forked from mindspore-Ecosystem/mindspore
134 lines
4.2 KiB
Python
134 lines
4.2 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.common.parameter import Parameter
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from mindspore.nn.optim.momentum import Momentum
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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 mindspore.train import Model
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from mindspore.context import ParallelMode
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from tests.dataset_mock import MindData
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from tests.ut.python.ops.test_math_ops import VirtualLoss
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context.set_context(mode=context.GRAPH_MODE)
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grad_all = C.GradOperation(get_all=True)
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class Dataset(MindData):
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def __init__(self, predict, label, length=3):
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super(Dataset, self).__init__(size=length)
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self.predict = predict
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self.label = label
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self.index = 0
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self.length = length
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def __iter__(self):
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return self
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def __next__(self):
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if self.index >= self.length:
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raise StopIteration
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self.index += 1
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return self.predict, self.label
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def reset(self):
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self.index = 0
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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, y, b):
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predict = self.network(x, y, b)
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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, y, b):
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return grad_all(self.network)(x, y, b)
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def test_auto_parallel_arithmetic():
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class Net(nn.Cell):
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def __init__(self):
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super().__init__()
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self.matmul = P.MatMul()
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self.one_hot = P.OneHot()
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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.matmul2 = P.MatMul()
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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out1 = self.one_hot(b, 64, self.on_value, self.off_value)
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out2 = self.matmul2(out, out1)
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return out2
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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net = GradWrap(NetWithLoss(Net()))
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context.set_auto_parallel_context(parallel_mode="auto_parallel")
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net.set_auto_parallel()
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([64]), dtype=ms.int32)
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_executor.compile(net, x, y, b)
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def test_auto_parallel_arithmetic_model():
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class NetOneHot(nn.Cell):
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def __init__(self):
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super().__init__()
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self.matmul = P.MatMul()
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self.one_hot = P.OneHot().set_strategy(((1, 8), (), ()))
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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.matmul2 = P.MatMul()
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self.w = Parameter(Tensor(np.zeros([32, 64]).astype(np.float32)), "weight", requires_grad=True)
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def construct(self, x, b):
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out = self.matmul(x, self.w)
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out1 = self.one_hot(b, 64, self.on_value, self.off_value)
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out2 = self.matmul2(out, out1)
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return out2
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode=ParallelMode.AUTO_PARALLEL)
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net = NetOneHot()
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x = Tensor(np.ones([8, 32]), dtype=ms.float32)
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b = Tensor(np.ones([8]), dtype=ms.int32)
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dataset = Dataset(x, b, 2)
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opt = Momentum(net.trainable_params(), 0.1, 0.9)
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model = Model(net, optimizer=opt)
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model.train(2, dataset, dataset_sink_mode=False)
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