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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, wo, stra1=None, stra2=None, stra3=None, stra4=None,
- stra5=None, stra6=None):
- super(Net, self).__init__()
- self.relu = P.ReLU().shard(stra1)
- self.transpose = P.Transpose().shard(stra2)
- self.wi = Parameter(wi, "wi")
- self.batch_mm = P.BatchMatMul().shard(stra3)
- self.wo = Parameter(wo, "wo")
- self.batch_mm2 = P.BatchMatMul().shard(stra4)
- self.transpose2 = P.Transpose().shard(stra5)
- self.relu2 = P.ReLU().shard(stra6)
- self.reshape = P.Reshape()
- self.reshape2 = P.Reshape()
-
- def construct(self, x):
- output = self.relu(x)
- trans_out = self.transpose(output, (2, 0, 3, 1))
- output = self.reshape(trans_out,
- (trans_out.shape[0], trans_out.shape[1]*trans_out.shape[2], trans_out.shape[3]))
- output = self.batch_mm(output, self.wi)
- output = self.batch_mm2(output, self.wo)
- output = self.reshape2(output, trans_out.shape)
- output = self.transpose2(output, (1, 3, 0, 2))
- output = self.relu2(output)
- return output
-
- _x = Tensor(np.ones([32, 16, 48, 128]), dtype=ms.float32)
- _wi = Tensor(np.ones([48, 16, 64]), dtype=ms.float32)
- _wo = Tensor(np.ones([48, 64, 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_batchmm():
- 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, 1),)
- stra3 = ((8, 1, 1), (8, 1, 1))
- stra4 = ((8, 1, 1), (8, 1, 1))
- stra5 = ((8, 1, 1, 1),)
- stra6 = ((8, 1, 1, 1),)
- net = Net(_wi, _wo, stra1=stra1, stra2=stra2, stra3=stra3, stra4=stra4, stra5=stra5, stra6=stra6)
- compile_net(net)
-
-
- def test_batchmm2():
- context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", enable_alltoall=True,
- device_num=32, global_rank=0)
- stra1 = ((4, 1, 1, 1),)
- stra2 = ((4, 1, 1, 1),)
- stra3 = ((4, 1, 1), (4, 1, 8))
- stra4 = ((4, 1, 8), (4, 8, 1))
- stra5 = ((4, 1, 1, 1),)
- stra6 = ((4, 1, 1, 1),)
- net = Net(_wi, _wo, stra1=stra1, stra2=stra2, stra3=stra3, stra4=stra4, stra5=stra5, stra6=stra6)
- compile_net(net)
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