forked from mindspore-Ecosystem/mindspore
106 lines
3.6 KiB
Python
106 lines
3.6 KiB
Python
# Copyright 2021 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 os
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import numpy as np
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import mindspore as ms
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from mindspore import context, Tensor, Parameter
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from mindspore.nn import Cell, Momentum
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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.train.callback import CheckpointConfig, ModelCheckpoint
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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 Net(Cell):
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def __init__(self, weight, w2, begin, end, strides, strategy1=None, strategy2=None, mask=0):
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super().__init__()
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self.mul = P.Mul().shard(strategy1)
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self.strided_slice = P.StridedSlice(begin_mask=mask).shard(strategy2)
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self.weight = Parameter(weight, "w1")
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self.mul2 = P.Mul()
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self.weight2 = Parameter(w2, "w2")
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self.begin = begin
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self.end = end
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self.strides = strides
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def construct(self, x, b):
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out = self.strided_slice(
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self.weight, self.begin, self.end, self.strides)
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out = self.mul(x, out)
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out = self.mul2(out, self.weight2)
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return out
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_x = Tensor(np.ones([16, 64, 1]), dtype=ms.float32)
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_b = Tensor(np.ones([16, 64, 32]), dtype=ms.float32)
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_w1 = Tensor(np.ones([256, 64, 32]), dtype=ms.float32)
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_w2 = Tensor(np.ones([128, 64, 1]), dtype=ms.float32)
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def clean_all_ckpt_files(folder_path):
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if os.path.exists(folder_path):
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for file_name in os.listdir(folder_path):
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if file_name.endswith('.ckpt') or file_name.endswith('.meta'):
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os.remove(os.path.join(folder_path, file_name))
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def compile_net(net):
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context.set_context(save_graphs=False)
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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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dataset = Dataset(_x, _b)
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opt = Momentum(net.trainable_params(), learning_rate, momentum)
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model = Model(net, optimizer=opt)
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ckpt_config = CheckpointConfig(keep_checkpoint_max=1)
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ckpt_path = "./parallel_ckpt"
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ckpt_cb = ModelCheckpoint(prefix="parallel", directory=ckpt_path, config=ckpt_config)
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model.train(epoch_size, dataset, dataset_sink_mode=False, callbacks=[ckpt_cb])
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assert len(model._train_network.parallel_parameter_merge_net_dict) == 4
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clean_all_ckpt_files(ckpt_path)
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context.reset_auto_parallel_context()
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def test_stridedslice_parameter():
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context.set_auto_parallel_context(
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parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((1, 4, 1), (1, 4, 2))
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strategy2 = ((1, 4, 2),)
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net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1),
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strategy1, strategy2)
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compile_net(net)
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