forked from mindspore-Ecosystem/mindspore
358 lines
13 KiB
Python
358 lines
13 KiB
Python
# Copyright 2022 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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import pytest
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import numpy as np
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import mindspore as ms
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from mindspore.nn import Cell
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from mindspore.common.parameter import Parameter
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from mindspore.common import ParameterTuple
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from mindspore import Tensor, context
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@pytest.mark.level1
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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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@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
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def test_parameter_1_1(mode):
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"""
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Feature: Check the names of parameters and the names of inputs of construct.
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Description: If the name of the input of construct is same as the parameters, add suffix to the name of the input.
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Expectation: No exception.
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"""
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class ParamNet(Cell):
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def __init__(self):
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super(ParamNet, self).__init__()
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self.param_a = Parameter(Tensor([1], ms.float32), name="name_a")
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self.param_b = Parameter(Tensor([2], ms.float32), name="name_b")
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def construct(self, name_a):
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return self.param_a + self.param_b - name_a
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context.set_context(mode=mode)
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net = ParamNet()
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res = net(Tensor([3], ms.float32))
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assert res == 0
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assert net.param_a.name == "name_a"
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assert net.param_b.name == "name_b"
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@pytest.mark.level1
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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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@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
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def test_parameter_1_2(mode):
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"""
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Feature: Check the names of parameters and the names of inputs of construct.
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Description: If the name of the input of construct is same as the parameters, add suffix to the name of the input.
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Expectation: No exception.
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"""
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class ParamNet(Cell):
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def __init__(self):
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super(ParamNet, self).__init__()
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self.param_a = Parameter(Tensor([1], ms.float32), name="name_a")
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self.param_b = ParameterTuple((Parameter(Tensor([2], ms.float32), name="name_b"), self.param_a))
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def construct(self, name_b):
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return self.param_a + self.param_b[0] - name_b
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context.set_context(mode=mode)
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net = ParamNet()
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res = net(Tensor([3], ms.float32))
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assert res == 0
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assert net.param_a.name == "name_a"
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assert net.param_b[0].name == "name_b"
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assert net.param_b[1].name == "name_a"
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@pytest.mark.level1
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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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@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
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def test_parameter_3(mode):
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"""
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Feature: Check the names of parameters.
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Description: Check the name of parameter in init.
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Expectation: No exception.
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"""
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class ParamNet(Cell):
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def __init__(self):
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super(ParamNet, self).__init__()
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self.param_a = Parameter(Tensor([1], ms.float32))
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self.param_b = Parameter(Tensor([2], ms.float32))
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def construct(self):
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return self.param_a + self.param_b
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context.set_context(mode=mode)
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net = ParamNet()
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res = net()
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assert res == 3
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assert net.param_a.name == "param_a"
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assert net.param_b.name == "param_b"
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@pytest.mark.level1
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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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@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
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def test_parameter_5_2(mode):
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"""
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Feature: Check the names of parameters.
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Description: Check the name of parameter in init.
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Expectation: No exception.
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"""
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class ParamNet(Cell):
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def __init__(self):
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super(ParamNet, self).__init__()
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self.param_a = Parameter(Tensor([1], ms.float32), name="name_a")
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self.res1 = ParameterTuple((Parameter(Tensor([2], ms.float32)), self.param_a))
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self.param_a = Parameter(Tensor([3], ms.float32), name="name_b")
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self.res2 = self.res1[0] + self.param_a
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def construct(self):
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return self.param_a + self.res1[0] + self.res2
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context.set_context(mode=mode)
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net = ParamNet()
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res = net()
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assert res == 10
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assert net.param_a.name == "name_b"
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assert net.res1[0].name == "Parameter$1"
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assert net.res1[1].name == "name_a"
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@pytest.mark.level1
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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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@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
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def test_parameter_list_tuple_no_name(mode):
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"""
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Feature: Check the names of parameters.
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Description: Check the name of parameter in init.
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Expectation: No exception.
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"""
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class ParamNet(Cell):
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def __init__(self):
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super(ParamNet, self).__init__()
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self.param_tuple = (Parameter(Tensor([5], ms.float32)), Parameter(Tensor([6], ms.float32)))
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self.param_list = [Parameter(Tensor([7], ms.float32)), Parameter(Tensor([8], ms.float32))]
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def construct(self):
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return self.param_tuple[0] + self.param_tuple[1] + self.param_list[0] + self.param_list[1]
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context.set_context(mode=mode)
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net = ParamNet()
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res = net()
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assert res == 26
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assert net.param_tuple[0].name == "Parameter$1"
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assert net.param_tuple[1].name == "Parameter$2"
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assert net.param_list[0].name == "Parameter$3"
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assert net.param_list[1].name == "Parameter$4"
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@pytest.mark.level1
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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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@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
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def test_parameter_in_tuple(mode):
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"""
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Feature: Check the names of parameters.
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Description: Check the name of parameter in init.
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Expectation: No exception.
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"""
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class ParamNet(Cell):
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def __init__(self):
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super(ParamNet, self).__init__()
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self.param_a = Parameter(Tensor([1], ms.float32), name="name_a")
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self.param_b = Parameter(Tensor([2], ms.float32), name="name_b")
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self.param_tuple = ParameterTuple((self.param_a, self.param_b))
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def construct(self):
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return self.param_a + self.param_b + self.param_tuple[0] + self.param_tuple[1]
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context.set_context(mode=mode)
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net = ParamNet()
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res = net()
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assert res == 6
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assert net.param_a.name == "name_a"
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assert net.param_b.name == "name_b"
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assert net.param_tuple[0].name == "name_a"
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assert net.param_tuple[1].name == "name_b"
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@pytest.mark.level1
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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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@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
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def test_parameter_parameter_tuple_2(mode):
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"""
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Feature: Check the names of parameters.
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Description: Check the name of parameter in init.
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Expectation: No exception.
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"""
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class ParamNet(Cell):
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def __init__(self):
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super(ParamNet, self).__init__()
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self.param_a = Parameter(Tensor([1], ms.float32), name="name_a")
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self.param_tuple = ParameterTuple((self.param_a, self.param_a, self.param_a))
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def construct(self):
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return self.param_a + self.param_tuple[0] + self.param_tuple[1] + self.param_tuple[2]
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context.set_context(mode=mode)
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net = ParamNet()
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res = net()
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assert res == 4
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assert net.param_a.name == "name_a"
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assert net.param_tuple[0].name == "name_a"
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assert net.param_tuple[1].name == "name_a"
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assert net.param_tuple[2].name == "name_a"
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
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def test_parameter(mode):
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"""
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Feature: Check the names of parameters.
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Description: If parameter in list or tuple is not given a name, will give it a unique name.
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Expectation: No exception.
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"""
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class ParamNet(Cell):
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def __init__(self):
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super(ParamNet, self).__init__()
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self.param_a = Parameter(Tensor([1], ms.float32), name="name_a")
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self.param_b = Parameter(Tensor([2], ms.float32), name="name_b")
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self.param_c = Parameter(Tensor([3], ms.float32))
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self.param_d = Parameter(Tensor([4], ms.float32))
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self.param_tuple = (Parameter(Tensor([5], ms.float32)),
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Parameter(Tensor([6], ms.float32)))
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self.param_list = [Parameter(Tensor([5], ms.float32)),
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Parameter(Tensor([6], ms.float32))]
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def construct(self, x):
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res1 = self.param_a + self.param_b + self.param_c + self.param_d
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res1 = res1 - self.param_list[0] + self.param_list[1] + x
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res2 = self.param_list[0] + self.param_list[1]
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return res1, res2
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context.set_context(mode=mode)
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net = ParamNet()
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x = Tensor([10], ms.float32)
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output1, output2 = net(x)
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output1_expect = Tensor(21, ms.float32)
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output2_expect = Tensor(11, ms.float32)
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assert output1 == output1_expect
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assert output2 == output2_expect
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assert net.param_a.name == "name_a"
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assert net.param_b.name == "name_b"
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assert net.param_c.name == "param_c"
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assert net.param_d.name == "param_d"
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assert net.param_tuple[0].name == "Parameter$1"
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assert net.param_tuple[1].name == "Parameter$2"
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assert net.param_list[0].name == "Parameter$3"
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assert net.param_list[1].name == "Parameter$4"
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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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.platform_x86_gpu_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
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def test_parameter_argument_and_fv(mode):
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"""
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Feature: Parameter argmument in top func graph.
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Description: Use Parameter as input argmument.
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Expectation: Parameter used as argument should equal to used as FV.
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"""
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y = Parameter(Tensor([1]))
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class Demo(Cell):
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def construct(self, x):
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ms.ops.Assign()(x, Tensor([0]))
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ms.ops.Assign()(y, Tensor([0]))
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return True
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context.set_context(mode=mode)
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x = Parameter(Tensor([1]))
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net = Demo()
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net(x)
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print(Tensor(x))
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print(Tensor(y))
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assert x == y
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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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.platform_x86_gpu_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
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def test_parameter_argument_grad(mode):
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"""
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Feature: Parameter argmument in top func graph.
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Description: Use Parameter as input argmument, and pass it to varargs.
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Expectation: Parameter used as argument should equal to used as FV.
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"""
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class ParameterArgumentCell(Cell):
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def __init__(self):
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super(ParameterArgumentCell, self).__init__()
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self.z = Parameter(Tensor(np.array([[1.0, 4.0], [-1, 8.0]]), ms.float32), name='z')
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def construct(self, param, x, y):
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ms.ops.Assign()(param, x * self.z)
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ms.ops.Assign()(x, x + y)
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ms.ops.Assign()(y, param)
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return param
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context.set_context(mode=mode)
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param = Parameter(Tensor(np.array([[0, 0], [0, 0]]), ms.float32), name='param')
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x = Parameter(Tensor(np.array([[4.0, -8.0], [-2.0, -5.0]]), ms.float32), name='x')
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y = Parameter(Tensor(np.array([[1, 0], [1, 1]]), ms.float32), name='y')
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net = ParameterArgumentCell()
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net(param, x, y)
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bparam = Parameter(Tensor(np.array([[0, 0], [0, 0]]), ms.float32), name='bparam')
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bx = Parameter(Tensor(np.array([[4.0, -8.0], [-2.0, -5.0]]), ms.float32), name='bx')
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by = Parameter(Tensor(np.array([[1, 0], [1, 1]]), ms.float32), name='by')
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grad_by_list = ms.ops.GradOperation(get_by_list=True)
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grad_by_list(net, ParameterTuple(net.trainable_params()))(bparam, bx, by)
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assert np.array_equal(param.asnumpy(), bparam.asnumpy())
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assert np.array_equal(x.asnumpy(), bx.asnumpy())
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assert np.array_equal(y.asnumpy(), by.asnumpy())
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