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- # Copyright 2021 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
- import mindspore.context as context
- from mindspore import Tensor, Parameter
- import mindspore.nn as nn
- from mindspore.common.api import _executor
- from mindspore.nn import TrainOneStepCell, Momentum
- from mindspore.ops import operations as P
-
- class Net(nn.Cell):
- def __init__(self, wi, stra1=None, stra2=None, stra3=None):
- super(Net, self).__init__()
- self.wi = Parameter(wi, "wi")
- self.matmul = P.MatMul().shard(stra1)
- self.onehot = P.OneHot(axis=-1).shard(stra2)
- self.mul = P.Mul().shard(stra3)
- self.on_value = Tensor(1.0, ms.float32)
- self.off_value = Tensor(0.0, ms.float32)
- self.cast = P.Cast()
- self.depth = 48
-
- def construct(self, x):
- output = self.matmul(x, self.wi)
- output = self.cast(output, ms.int32)
- output = self.onehot(output, self.depth, self.on_value, self.off_value)
- output = self.mul(output, output)
- return output
-
- _x = Tensor(np.ones([16, 48]), dtype=ms.float32)
- _wi = Tensor(np.ones([48, 16]), dtype=ms.float32)
-
-
- def compile_net(net):
- context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
- optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
- train_net = TrainOneStepCell(net, optimizer)
- train_net.set_auto_parallel()
- train_net.set_train()
- _executor.compile(train_net, _x)
- context.reset_auto_parallel_context()
-
-
- def test_onehot():
- context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, enable_alltoall=True,
- global_rank=0)
- stra1 = ((8, 1), (1, 1))
- stra2 = ((8, 1, 1), (), ())
- stra3 = ((8, 1, 1), (8, 1, 1))
- net = Net(_wi, stra1=stra1, stra2=stra2, stra3=stra3)
- compile_net(net)
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