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79 lines
2.8 KiB
79 lines
2.8 KiB
# 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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from mindspore import context
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import mindspore.nn as nn
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from mindspore.ops import operations as P
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from mindspore import Tensor, Parameter, ParameterTuple
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import mindspore as ms
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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 functional as F
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class NetWithLoss(nn.Cell):
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def __init__(self, network, strategy3):
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super(NetWithLoss, self).__init__()
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self.loss = P.SoftmaxCrossEntropyWithLogits().set_strategy(strategy3)
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self.network = network
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def construct(self, x, b):
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predict = self.network(x)
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return self.loss(predict, b)[0]
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class OneStepCell(nn.Cell):
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def __init__(self, network):
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super(OneStepCell, self).__init__(auto_prefix=False)
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self.network = network
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self.weights = ParameterTuple(network.network.trainable_params())
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def construct(self, data, label):
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weights = self.weights
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grads = C.grad_by_list(self.network, weights)(data, label)
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return grads
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def test_two_weights_parameter():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2, weight, weight2):
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super().__init__()
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self.weight = Parameter(weight, "w1", requires_grad=True)
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self.weight2 = Parameter(weight2, "w2", requires_grad=True)
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self.matmul = P.MatMul().set_strategy(strategy1)
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self.matmul2 = P.MatMul().set_strategy(strategy2)
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def construct(self, x):
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out = self.matmul(x, self.weight)
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out = self.matmul2(out, self.weight2)
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return out
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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strategy1 = ((4, 1), (1, 2))
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strategy2 = ((4, 2), (2, 1))
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strategy3 = ((8, 1), (8, 1))
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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weight = Tensor(np.ones([32, 64]), dtype=ms.float32)
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weight2 = Tensor(np.ones([64, 64]), dtype=ms.float32)
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b = Tensor(np.ones([64, 64]), dtype=ms.float32)
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net = Net(strategy1, strategy2, weight, weight2)
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net_with_loss = NetWithLoss(net, strategy3)
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train_net = OneStepCell(net_with_loss)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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train_net.set_auto_parallel()
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_executor.compile(train_net, x, b)
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