mindspore2022/tests/ut/python/ops/test_math_ops_check.py

# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
""" test ops """
import functools
import numpy as np

import mindspore.nn as nn
import mindspore.ops.composite as C
from mindspore import Tensor
from mindspore import ops
from mindspore.common import dtype as mstype
from mindspore.common.api import _executor
from mindspore.common.parameter import Parameter
from mindspore.ops import functional as F
from mindspore.ops import operations as P
from mindspore.ops.operations import _grad_ops as G
from ..ut_filter import non_graph_engine
from ....mindspore_test_framework.mindspore_test import mindspore_test
from ....mindspore_test_framework.pipeline.forward.compile_forward \
    import (pipeline_for_compile_forward_ge_graph_for_case_by_case_config,
            pipeline_for_compile_forward_ge_graph_for_case_by_case_config_exception)
from ....mindspore_test_framework.pipeline.gradient.compile_gradient \
    import pipeline_for_compile_grad_ge_graph_for_case_by_case_config


class AssignAddNet(nn.Cell):
    def __init__(self, ):
        super(AssignAddNet, self).__init__()
        self.op = P.AssignAdd()
        self.inputdata = Parameter(Tensor(np.zeros([1]).astype(np.bool_), mstype.bool_), name="assign_add1")

    def construct(self, x):
        self.op(self.inputdata, x)
        return self.inputdata


class AssignSubNet(nn.Cell):
    def __init__(self, ):
        super(AssignSubNet, self).__init__()
        self.op = P.AssignSub()
        self.inputdata = Parameter(Tensor(np.zeros([1]).astype(np.bool_), mstype.bool_), name="assign_sub1")

    def construct(self, x):
        self.op(self.inputdata, x)
        return self.inputdata


class ReduceNet(nn.Cell):
    def __init__(self, op_class, keep_dims, axis):
        super(ReduceNet, self).__init__()
        self.axis = axis
        self.op = op_class(keep_dims=keep_dims)

    def construct(self, x):
        return self.op(x, self.axis)


class CumProdNet(nn.Cell):
    def __init__(self):
        super(CumProdNet, self).__init__()
        self.op = P.CumProd()

    def construct(self, x, axis):
        return self.op(x, axis)


class CumSumNet(nn.Cell):
    def __init__(self, axis):
        super(CumSumNet, self).__init__()
        self.axis = axis
        self.op = P.CumSum()

    def construct(self, x):
        return self.op(x, self.axis)


raise_set = [
    # input two tensors, but element types are not same
    ('TensorAdd1', {
        'block': (P.TensorAdd(), {'exception': TypeError, 'error_keywords': ['TensorAdd']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
        'skip': ['backward']}),
    # input two tensors, their shapes do not match
    ('TensorAdd2', {
        'block': (P.TensorAdd(), {'exception': ValueError, 'error_keywords': ['TensorAdd']}),
        'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))],
        'skip': ['backward']}),

    # check input Tensor(bool_)
    ('AssignAdd', {
        'block': (AssignAddNet(), {'exception': TypeError, 'error_keywords': ['AssignAdd']}),
        'desc_inputs': [Tensor(np.ones([1]).astype(np.bool_), mstype.bool_)],
        'skip': ['backward']}),

    # check input Tensor(bool_)
    ('AssignSub', {
        'block': (AssignSubNet(), {'exception': TypeError, 'error_keywords': ['AssignSub']}),
        'desc_inputs': [Tensor(np.ones([1]).astype(np.bool_), mstype.bool_)],
        'skip': ['backward']}),

    # type of axis is float, not int
    ('ReduceMean1', {
        'block': (ReduceNet(P.ReduceMean, keep_dims=True, axis=5.0),
                  {'exception': TypeError, 'error_keywords': ['ReduceMean']}),
        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32))],
        'skip': ['backward']}),
    # axis is out of range
    ('ReduceMean2', {
        'block': (ReduceNet(P.ReduceMean, keep_dims=True, axis=5),
                  {'exception': ValueError, 'error_keywords': ['ReduceMean']}),
        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32))],
        'skip': ['backward']}),

    # type of axis is float, not int
    ('ReduceSum1', {
        'block': (ReduceNet(P.ReduceSum, keep_dims=True, axis=5.0),
                  {'exception': TypeError, 'error_keywords': ['ReduceSum']}),
        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32))],
        'skip': ['backward']}),
    # axis is out of range
    ('ReduceSum2', {
        'block': (ReduceNet(P.ReduceSum, keep_dims=True, axis=5),
                  {'exception': ValueError, 'error_keywords': ['ReduceSum']}),
        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32))],
        'skip': ['backward']}),

    # type of axis is float, not int
    ('ReduceAll1', {
        'block': (ReduceNet(P.ReduceAll, keep_dims=True, axis=5.0),
                  {'exception': TypeError, 'error_keywords': ['ReduceAll']}),
        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.bool_))],
        'skip': ['backward']}),
    # axis is out of range
    ('ReduceAll2', {
        'block': (ReduceNet(P.ReduceAll, keep_dims=True, axis=5),
                  {'exception': ValueError, 'error_keywords': ['ReduceAll']}),
        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.bool_))],
        'skip': ['backward']}),

    # type of axis is float, not int
    ('ReduceMax1', {
        'block': (ReduceNet(P.ReduceMax, keep_dims=True, axis=5.0),
                  {'exception': TypeError, 'error_keywords': ['ReduceMax']}),
        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32))],
        'skip': ['backward']}),
    # axis is out of range
    ('ReduceMax2', {
        'block': (ReduceNet(P.ReduceMax, keep_dims=True, axis=5),
                  {'exception': ValueError, 'error_keywords': ['ReduceMax']}),
        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32))],
        'skip': ['backward']}),

    # type of axis is float, not int
    ('ReduceMin1', {
        'block': (ReduceNet(P.ReduceMin, keep_dims=True, axis=5.0),
                  {'exception': TypeError, 'error_keywords': ['ReduceMin']}),
        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32))],
        'skip': ['backward']}),
    # axis is out of range
    ('ReduceMin2', {
        'block': (ReduceNet(P.ReduceMin, keep_dims=True, axis=5),
                  {'exception': ValueError, 'error_keywords': ['ReduceMin']}),
        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32))],
        'skip': ['backward']}),

    # type of axis is float, not int
    ('ReduceProd1', {
        'block': (ReduceNet(P.ReduceProd, keep_dims=True, axis=5.0),
                  {'exception': TypeError, 'error_keywords': ['ReduceProd']}),
        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32))],
        'skip': ['backward']}),
    # axis is out of range
    ('ReduceProd2', {
        'block': (ReduceNet(P.ReduceProd, keep_dims=True, axis=5),
                  {'exception': ValueError, 'error_keywords': ['ReduceProd']}),
        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32))],
        'skip': ['backward']}),

    # type of x is Tensor(bool)
    ('CumProd1', {
        'block': (CumProdNet(),
                  {'exception': TypeError, 'error_keywords': ['CumProd']}),
        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.bool)), 1],
        'skip': ['backward']}),
    # type of axis in float, not int
    ('CumProd2', {
        'block': (CumProdNet(),
                  {'exception': TypeError, 'error_keywords': ['CumProd']}),
        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.float32)), 5.0],
        'skip': ['backward']}),

    # type of x and y are Tensor(uint32)
    ('MatMul1', {
        'block': (P.MatMul(),
                  {'exception': TypeError, 'error_keywords': ['MatMul']}),
        'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.uint32)), Tensor(np.ones([3, 2]).astype(np.uint32))],
        'skip': ['backward']}),
    # type of x and y not match
    ('MatMul2', {
        'block': (P.MatMul(),
                  {'exception': TypeError, 'error_keywords': ['MatMul']}),
        'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.int32))],
        'skip': ['backward']}),
    # shape of x and y not match
    ('MatMul3', {
        'block': (P.MatMul(),
                  {'exception': ValueError, 'error_keywords': ['MatMul']}),
        'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.float32)), Tensor(np.ones([2, 3]).astype(np.float32))],
        'skip': ['backward']}),

    # dims of x and y are less than 3
    ('BatchMatMul1', {
        'block': (P.BatchMatMul(),
                  {'exception': ValueError, 'error_keywords': ['BatchMatMul']}),
        'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.int32)), Tensor(np.ones([3, 2]).astype(np.int32))],
        'skip': ['backward']}),

    # type of x is Tensor(bool)
    ('CumSum1', {
        'block': (CumSumNet(axis=1),
                  {'exception': TypeError, 'error_keywords': ['CumSum']}),
        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.bool))],
        'skip': ['backward']}),
    # type of axis in float, not int
    ('CumSum2', {
        'block': (CumSumNet(axis=1.0),
                  {'exception': TypeError, 'error_keywords': ['CumSum']}),
        'desc_inputs': [Tensor(np.ones([2, 3, 5]).astype(np.bool))],
        'skip': ['backward']}),

    # intput is not tuple or list
    ('AddN1', {
        'block': (P.AddN(),
                  {'exception': TypeError, 'error_keywords': ['AddN']}),
        'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.uint32))],
        'skip': ['backward']}),
    # type not match
    ('AddN2', {
        'block': (P.AddN(),
                  {'exception': TypeError, 'error_keywords': ['AddN']}),
        'desc_inputs': [(Tensor(np.ones([2, 3]).astype(np.uint32)), Tensor(np.ones([3, 2]).astype(np.int32)))],
        'skip': ['backward']}),
    # shape not match
    ('AddN3', {
        'block': (P.AddN(),
                  {'exception': ValueError, 'error_keywords': ['AddN']}),
        'desc_inputs': [(Tensor(np.ones([2, 3]).astype(np.int32)), Tensor(np.ones([3, 2]).astype(np.int32)))],
        'skip': ['backward']}),

    # input is Tensor(bool)
    ('Neg1', {
        'block': (P.Neg(),
                  {'exception': TypeError, 'error_keywords': ['Neg']}),
        'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.bool_))],
        'skip': ['backward']}),

    # input two tensors, but element types are not same
    ('Sub1', {
        'block': (P.Sub(), {'exception': TypeError, 'error_keywords': ['Sub']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
        'skip': ['backward']}),
    # input two tensors, their shapes do not match
    ('Sub2', {
        'block': (P.Sub(), {'exception': ValueError, 'error_keywords': ['Sub']}),
        'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))],
        'skip': ['backward']}),

    # input two tensors, but element types are not same
    ('Mul1', {
        'block': (P.Mul(), {'exception': TypeError, 'error_keywords': ['Mul']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
        'skip': ['backward']}),
    # input two tensors, their shapes do not match
    ('Mul2', {
        'block': (P.Mul(), {'exception': ValueError, 'error_keywords': ['Mul']}),
        'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))],
        'skip': ['backward']}),

    # input is Tensor(bool)
    ('Square1', {
        'block': (P.Square(),
                  {'exception': TypeError, 'error_keywords': ['Square']}),
        'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.bool_))],
        'skip': ['backward']}),

    # input is Tensor(bool)
    ('Rsqrt1', {
        'block': (P.Rsqrt(),
                  {'exception': TypeError, 'error_keywords': ['Rsqrt']}),
        'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.bool_))],
        'skip': ['backward']}),

    # input is Tensor(bool)
    ('Sqrt1', {
        'block': (P.Sqrt(),
                  {'exception': TypeError, 'error_keywords': ['Sqrt']}),
        'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.bool_))],
        'skip': ['backward']}),

    # input is not Tensor
    ('Reciprocal1', {
        'block': (P.Reciprocal(),
                  {'exception': TypeError, 'error_keywords': ['Reciprocal']}),
        'desc_inputs': [5.0],
        'skip': ['backward']}),

    # input x is Tensor(bool)
    ('Pow1', {
        'block': (P.Pow(),
                  {'exception': TypeError, 'error_keywords': ['Pow']}),
        'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.bool_)), 2.0],
        'skip': ['backward']}),

    # input is not Tensor
    ('Exp1', {
        'block': (P.Exp(),
                  {'exception': TypeError, 'error_keywords': ['Exp']}),
        'desc_inputs': [5.0],
        'skip': ['backward']}),

    # input is not Tensor
    ('Log1', {
        'block': (P.Log(),
                  {'exception': TypeError, 'error_keywords': ['Log']}),
        'desc_inputs': [5.0],
        'skip': ['backward']}),

    # input two tensors, but element types are not same
    ('Minimum1', {
        'block': (P.Minimum(), {'exception': TypeError, 'error_keywords': ['Minimum']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
        'skip': ['backward']}),
    # input two tensors, their shapes do not match
    ('Minimum2', {
        'block': (P.Minimum(), {'exception': ValueError, 'error_keywords': ['Minimum']}),
        'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))],
        'skip': ['backward']}),

    # input two tensors, but element types are not same
    ('Maximum1', {
        'block': (P.Maximum(), {'exception': TypeError, 'error_keywords': ['Maximum']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
        'skip': ['backward']}),
    # input two tensors, their shapes do not match
    ('Maximum2', {
        'block': (P.Maximum(), {'exception': ValueError, 'error_keywords': ['Maximum']}),
        'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))],
        'skip': ['backward']}),

    # input two tensors, but element types are not same
    ('RealDiv1', {
        'block': (P.RealDiv(), {'exception': TypeError, 'error_keywords': ['RealDiv']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
        'skip': ['backward']}),
    # input two tensors, their shapes do not match
    ('RealDiv2', {
        'block': (P.RealDiv(), {'exception': ValueError, 'error_keywords': ['RealDiv']}),
        'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))],
        'skip': ['backward']}),

    # input two tensors, but element types are not same
    ('Div1', {
        'block': (P.Div(), {'exception': TypeError, 'error_keywords': ['Div']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
        'skip': ['backward']}),
    # input two tensors, their shapes do not match
    ('Div2', {
        'block': (P.Div(), {'exception': ValueError, 'error_keywords': ['Div']}),
        'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))],
        'skip': ['backward']}),

    # input two tensors, but element types are not same
    ('FloorDiv1', {
        'block': (P.FloorDiv(), {'exception': TypeError, 'error_keywords': ['FloorDiv']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
        'skip': ['backward']}),
    # input two tensors, their shapes do not match
    ('FloorDiv2', {
        'block': (P.FloorDiv(), {'exception': ValueError, 'error_keywords': ['FloorDiv']}),
        'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))],
        'skip': ['backward']}),

    # input x is Tensor(int32), not Tensor(float)
    ('Floor1', {
        'block': (P.Floor(),
                  {'exception': TypeError, 'error_keywords': ['Floor']}),
        'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.int32))],
        'skip': ['backward']}),

    # input two tensors, but element types are not same
    ('FloorMod1', {
        'block': (P.FloorMod(), {'exception': TypeError, 'error_keywords': ['FloorMod']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
        'skip': ['backward']}),
    # input two tensors, their shapes do not match
    ('FFloorMod2', {
        'block': (P.FloorMod(), {'exception': ValueError, 'error_keywords': ['FloorMod']}),
        'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))],
        'skip': ['backward']}),

    # input x is Tensor(int32), not Tensor(float)
    ('Acosh1', {
        'block': (P.Acosh(),
                  {'exception': TypeError, 'error_keywords': ['Acosh']}),
        'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.bool_))],
        'skip': ['backward']}),

    # type of x and y not match
    ('Equal1', {
        'block': (P.Equal(), {'exception': TypeError, 'error_keywords': ['Equal']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
        'skip': ['backward']}),
    # shape of x and y not match
    ('Equal2', {
        'block': (P.Equal(), {'exception': ValueError, 'error_keywords': ['Equal']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.float32))],
        'skip': ['backward']}),

    # input is not tensor
    ('EqualCount0', {
        'block': (P.EqualCount(), {'exception': TypeError, 'error_keywords': ['EqualCount']}),
        'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))],
        'skip': ['backward']}),
    # type of x and y not match
    ('EqualCount1', {
        'block': (P.EqualCount(), {'exception': TypeError, 'error_keywords': ['EqualCount']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
        'skip': ['backward']}),
    # shape of x and y not match

    # type of x and y not match
    ('NotEqual1', {
        'block': (P.NotEqual(), {'exception': TypeError, 'error_keywords': ['NotEqual']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
        'skip': ['backward']}),
    # shape of x and y not match
    ('NotEqual2', {
        'block': (P.NotEqual(), {'exception': ValueError, 'error_keywords': ['NotEqual']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.float32))],
        'skip': ['backward']}),

    # type of x and y not match
    ('Greater1', {
        'block': (P.Greater(), {'exception': TypeError, 'error_keywords': ['Greater']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
        'skip': ['backward']}),
    # shape of x and y not match
    ('Greater2', {
        'block': (P.Greater(), {'exception': ValueError, 'error_keywords': ['Greater']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.float32))],
        'skip': ['backward']}),

    # type of x and y not match
    ('GreaterEqual1', {
        'block': (P.GreaterEqual(), {'exception': TypeError, 'error_keywords': ['GreaterEqual']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
        'skip': ['backward']}),
    # shape of x and y not match
    ('GreaterEqual2', {
        'block': (P.GreaterEqual(), {'exception': ValueError, 'error_keywords': ['GreaterEqual']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.float32))],
        'skip': ['backward']}),

    # type of x and y not match
    ('Less1', {
        'block': (P.Less(), {'exception': TypeError, 'error_keywords': ['Less']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
        'skip': ['backward']}),
    # shape of x and y not match
    ('Less2', {
        'block': (P.Less(), {'exception': ValueError, 'error_keywords': ['Less']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.float32))],
        'skip': ['backward']}),

    # type of x and y not match
    ('LessEqual1', {
        'block': (P.LessEqual(), {'exception': TypeError, 'error_keywords': ['LessEqual']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
        'skip': ['backward']}),
    # shape of x and y not match
    ('LessEqual2', {
        'block': (P.LessEqual(), {'exception': ValueError, 'error_keywords': ['LessEqual']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.float32))],
        'skip': ['backward']}),

    # input x is not Tensor(bool)
    ('LogicalNot1', {
        'block': (P.LogicalNot(),
                  {'exception': TypeError, 'error_keywords': ['LogicalNot']}),
        'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.int32))],
        'skip': ['backward']}),

    # type of x and y not match
    ('LogicalAnd1', {
        'block': (P.LogicalAnd(), {'exception': TypeError, 'error_keywords': ['LogicalAnd']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.bool_))],
        'skip': ['backward']}),
    # shape of x and y not match
    ('LogicalAnd2', {
        'block': (P.LogicalAnd(), {'exception': ValueError, 'error_keywords': ['LogicalAnd']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_)), Tensor(np.ones([3, 2]).astype(np.bool_))],
        'skip': ['backward']}),

    # type of x and y not match
    ('LogicalOr1', {
        'block': (P.LogicalOr(), {'exception': TypeError, 'error_keywords': ['LogicalOr']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.bool_))],
        'skip': ['backward']}),
    # shape of x and y not match
    ('LogicalOr2', {
        'block': (P.LogicalOr(), {'exception': ValueError, 'error_keywords': ['LogicalOr']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_)), Tensor(np.ones([3, 2]).astype(np.bool_))],
        'skip': ['backward']}),

    # input is not tensor
    ('NPUGetFloatStatus0', {
        'block': (P.NPUGetFloatStatus(), {'exception': TypeError, 'error_keywords': ['NPUGetFloatStatus']}),
        'desc_inputs': [5.0],
        'skip': ['backward']}),
    # input is Tensor(int32), not Tensor(float32)
    ('NPUGetFloatStatus1', {
        'block': (P.NPUGetFloatStatus(), {'exception': TypeError, 'error_keywords': ['NPUGetFloatStatus']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32))],
        'skip': ['backward']}),
    # dims is not 1
    ('NPUGetFloatStatus2', {
        'block': (P.NPUGetFloatStatus(), {'exception': ValueError, 'error_keywords': ['NPUGetFloatStatus']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32))],
        'skip': ['backward']}),
    # shape[0] is not 8
    ('NPUGetFloatStatus3', {
        'block': (P.NPUGetFloatStatus(), {'exception': ValueError, 'error_keywords': ['NPUGetFloatStatus']}),
        'desc_inputs': [Tensor(np.ones([3]).astype(np.float32))],
        'skip': ['backward']}),

    # input is not tensor
    ('NPUClearFloatStatus0', {
        'block': (P.NPUClearFloatStatus(), {'exception': TypeError, 'error_keywords': ['NPUClearFloatStatus']}),
        'desc_inputs': [5.0],
        'skip': ['backward']}),
    # input is Tensor(int32), not Tensor(float32)
    ('NPUClearFloatStatus1', {
        'block': (P.NPUClearFloatStatus(), {'exception': TypeError, 'error_keywords': ['NPUClearFloatStatus']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32))],
        'skip': ['backward']}),
    # dims is not 1
    ('NPUClearFloatStatus2', {
        'block': (P.NPUClearFloatStatus(), {'exception': ValueError, 'error_keywords': ['NPUClearFloatStatus']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32))],
        'skip': ['backward']}),
    # shape[0] is not 8
    ('NPUClearFloatStatus3', {
        'block': (P.NPUClearFloatStatus(), {'exception': ValueError, 'error_keywords': ['NPUClearFloatStatus']}),
        'desc_inputs': [Tensor(np.ones([3]).astype(np.float32))],
        'skip': ['backward']}),

    # input is not tensor
    ('Cos0', {
        'block': (P.Cos(), {'exception': TypeError, 'error_keywords': ['Cos']}),
        'desc_inputs': [5.0],
        'skip': ['backward']}),
    # input is Tensor(bool)
    ('Cos1', {
        'block': (P.Cos(), {'exception': TypeError, 'error_keywords': ['Cos']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_))],
        'skip': ['backward']}),

    # input is not tensor
    ('ACos0', {
        'block': (P.ACos(), {'exception': TypeError, 'error_keywords': ['ACos']}),
        'desc_inputs': [5.0],
        'skip': ['backward']}),
    # input is Tensor(bool)
    ('ACos1', {
        'block': (P.ACos(), {'exception': TypeError, 'error_keywords': ['ACos']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_))],
        'skip': ['backward']}),

    # input is not tensor
    ('Sin0', {
        'block': (P.Sin(), {'exception': TypeError, 'error_keywords': ['Sin']}),
        'desc_inputs': [5.0],
        'skip': ['backward']}),
    # input is Tensor(bool)
    ('Sin1', {
        'block': (P.Sin(), {'exception': TypeError, 'error_keywords': ['Sin']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_))],
        'skip': ['backward']}),

    # input is not tensor
    ('NMSWithMask0', {
        'block': (P.NMSWithMask(), {'exception': TypeError, 'error_keywords': ['NMSWithMask']}),
        'desc_inputs': [5.0],
        'skip': ['backward']}),
    # input is not Tensor(float16) or Tensor(float32)
    ('NMSWithMask1', {
        'block': (P.NMSWithMask(), {'exception': TypeError, 'error_keywords': ['NMSWithMask']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32))],
        'skip': ['backward']}),
    # dims is not 2
    ('NMSWithMask2', {
        'block': (P.NMSWithMask(), {'exception': ValueError, 'error_keywords': ['NMSWithMask']}),
        'desc_inputs': [Tensor(np.ones([3, 4, 2]).astype(np.float32))],
        'skip': ['backward']}),
    # shape[1] is not 5
    ('NMSWithMask3', {
        'block': (P.NMSWithMask(), {'exception': ValueError, 'error_keywords': ['NMSWithMask']}),
        'desc_inputs': [Tensor(np.ones([3, 2]).astype(np.float32))],
        'skip': ['backward']}),

    # input is not tensor
    ('Abs0', {
        'block': (P.Abs(), {'exception': TypeError, 'error_keywords': ['Abs']}),
        'desc_inputs': [5.0],
        'skip': ['backward']}),
    # input is Tensor(bool)
    ('Abs1', {
        'block': (P.Abs(), {'exception': TypeError, 'error_keywords': ['Abs']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_))],
        'skip': ['backward']}),

    # input is not tensor
    ('Sign0', {
        'block': (P.Sign(), {'exception': TypeError, 'error_keywords': ['Sign']}),
        'desc_inputs': [5.0],
        'skip': ['backward']}),
    # input is Tensor(bool)
    ('Sign1', {
        'block': (P.Sign(), {'exception': TypeError, 'error_keywords': ['Sign']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_))],
        'skip': ['backward']}),

    # input is not tensor
    ('Round0', {
        'block': (P.Round(), {'exception': TypeError, 'error_keywords': ['Round']}),
        'desc_inputs': [5.0],
        'skip': ['backward']}),
    # input is Tensor(bool)
    ('Round1', {
        'block': (P.Round(), {'exception': TypeError, 'error_keywords': ['Round']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_))],
        'skip': ['backward']}),

    # input two tensors, but element types are not same
    ('Atan21', {
        'block': (P.Atan2(), {'exception': TypeError, 'error_keywords': ['Atan2']}),
        'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
        'skip': ['backward']}),
    # input two tensors, their shapes do not match
    ('Atan22', {
        'block': (P.Atan2(), {'exception': ValueError, 'error_keywords': ['Atan2']}),
        'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))],
        'skip': ['backward']}),
]


@mindspore_test(pipeline_for_compile_forward_ge_graph_for_case_by_case_config_exception)
def test_check_exception():
    return raise_set