mindspore/tests/ut/python/parallel/test_optimizer_clone_weight.py

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# Copyright 2019 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
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import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor, Parameter
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from mindspore import context
from mindspore.common.api import _CellGraphExecutor
from mindspore.nn import TrainOneStepCell
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from mindspore.nn.optim import AdamWeightDecay
from mindspore.ops import operations as P
class NetWithLoss(nn.Cell):
def __init__(self, network, strategy3):
super(NetWithLoss, self).__init__()
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self.loss = P.SoftmaxCrossEntropyWithLogits().shard(strategy3)
self.network = network
def construct(self, x, b):
predict = self.network(x)
return self.loss(predict, b)[0]
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def compile_net(net, x, b):
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net.set_auto_parallel()
_CellGraphExecutor().compile(net, x, b)
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def test_optimizer_clone_weight():
class Net(nn.Cell):
def __init__(self, strategy1, strategy2, weight):
super().__init__()
self.weight = Parameter(weight, "w1")
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self.matmul = P.MatMul(transpose_a=False, transpose_b=True).shard(strategy1)
self.relu = P.ReLU().shard(strategy2)
def construct(self, x):
out = self.matmul(x, self.weight)
out = self.relu(out)
return out
context.set_auto_parallel_context(device_num=4, global_rank=0)
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy1 = ((2, 1), (2, 1))
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strategy2 = ((4, 1),)
strategy3 = ((4, 1), (4, 1))
x = Tensor(np.ones([64, 32]), dtype=ms.float32)
weight = Tensor(np.ones([64, 32]), dtype=ms.float32)
b = Tensor(np.ones([64, 64]), dtype=ms.float32)
net = Net(strategy1, strategy2, weight)
optimizer = AdamWeightDecay(net.trainable_params())
net_with_loss = NetWithLoss(net, strategy3)
train_net = TrainOneStepCell(net_with_loss, optimizer)
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compile_net(train_net, x, b)
def test_optimizer_clone_weight2():
class Net(nn.Cell):
def __init__(self, strategy1, strategy2, weight):
super().__init__()
self.weight = Parameter(weight, "w1")
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self.matmul = P.MatMul(transpose_a=False, transpose_b=True).shard(strategy1)
self.relu = P.ReLU().shard(strategy2)
def construct(self, x):
out = self.matmul(x, self.weight)
out = self.relu(out)
return out
context.set_auto_parallel_context(device_num=4, global_rank=0)
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy1 = ((2, 1), (2, 1))
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strategy2 = ((4, 1),)
strategy3 = ((4, 1), (4, 1))
x = Tensor(np.ones([64, 32]), dtype=ms.float32)
weight = Tensor(np.ones([64, 32]), dtype=ms.float32)
b = Tensor(np.ones([64, 64]), dtype=ms.float32)
net = Net(strategy1, strategy2, weight)
optimizer = AdamWeightDecay(net.trainable_params())
net_with_loss = NetWithLoss(net, strategy3)
train_net = TrainOneStepCell(net_with_loss, optimizer)
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compile_net(train_net, x, b)