!29516 fix GPU front-end exception interception information which is simple

Merge pull request !29516 from 沈竞兴/fix_error1

mindspore/python/mindspore/ops/operations/array_ops.py

@@ -5081,12 +5081,12 @@ class SpaceToDepth(PrimitiveWithInfer):
         """Initialize SpaceToDepth"""
         self.init_prim_io_names(inputs=['x'], outputs=['y'])
         validator.check_value_type('block_size', block_size, [int], self.name)
-        validator.check('block_size', block_size, '', 2, Rel.GE)
+        validator.check('block_size', block_size, self.name, 2, Rel.GE)
         self.block_size = block_size
         self.add_prim_attr("data_format", "NCHW")
 
     def infer_shape(self, x_shape):
-        validator.check('x dimension', len(x_shape), '', 4, Rel.EQ)
+        validator.check('x dimension', len(x_shape), self.name, 4, Rel.EQ)
         out_shape = copy.deepcopy(x_shape)
         for i in range(2):
             if out_shape[i + 2] % self.block_size != 0:
@@ -5218,9 +5218,9 @@ class SpaceToBatch(PrimitiveWithInfer):
         logger.warning("WARN_DEPRECATED: The usage of SpaceToBatch is deprecated."
                        " Please use SpaceToBatchND.")
         validator.check_value_type('block_size', block_size, [int], self.name)
-        validator.check('block_size', block_size, '', 2, Rel.GE, self.name)
+        validator.check('block_size', block_size, self.name, 2, Rel.GE, self.name)
         self.block_size = block_size
-        validator.check('paddings shape', np.array(paddings).shape, '', (2, 2), Rel.EQ, self.name)
+        validator.check('paddings shape', np.array(paddings).shape, self.name, (2, 2), Rel.EQ, self.name)
         for elem in itertools.chain(*paddings):
             validator.check_non_negative_int(elem, 'paddings element', self.name)
             validator.check_value_type('paddings element', elem, [int], self.name)
@@ -5308,7 +5308,7 @@ class BatchToSpace(PrimitiveWithInfer):
         validator.check('block_size', block_size, '', 2, Rel.GE, self.name)
         self.block_size = block_size
         validator.check_value_type('crops type', crops, [list, tuple], self.name)
-        validator.check('crops shape', np.array(crops).shape, '', (2, 2))
+        validator.check('crops shape', np.array(crops).shape, self.name, (2, 2))
         for elem in itertools.chain(*crops):
             validator.check_non_negative_int(elem, 'crops element', self.name)
             validator.check_value_type('crops element', elem, [int], self.name)
@@ -5319,7 +5319,7 @@ class BatchToSpace(PrimitiveWithInfer):
         return x_dtype
 
     def infer_shape(self, x_shape):
-        validator.check('rank of input_x', len(x_shape), '', 4)
+        validator.check('rank of input_x', len(x_shape), self.name, 4)
         out_shape = copy.deepcopy(x_shape)
         for i in range(2):
             x_block_prod = out_shape[i + 2] * self.block_size