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114 lines
3.6 KiB
114 lines
3.6 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 re
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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.loss import SoftmaxCrossEntropyWithLogits
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from mindspore.nn.optim.momentum import Momentum
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from mindspore.ops import operations as P
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from mindspore.parallel._utils import _reset_op_id
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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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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 AllToAllNet(nn.Cell):
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def __init__(self, strategy1):
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super(AllToAllNet, self).__init__()
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self.matmul = P.MatMul().shard(((1, 1), (1, 8)))
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self.matmul_weight = Parameter(Tensor(np.ones([128, 256]), dtype=ms.float32), name="weight")
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self.transpose1 = P.Transpose().shard(strategy1)
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def construct(self, x):
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x = self.matmul(x, self.matmul_weight)
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x = self.transpose1(x, (1, 0))
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return x
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def all_to_all_net(strategy1):
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return AllToAllNet(strategy1=strategy1)
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def all_to_all_common(strategy1):
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learning_rate = 0.1
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momentum = 0.9
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epoch_size = 2
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL, device_num=8)
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predict = Tensor(np.ones([32, 128]), dtype=ms.float32)
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label = Tensor(np.ones([32]), dtype=ms.int32)
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dataset = Dataset(predict, label, 2)
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net = all_to_all_net(strategy1)
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loss = SoftmaxCrossEntropyWithLogits(sparse=True)
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loss.softmax_cross_entropy.shard(((8, 1), (8, 1)))
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loss.one_hot.shard(((8, 1), (), ()))
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opt = Momentum(net.trainable_params(), learning_rate, momentum)
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model = Model(net, loss, opt)
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model.train(epoch_size, dataset, dataset_sink_mode=False)
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strategys = _executor._get_shard_strategy(model._train_network)
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return strategys
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def test_all_to_all():
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strategy1 = ((8, 1),)
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context.set_context(mode=context.GRAPH_MODE, save_graphs=False)
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_reset_op_id()
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strategys = all_to_all_common(strategy1)
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print(strategys)
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for (k, v) in strategys.items():
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if re.search('SoftmaxCrossEntropyWithLogits-op', k) is not None:
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assert v == [[8, 1], [8, 1]]
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elif re.search('OneHot-op', k) is not None:
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assert v == [[8, 1], [], []]
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elif re.search('Transpose-op', k) is not None:
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assert v == [[8, 1]]
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elif re.search('MatMul-op', k) is not None:
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assert v == [[1, 1], [1, 8]]
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context.set_context(save_graphs=False)
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if __name__ == '__main__':
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test_all_to_all()
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