fix bug in infer_dtype function of hcom operations

mindspore/ops/operations/comm_ops.py

@@ -45,7 +45,6 @@ class AllReduce(PrimitiveWithInfer):
 
     Note:
         The operation of AllReduce does not support "prod" currently.
-        The input of AllReduce does not support dtype "Bool".
         Tensor must have same shape and format in all processes participating in the collective.
 
     Args:
@@ -103,7 +102,7 @@ class AllReduce(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_dtype):
-        if x_dtype == mstype.bool_:
+        if x_dtype.element_type() == mstype.bool_:
             raise TypeError("AllReduce does not support 'Bool' as the dtype of input!")
         return x_dtype
 
@@ -161,7 +160,7 @@ class AllGather(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_dtype):
-        if x_dtype == mstype.bool_:
+        if x_dtype.element_type() == mstype.bool_:
             raise TypeError(f"{self.name} does not support 'Bool' as the dtype of input!")
         return x_dtype
 
@@ -176,6 +175,7 @@ class ReduceScatter(PrimitiveWithInfer):
     Note:
         The back propagation of the op is not surported yet. Stay tuned for more.
         Tensor must have the same shape and format in all processes participating in the collective.
+
     Args:
         op (str): Specifies an operation used for element-wise reductions,
                   like sum, max, avg. Default: ReduceOp.SUM.
@@ -218,7 +218,7 @@ class ReduceScatter(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_dtype):
-        if x_dtype == mstype.bool_:
+        if x_dtype.element_type() == mstype.bool_:
             raise TypeError(f"{self.name} does not support 'Bool' as the dtype of input!")
         return x_dtype
 
@@ -275,8 +275,11 @@ class Broadcast(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_dtype):
-        if x_dtype == mstype.bool_:
-            raise TypeError(f"{self.name} does not support 'Bool' as the dtype of input!")
+        if not isinstance(x_dtype, tuple):
+            raise TypeError(f"{self.name}'s input should be a tuple!")
+        for _ele in x_dtype:
+            if _ele.element_type() == mstype.bool_:
+                raise TypeError(f"{self.name} does not support 'Bool' as the dtype of input!")
         return x_dtype
 
 
@@ -318,7 +321,7 @@ class _AlltoAll(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_dtype):
-        if x_dtype == mstype.bool_:
+        if x_dtype.element_type() == mstype.bool_:
             raise TypeError(f"{self.name} does not support 'Bool' as the dtype of input!")
         return x_dtype
 

tests/ut/python/communication/test_comm.py

@@ -55,7 +55,7 @@ class BroadCastNet(nn.Cell):
         self.broadcast = Broadcast(0)
 
     def construct(self, x):
-        x = self.broadcast((x))
+        x, = self.broadcast((x,))
         x = self.dense(x)
         return x
 

tests/ut/python/parallel/test_bool_grad.py

@@ -52,7 +52,7 @@ class CommonNet(nn.Cell):
     def __init__(self):
         super(CommonNet, self).__init__()
         self.weight = Parameter(Tensor(np.ones([256, 64]), dtype=ms.float32), name="mul_weight")
-        self.logicalnot = P.LogicalNot().set_strategy(((4,1),))
+        self.logicalnot = P.LogicalNot().set_strategy(((4,2),))
         self.equal = P.Equal().set_strategy(((4,2),(4,2)))
 
     def construct(self, x, label):
@@ -78,4 +78,5 @@ def common_net():
 
 
 def test_bool_grad():
-    common_net()
+    common_net()
+