fix bug of Acosh bprop and Elu bprop

mindspore/_checkparam.py

@@ -128,7 +128,7 @@ class Validator:
 
     @staticmethod
     def check_number(arg_name, arg_value, value, rel, prim_name):
-        """Integer value judgment."""
+        """Number value judgment."""
         rel_fn = Rel.get_fns(rel)
         if not rel_fn(arg_value, value):
             rel_str = Rel.get_strs(rel).format(value)

mindspore/ops/_grad/grad_math_ops.py

@@ -727,7 +727,7 @@ def get_bprop_acosh(self):
     input_grad = G.AcoshGrad()
 
     def bprop(x, out, dout):
-        dx = input_grad(x, dout)
+        dx = input_grad(out, dout)
         return (dx,)
     return bprop
 

mindspore/ops/_grad/grad_nn_ops.py

@@ -281,7 +281,7 @@ def get_bprop_elu(self):
     input_grad = G.EluGrad()
 
     def bprop(x, out, dout):
-        dx = input_grad(dout, x)
+        dx = input_grad(dout, out)
         return (dx,)
 
     return bprop

mindspore/ops/operations/nn_ops.py

@@ -308,7 +308,8 @@ class Elu(PrimitiveWithInfer):
     The data type of input tensor should be float.
 
     Args:
-        alpha (float): The coefficient of negative factor whose type is float. Default: 1.0.
+        alpha (float): The coefficient of negative factor whose type is float,
+            only support '1.0' currently. Default: 1.0.
 
     Inputs:
         - **input_x** (Tensor) - The input tensor whose data type should be float.
@@ -328,6 +329,7 @@ class Elu(PrimitiveWithInfer):
     def __init__(self, alpha=1.0):
         """Init Elu"""
         validator.check_value_type("alpha", alpha, [float], self.name)
+        validator.check_number("alpha", alpha, 1.0, Rel.EQ, self.name)
 
     def infer_shape(self, input_x):
         return input_x

tests/ut/python/ops/test_nn_ops_check.py

@@ -123,7 +123,7 @@ raise_set = [
         'skip': ['backward']}),
     # input is Tensor(int32)
     ('Elu1', {
-        'block': (P.Elu(alpha=0.9), {'exception': TypeError, 'error_keywords': ['Elu']}),
+        'block': (P.Elu(), {'exception': TypeError, 'error_keywords': ['Elu']}),
         'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32))],
         'skip': ['backward']}),