!645 auto parallel prelu operator support broadcast

Merge pull request !645 from yao_yf/auto_parallel_prelu_support_broadcast

mindspore/ccsrc/parallel/ops_info/prelu_info.cc

@@ -32,7 +32,7 @@ namespace parallel {
  * prelu has 2 input
  *  A: A float tensor of shape [NCHW] representing the output of the preview layer.
  *  w: Float Tensor, w > 0: there is only two shapes are legitimate: 1, or the number of channels at input.
- *  the strategy of w should equal to the channel dimension of strategy of A
+ *  the strategy of w should equal to the channel dimension of strategy of A, or equal to 1
  */
 Status PReLUInfo::CheckStrategy(const StrategyPtr &strategy) {
   if (CheckStrategyValue(strategy, inputs_shape_, is_auto_parallel_) != SUCCESS) {
@@ -52,7 +52,7 @@ Status PReLUInfo::CheckStrategy(const StrategyPtr &strategy) {
     }
     return FAILED;
   }
-  if (stra[0][PRELU_CHANNEL_INDEX] != stra[1][0]) {
+  if (stra[0][PRELU_CHANNEL_INDEX] != stra[1][0] && inputs_shape_[1][0] != 1) {
     if (is_auto_parallel_) {
       MS_LOG(DEBUG) << name_ << ": Invalid channel strategy.";
     } else {
@@ -107,7 +107,11 @@ Status PReLUInfo::InferTensorMap() {
   }
 
   TensorMap param_tensor_map;
-  param_tensor_map.push_back(input_tensor_map.at(1));
+  if (inputs_shape_[1][0] == 1) {
+    param_tensor_map.push_back(-1);
+  } else {
+    param_tensor_map.push_back(input_tensor_map.at(1));
+  }
   inputs_tensor_map_.push_back(input_tensor_map);
   inputs_tensor_map_.push_back(param_tensor_map);
   outputs_tensor_map_.push_back(input_tensor_map);

tests/ut/python/parallel/test_prelu.py

@@ -166,3 +166,21 @@ def test_prelu_parallel_success4():
     w = Tensor(np.random.rand(16),dtype=ms.float32)
     net = GradWrap(NetWithLoss(Net(strategy)))
     _executor.compile(net, x, w)
+
+def test_prelu_parallel_success5():
+    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), (1, ))
+    x = Tensor(np.random.rand(4, 16, 32, 64),dtype=ms.float32)
+    w = Tensor(np.random.rand(1),dtype=ms.float32)
+    net = GradWrap(NetWithLoss(Net(strategy)))
+    _executor.compile(net, x, w)
+