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- # Copyright 2022 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
- import numpy as np
-
- import mindspore as ms
- from mindspore import context, Tensor
- from mindspore.nn import Cell
- from mindspore.ops import operations as P
- from mindspore.train import Model
-
-
- class Net(Cell):
- def __init__(self, strategy):
- super().__init__()
- self.invert = P.Invert().shard(strategy)
- self.pop = P.PopulationCount().shard(strategy)
- self.cast = P.Cast().shard(strategy)
- self.relu = P.ReLU().shard(strategy)
-
- def construct(self, x, b):
- out = self.invert(x)
- out = self.pop(out)
- out = self.cast(out, ms.float32)
- out = self.relu(out)
- return out
-
-
- _x = Tensor(np.ones([16, 16]), dtype=ms.int16)
- _w = Tensor(np.ones([16, 16]), dtype=ms.int16)
- _b = Tensor(np.ones([16, 16]), dtype=ms.int16)
-
-
- def compile_net(net):
- model = Model(net)
- model.predict(_x, _b)
- context.reset_auto_parallel_context()
-
-
- def test_invert_population_count_semi():
- """
- Feature: semi auto parallel
- Description:
- Expectation: compile success
- """
- context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, full_batch=True)
- strategy = ((2, 4),)
- net = Net(strategy)
- compile_net(net)
-
-
- def test_invert_population_count_auto():
- """
- Feature: auto parallel
- Description:
- Expectation: compile success
- """
- context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, full_batch=True)
- strategy = None
- net = Net(strategy)
- compile_net(net)
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