forked from OSSInnovation/mindspore
!2782 New control sink testcase
Merge pull request !2782 from zhoufeng/control-sink-test
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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""" test_ascend_control_sink """
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import pytest
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import numpy as np
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore.ops import operations as op
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from mindspore.common import dtype as mstype
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from mindspore.common.tensor import Tensor
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from mindspore.common.parameter import Parameter
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from mindspore.common.initializer import initializer
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class ControlSimpleIf(nn.Cell):
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def __init__(self):
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super().__init__()
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self.addn = op.AddN()
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def construct(self, x, y, z, input1, input2):
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addn1 = self.addn([input1, input1, input1])
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addn2 = self.addn([input2, input2, input2])
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addn11 = self.addn([addn1, addn1, addn1])
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addn22 = self.addn([addn2, addn2, addn2])
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cond1 = x > y
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cond2 = y > z
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# dodge pylint
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if cond1 and cond2:
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out = self.addn([addn11, addn11])
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else:
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out = self.addn([addn22, addn22])
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out_me = self.addn([out, input1])
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return out_me
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class ControlSimpleIfWithAssign(nn.Cell):
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def __init__(self, input_shape):
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super().__init__()
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self.addn = op.AddN()
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self.assign = op.Assign()
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self.input_data = Parameter(initializer(1, input_shape, mstype.float32), name="var")
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def construct(self, x, y, input_data):
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if x > y:
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out = self.addn([input_data, input_data, input_data])
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else:
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out = self.assign(self.input_data, input_data)
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return out
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class ControlIfinIf(nn.Cell):
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def __init__(self):
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super().__init__()
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def construct(self, x, y):
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if x > y:
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x = x + 1
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if y < 0:
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y = y + 1
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else:
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y = y + 2
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else:
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x = x + 2
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x = x + y
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return x
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class ControlIfbyIfbyIf(nn.Cell):
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def __init__(self):
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super().__init__()
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self.addn = op.AddN()
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def construct(self, x, y, cond1, cond2, input_data):
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tri_in = self.addn([input_data, input_data, input_data])
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if x > y:
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addn_1 = self.addn([tri_in, tri_in])
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else:
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addn_1 = self.addn([tri_in, tri_in, tri_in])
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if cond1:
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addn_2 = self.addn([addn_1, addn_1])
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else:
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addn_2 = self.addn([addn_1, addn_1, addn_1])
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if cond2:
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out = self.addn([addn_2, addn_2, addn_2])
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else:
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out = self.addn([addn_2, addn_2])
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return out
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class ControlMixedWhileIf(nn.Cell):
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def __init__(self):
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super().__init__()
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def construct(self, x, y):
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y = y + 4
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while x < y:
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if 2 * x < y:
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x = x + 1
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else:
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x = x + 2
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x = x + 3
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return x
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_simple_if():
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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x = np.array(3).astype(np.float32)
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y = np.array(2).astype(np.float32)
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z = np.array(3).astype(np.float32)
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input_shape = (127, 7, 53, 31)
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input1 = np.random.randn(*input_shape).astype(np.float32)
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input2 = np.random.randn(*input_shape).astype(np.float32)
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net = ControlSimpleIf()
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output = net(Tensor(x), Tensor(y), Tensor(z), Tensor(input1), Tensor(input2))
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expect = input2 * 3 * 3 * 2 + input1
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assert np.allclose(expect, output.asnumpy(), 0.0001, 0.0001)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_simple_if_with_assign():
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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x = np.array(0).astype(np.float32)
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y = np.array(1).astype(np.float32)
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input_shape = (127, 7, 53, 31)
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input_data = np.random.randn(*input_shape).astype(np.float32)
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net = ControlSimpleIfWithAssign(input_shape)
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output = net(Tensor(x), Tensor(y), Tensor(input_data))
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expect = input_data
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assert np.allclose(expect, output.asnumpy(), 0.0001, 0.0001)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_if_in_if():
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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x = np.array(2.345678).astype(np.float32)
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y = np.array(1.234567).astype(np.float32)
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net = ControlIfinIf()
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output = net(Tensor(x), Tensor(y))
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expect = x + y + 3
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assert np.allclose(expect, output.asnumpy(), 0.0001, 0.0001)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_if_by_if_by_if():
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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x = np.array(2.345678).astype(np.float32)
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y = np.array(1.234567).astype(np.float32)
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cond1 = np.array(True).astype(np.bool)
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cond2 = np.array(False).astype(np.bool)
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input_shape = (127, 7, 53, 31)
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input_data = np.random.randn(*input_shape).astype(np.float32)
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net = ControlIfbyIfbyIf()
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output = net(Tensor(x), Tensor(y), Tensor(cond1), Tensor(cond2), Tensor(input_data))
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expect = input_data * 3 * 2 * 2 * 2
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assert np.allclose(expect, output.asnumpy(), 0.0001, 0.0001)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_mixed_while_if():
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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x = np.array(2).astype(np.int32)
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y = np.array(14).astype(np.int32)
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net = ControlMixedWhileIf()
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output = net(Tensor(x), Tensor(y))
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expect = np.array(22).astype(np.int32)
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assert np.allclose(expect, output.asnumpy(), 0.0001, 0.0001)
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