fixed CumSum, BesselI1e etc

mindspore/ops/_grad/grad_math_ops.py

@@ -1001,15 +1001,16 @@ def get_bprop_bessel_i1e(self):
     reciprocal = P.Reciprocal()
     cast = P.Cast()
     dtype = P.DType()
+    abs_ops = P.Abs()
 
     def bprop(x, out, dout):
         zeros = zeros_like(x)
         np_eps = const_utils.get_np_eps(dtype(x))
         eps = cast(np_eps, dtype(x))
-        x_is_valid = less(eps, x)
+        x_is_valid = less(eps, abs_ops(x))
         x_safe = select(x_is_valid, x, eps + zeros)
-        tmp = bessel_i0e(x_safe) - out * (sign(x) + reciprocal(x_safe))
-        dx = select(x_is_valid, tmp, 0.5 + zeros)
+        tmp = bessel_i0e(x_safe) - out * (sign(x_safe) + reciprocal(x_safe))
+        dx = select(x_is_valid, tmp, cast(0.5, dtype(x)) + zeros) * dout
         return (dx,)
     return bprop
 

mindspore/ops/operations/math_ops.py

@@ -672,6 +672,8 @@ class CumSum(PrimitiveWithInfer):
     def __infer__(self, x, axis):
         cls_name = self.name
         x_shp = x['shape']
+        if axis['value'] is None:
+            raise ValueError(f"For {self.name}, axis must be const.")
         validator.check_value_type('axis', axis['value'], [int], cls_name)
         valid_types = [mstype.uint8, mstype.int8, mstype.int32, mstype.float16, mstype.float32]
         validator.check_tensor_type_same({'x': x['dtype']}, valid_types, cls_name)
@@ -679,10 +681,6 @@ class CumSum(PrimitiveWithInfer):
                 'dtype': x['dtype'],
                 'value': None}
 
-    def infer_value(self, x, axis):
-        if axis is None:
-            raise ValueError(f"For {self.name}, axis must be const.")
-
 
 class AddN(PrimitiveWithInfer):
     """

mindspore/ops/operations/nn_ops.py

@@ -1767,9 +1767,6 @@ class ApplyRMSProp(PrimitiveWithInfer):
     def infer_value(self, var, mean_square, moment, learning_rate, grad, decay, momentum, epsilon):
         if decay is None or momentum is None or epsilon is None:
             raise ValueError(f"For {self.name}, decay, momentum, epsilon must be const.")
-        if not self.is_ge and self.is_d:
-            return None, None, None
-        return None
 
 
 class ApplyCenteredRMSProp(PrimitiveWithInfer):