forked from mindspore-Ecosystem/mindspore
116 lines
3.6 KiB
Python
116 lines
3.6 KiB
Python
# Copyright 2019 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.nn as nn
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from mindspore import Tensor
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from mindspore import context
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from mindspore.common.api import _executor
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from mindspore.ops import composite as C
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from mindspore.ops import operations as P
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from tests.ut.python.ops.test_math_ops import VirtualLoss
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grad_all = C.GradOperation(get_all=True)
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class NetWithLoss(nn.Cell):
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def __init__(self, network):
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super(NetWithLoss, self).__init__()
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self.loss = VirtualLoss()
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self.network = network
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def construct(self, x, w1, w2):
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predict = self.network(x, w1, w2)
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return self.loss(predict)
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class GradWrap(nn.Cell):
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def __init__(self, network):
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super(GradWrap, self).__init__()
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self.network = network
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def construct(self, x, w1, w2):
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return grad_all(self.network)(x, w1, w2)
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class NetConv(nn.Cell):
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def __init__(self,
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cin,
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cout,
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kernel_size,
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stride=1,
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pad_mode='pad',
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padding=0,
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dilation=1,
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group=1,
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has_bias=False,
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weight_init='normal',
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bias_init='zeros',
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strategy=None):
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super(NetConv, self).__init__()
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self.conv = nn.Conv2d(cin,
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cout,
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kernel_size,
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stride,
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pad_mode,
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padding,
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dilation,
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group,
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has_bias,
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weight_init,
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bias_init)
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self.conv.conv2d.set_strategy(strategy)
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def construct(self, input_x):
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return self.conv(input_x)
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def test_batch():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2, strategy3):
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super().__init__()
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self.conv1 = NetConv(16, 8, (3, 3), bias_init='zeros', strategy=strategy1)
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self.mul1 = P.Mul().set_strategy(strategy2)
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self.conv2 = NetConv(8, 64, (9, 9), bias_init='zeros', strategy=strategy1)
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self.mul2 = P.Mul().set_strategy(strategy3)
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def construct(self, x, w1, w2):
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out1 = self.conv1(x)
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out2 = self.mul1(out1, w1)
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out3 = self.conv2(out2)
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out4 = self.mul2(out3, w2)
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return out4
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy1 = ((8, 1, 1, 1), (1, 1, 1, 1))
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strategy2 = ((1, 1, 1, 8), (1, 1, 1, 8))
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strategy3 = ((4, 1, 1, 2), (4, 1, 1, 2))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
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net.set_auto_parallel()
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x = Tensor(np.ones([128, 16, 34, 34]), dtype=ms.float32)
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w1 = Tensor(np.ones([128, 8, 32, 32]), dtype=ms.float32)
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w2 = Tensor(np.ones([128, 64, 24, 24]), dtype=ms.float32)
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_executor.compile(net, x, w1, w2)
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if __name__ == '__main__':
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test_batch()
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