forked from mindspore-Ecosystem/mindspore
106 lines
3.2 KiB
Python
106 lines
3.2 KiB
Python
# Copyright 2022 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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# ============================================================================
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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 context
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from mindspore import Tensor
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from mindspore.ops import operations as P
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from mindspore.common.parameter import Parameter
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from mindspore.common.initializer import initializer
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from mindspore.train.model import Model
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from mindspore.nn.wrap.cell_wrapper import PipelineCell, Cell
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class DatasetLenet():
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def __init__(self, data, length=3):
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self.data = data
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self.index = 1
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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.data
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@staticmethod
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def get_dataset_size():
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return 32
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@staticmethod
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def get_repeat_count():
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return 1
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@staticmethod
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def get_batch_size():
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return 32
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def create_tuple_iterator(self, num_epochs=1, do_copy=True):
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return self
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def reset(self):
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self.index = 0
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class MatMulCell(Cell):
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def __init__(self, strategy1, strategy2):
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super().__init__()
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self.param = Parameter(initializer("zeros", [64, 64]), name="param")
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self.param1 = Parameter(initializer("zeros", [64, 64]), name="param1")
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self.matmul = P.MatMul().shard(strategy1)
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self.matmul1 = P.MatMul().shard(strategy2)
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def construct(self, x):
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out = self.matmul(x, self.param)
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out = self.matmul1(out, self.param1)
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return out
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2):
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super().__init__()
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self.block = nn.CellList()
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for i in range(2):
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cell = MatMulCell(strategy1, strategy2)
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cell.pipeline_stage = i
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self.block.append(cell)
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def construct(self, x):
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for i in range(2):
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x = self.block[i](x)
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return x
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def test_pipeline_split_stage1():
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"""
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Feature:pipeline stage1
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Description:pipeline end virtual node
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Expectation:success
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"""
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context.set_auto_parallel_context(device_num=16, global_rank=8, pipeline_stages=2)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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data = Tensor(np.ones([32, 64]), dtype=ms.float32)
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dataset = DatasetLenet(data, 3)
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strategy1 = ((4, 1), (1, 2))
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strategy2 = ((2, 2), (2, 1))
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net = PipelineCell(Net(strategy1, strategy2), 4)
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optimizer = nn.Lamb(net.trainable_params(), learning_rate=0.01)
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model = Model(net, optimizer=optimizer)
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model.train(2, dataset, dataset_sink_mode=False)
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