62 lines
2.2 KiB
Python
62 lines
2.2 KiB
Python
# 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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# ============================================================================
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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore.common.parameter import Parameter
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from mindspore import Tensor
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from mindspore.ops import operations as P
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class MomentumFusionNet(nn.Cell):
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def __init__(self, var, accum):
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super(MomentumFusionNet, self).__init__()
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self.op = P.ApplyMomentum()
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self.add = P.AddN()
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self.mul = P.Mul()
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self.var = Parameter(var, name="variable")
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self.accum = Parameter(accum, name="accumulate")
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self.lr = 0.1
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self.weight_decay = 0.002
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self.moment = 0.98
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def construct(self, grad):
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wd = self.mul(self.var, self.weight_decay)
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g = self.add((wd, grad))
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return self.op(self.var, self.accum, self.lr, g, self.moment)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_momentum_fusion():
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np.random.seed(42)
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var = Tensor(np.random.randn(10, 20).astype(np.float32))
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accum = Tensor(np.random.randn(10, 20).astype(np.float32))
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grad = Tensor(np.random.randn(10, 20).astype(np.float32))
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context.set_context(device_target='GPU', mode=context.GRAPH_MODE)
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net1 = MomentumFusionNet(var, accum)
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_ = net1(grad)
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context.set_context(device_target='GPU', mode=context.PYNATIVE_MODE)
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net2 = MomentumFusionNet(var, accum)
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_ = net2(grad)
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assert np.allclose(net1.var.data.asnumpy(), net2.var.data.asnumpy(), atol=1e-5)
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assert np.allclose(net1.accum.data.asnumpy(), net2.accum.data.asnumpy(), atol=1e-5)
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