED;
   }
-  if ((stra[0][PRELU_CHANNEL_INDEX] != PRELU_CHANNEL_STRATEGY) || (stra[1][0] != PRELU_CHANNEL_STRATEGY)) {
+  if (stra[0][PRELU_CHANNEL_INDEX] != stra[1][0]) {
     if (is_auto_parallel_) {
       MS_LOG(DEBUG) << name_ << ": Invalid channel strategy.";
     } else {

tests/ut/cpp/parallel/ops_info/prelu_test.cc

@@ -146,11 +146,10 @@ TEST_F(TestPReLUInfo, CheckStrategy1) {
 }
 
 TEST_F(TestPReLUInfo, CheckStrategy2) {
-  // Success: {{2,1,8,16},{1}}
   std::vector<Dimensions> inputs = {{2, 4, 8, 16}, {4}};
   StrategyPtr strategy = NewStrategy(0, inputs);
   Status ret = prelu->Init(strategy);
-  ASSERT_EQ(ret, FAILED);
+  ASSERT_EQ(ret, SUCCESS);
 }
 
 TEST_F(TestPReLUInfo, AutoStrategy1) {
@@ -252,11 +251,10 @@ TEST_F(TestPReLUInfo, CheckStrategy_2d1) {
 }
 
 TEST_F(TestPReLUInfo, CheckStrategy_2d2) {
-  // Success: {{2,1,8,16},{1}}
   std::vector<Dimensions> inputs = {{128, 4}, {4}};
   StrategyPtr strategy = NewStrategy(0, inputs);
   Status ret = prelu_2d->Init(strategy);
-  ASSERT_EQ(ret, FAILED);
+  ASSERT_EQ(ret, SUCCESS);
 }
 
 TEST_F(TestPReLUInfo, AutoStrategy_2d1) {

tests/ut/python/parallel/test_prelu.py

@@ -149,3 +149,20 @@ def test_prelu_parallel_success3():
     w = Tensor(np.random.rand(16),dtype=ms.float32)
     net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
     _executor.compile(net, x, y, w)
+
+def test_prelu_parallel_success4():
+    class Net(nn.Cell):
+        def __init__(self, strategy):
+            super().__init__()
+            self.prelu = P.PReLU().set_strategy(strategy)
+        def construct(self, x, y):
+            out = self.prelu(x, y)
+            return out
+    context.reset_auto_parallel_context()
+    context.set_auto_parallel_context(device_num=64, global_rank=0)
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+    strategy = ((2, 4, 4, 2), (4, ))
+    x = Tensor(np.random.rand(4, 16, 32, 64),dtype=ms.float32)
+    w = Tensor(np.random.rand(16),dtype=ms.float32)
+    net = GradWrap(NetWithLoss(Net(strategy)))
+    _executor.compile(net, x, w)