forked from mindspore-Ecosystem/mindspore
51 lines
1.6 KiB
Python
51 lines
1.6 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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import numpy as np
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import pytest
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import mindspore
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from mindspore import context, ops, nn, Tensor
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class Net(nn.Cell):
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def __init__(self):
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super().__init__()
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self.relu = ops.ReLU()
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self.add = ops.Add()
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def construct(self, input_x):
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output = self.relu(input_x)
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for _ in range(200):
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output = self.add(output, 1)
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return output
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@pytest.mark.level1
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_multi_actor_fusion():
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"""
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Feature: Multi actor fusion.
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Description: Test the net which is non concurrent, that can trigger the function of multi actor fusion.
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Expectation: The value and shape of output are the expected values.
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"""
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context.set_context(mode=context.GRAPH_MODE)
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x = Tensor(np.ones(2), mindspore.float32)
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net = Net()
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expect = np.array([201, 201])
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for _ in range(20):
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output = net(x)
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assert (output.asnumpy() == expect).all()
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