73 lines
2.1 KiB
Python
73 lines
2.1 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 numpy as np
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import mindspore as ms
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from mindspore import context, Tensor
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from mindspore.nn import Cell
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from mindspore.ops import operations as P
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from mindspore.train import Model
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class Net(Cell):
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def __init__(self, strategy):
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super().__init__()
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self.invert = P.Invert().shard(strategy)
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self.pop = P.PopulationCount().shard(strategy)
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self.cast = P.Cast().shard(strategy)
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self.relu = P.ReLU().shard(strategy)
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def construct(self, x, b):
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out = self.invert(x)
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out = self.pop(out)
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out = self.cast(out, ms.float32)
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out = self.relu(out)
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return out
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_x = Tensor(np.ones([16, 16]), dtype=ms.int16)
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_w = Tensor(np.ones([16, 16]), dtype=ms.int16)
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_b = Tensor(np.ones([16, 16]), dtype=ms.int16)
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def compile_net(net):
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model = Model(net)
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model.predict(_x, _b)
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context.reset_auto_parallel_context()
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def test_invert_population_count_semi():
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"""
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Feature: semi auto parallel
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Description:
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Expectation: compile success
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, full_batch=True)
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strategy = ((2, 4),)
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net = Net(strategy)
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compile_net(net)
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def test_invert_population_count_auto():
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"""
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Feature: auto parallel
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Description:
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Expectation: compile success
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"""
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, full_batch=True)
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strategy = None
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net = Net(strategy)
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compile_net(net)
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