forked from mindspore-Ecosystem/mindspore
452 lines
12 KiB
Python
452 lines
12 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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""" test jit_class """
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import pytest
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import numpy as np
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import mindspore.nn as nn
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import mindspore.common.dtype as mstype
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from mindspore import Tensor, context, jit_class
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context.set_context(mode=context.GRAPH_MODE)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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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_ms_class_attr():
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"""
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Feature: JIT Fallback
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Description: Access the attributes of user-defined classes decorated with jit_class.
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Expectation: No exception.
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"""
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@jit_class
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class InnerNet:
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def __init__(self):
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self.number = Tensor(1, dtype=mstype.int32)
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.inner_net = InnerNet()
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def construct(self):
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out = self.inner_net.number
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return out
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net = Net()
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out = net()
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assert out.asnumpy() == 1
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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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_ms_class_method():
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"""
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Feature: JIT Fallback
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Description: Access the methods of user-defined classes decorated with jit_class.
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Expectation: No exception.
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"""
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@jit_class
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class InnerNet:
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def __init__(self):
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self.val = Tensor(2, dtype=mstype.int32)
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def act(self, x, y):
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return self.val * (x + y)
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.inner_net = InnerNet()
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def construct(self, x, y):
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out = self.inner_net.act(x, y)
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return out
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x = Tensor(2, dtype=mstype.int32)
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y = Tensor(3, dtype=mstype.int32)
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net = Net()
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out = net(x, y)
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assert out.asnumpy() == 10
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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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_ms_class_call():
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"""
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Feature: JIT Fallback
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Description: Call the __call__ function of user-defined classes decorated with jit_class.
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Expectation: No exception.
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"""
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@jit_class
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class InnerNet:
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def __init__(self, val):
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self.val = val
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def __call__(self, x, y):
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return self.val * (x + y)
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class Net(nn.Cell):
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def __init__(self, val):
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super(Net, self).__init__()
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self.inner_net = InnerNet(val)
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def construct(self, x, y):
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out = self.inner_net(x, y)
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return out
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val = Tensor(2, dtype=mstype.int32)
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x = Tensor(3, dtype=mstype.int32)
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y = Tensor(4, dtype=mstype.int32)
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net = Net(val)
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out = net(x, y)
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assert out.asnumpy() == 14
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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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_ms_class_input_attr():
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"""
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Feature: JIT Fallback
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Description: Access the attributes of user-defined classes decorated with jit_class.
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Expectation: No exception.
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"""
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@jit_class
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class InnerNet:
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def __init__(self):
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self.number = Tensor(np.array([1, 2, 3]))
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class Net(nn.Cell):
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def __init__(self, net):
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super(Net, self).__init__()
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self.inner_net = net()
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def construct(self):
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out = self.inner_net.number
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return out
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net = Net(InnerNet)
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out = net()
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expect_res = np.array([1, 2, 3])
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assert np.all(out.asnumpy() == expect_res)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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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_ms_class_input_method():
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"""
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Feature: JIT Fallback
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Description: Access the methods of user-defined classes decorated with jit_class.
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Expectation: No exception.
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"""
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@jit_class
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class InnerNet:
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def __init__(self):
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self.val = Tensor(2, dtype=mstype.int32)
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def act(self, x, y):
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return self.val * (x + y)
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class Net(nn.Cell):
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def __init__(self, net):
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super(Net, self).__init__()
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self.inner_net = net()
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def construct(self):
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out = self.inner_net.act(1, 2)
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return out
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net = Net(InnerNet)
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out = net()
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assert out.asnumpy() == 6
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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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_ms_class_nested():
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"""
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Feature: JIT Fallback
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Description: Test nested jit_class in graph.
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Expectation: No exception.
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"""
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@jit_class
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class Inner:
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def __init__(self):
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self.number = Tensor(1, dtype=mstype.int32)
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@jit_class
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class InnerNet:
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def __init__(self):
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self.inner = Inner()
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.inner_net = InnerNet()
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def construct(self):
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out = self.inner_net.inner.number
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return out
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net = Net()
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out = net()
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assert out.asnumpy() == 1
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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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_ms_class_cell_nested():
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"""
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Feature: JIT Fallback
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Description: Test nested jit_class and cell in graph.
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Expectation: No exception.
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"""
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class Net(nn.Cell):
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def __init__(self, val):
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super().__init__()
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self.val = val
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def construct(self, x):
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return x + self.val
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@jit_class
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class TrainNet():
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class Loss(nn.Cell):
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def __init__(self, net):
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super().__init__()
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self.net = net
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def construct(self, x):
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out = self.net(x)
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return out * 2
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def __init__(self, net):
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self.net = net
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loss_net = self.Loss(self.net)
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self.number = loss_net(10)
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global_net = Net(1)
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class LearnNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.value = TrainNet(global_net).number
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def construct(self, x):
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return x + self.value
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leanrn_net = LearnNet()
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out = leanrn_net(3)
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print(out)
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assert out == 25
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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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_ms_class_type_attr():
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"""
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Feature: JIT Fallback
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Description: Access the attributes of class type.
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Expectation: No exception.
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"""
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@jit_class
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class InnerNet:
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val = Tensor(2, dtype=mstype.int32)
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def act(self, x, y):
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return self.val * (x + y)
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.inner_net = InnerNet
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# Support accessing attributes of class type, but do not support
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# accessing methods, e.g. self.inner_net.act(1, 2)
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def construct(self):
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out = self.inner_net.val
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return out
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net = Net()
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out = net()
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assert out == 2
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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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_ms_class_create_instance_attr():
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"""
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Feature: JIT Fallback
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Description: Access the attributes of the created class instance.
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Expectation: No exception.
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"""
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@jit_class
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class InnerNet:
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def __init__(self, val):
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self.number = val + 3
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.inner_net = InnerNet
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def construct(self, x):
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net = self.inner_net(x)
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return net.number
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net = Net()
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out = net(2)
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assert out == 5
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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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_ms_class_create_instance_method():
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"""
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Feature: JIT Fallback
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Description: Access the methods of the created class instance.
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Expectation: No exception.
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"""
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@jit_class
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class InnerNet:
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def __init__(self, val):
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self.number = val
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def act(self, x, y):
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return self.number * (x + y)
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.inner_net = InnerNet
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def construct(self, x, y, z):
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net = self.inner_net(x)
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return net.act(y, z)
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x = 2
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y = Tensor(2, dtype=mstype.int32)
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z = Tensor(3, dtype=mstype.int32)
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net = Net()
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out = net(x, y, z)
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assert out.asnumpy() == 10
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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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_ms_class_create_instance_call():
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"""
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Feature: JIT Fallback
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Description: Call the __call__ function of the created class instance.
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Expectation: No exception.
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"""
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@jit_class
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class InnerNet:
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def __init__(self, number):
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self.number = number
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def __call__(self, x, y):
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return self.number * (x + y)
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.inner_net = InnerNet
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def construct(self, x, y, z):
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net = self.inner_net(x)
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out = net(y, z)
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return out
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x = 2
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y = Tensor(2, dtype=mstype.int32)
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z = Tensor(3, dtype=mstype.int32)
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net = Net()
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out = net(x, y, z)
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assert out == 10
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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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_raise_error_not_class_type():
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"""
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Feature: JIT Fallback
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Description: Decorator jit_class cannot be used for non-class types.
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Expectation: No exception.
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"""
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with pytest.raises(TypeError):
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@jit_class
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def func(x, y):
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return x + y
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func(1, 2)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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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_raise_error_decorate_cell():
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"""
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Feature: JIT Fallback
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Description: Decorator jit_class cannot be used for nn.Cell
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Expectation: No exception.
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"""
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with pytest.raises(TypeError):
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@jit_class
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class Net(nn.Cell):
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def construct(self, x):
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return x
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x = Tensor(1)
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net = Net()
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net(x)
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