mindspore/tests/st/numpy_native/test_array_creations.py

# Copyright 2020-2021 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.
# ============================================================================
"""unit tests for numpy array operations"""

import functools

import pytest
import numpy as onp

import mindspore.context as context
import mindspore.numpy as mnp

context.set_context(mode=context.GRAPH_MODE, device_target='CPU')


class Cases():
    def __init__(self):
        self.all_shapes = [
            0, 1, 2, (), (1,), (2,), (1, 2, 3), [], [1], [2], [1, 2, 3]
        ]
        self.onp_dtypes = [onp.int32, 'int32', int,
                           onp.float32, 'float32', float,
                           onp.uint32, 'uint32',
                           onp.bool_, 'bool', bool]

        self.mnp_dtypes = [mnp.int32, 'int32', int,
                           mnp.float32, 'float32', float,
                           mnp.uint32, 'uint32',
                           mnp.bool_, 'bool', bool]

        self.array_sets = [1, 1.1, True, [1, 0, True], [1, 1.0, 2], (1,),
                           [(1, 2, 3), (4, 5, 6)], onp.random.random(  # pylint: disable=no-member
                               (100, 100)).astype(onp.float32),
                           onp.random.random((100, 100)).astype(onp.bool)]

        self.arrs = [
            rand_int(2),
            rand_int(2, 3),
            rand_int(2, 3, 4),
            rand_int(2, 3, 4, 5),
        ]

        # scalars expanded across the 0th dimension
        self.scalars = [
            rand_int(),
            rand_int(1),
            rand_int(1, 1),
            rand_int(1, 1, 1),
        ]

        # arrays of the same size expanded across the 0th dimension
        self.expanded_arrs = [
            rand_int(2, 3),
            rand_int(1, 2, 3),
            rand_int(1, 1, 2, 3),
            rand_int(1, 1, 1, 2, 3),
        ]

        # arrays with dimensions of size 1
        self.nested_arrs = [
            rand_int(1),
            rand_int(1, 2),
            rand_int(3, 1, 8),
            rand_int(1, 3, 9, 1),
        ]

        # arrays which can be broadcast
        self.broadcastables = [
            rand_int(5),
            rand_int(6, 1),
            rand_int(7, 1, 5),
            rand_int(8, 1, 6, 1)
        ]

        # boolean arrays which can be broadcast
        self.bool_broadcastables = [
            rand_bool(),
            rand_bool(1),
            rand_bool(5),
            rand_bool(6, 1),
            rand_bool(7, 1, 5),
            rand_bool(8, 1, 6, 1),
        ]

        self.mnp_prototypes = [
            mnp.ones((2, 3, 4)),
            mnp.ones((0, 3, 0, 2, 5)),
            onp.ones((2, 7, 0)),
            onp.ones(()),
            [mnp.ones(3), (1, 2, 3), onp.ones(3), [4, 5, 6]],
            ([(1, 2), mnp.ones(2)], (onp.ones(2), [3, 4])),
        ]

        self.onp_prototypes = [
            onp.ones((2, 3, 4)),
            onp.ones((0, 3, 0, 2, 5)),
            onp.ones((2, 7, 0)),
            onp.ones(()),
            [onp.ones(3), (1, 2, 3), onp.ones(3), [4, 5, 6]],
            ([(1, 2), onp.ones(2)], (onp.ones(2), [3, 4])),
        ]


def match_array(actual, expected, error=0):
    if error > 0:
        onp.testing.assert_almost_equal(actual.tolist(), expected.tolist(),
                                        decimal=error)
    else:
        onp.testing.assert_equal(actual.tolist(), expected.tolist())


def check_all_results(onp_results, mnp_results, error=0):
    """Check all results from numpy and mindspore.numpy"""
    for i, _ in enumerate(onp_results):
        match_array(onp_results[i], mnp_results[i].asnumpy())


def check_all_unique_results(onp_results, mnp_results):
    """
    Check all results from numpy and mindspore.numpy.

    Args:
        onp_results (Union[tuple of numpy.arrays, numpy.array])
        mnp_results (Union[tuple of Tensors, Tensor])
    """
    for i, _ in enumerate(onp_results):
        if isinstance(onp_results[i], tuple):
            for j in range(len(onp_results[i])):
                match_array(onp_results[i][j],
                            mnp_results[i][j].asnumpy(), error=7)
        else:
            match_array(onp_results[i], mnp_results[i].asnumpy(), error=7)


def run_non_kw_test(mnp_fn, onp_fn):
    """Run tests on functions with non keyword arguments"""
    test_case = Cases()
    for i in range(len(test_case.arrs)):
        arrs = test_case.arrs[:i]
        match_res(mnp_fn, onp_fn, *arrs)

    for i in range(len(test_case.scalars)):
        arrs = test_case.scalars[:i]
        match_res(mnp_fn, onp_fn, *arrs)

    for i in range(len(test_case.expanded_arrs)):
        arrs = test_case.expanded_arrs[:i]
        match_res(mnp_fn, onp_fn, *arrs)

    for i in range(len(test_case.nested_arrs)):
        arrs = test_case.nested_arrs[:i]
        match_res(mnp_fn, onp_fn, *arrs)


def rand_int(*shape):
    """return an random integer array with parameter shape"""
    res = onp.random.randint(low=1, high=5, size=shape)
    if isinstance(res, onp.ndarray):
        return res.astype(onp.float32)
    return float(res)


# return an random boolean array
def rand_bool(*shape):
    return onp.random.rand(*shape) > 0.5


def match_res(mnp_fn, onp_fn, *arrs, **kwargs):
    """Checks results from applying mnp_fn and onp_fn on arrs respectively"""
    mnp_arrs = map(functools.partial(mnp.asarray, dtype='float32'), arrs)
    mnp_res = mnp_fn(*mnp_arrs, **kwargs)
    onp_res = onp_fn(*arrs, **kwargs)
    match_all_arrays(mnp_res, onp_res)


def match_all_arrays(mnp_res, onp_res, error=0):
    if isinstance(mnp_res, (tuple, list)):
        for actual, expected in zip(mnp_res, onp_res):
            match_array(actual.asnumpy(), expected, error)
    else:
        match_array(mnp_res.asnumpy(), onp_res, error)


def match_meta(actual, expected):
    # float64 and int64 are not supported, and the default type for
    # float and int are float32 and int32, respectively
    if expected.dtype == onp.float64:
        expected = expected.astype(onp.float32)
    elif expected.dtype == onp.int64:
        expected = expected.astype(onp.int32)
    assert actual.shape == expected.shape
    assert actual.dtype == expected.dtype


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_asarray():
    test_case = Cases()
    for array in test_case.array_sets:
        # Check for dtype matching
        actual = onp.asarray(array)
        expected = mnp.asarray(array).asnumpy()
        # Since we set float32/int32 as the default dtype in mindspore, we need
        # to make a conversion between numpy.asarray and mindspore.numpy.asarray
        if actual.dtype is onp.dtype('float64'):
            assert expected.dtype == onp.dtype('float32')
        elif actual.dtype is onp.dtype('int64'):
            assert expected.dtype == onp.dtype('int32')
        else:
            assert actual.dtype == expected.dtype
        match_array(actual, expected, error=7)

        for i in range(len(test_case.onp_dtypes)):
            actual = onp.asarray(array, test_case.onp_dtypes[i])
            expected = mnp.asarray(array, test_case.mnp_dtypes[i]).asnumpy()
            match_array(actual, expected, error=7)

    # Additional tests for nested tensor/numpy_array mixture
    mnp_input = [(onp.ones(3,), mnp.ones(3)), [[1, 1, 1], (1, 1, 1)]]
    onp_input = [(onp.ones(3,), onp.ones(3)), [[1, 1, 1], (1, 1, 1)]]

    actual = onp.asarray(onp_input)
    expected = mnp.asarray(mnp_input).asnumpy()
    match_array(actual, expected, error=7)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_array():
    # array's function is very similar to asarray, so we mainly test the
    # `copy` argument.
    test_case = Cases()
    for array in test_case.array_sets:
        arr1 = mnp.asarray(array)
        arr2 = mnp.array(arr1, copy=False)
        arr3 = mnp.array(arr1)
        arr4 = mnp.asarray(array, dtype='int32')
        arr5 = mnp.asarray(arr4, dtype=mnp.int32)
        assert arr1 is arr2
        assert arr1 is not arr3
        assert arr4 is arr5

    # Additional tests for nested tensor/numpy_array mixture
    mnp_input = [(onp.ones(3,), mnp.ones(3)), [[1, 1, 1], (1, 1, 1)]]
    onp_input = [(onp.ones(3,), onp.ones(3)), [[1, 1, 1], (1, 1, 1)]]

    actual = onp.asarray(onp_input)
    expected = mnp.asarray(mnp_input).asnumpy()
    match_array(actual, expected, error=7)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_asfarray():
    test_case = Cases()
    for array in test_case.array_sets:
        # Check for dtype matching
        actual = onp.asfarray(array)
        expected = mnp.asfarray(array).asnumpy()
        # Since we set float32/int32 as the default dtype in mindspore, we need
        # to make a conversion between numpy.asarray and mindspore.numpy.asarray
        if actual.dtype is onp.dtype('float64'):
            assert expected.dtype == onp.dtype('float32')
        else:
            assert actual.dtype == expected.dtype
        match_array(actual, expected, error=7)

        for i in range(len(test_case.onp_dtypes)):
            actual = onp.asfarray(array, test_case.onp_dtypes[i])
            expected = mnp.asfarray(array, test_case.mnp_dtypes[i]).asnumpy()
            match_array(actual, expected, error=7)

    # Additional tests for nested tensor/numpy_array mixture
    mnp_input = [(onp.ones(3,), mnp.ones(3)), [[1, 1, 1], (1, 1, 1)]]
    onp_input = [(onp.ones(3,), onp.ones(3)), [[1, 1, 1], (1, 1, 1)]]

    actual = onp.asarray(onp_input)
    expected = mnp.asarray(mnp_input).asnumpy()
    match_array(actual, expected, error=7)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_zeros():
    test_case = Cases()
    for shape in test_case.all_shapes:
        for i in range(len(test_case.onp_dtypes)):
            actual = onp.zeros(shape, test_case.onp_dtypes[i])
            expected = mnp.zeros(shape, test_case.mnp_dtypes[i]).asnumpy()
            match_array(actual, expected)
        actual = onp.zeros(shape)
        expected = mnp.zeros(shape).asnumpy()
        match_array(actual, expected)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_ones():
    test_case = Cases()
    for shape in test_case.all_shapes:
        for i in range(len(test_case.onp_dtypes)):
            actual = onp.ones(shape, test_case.onp_dtypes[i])
            expected = mnp.ones(shape, test_case.mnp_dtypes[i]).asnumpy()
            match_array(actual, expected)
        actual = onp.ones(shape)
        expected = mnp.ones(shape).asnumpy()
        match_array(actual, expected)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_full():
    actual = onp.full((2, 2), [1, 2])
    expected = mnp.full((2, 2), [1, 2]).asnumpy()
    match_array(actual, expected)

    actual = onp.full((2, 0), onp.inf)
    expected = mnp.full((2, 0), mnp.inf).asnumpy()
    match_array(actual, expected)

    actual = onp.full((2, 3), True)
    expected = mnp.full((2, 3), True).asnumpy()
    match_array(actual, expected)

    actual = onp.full((3, 4, 5), 7.5)
    expected = mnp.full((3, 4, 5), 7.5).asnumpy()
    match_array(actual, expected)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_eye():
    test_case = Cases()
    for i in range(len(test_case.onp_dtypes)):
        for m in range(1, 5):
            actual = onp.eye(m, dtype=test_case.onp_dtypes[i])
            expected = mnp.eye(m, dtype=test_case.mnp_dtypes[i]).asnumpy()
            match_array(actual, expected)
            for n in range(1, 5):
                for k in range(0, 5):
                    actual = onp.eye(m, n, k, dtype=test_case.onp_dtypes[i])
                    expected = mnp.eye(
                        m, n, k, dtype=test_case.mnp_dtypes[i]).asnumpy()
                    match_array(actual, expected)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_identity():
    test_case = Cases()
    for i in range(len(test_case.onp_dtypes)):
        for m in range(1, 5):
            actual = onp.identity(m, dtype=test_case.onp_dtypes[i])
            expected = mnp.identity(m, dtype=test_case.mnp_dtypes[i]).asnumpy()
            match_array(actual, expected)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_arange():
    actual = onp.arange(10)
    expected = mnp.arange(10).asnumpy()
    match_array(actual, expected)

    actual = onp.arange(0, 10)
    expected = mnp.arange(0, 10).asnumpy()
    match_array(actual, expected)

    actual = onp.arange(start=10)
    expected = mnp.arange(start=10).asnumpy()
    match_array(actual, expected)

    actual = onp.arange(start=10, step=0.1)
    expected = mnp.arange(start=10, step=0.1).asnumpy()
    match_array(actual, expected, error=6)

    actual = onp.arange(10, step=0.1)
    expected = mnp.arange(10, step=0.1).asnumpy()
    match_array(actual, expected, error=6)

    actual = onp.arange(0.1, 9.9)
    expected = mnp.arange(0.1, 9.9).asnumpy()
    match_array(actual, expected, error=6)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_linspace():
    actual = onp.linspace(2.0, 3.0, dtype=onp.float32)
    expected = mnp.linspace(2.0, 3.0).asnumpy()
    match_array(actual, expected, error=7)

    actual = onp.linspace(2.0, 3.0, num=5, dtype=onp.float32)
    expected = mnp.linspace(2.0, 3.0, num=5).asnumpy()
    match_array(actual, expected, error=7)

    actual = onp.linspace(
        2.0, 3.0, num=5, endpoint=False, dtype=onp.float32)
    expected = mnp.linspace(2.0, 3.0, num=5, endpoint=False).asnumpy()
    match_array(actual, expected, error=7)

    actual = onp.linspace(2.0, 3.0, num=5, retstep=True, dtype=onp.float32)
    expected = mnp.linspace(2.0, 3.0, num=5, retstep=True)
    match_array(actual[0], expected[0].asnumpy())
    assert actual[1] == expected[1]

    actual = onp.linspace(2.0, [3, 4, 5], num=5,
                          endpoint=False, dtype=onp.float32)
    expected = mnp.linspace(
        2.0, [3, 4, 5], num=5, endpoint=False).asnumpy()
    match_array(actual, expected)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_logspace():
    actual = onp.logspace(2.0, 3.0, dtype=onp.float32)
    expected = mnp.logspace(2.0, 3.0).asnumpy()
    match_array(actual, expected)

    actual = onp.logspace(2.0, 3.0, num=5, dtype=onp.float32)
    expected = mnp.logspace(2.0, 3.0, num=5).asnumpy()
    match_array(actual, expected)

    actual = onp.logspace(
        2.0, 3.0, num=5, endpoint=False, dtype=onp.float32)
    expected = mnp.logspace(2.0, 3.0, num=5, endpoint=False).asnumpy()
    match_array(actual, expected)

    actual = onp.logspace(2.0, [3, 4, 5], num=5,
                          endpoint=False, dtype=onp.float32)
    expected = mnp.logspace(
        2.0, [3, 4, 5], num=5, endpoint=False).asnumpy()
    match_array(actual, expected)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_empty():
    test_case = Cases()
    for shape in test_case.all_shapes:
        for mnp_dtype, onp_dtype in zip(test_case.mnp_dtypes,
                                        test_case.onp_dtypes):
            actual = mnp.empty(shape, mnp_dtype).asnumpy()
            expected = onp.empty(shape, onp_dtype)
            match_meta(actual, expected)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_empty_like():
    test_case = Cases()
    for mnp_proto, onp_proto in zip(test_case.mnp_prototypes, test_case.onp_prototypes):
        actual = mnp.empty_like(mnp_proto).asnumpy()
        expected = onp.empty_like(onp_proto)
        assert actual.shape == expected.shape

        for mnp_dtype, onp_dtype in zip(test_case.mnp_dtypes,
                                        test_case.onp_dtypes):
            actual = mnp.empty_like(mnp_proto, dtype=mnp_dtype).asnumpy()
            expected = onp.empty_like(onp_proto, dtype=onp_dtype)
            match_meta(actual, expected)


def run_x_like(mnp_fn, onp_fn):
    test_case = Cases()
    for mnp_proto, onp_proto in zip(test_case.mnp_prototypes, test_case.onp_prototypes):
        actual = mnp_fn(mnp_proto).asnumpy()
        expected = onp_fn(onp_proto)
        match_array(actual, expected)

        for shape in test_case.all_shapes:
            actual = mnp_fn(mnp_proto, shape=shape).asnumpy()
            expected = onp_fn(onp_proto, shape=shape)
            match_array(actual, expected)

            for mnp_dtype, onp_dtype in zip(test_case.mnp_dtypes,
                                            test_case.onp_dtypes):
                actual = mnp_fn(mnp_proto, dtype=mnp_dtype).asnumpy()
                expected = onp_fn(onp_proto, dtype=onp_dtype)
                match_array(actual, expected)

                actual = mnp_fn(mnp_proto, dtype=mnp_dtype,
                                shape=shape).asnumpy()
                expected = onp_fn(onp_proto, dtype=onp_dtype, shape=shape)
                match_array(actual, expected)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_ones_like():
    run_x_like(mnp.ones_like, onp.ones_like)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_zeros_like():
    run_x_like(mnp.zeros_like, onp.zeros_like)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_full_like():
    test_case = Cases()
    for mnp_proto, onp_proto in zip(test_case.mnp_prototypes, test_case.onp_prototypes):
        shape = onp.zeros_like(onp_proto).shape
        fill_value = rand_int()
        actual = mnp.full_like(mnp_proto, fill_value).asnumpy()
        expected = onp.full_like(onp_proto, fill_value)
        match_array(actual, expected)

        for i in range(len(shape) - 1, 0, -1):
            fill_value = rand_int(*shape[i:])
            actual = mnp.full_like(mnp_proto, fill_value).asnumpy()
            expected = onp.full_like(onp_proto, fill_value)
            match_array(actual, expected)

            fill_value = rand_int(1, *shape[i + 1:])
            actual = mnp.full_like(mnp_proto, fill_value).asnumpy()
            expected = onp.full_like(onp_proto, fill_value)
            match_array(actual, expected)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_tri_triu_tril():
    x = mnp.ones((16, 32), dtype="bool")
    match_array(mnp.tril(x).asnumpy(), onp.tril(x.asnumpy()))
    match_array(mnp.tril(x, -1).asnumpy(), onp.tril(x.asnumpy(), -1))
    match_array(mnp.triu(x).asnumpy(), onp.triu(x.asnumpy()))
    match_array(mnp.triu(x, -1).asnumpy(), onp.triu(x.asnumpy(), -1))

    x = mnp.ones((64, 64), dtype="uint8")
    match_array(mnp.tril(x).asnumpy(), onp.tril(x.asnumpy()))
    match_array(mnp.tril(x, 25).asnumpy(), onp.tril(x.asnumpy(), 25))
    match_array(mnp.triu(x).asnumpy(), onp.triu(x.asnumpy()))
    match_array(mnp.triu(x, 25).asnumpy(), onp.triu(x.asnumpy(), 25))

    match_array(mnp.tri(64, 64).asnumpy(), onp.tri(64, 64))
    match_array(mnp.tri(64, 64, -10).asnumpy(), onp.tri(64, 64, -10))


def mnp_diagonal(arr):
    return mnp.diagonal(arr, offset=2, axis1=-1, axis2=0)


def onp_diagonal(arr):
    return onp.diagonal(arr, offset=2, axis1=-1, axis2=0)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_diagonal():
    arr = rand_int(0, 0)
    match_res(mnp.diagonal, onp.diagonal, arr, offset=1)

    arr = rand_int(3, 5)
    for i in [-10, -5, -1, 0, 2, 5, 6, 10]:
        match_res(mnp.diagonal, onp.diagonal, arr, offset=i, axis1=0, axis2=1)
        match_res(mnp.diagonal, onp.diagonal, arr, offset=i, axis1=1, axis2=0)

    arr = rand_int(7, 4, 9)
    for i in [-10, -5, -1, 0, 2, 5, 6, 10]:
        match_res(mnp.diagonal, onp.diagonal, arr, offset=i, axis1=0, axis2=-1)
        match_res(mnp.diagonal, onp.diagonal, arr, offset=i, axis1=-2, axis2=2)
        match_res(mnp.diagonal, onp.diagonal, arr,
                  offset=i, axis1=-1, axis2=-2)

    arr = rand_int(2, 5, 8, 1)
    match_res(mnp_diagonal, onp_diagonal, arr)
    for i in [-10, -5, -1, 0, 2, 5, 6, 10]:
        match_res(mnp.diagonal, onp.diagonal, arr, offset=i, axis1=-3, axis2=2)
        match_res(mnp.diagonal, onp.diagonal, arr, offset=i, axis1=1, axis2=3)
        match_res(mnp.diagonal, onp.diagonal, arr, offset=i, axis1=0, axis2=-2)
        match_res(mnp.diagonal, onp.diagonal, arr, offset=i, axis1=2, axis2=-1)


def mnp_trace(arr):
    return mnp.trace(arr, offset=4, axis1=1, axis2=2)


def onp_trace(arr):
    return onp.trace(arr, offset=4, axis1=1, axis2=2)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_trace():
    arr = rand_int(0, 0)
    match_res(mnp.trace, onp.trace, arr, offset=1)

    arr = rand_int(3, 5)
    for i in [-10, -5, -1, 0, 2, 5, 6, 10]:
        match_res(mnp.trace, onp.trace, arr, offset=i, axis1=0, axis2=1)
        match_res(mnp.trace, onp.trace, arr, offset=i, axis1=1, axis2=0)

    arr = rand_int(7, 4, 9)
    for i in [-10, -5, -1, 0, 2, 5, 6, 10]:
        match_res(mnp.trace, onp.trace, arr, offset=i, axis1=0, axis2=-1)
        match_res(mnp.trace, onp.trace, arr, offset=i, axis1=-2, axis2=2)
        match_res(mnp.trace, onp.trace, arr, offset=i, axis1=-1, axis2=-2)

    arr = rand_int(2, 5, 8, 1)
    match_res(mnp_trace, onp_trace, arr)
    for i in [-10, -5, -1, 0, 2, 5, 6, 10]:
        match_res(mnp.trace, onp.trace, arr, offset=i, axis1=-3, axis2=2)
        match_res(mnp.trace, onp.trace, arr, offset=i, axis1=1, axis2=3)
        match_res(mnp.trace, onp.trace, arr, offset=i, axis1=0, axis2=-2)
        match_res(mnp.trace, onp.trace, arr, offset=i, axis1=2, axis2=-1)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_asarray_exception():
    with pytest.raises(TypeError):
        mnp.asarray({1, 2, 3})


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_linspace_exception():
    with pytest.raises(TypeError):
        mnp.linspace(0, 1, num=2.5)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_empty_like_exception():
    with pytest.raises(ValueError):
        mnp.empty_like([[1, 2, 3], [4, 5]])