|
- # 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, TrainOneStepCell, Momentum
- 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().set_strategy(strategy1)
- self.neg = P.Neg().set_strategy(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
-
-
- _x = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
- _w1 = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
- _b = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
-
-
- def compile_net(net):
- optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
- train_net = TrainOneStepCell(net, optimizer)
- train_net.set_auto_parallel()
- _executor.compile(train_net, _x, _b)
- context.reset_auto_parallel_context()
-
-
- def test_neg_data_parallel():
- context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
- strategy1 = ((16, 1, 1), (16, 1, 1))
- strategy2 = ((16, 1, 1),)
- net = Net(_w1, strategy1, strategy2)
- compile_net(net)
-
-
- def test_neg_model_parallel():
- context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
- strategy1 = ((1, 1, 16), (1, 1, 16))
- strategy2 = ((1, 1, 16),)
- net = Net(_w1, strategy1, strategy2)
- compile_net(net)
-
-
- def test_neg_hybrid_parallel():
- context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
- strategy1 = ((2, 2, 4), (2, 2, 4))
- strategy2 = ((2, 2, 4),)
- net = Net(_w1, strategy1, strategy2)
- compile_net(net)
-
-
- def test_neg_auto_parallel():
- context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=16, global_rank=0)
- net = Net(_w1)
- compile_net(net)
-
-
- def test_neg_repeat_calc():
- context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
- strategy1 = ((2, 2, 4), (2, 2, 4))
- strategy2 = ((1, 2, 2),)
- net = Net(_w1, strategy1, strategy2)
- compile_net(net)
|
