forked from huawei/mindspore2022
417 lines
9.7 KiB
Python
417 lines
9.7 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 graph fallback """
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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, ms_class, ms_function
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from . import test_graph_fallback
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context.set_context(mode=context.GRAPH_MODE)
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def test_fallback_self_attr():
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"""
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Feature: JIT Fallback
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Description: Test self.attr in graph.
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Expectation: No exception.
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"""
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class Network(nn.Cell):
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def __init__(self):
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super(Network, self).__init__()
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self.dim = 1
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def construct(self, x):
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batch = x.shape[0]
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one = Tensor(np.ones([batch, self.dim]), mstype.float32)
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return one * x
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net = Network()
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x = Tensor([1, 2], mstype.float32)
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out = net(x)
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expect = np.array([[1., 2.], [1., 2.]])
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assert np.allclose(out.asnumpy(), expect, 1.e-2, 1.e-2)
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def test_fallback_self_attr_fn():
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"""
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Feature: JIT Fallback
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Description: Test self.attr in graph.
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Expectation: No exception.
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"""
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class Network(nn.Cell):
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def __init__(self, fn):
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super(Network, self).__init__()
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self.fn = fn
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def construct(self):
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x = np.array([1, 2, 3])
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y = np.array([3, 4, 5])
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out = Tensor(self.fn(x, y))
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return out
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def fn(x, y):
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return x + y
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net = Network(fn)
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out = net()
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expect = np.array([4, 6, 8])
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assert np.all(out.asnumpy() == expect)
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def test_fallback_self_attr_attr():
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"""
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Feature: JIT Fallback
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Description: Test self.attr in graph.
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Expectation: No exception.
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"""
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class Network(nn.Cell):
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def __init__(self):
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super(Network, self).__init__()
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self.value = [2, 2, 3]
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def construct(self):
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x = np.array(self.value.count(2))
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return Tensor(x)
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net = Network()
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out = net()
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assert out == 2
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def test_fallback_self_method():
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"""
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Feature: JIT Fallback
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Description: Test self.method in graph.
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Expectation: No exception.
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"""
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class Network(nn.Cell):
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def construct(self):
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x = np.array([1, 2, 3])
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y = np.array([3, 4, 5])
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out = Tensor(self.fn(x, y))
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return out
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def fn(self, x, y):
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return x + y
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net = Network()
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out = net()
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expect = np.array([4, 6, 8])
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assert np.all(out.asnumpy() == expect)
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@pytest.mark.skip(reason='Not support in graph jit fallback feature yet')
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def test_fallback_self_method_tensor():
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"""
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Feature: JIT Fallback
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Description: Test self.method in graph.
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Expectation: No exception.
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"""
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class Network(nn.Cell):
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def construct(self):
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x = np.array([1, 2, 3])
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y = np.array([3, 4, 5])
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z = self.fn(x, y)
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out = Tensor(z)
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return out
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def fn(self, x, y):
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return x + y
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net = Network()
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out = net()
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print(out)
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def test_fallback_import_modules():
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"""
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Feature: JIT Fallback
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Description: add_func is defined in test_graph_fallback.py
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Expectation: No exception.
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"""
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@ms_function
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def use_imported_module(x, y):
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out = test_graph_fallback.add_func(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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out = use_imported_module(x, y)
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print(out)
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def test_fallback_class_attr():
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"""
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Feature: JIT Fallback
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Description: Test user-defined class attributes in graph.
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Expectation: No exception.
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"""
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@ms_class
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class InnerNet:
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def __init__(self):
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self.number = 1
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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 == 1
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def test_fallback_class_method():
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"""
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Feature: JIT Fallback
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Description: Test user-defined class methods in graph.
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Expectation: No exception.
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"""
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@ms_class
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class InnerNet:
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def __init__(self):
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self.val = 2
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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):
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out = self.inner_net.act(1, 2)
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return out
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net = Net()
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out = net()
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assert out == 6
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def test_fallback_class_input_attr():
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"""
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Feature: JIT Fallback
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Description: Test user-defined class attributes in graph.
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Expectation: No exception.
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"""
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@ms_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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def test_fallback_class_input_method():
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"""
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Feature: JIT Fallback
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Description: Test user-defined class methods in graph.
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Expectation: No exception.
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"""
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@ms_class
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class InnerNet:
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def __init__(self):
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self.val = 2
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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 == 6
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def test_fallback_class_class_nested():
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"""
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Feature: JIT Fallback
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Description: Test nested ms_class in graph.
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Expectation: No exception.
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"""
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@ms_class
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class Inner:
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def __init__(self):
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self.number = 1
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@ms_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 == 1
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def test_fallback_class_cell_nested():
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"""
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Feature: JIT Fallback
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Description: Test nested ms_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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@ms_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.skip(reason='Not support in graph yet')
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def test_fallback_class_isinstance():
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"""
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Feature: JIT Fallback
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Description: Test ms_class in graph.
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Expectation: No exception.
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"""
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@ms_class
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class InnerNet:
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def __init__(self):
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self.number = 1
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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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if isinstance(self.inner_net, InnerNet):
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return x + 10
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return x
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net = Net()
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out = net(5)
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assert out == 15
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def test_fallback_raise_error_not_class_type():
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"""
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Feature: JIT Fallback
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Description: Test ms_class in graph.
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Expectation: No exception.
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"""
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with pytest.raises(TypeError):
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@ms_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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def test_fallback_raise_error_not_class_instance():
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"""
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Feature: JIT Fallback
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Description: Test ms_class in graph.
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Expectation: No exception.
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"""
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@ms_class
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class InnerNet:
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def __init__(self):
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self.number = 1
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class Net(nn.Cell):
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def construct(self):
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out = InnerNet().number
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return out
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with pytest.raises(ValueError):
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net = Net()
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net()
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def test_fallback_raise_error_decorate_cell():
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"""
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Feature: JIT Fallback
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Description: Test ms_class in graph.
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Expectation: No exception.
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"""
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@ms_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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with pytest.raises(TypeError):
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x = Tensor(1)
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net = Net()
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net(x)
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