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79 lines
2.8 KiB
79 lines
2.8 KiB
# Copyright 2020 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.context as context
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from mindspore.common.api import _executor
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from mindspore import Tensor, Parameter
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import mindspore.nn as nn
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from mindspore.nn import Cell, TrainOneStepCell, Momentum
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from mindspore.ops import operations as P
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class TwoInputBpropOperator(Cell):
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def __init__(self):
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super().__init__()
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self.op = P.Mul()
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self.bp = P.Add()
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def construct(self, x, y):
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return self.op(x, y)
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def bprop(self, x, y, out, dout):
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return self.bp(5, x), self.bp(y, 8)
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class ParallelFloorDivBpropNet(Cell):
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def __init__(self, mul_size, test_size, strategy=None, strategy2=None):
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super().__init__()
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mul_np = np.full(mul_size, 0.5, dtype=np.float32)
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floordiv_np = np.full(test_size, 0.1, dtype=np.float32)
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self.mul_weight = Parameter(Tensor(mul_np), name="mul_weight")
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self.floordiv_weight = Parameter(Tensor(floordiv_np), name="floordiv_weight")
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self.mul = TwoInputBpropOperator()
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self.floor_div = P.FloorDiv()
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self.bn = nn.BatchNorm1d(num_features=96)
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if strategy is not None:
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self.mul.op.shard(strategy2)
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self.mul.bp.shard(strategy2)
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self.floor_div.shard(strategy)
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def construct(self, inputs, label):
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x = self.mul(inputs, self.mul_weight)
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x = self.floor_div(x, self.floordiv_weight)
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x = self.bn(x)
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return x
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inputs_ = Tensor(np.random.randn(128, 96).astype(np.float32), dtype=ms.float32)
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label_ = Tensor(np.random.randn(128, 96).astype(np.float32), dtype=ms.float32)
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def compile_net(net):
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optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
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train_net = TrainOneStepCell(net, optimizer)
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train_net.set_auto_parallel()
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train_net.set_train()
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_executor.compile(train_net, inputs_, label_)
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context.reset_auto_parallel_context()
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def test_net():
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=4, global_rank=0)
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strategy = ((4, 1), (4, 1))
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net = ParallelFloorDivBpropNet(mul_size=(128, 96), test_size=(128, 96), strategy=strategy, strategy2=strategy)
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compile_net(net)
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