mindspore2022/tests/ut/python/pynative_mode/test_cont_cases.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 control ops """
import numpy as np
from mindspore import dtype as ms
from mindspore import Tensor
from mindspore import context
from mindspore import nn
from mindspore import ms_function
from mindspore.common.parameter import Parameter, ParameterTuple
from mindspore.ops import composite as C
from mindspore.ops import operations as P
# from tests.vm_impl.math_ops_vm_impl import *
# from tests.vm_impl.vm_interface import *
# from tests.vm_impl import *

grad_by_list = C.GradOperation(get_by_list=True)
grad_all = C.GradOperation(get_all=True)


def setup_module():
    context.set_context(mode=context.PYNATIVE_MODE, enable_sparse=False)


def test_while_with_param_forward_with_const_branch():
    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
            self.reduce = P.ReduceSum()

        @ms_function
        def construct(self, idx, end, x):
            out = self.zero
            while idx < end:
                if 2 > 1:
                    out = out + self.param
                else:
                    out = out + idx + self.param
                idx = idx + 1
            return out

    while_net = MyWhileNet()
    net = while_net
    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(4), dtype=ms.int32)
    x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
    net(idx, end, x)


def test_while_opt_endless():
    """endless during optimization case"""
    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
            self.reduce = P.ReduceSum()
            self.addn = P.AddN()

        def construct(self, idx, end, x):
            addn1 = self.addn((x, x, x))
            out = addn1
            while idx < end:
                out = self.addn((out, addn1))
                idx = idx + 1
            out = self.addn((out, x))
            return out

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net

        @ms_function
        def construct(self, *inputs):
            return grad_all(self.net)(*inputs)

    while_net = MyWhileNet()
    net = GradNet(while_net)
    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(4), dtype=ms.int32)
    x = Tensor(np.ones([2, 2, 2]).astype(np.float32) * 3, dtype=ms.float32)
    net(idx, end, x)


def test_no_while_call():
    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
            self.reduce = P.ReduceSum()

        @ms_function
        def construct(self, idx, end, x):
            out = self.zero
            if 2 > 1:
                out = out + self.param
            else:
                out = out + idx + self.param
            return out

    while_net = MyWhileNet()
    net = while_net
    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(4), dtype=ms.int32)
    x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
    net(idx, end, x)


def test_while_with_param_grad_with_const_branch():
    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
            self.reduce = P.ReduceSum()

        def construct(self, idx, end, x):
            out = self.zero
            while idx < end:
                if 2 > 1:
                    out = out + self.param
                else:
                    out = out + idx + self.param
                idx = idx + 1
            return out

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.weights = ParameterTuple(net.trainable_params())

        @ms_function
        def construct(self, a, b, c):
            return grad_by_list(self.net, self.weights)(a, b, c)

    while_net = MyWhileNet()
    net = GradNet(while_net)
    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(4), dtype=ms.int32)
    x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
    net(idx, end, x)


def test_for_while_with_param_grad_with_const_branch():
    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
            self.reduce = P.ReduceSum()
            self.start = Tensor(np.array(0), dtype=ms.int32)

        def construct(self, idx, end, x):
            out = self.zero
            for _ in range(0, 2):
                idx = self.start
                while idx < end:
                    if 2 > 1:
                        out = out + self.param
                    else:
                        out = out + idx + self.param
                    idx = idx + 1
            return out

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.weights = ParameterTuple(net.trainable_params())

        @ms_function
        def construct(self, a, b, c):
            return grad_by_list(self.net, self.weights)(a, b, c)

    while_net = MyWhileNet()
    net = GradNet(while_net)
    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(4), dtype=ms.int32)
    x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
    net(idx, end, x)


def test_for_while_with_param_grad_basic():
    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
            self.reduce = P.ReduceSum()
            self.start = Tensor(np.array(0), dtype=ms.int32)

        def construct(self, idx, end, x):
            out = self.zero
            for _ in range(0, 2):
                idx = self.start
                while idx < end:
                    out = out + self.param
                    idx = idx + 1
            return out

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.weights = ParameterTuple(net.trainable_params())

        @ms_function
        def construct(self, a, b, c):
            return grad_by_list(self.net, self.weights)(a, b, c)

    while_net = MyWhileNet()
    net = GradNet(while_net)
    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(4), dtype=ms.int32)
    x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
    net(idx, end, x)


def test_for_while_with_param_grad_normal():
    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
            self.reduce = P.ReduceSum()
            self.start = Tensor(np.array(0), dtype=ms.int32)

        def construct(self, idx, end, x):
            out = x
            for _ in range(0, 2):
                idx = self.start
                while idx < end:
                    out = out + self.param
                    idx = idx + 1
            return out

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.weights = ParameterTuple(net.trainable_params())

        @ms_function
        def construct(self, a, b, c):
            return grad_by_list(self.net, self.weights)(a, b, c)

    while_net = MyWhileNet()
    net = GradNet(while_net)
    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(4), dtype=ms.int32)
    x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
    net(idx, end, x)


def test_while_with_param_basic_grad():
    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
            self.t2 = Tensor(np.array(2), dtype=ms.float32)

        def construct(self, idx, end, x):
            out = self.zero
            while idx < end:
                out = out + self.param
                idx = idx + 1
            return out + self.param

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.weights = ParameterTuple(net.trainable_params())

        @ms_function
        def construct(self, a, b, c):
            return grad_by_list(self.net, self.weights)(a, b, c)

    while_net = MyWhileNet()
    net = GradNet(while_net)
    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(3), dtype=ms.int32)
    x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
    net(idx, end, x)


def test_while_with_param_basic_grad_mul():
    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.ones(([2, 2, 2])), ms.float32)
            self.t2 = Tensor(np.array(2), dtype=ms.float32)

        def construct(self, idx, end, x):
            out = self.zero
            while idx < end:
                out = out * self.param
                idx = idx + 1
            return out + self.param

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.weights = ParameterTuple(net.trainable_params())

        @ms_function
        def construct(self, a, b, c):
            return grad_by_list(self.net, self.weights)(a, b, c)

    while_net = MyWhileNet()
    net = GradNet(while_net)
    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(3), dtype=ms.int32)
    x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
    net(idx, end, x)


def test_while_with_param_basic_grad_two():
    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.weight = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="loss")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
            self.t2 = Tensor(np.array(2), dtype=ms.float32)

        def construct(self, idx, end, x):
            out = self.zero
            while idx < end:
                out = out + self.param + self.weight
                idx = idx + 1
            return out + self.param

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.weights = ParameterTuple(net.trainable_params())

        @ms_function
        def construct(self, a, b, c):
            return grad_by_list(self.net, self.weights)(a, b, c)

    while_net = MyWhileNet()
    net = GradNet(while_net)
    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(3), dtype=ms.int32)
    x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
    net(idx, end, x)


def test_while_with_param_basic_grad_three():
    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.weight = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="loss")
            self.key = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="key")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
            self.t2 = Tensor(np.array(2), dtype=ms.float32)

        def construct(self, idx, end, x):
            out = self.zero
            while idx < end:
                out = out + self.param + self.weight + self.key
                idx = idx + 1
            return out + self.param

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.weights = ParameterTuple(net.trainable_params())

        @ms_function
        def construct(self, a, b, c):
            return grad_by_list(self.net, self.weights)(a, b, c)

    while_net = MyWhileNet()
    net = GradNet(while_net)
    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(3), dtype=ms.int32)
    x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
    net(idx, end, x)


def test_while_if_with_param_grad():
    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
            self.t2 = Tensor(np.array(2), dtype=ms.float32)

        def construct(self, idx, end, x):
            out = self.zero
            while idx < end:
                if self.max(out) < self.max(x):
                    out = out + self.param * 2
                else:
                    out = out + self.param
                idx = idx + 1
            return out + self.param

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.weights = ParameterTuple(net.trainable_params())

        @ms_function
        def construct(self, a, b, c):
            return grad_by_list(self.net, self.weights)(a, b, c)

    while_net = MyWhileNet()
    net = GradNet(while_net)
    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(3), dtype=ms.int32)
    x = Tensor(np.ones([2, 2, 2]).astype(np.float32), dtype=ms.float32)
    net(idx, end, x)


def test_while_with_param_grad_not_enter_while():
    class MyWhileNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)

        def construct(self, idx, end, x):
            out = self.zero
            while idx < end:
                out = out + self.param * 3
                idx = idx + 1
            return out + self.param

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.weights = ParameterTuple(net.trainable_params())

        @ms_function
        def construct(self, a, b, c):
            return grad_by_list(self.net, self.weights)(a, b, c)

    while_net = MyWhileNet()
    net = GradNet(while_net)
    idx = Tensor(np.array(3), dtype=ms.int32)
    end = Tensor(np.array(0), dtype=ms.int32)
    x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
    net(idx, end, x)


def test_with_param_if_by_if_forward():
    class MyIfByIfNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)

        @ms_function
        def construct(self, a, b, x):
            out = self.zero
            if a < b:
                out = out + x + self.param
            else:
                out = out + x
            if a == b:
                out = out + x*3 + self.param
            else:
                out = out + x*2
            return out

    if_net = MyIfByIfNet()
    net = if_net
    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(4), dtype=ms.int32)
    x = Tensor(np.ones([2, 2, 2]).astype(np.float32), dtype=ms.float32)
    net(idx, end, x)


def test_with_param_if_by_if_grad_inputs():
    class MyIfByIfNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)

        def construct(self, a, b, x):
            out = self.zero
            if a < b:
                out = out + x + self.param * 4
            if a == b:
                out = out + x*3 + self.param * 3
            return out

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net

        @ms_function
        def construct(self, *inputs):
            return grad_all(self.net)(*inputs)

    if_net = MyIfByIfNet()
    net = GradNet(if_net)
    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(0), dtype=ms.int32)
    x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
    net(idx, end, x)


def test_with_param_if_by_if_grad_parameter():
    class MyIfByIfNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)

        def construct(self, a, b, x):
            out = self.zero
            if a < b:
                out = out + x + self.param * 2
            if a == b:
                out = out + x*3 + self.param
            return out

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.weights = ParameterTuple(net.trainable_params())

        @ms_function
        def construct(self, *inputs):
            return grad_by_list(self.net, self.weights)(*inputs)

    if_net = MyIfByIfNet()
    net = GradNet(if_net)
    idx = Tensor(np.array(0), dtype=ms.int32)
    end = Tensor(np.array(2), dtype=ms.int32)
    x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
    net(idx, end, x)


def test_with_param_if_by_if_grad_param_excute_null():
    class MyIfByIfNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)

        def construct(self, a, b, x):
            out = self.zero
            if a < b:
                out = out + x + self.param * 2
            return out

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.weights = ParameterTuple(net.trainable_params())

        @ms_function
        def construct(self, *inputs):
            return grad_by_list(self.net, self.weights)(*inputs)

    if_net = MyIfByIfNet()
    net = GradNet(if_net)
    idx = Tensor(np.array(4), dtype=ms.int32)
    end = Tensor(np.array(0), dtype=ms.int32)
    x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
    net(idx, end, x)


def test_if_by_if_return_inside_grad():
    class MyIfByIfNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.max = P.ReduceMax()
            self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
            self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)

        def construct(self, a, b, x):
            out = self.zero
            if a < b:
                return out + x + self.param
            if a == b:
                return out + self.param * 2
            return out + self.param * 3

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.weights = ParameterTuple(net.trainable_params())

        @ms_function
        def construct(self, *inputs):
            return grad_by_list(self.net, self.weights)(*inputs)

    if_net = MyIfByIfNet()
    net = GradNet(if_net)
    idx = Tensor(np.array(1), dtype=ms.int32)
    end = Tensor(np.array(0), dtype=ms.int32)
    x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
    net(idx, end, x)


def test_if_by_if_forward():
    class MyIfByIfNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.add = P.TensorAdd()
            self.sub = P.Sub()
            self.mul = P.Mul()
            self.div = P.RealDiv()

        @ms_function
        def construct(self, a, b, x):
            if a < b:
                a = self.add(a, b)
            else:
                a = self.sub(a, b)
            if a == x:
                a = self.mul(a, b)
            else:
                a = self.div(a, b)
            if b == x:
                b = self.add(a, b)
            else:
                b = self.add(a, x)
            a = a * b
            out = a + b + x
            return out

    if_net = MyIfByIfNet()
    net = if_net
    idx = Tensor(np.array(2), dtype=ms.float32)
    end = Tensor(np.array(3), dtype=ms.float32)
    x = Tensor(np.array(4), dtype=ms.float32)
    net(idx, end, x)


def test_if_by_if_forward_control_tuple_switch():
    """tuple_get from  swtich op will generate new switch inside to eliminate tuple_get"""
    class Branch3Net(nn.Cell):
        def __init__(self):
            super().__init__()
            self.add = P.TensorAdd()
            self.sub = P.Sub()
            self.mul = P.Mul()
            self.div = P.RealDiv()

        def construct(self, a, b, x):
            if b == x:
                b = self.add(a, b)
            else:
                b = self.add(a, x)
            return a, b, x

    class Branch2Net(nn.Cell):
        def __init__(self):
            super().__init__()
            self.add = P.TensorAdd()
            self.sub = P.Sub()
            self.mul = P.Mul()
            self.div = P.RealDiv()
            self.net = Branch3Net()

        def construct(self, a, b, x):
            if a == x:
                a = self.mul(a, b)
            else:
                a = self.div(a, b)
            return self.net(a, b, x)

    class MyIfByIfNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.add = P.TensorAdd()
            self.sub = P.Sub()
            self.mul = P.Mul()
            self.div = P.RealDiv()
            self.net = Branch2Net()

        @ms_function
        def construct(self, a, b, x):
            if a < b:
                a = self.add(a, b)
            else:
                a = self.sub(a, b)
            a, b, x = self.net(a, b, x)
            a = a * b
            out = a + b + x
            return out

    if_net = MyIfByIfNet()
    net = if_net
    idx = Tensor(np.array(2), dtype=ms.float32)
    end = Tensor(np.array(3), dtype=ms.float32)
    x = Tensor(np.array(0), dtype=ms.float32)
    net(idx, end, x)


def test_if_by_if_forward_control_inside_net():
    class Branch3Net(nn.Cell):
        def __init__(self):
            super().__init__()
            self.add = P.TensorAdd()
            self.sub = P.Sub()
            self.mul = P.Mul()
            self.div = P.RealDiv()

        def construct(self, a, b, x):
            if b == x:
                b = self.add(a, b)
            else:
                b = self.add(a, x)
            a = a * b
            out = a + b + x
            return out

    class Branch2Net(nn.Cell):
        def __init__(self):
            super().__init__()
            self.add = P.TensorAdd()
            self.sub = P.Sub()
            self.mul = P.Mul()
            self.div = P.RealDiv()
            self.net = Branch3Net()

        def construct(self, a, b, x):
            if a == x:
                a = self.mul(a, b)
            else:
                a = self.div(a, b)
            return self.net(a, b, x)

    class MyIfByIfNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.add = P.TensorAdd()
            self.sub = P.Sub()
            self.mul = P.Mul()
            self.div = P.RealDiv()
            self.net = Branch2Net()

        @ms_function
        def construct(self, a, b, x):
            if a < b:
                a = self.add(a, b)
            else:
                a = self.sub(a, b)
            out = self.net(a, b, x)
            return out

    if_net = MyIfByIfNet()
    net = if_net
    idx = Tensor(np.array(2), dtype=ms.float32)
    end = Tensor(np.array(3), dtype=ms.float32)
    x = Tensor(np.array(0), dtype=ms.float32)
    net(idx, end, x)


def test_if_by_if_forward_use_namespace():
    class MyIfByIfNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.add = P.TensorAdd()
            self.sub = P.Sub()
            self.mul = P.Mul()
            self.div = P.RealDiv()

        @ms_function
        def construct(self, a, b, x):
            if a < b:
                a = P.TensorAdd()(a, b)
            else:
                a = P.Sub()(a, b)
            if a == x:
                a = P.Mul()(a, b)
            else:
                a = P.RealDiv()(a, b)
            if b == x:
                b = P.TensorAdd()(a, b)
            else:
                b = P.TensorAdd()(a, x)
            a = a * b
            out = a + b + x
            return out

    if_net = MyIfByIfNet()
    net = if_net
    idx = Tensor(np.array(2), dtype=ms.float32)
    end = Tensor(np.array(3), dtype=ms.float32)
    x = Tensor(np.array(0), dtype=ms.float32)
    net(idx, end, x)


def test_if_by_if_forward_use_global_op():
    class MyIfByIfNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.add = P.TensorAdd()
            self.sub = P.Sub()
            self.mul = P.Mul()
            self.div = P.RealDiv()

        @ms_function
        def construct(self, a, b, x):
            add = P.TensorAdd()
            sub = P.Sub()
            mul = P.Mul()
            div = P.RealDiv()
            if a < b:
                a = add(a, b)
            else:
                a = sub(a, b)
            if a == x:
                a = mul(a, b)
            else:
                a = div(a, b)
            if b == x:
                b = add(a, b)
            else:
                b = add(a, x)
            a = a * b
            out = a + b + x
            return out

    if_net = MyIfByIfNet()
    net = if_net
    idx = Tensor(np.array(2), dtype=ms.float32)
    end = Tensor(np.array(3), dtype=ms.float32)
    x = Tensor(np.array(0), dtype=ms.float32)
    net(idx, end, x)


def test_for_with_if_by_if_forward():
    class MyIfByIfNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.add = P.TensorAdd()
            self.sub = P.Sub()

        @ms_function
        def construct(self, a, b, x):
            for _ in range(0, 4):
                if a < b:
                    a = self.add(a, b)
                else:
                    b = self.sub(b, x)
            a = a * b
            out = a + b + x
            return out

    if_net = MyIfByIfNet()
    net = if_net
    idx = Tensor(np.array(2), dtype=ms.float32)
    end = Tensor(np.array(3), dtype=ms.float32)
    x = Tensor(np.array(0), dtype=ms.float32)
    net(idx, end, x)


def test_for_with_if_by_if_forward_namespace():
    class MyIfByIfNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.add = P.TensorAdd()
            self.sub = P.Sub()
            self.mul = P.Mul()
            self.div = P.RealDiv()

        @ms_function
        def construct(self, a, b, x):
            for _ in range(0, 6):
                if a < b:
                    a = P.TensorAdd()(a, b)
                else:
                    b = P.Sub()(b, x)
            a = a * b
            out = a + b + x
            return out

    if_net = MyIfByIfNet()
    net = if_net
    idx = Tensor(np.array(2), dtype=ms.float32)
    end = Tensor(np.array(3), dtype=ms.float32)
    x = Tensor(np.array(0), dtype=ms.float32)
    net(idx, end, x)


def test_if_by_if_forward_const_branch_inner():
    class MyIfByIfNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.add = P.TensorAdd()
            self.sub = P.Sub()
            self.mul = P.Mul()
            self.div = P.RealDiv()

        @ms_function
        def construct(self, a, b, x):
            add = P.TensorAdd()
            sub = P.Sub()
            mul = P.Mul()
            div = P.RealDiv()
            if a < b:
                a = add(a, b)
            else:
                a = sub(a, b)
            if 2 > 1:
                a = mul(a, b)
            else:
                a = div(a, b)
            if b == x:
                b = add(a, b)
            else:
                b = add(a, x)
            a = a * b
            out = a + b + x
            return out

    if_net = MyIfByIfNet()
    net = if_net
    idx = Tensor(np.array(2), dtype=ms.float32)
    end = Tensor(np.array(3), dtype=ms.float32)
    x = Tensor(np.array(0), dtype=ms.float32)
    net(idx, end, x)


def test_if_by_if_forward_all_const_branch():
    class MyIfByIfNet(nn.Cell):
        def __init__(self):
            super().__init__()
            self.add = P.TensorAdd()
            self.sub = P.Sub()
            self.mul = P.Mul()
            self.div = P.RealDiv()

        @ms_function
        def construct(self, a, b, x):
            add = P.TensorAdd()
            sub = P.Sub()
            mul = P.Mul()
            div = P.RealDiv()
            if 2 < 12:
                a = add(a, b)
            else:
                a = sub(a, b)
            if 2 > 1:
                a = mul(a, b)
            else:
                a = div(a, b)
            if 2 == 1:
                b = add(a, b)
            else:
                b = add(a, x)
            a = a * b
            out = a + b + x
            return out

    if_net = MyIfByIfNet()
    net = if_net
    idx = Tensor(np.array(2), dtype=ms.float32)
    end = Tensor(np.array(3), dtype=ms.float32)
    x = Tensor(np.array(0), dtype=ms.float32)
    net(idx, end, x)