forked from mindspore-Ecosystem/mindspore
147 lines
5.1 KiB
Python
147 lines
5.1 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 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 _cell_graph_executor
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from mindspore.common.initializer import initializer
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from mindspore.common.parameter import Parameter, ParameterTuple
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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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context.set_context(mode=context.GRAPH_MODE)
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grad_by_list = C.GradOperation(get_by_list=True)
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class Net(nn.Cell):
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def __init__(self, channel=1, w=0.25, strategy1=None, strategy2=None):
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super().__init__()
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self.norm = P.L2Normalize().shard(strategy1)
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self.prelu = P.PReLU().shard(strategy2)
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self.w = Parameter(initializer(w, [channel,]), name='w')
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def construct(self, data):
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x = self.norm(data)
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x = self.prelu(x, self.w)
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return x
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class NetWithLoss(nn.Cell):
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def __init__(self, network, types, shapes, output_num, strategy3=None, strategy4=None, axis=-1):
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super(NetWithLoss, self).__init__()
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self.get_next = P.GetNext(types, shapes, output_num, "")
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self.one_hot = P.OneHot(axis=axis).shard(strategy3)
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self.on_value = Tensor(1.0, ms.float32)
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self.off_value = Tensor(0.0, ms.float32)
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self.loss = P.SoftmaxCrossEntropyWithLogits().shard(strategy4)
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self.network = network
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def construct(self):
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data, label = self.get_next()
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predict = self.network(data)
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label = self.one_hot(label, 64, self.on_value, self.off_value)
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return self.loss(predict, label)[0]
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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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self.weights = ParameterTuple(network.trainable_params())
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def construct(self):
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return grad_by_list(self.network, self.weights)()
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def compile_net(net):
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net.set_auto_parallel()
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_cell_graph_executor.compile(net)
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def test_get_next_single():
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"""
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Feature: test get next ops
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Description: standalone, getnext-norm-prelu-loss.
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Expectation: compile well done.
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"""
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net = GradWrap(NetWithLoss(Net(), [ms.float32, ms.int32], [[32, 64], [32]], 2))
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_cell_graph_executor.compile(net)
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def test_get_next_semi_auto_parallel():
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"""
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Feature: test get next ops
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Description: semi-auto parallel, getnext-norm-prelu-loss.
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Expectation: compile well done.
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"""
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context.set_auto_parallel_context(device_num=4, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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network = Net(strategy1=((1, 4),), strategy2=((4, 1), (1,)))
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strategy3 = ((4, 1), (), ())
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strategy4 = ((4, 1), (4, 1))
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net_with_loss = NetWithLoss(network, [ms.float32, ms.int32], [[32, 64], [32]], 2, strategy3=strategy3,
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strategy4=strategy4)
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net = GradWrap(net_with_loss)
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compile_net(net)
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def test_get_next_semi_auto_parallel1():
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"""
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Feature: test get next ops
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Description: semi-auto parallel, getnext-norm-prelu-loss.
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Expectation: compile well done.
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"""
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context.set_auto_parallel_context(device_num=4, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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network = Net(strategy1=((1, 4),), strategy2=((4, 1), (1,)))
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strategy3 = ((1, 4), (), ())
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strategy4 = ((4, 1), (4, 1))
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net_with_loss = NetWithLoss(network, [ms.float32, ms.int32], [[32, 64], [32]], 2, strategy3=strategy3,
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strategy4=strategy4)
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net = GradWrap(net_with_loss)
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compile_net(net)
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def test_get_next_auto_parallel():
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"""
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Feature: test get next ops
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Description: auto parallel, getnext-norm-prelu-loss.
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Expectation: compile well done.
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"""
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context.set_auto_parallel_context(device_num=4, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="auto_parallel")
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network = Net()
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net_with_loss = NetWithLoss(network, [ms.float32, ms.int32], [[32, 64], [32]], 2)
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net = GradWrap(net_with_loss)
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compile_net(net)
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def test_only_one_get_next():
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"""
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Feature: test get next ops
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Description: semi-auto parallel, only getnext.
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Expectation: compile well done.
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"""
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class Net1(nn.Cell):
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def __init__(self):
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super().__init__()
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self.get_next = P.GetNext([ms.float32, ms.int32], [[32, 64], [32]], 2, "")
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def construct(self):
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return self.get_next()
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context.set_auto_parallel_context(device_num=4, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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net = Net1()
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net.set_train()
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compile_net(net)
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