90 lines
3.5 KiB
Python
90 lines
3.5 KiB
Python
# Copyright 2021 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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import numpy as np
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import mindspore as ms
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import mindspore.context as context
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from mindspore import Tensor, Parameter
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import mindspore.nn as nn
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from mindspore.common.api import _cell_graph_executor
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from mindspore.nn import TrainOneStepCell, Momentum
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from mindspore.ops import operations as P
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class Net(nn.Cell):
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def __init__(self, wi, wo, stra1=None, stra2=None, stra3=None, stra4=None,
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stra5=None, stra6=None):
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super(Net, self).__init__()
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self.relu = P.ReLU().shard(stra1)
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self.transpose = P.Transpose().shard(stra2)
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self.wi = Parameter(wi, "wi")
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self.batch_mm = P.BatchMatMul().shard(stra3)
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self.wo = Parameter(wo, "wo")
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self.batch_mm2 = P.BatchMatMul().shard(stra4)
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self.transpose2 = P.Transpose().shard(stra5)
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self.relu2 = P.ReLU().shard(stra6)
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self.reshape = P.Reshape()
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self.reshape2 = P.Reshape()
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def construct(self, x):
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output = self.relu(x)
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trans_out = self.transpose(output, (2, 0, 3, 1))
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output = self.reshape(trans_out,
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(trans_out.shape[0], trans_out.shape[1]*trans_out.shape[2], trans_out.shape[3]))
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output = self.batch_mm(output, self.wi)
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output = self.batch_mm2(output, self.wo)
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output = self.reshape2(output, trans_out.shape)
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output = self.transpose2(output, (1, 3, 0, 2))
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output = self.relu2(output)
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return output
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_x = Tensor(np.ones([32, 16, 48, 128]), dtype=ms.float32)
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_wi = Tensor(np.ones([48, 16, 64]), dtype=ms.float32)
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_wo = Tensor(np.ones([48, 64, 16]), dtype=ms.float32)
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def compile_net(net):
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context.set_context(mode=context.GRAPH_MODE)
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optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
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train_net = TrainOneStepCell(net, optimizer)
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train_net.set_auto_parallel()
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train_net.set_train()
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_cell_graph_executor.compile(train_net, _x)
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context.reset_auto_parallel_context()
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def test_batchmm():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, enable_alltoall=True,
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global_rank=0)
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stra1 = ((8, 1, 1, 1),)
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stra2 = ((8, 1, 1, 1),)
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stra3 = ((8, 1, 1), (8, 1, 1))
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stra4 = ((8, 1, 1), (8, 1, 1))
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stra5 = ((8, 1, 1, 1),)
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stra6 = ((8, 1, 1, 1),)
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net = Net(_wi, _wo, stra1=stra1, stra2=stra2, stra3=stra3, stra4=stra4, stra5=stra5, stra6=stra6)
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compile_net(net)
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def test_batchmm2():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", enable_alltoall=True,
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device_num=32, global_rank=0)
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stra1 = ((4, 1, 1, 1),)
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stra2 = ((4, 1, 1, 1),)
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stra3 = ((4, 1, 1), (4, 1, 8))
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stra4 = ((4, 1, 8), (4, 8, 1))
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stra5 = ((4, 1, 1, 1),)
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stra6 = ((4, 1, 1, 1),)
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net = Net(_wi, _wo, stra1=stra1, stra2=stra2, stra3=stra3, stra4=stra4, stra5=stra5, stra6=stra6)
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compile_net(net)
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