mindspore2022/tests/syntax/simple_expression/test_hypermap.py

# Copyright 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.
# ============================================================================

import pytest
import numpy as np

from mindspore import Tensor
from mindspore.common.api import ms_function
from mindspore.ops import composite as C
from mindspore.ops import operations as P
from mindspore.nn import Cell

add = P.Add()
hyper_map = C.HyperMap()

@ms_function
def main_noleaf(x, y):
    return hyper_map(add, x, y)


def test_hypermap_noleaf_tuple_list_mix():
    """
    Feature: Check the types of inputs of HyperMap.
    Description: The types of inputs of HyperMap must be the same.
    Expectation: The types of inputs of HyperMap must be the same.
    """
    tensor1 = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]]).astype('float32'))
    tensor2 = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]]).astype('float32'))
    with pytest.raises(Exception, match="the types of arguments in HyperMap must be consistent"):
        main_noleaf((tensor1, 1), [tensor2, 2])


def test_hypermap_noleaf_tuple_length():
    """
    Feature: Check the length of arg of Tuple in HyperMap.
    Description: The length of inputs of HyperMap must be the same.
    Expectation: The length of inputs of HyperMap must be the same.
    """
    tensor1 = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]]).astype('float32'))
    tensor2 = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]]).astype('float32'))
    with pytest.raises(Exception, match="The length of tuples in HyperMap must be the same"):
        main_noleaf((tensor1, 1), (tensor2, 2, 2))


def test_hypermap_noleaf_list_length():
    """
    Feature: Check the length of arg of List in HyperMap.
    Description: Check the length of arg of List in HyperMap.
    Expectation: Check the length of arg of List in HyperMap.
    """
    tensor1 = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]]).astype('float32'))
    tensor2 = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]]).astype('float32'))
    with pytest.raises(Exception, match="The lists in HyperMap should have the same length"):
        main_noleaf([tensor1], [tensor2, tensor2])


def test_hypermap_noleaf_list_tuple():
    """
    Feature: Check the types of inputs of HyperMap.
    Description: The types of inputs of HyperMap must be the same.
    Expectation: The types of inputs of HyperMap must be the same.
    """
    tensor1 = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]]).astype('float32'))
    tensor2 = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]]).astype('float32'))
    with pytest.raises(Exception, match="the types of arguments in HyperMap must be consistent"):
        main_noleaf([tensor1], (tensor2, tensor2))


def test_tuple_slice_stop_index():
    """
    Feature: Check the type of stop index of slice.
    Description: The type of stop index of slice must be scalar, None or Tensor.
    Expectation: The type of stop index of slice must be scalar, None or Tensor.
    """
    class TupleSliceNet(Cell):
        def __init__(self):
            super(TupleSliceNet, self).__init__()
            self.addn = P.AddN()
            self.index_0 = Tensor(3)

        def construct(self, tensor_tuple):
            tensor_tuple_slice0 = tensor_tuple[:]
            tensor_tuple_slice1 = tensor_tuple[self.index_0:"str"]  # slice should be Scalar or None, rather than string
            sum0 = self.addn(tensor_tuple_slice0)
            sum1 = self.addn(tensor_tuple_slice1)
            ret = sum0 + sum1
            return ret

    data = (Tensor(np.ones([2, 3, 4], np.int32)),
            Tensor(np.zeros([2, 3, 4], np.int32)),
            Tensor(np.ones([2, 3, 4], np.int32)),
            Tensor(np.ones([2, 3, 4], np.int32)),
            Tensor(np.zeros([2, 3, 4], np.int32)),
            Tensor(np.ones([2, 3, 4], np.int32)))

    net = TupleSliceNet()
    with pytest.raises(Exception, match="Slice indices must be integers or bool."):
        output = net(data)
        print("output:", output)


def test_tuple_slice_start_index():
    """
    Feature: Check the type of start index of slice.
    Description: The type of start index of slice must be scalar, None or Tensor.
    Expectation: The type of start index of slice must be scalar, None or Tensor.
    """
    class TupleSliceNet(Cell):
        def __init__(self):
            super(TupleSliceNet, self).__init__()
            self.addn = P.AddN()
            self.index_0 = Tensor(3)
            self.index_1 = Tensor([5])
            self.index_3 = Tensor([True])

        def construct(self, tensor_tuple):
            tensor_tuple_slice0 = tensor_tuple[:]
            tensor_tuple_slice1 = tensor_tuple["str":self.index_0]
            tensor_tuple_slice2 = tensor_tuple[self.index_3:]
            tensor_tuple_slice3 = tensor_tuple[2:self.index_1:]
            sum0 = self.addn(tensor_tuple_slice0)
            sum1 = self.addn(tensor_tuple_slice1)
            sum2 = self.addn(tensor_tuple_slice2)
            sum3 = self.addn(tensor_tuple_slice3)
            ret = sum0 + sum1 + sum2 + sum3
            return ret

    data = (Tensor(np.ones([2, 3, 4], np.int32)),
            Tensor(np.zeros([2, 3, 4], np.int32)),
            Tensor(np.ones([2, 3, 4], np.int32)),
            Tensor(np.ones([2, 3, 4], np.int32)),
            Tensor(np.zeros([2, 3, 4], np.int32)),
            Tensor(np.ones([2, 3, 4], np.int32)))

    net = TupleSliceNet()
    with pytest.raises(Exception, match="Slice indices must be integers or bool."):
        output = net(data)
        print("output:", output)


def test_tuple_slice_step():
    """
    Feature: Check the type of step of slice.
    Description: The type of step of slice must not be 0.
    Expectation: The type of step of slice must be scalar, None or Tensor.
    """
    class TupleSliceNet(Cell):
        def __init__(self):
            super(TupleSliceNet, self).__init__()
            self.addn = P.AddN()
            self.index_0 = Tensor(3)
            self.index_1 = Tensor([5])
            self.index_3 = Tensor([True])

        def construct(self, tensor_tuple):
            tensor_tuple_slice0 = tensor_tuple[:]
            tensor_tuple_slice1 = tensor_tuple[:self.index_0]
            tensor_tuple_slice2 = tensor_tuple[self.index_3:]
            tensor_tuple_slice3 = tensor_tuple[2:self.index_1:0]
            sum0 = self.addn(tensor_tuple_slice0)
            sum1 = self.addn(tensor_tuple_slice1)
            sum2 = self.addn(tensor_tuple_slice2)
            sum3 = self.addn(tensor_tuple_slice3)
            ret = sum0 + sum1 + sum2 + sum3
            return ret

    data = (Tensor(np.ones([2, 3, 4], np.int32)),
            Tensor(np.zeros([2, 3, 4], np.int32)),
            Tensor(np.ones([2, 3, 4], np.int32)),
            Tensor(np.ones([2, 3, 4], np.int32)),
            Tensor(np.zeros([2, 3, 4], np.int32)),
            Tensor(np.ones([2, 3, 4], np.int32)))

    net = TupleSliceNet()
    with pytest.raises(Exception, match="Slice step cannot be zero."):
        output = net(data)
        print("output:", output)