forked from mindspore-Ecosystem/mindspore
72 lines
2.4 KiB
Python
72 lines
2.4 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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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, context
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from mindspore.ops import operations as P
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from mindspore.common.api import _cell_graph_executor
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from mindspore.nn.wrap.cell_wrapper import MicroBatchInterleaved
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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.matmul1 = P.MatMul().shard(strategy1)
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self.matmul2 = P.MatMul().shard(strategy2)
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def construct(self, x, y, b):
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out = self.matmul1(x, y)
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out = self.matmul2(out, b)
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return out
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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 = P.ReLU()
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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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def compile_net(net, x, y, b):
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net.set_auto_parallel()
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net.set_train()
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_cell_graph_executor.compile(net, x, y, b)
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def test_micro_batch_interleaved():
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"""
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Feature: test MicroBatchInterleaved in auto parallel.
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Description: net with MicroBatchInterleaved in semi auto parallel.
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Expectation: compile done without error.
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"""
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context.set_context(mode=context.GRAPH_MODE)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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context.set_auto_parallel_context(device_num=8, global_rank=0, gradients_mean=True)
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strategy1 = ((4, 2), (2, 1))
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strategy2 = ((2, 4), (4, 1))
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micro_batch_interleaved = 2
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net = MicroBatchInterleaved(NetWithLoss(Net(strategy1, strategy2)), micro_batch_interleaved)
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x = Tensor(np.ones([128, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32 * micro_batch_interleaved, 64]), dtype=ms.float32)
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b = Tensor(np.ones([64 * micro_batch_interleaved, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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