mindspore/tests/st/control/test_ascend_control_sink.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_ascend_control_sink """
import pytest
import numpy as np
import mindspore.context as context
import mindspore.nn as nn
from mindspore.ops import operations as op
from mindspore.common import dtype as mstype
from mindspore.common.tensor import Tensor
from mindspore.common.parameter import Parameter
from mindspore.common.initializer import initializer


class ControlSimpleIf(nn.Cell):
    def __init__(self):
        super().__init__()
        self.addn = op.AddN()

    def construct(self, x, y, z, input1, input2):
        addn1 = self.addn([input1, input1, input1])
        addn2 = self.addn([input2, input2, input2])
        addn11 = self.addn([addn1, addn1, addn1])
        addn22 = self.addn([addn2, addn2, addn2])
        cond1 = x > y
        cond2 = y > z
        # dodge pylint
        if cond1 and cond2:
            out = self.addn([addn11, addn11])
        else:
            out = self.addn([addn22, addn22])
        out_me = self.addn([out, input1])
        return out_me


class ControlSimpleIfWithAssign(nn.Cell):
    def __init__(self, input_shape):
        super().__init__()
        self.addn = op.AddN()
        self.assign = op.Assign()
        self.input_data = Parameter(initializer(1, input_shape, mstype.float32), name="var")

    def construct(self, x, y, input_data):
        if x > y:
            out = self.addn([input_data, input_data, input_data])
        else:
            out = self.assign(self.input_data, input_data)
        return out


class ControlIfinIf(nn.Cell):
    def __init__(self):
        super().__init__()

    def construct(self, x, y):
        if x > y:
            x = x + 1
            if y < 0:
                y = y + 1
            else:
                y = y + 2
        else:
            x = x + 2
        x = x + y
        return x


class ControlIfbyIfbyIf(nn.Cell):
    def __init__(self):
        super().__init__()
        self.addn = op.AddN()

    def construct(self, x, y, cond1, cond2, input_data):
        tri_in = self.addn([input_data, input_data, input_data])
        if x > y:
            addn_1 = self.addn([tri_in, tri_in])
        else:
            addn_1 = self.addn([tri_in, tri_in, tri_in])
        if cond1:
            addn_2 = self.addn([addn_1, addn_1])
        else:
            addn_2 = self.addn([addn_1, addn_1, addn_1])
        if cond2:
            out = self.addn([addn_2, addn_2, addn_2])
        else:
            out = self.addn([addn_2, addn_2])
        return out


class ControlSimpleWhile(nn.Cell):
    def __init__(self):
        super().__init__()
        self.addn = op.AddN()

    def construct(self, x, y, input_data):
        out = input_data
        while x:
            out = self.addn([input_data, input_data, input_data])
            x = y
        return out


class ControlMixedWhileIf(nn.Cell):
    def __init__(self):
        super().__init__()
        self.assign = op.Assign()
        self.var = Parameter(initializer(1, (1), mstype.float32), name="var")

    def construct(self, x, y, z, c2, c4):
        out = self.assign(self.var, c4)
        while x < c2:
            y = self.assign(self.var, c4)
            while y < c2 and x < c2:
                if 2 * y < c2:
                    y = y + 2
                else:
                    y = y + 1
            out = out + y
            z = self.assign(self.var, c4)
            while z < c2:
                z = z + 1
            out = out + z
            x = x + 1
        out = out + x
        while x < 2 * c2:
            y = self.assign(self.var, c4)
            x = x + 1
            while y < c2:
                z = self.assign(self.var, c4)
                while z < c2:
                    z = z + 1
                if x < c2:
                    y = y - 1
                else:
                    y = y + 1
                out = out + z
            out = out + y
        out = out + x
        return out


@pytest.mark.level0
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_simple_if():
    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
    x = np.array(3).astype(np.float32)
    y = np.array(2).astype(np.float32)
    z = np.array(3).astype(np.float32)
    input_shape = (127, 7, 53, 31)
    input1 = np.random.randn(*input_shape).astype(np.float32)
    input2 = np.random.randn(*input_shape).astype(np.float32)
    net = ControlSimpleIf()
    output = net(Tensor(x), Tensor(y), Tensor(z), Tensor(input1), Tensor(input2))
    expect = input2 * 3 * 3 * 2 + input1
    assert np.allclose(expect, output.asnumpy(), 0.0001, 0.0001)


@pytest.mark.level0
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_simple_if_with_assign():
    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
    x = np.array(0).astype(np.float32)
    y = np.array(1).astype(np.float32)
    input_shape = (127, 7, 53, 31)
    input_data = np.random.randn(*input_shape).astype(np.float32)
    net = ControlSimpleIfWithAssign(input_shape)
    output = net(Tensor(x), Tensor(y), Tensor(input_data))
    expect = input_data
    assert np.allclose(expect, output.asnumpy(), 0.0001, 0.0001)


@pytest.mark.level0
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_if_in_if():
    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
    x = np.array(2.345678).astype(np.float32)
    y = np.array(1.234567).astype(np.float32)
    net = ControlIfinIf()
    output = net(Tensor(x), Tensor(y))
    expect = x + y + 3
    assert np.allclose(expect, output.asnumpy(), 0.0001, 0.0001)


@pytest.mark.level0
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_if_by_if_by_if():
    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
    x = np.array(2.345678).astype(np.float32)
    y = np.array(1.234567).astype(np.float32)
    cond1 = np.array(True).astype(np.bool)
    cond2 = np.array(False).astype(np.bool)
    input_shape = (127, 7, 53, 31)
    input_data = np.random.randn(*input_shape).astype(np.float32)
    net = ControlIfbyIfbyIf()
    output = net(Tensor(x), Tensor(y), Tensor(cond1), Tensor(cond2), Tensor(input_data))
    expect = input_data * 3 * 2 * 2 * 2
    assert np.allclose(expect, output.asnumpy(), 0.0001, 0.0001)


@pytest.mark.level0
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_simple_while():
    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
    x = np.array(True).astype(np.bool)
    y = np.array(False).astype(np.bool)
    input_shape = (127, 7, 53, 31)
    input_data = np.random.randn(*input_shape).astype(np.float32)
    net = ControlSimpleWhile()
    output = net(Tensor(x), Tensor(y), Tensor(input_data))
    expect = input_data * 3
    assert np.allclose(expect, output.asnumpy(), 0.0001, 0.0001)


@pytest.mark.level0
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_mixed_while_if():
    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
    x = np.array(2).astype(np.int32)
    y = np.array(14).astype(np.int32)
    z = np.array(1).astype(np.int32)
    c2 = Tensor([14], mstype.int32)
    c4 = Tensor([0], mstype.int32)
    net = ControlMixedWhileIf()
    output = net(Tensor(x), Tensor(y), Tensor(z), c2, c4)
    expect = np.array(3318).astype(np.int32)
    assert np.allclose(expect, output.asnumpy(), 0.0001, 0.0001)