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- # Copyright 2020 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, Parameter
- from mindspore.common.api import _executor
- from mindspore.nn import Cell
- from mindspore.ops import operations as P
-
-
- class Net(Cell):
- def __init__(self, mul_weight, strategy1=None, strategy2=None):
- super().__init__()
- self.mul = P.Mul().shard(strategy1)
- self.neg = P.Neg().shard(strategy2)
- self.mul_weight = Parameter(mul_weight, "w1")
-
- def construct(self, x, b):
- out = self.mul(x, self.mul_weight)
- out = self.neg(out)
- return out
-
-
- class EvalNet(Cell):
- def __init__(self, network, strategy2=None):
- super().__init__()
- self.network = network
- self.relu = P.ReLU().shard(strategy2)
-
- def construct(self, x, b):
- out = self.network(x, b)
- out1 = self.relu(out)
- return out, out1
-
-
- _x = Tensor(np.ones([64, 64]), dtype=ms.float32)
- _w1 = Tensor(np.ones([64, 64]), dtype=ms.float32)
- _b = Tensor(np.ones([64, 64]), dtype=ms.float32)
-
-
- def test_train_and_eval():
- context.set_context(save_graphs=False, mode=0)
- context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16)
- strategy1 = ((4, 4), (4, 4))
- strategy2 = ((4, 4),)
- net = Net(_w1, strategy1, strategy2)
- eval_net = EvalNet(net, strategy2=strategy2)
- net.set_auto_parallel()
- net.set_train()
- _executor.compile(net, _x, _b, phase='train', auto_parallel_mode=True)
-
- eval_net.set_train(mode=False)
- eval_net.set_auto_parallel()
- _executor.compile(eval_net, _x, _b, phase='eval', auto_parallel_mode=True)
-
- context.reset_auto_parallel_context()
-
- def test_train_and_eval_auto():
- context.set_context(save_graphs=False, mode=0)
- context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=16)
- strategy1 = ((4, 4), (4, 4))
- strategy2 = ((4, 4),)
- net = Net(_w1, strategy1, strategy2)
- eval_net = EvalNet(net, strategy2=strategy2)
- net.set_auto_parallel()
- net.set_train()
- _executor.compile(net, _x, _b, phase='train', auto_parallel_mode=True)
-
- eval_net.set_train(mode=False)
- eval_net.set_auto_parallel()
- _executor.compile(eval_net, _x, _b, phase='eval', auto_parallel_mode=True)
-
- context.reset_auto_parallel_context()
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