forked from mindspore-Ecosystem/mindspore
88 lines
3.8 KiB
Python
88 lines
3.8 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 import Tensor
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from mindspore.common.api import ms_function
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from mindspore.ops import operations as P
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from mindspore.ops import functional as F
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from mindspore.common import dtype as mstype
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from mindspore.common.parameter import Parameter
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU", save_graphs=True)
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class Net(nn.Cell):
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def __init__(self, decay_flag=True):
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super(Net, self).__init__()
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self.decay_flag = decay_flag
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self.op_mul = P.Mul()
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self.op_square = P.Square()
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self.op_sqrt = P.Sqrt()
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self.op_cast = P.Cast()
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self.op_reshape = P.Reshape()
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self.op_shape = P.Shape()
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self.param = Parameter(Tensor(np.array([0.1, 0.3, 0.5]).astype(np.float32)), name='param')
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self.m = Parameter(Tensor(np.array([0.1, 0.3, 0.5]).astype(np.float32)), name='m')
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self.v = Parameter(Tensor(np.array([0.1, 0.3, 0.5]).astype(np.float32)), name='v')
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@ms_function
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def construct(self, beta1, beta2, gradient, eps, weight_decay_tensor, lr):
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param_fp32 = self.op_cast(self.param, mstype.float32)
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m_fp32 = self.op_cast(self.m, mstype.float32)
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v_fp32 = self.op_cast(self.v, mstype.float32)
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gradient_fp32 = self.op_cast(gradient, mstype.float32)
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next_m = self.op_mul(beta1, m_fp32) + \
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self.op_mul(self.op_cast(F.tuple_to_array((1.0,)), mstype.float32) - beta1, gradient_fp32)
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next_v = self.op_mul(beta2, v_fp32) + self.op_mul(self.op_cast(F.tuple_to_array((1.0,)), mstype.float32) - \
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beta2, self.op_square(gradient_fp32))
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update = next_m / (eps + self.op_sqrt(next_v))
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if self.decay_flag:
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update = self.op_mul(weight_decay_tensor, param_fp32) + update
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update_with_lr = self.op_mul(lr, update)
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next_param = param_fp32 - self.op_reshape(update_with_lr, self.op_shape(param_fp32))
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next_v = F.depend(next_v, F.assign(self.param, next_param))
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next_v = F.depend(next_v, F.assign(self.m, next_m))
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next_v = F.depend(next_v, F.assign(self.v, next_v))
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return next_v
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test():
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beta1 = Tensor(np.array([0.9]).astype(np.float32))
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beta2 = Tensor(np.array([0.999]).astype(np.float32))
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lr = Tensor(np.array([0.001]).astype(np.float32))
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eps = Tensor(np.array([1e-6]).astype(np.float32))
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weight_decay_tensor = Tensor(np.array([0.001]).astype(np.float32))
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gradient = Tensor(np.array([0.01, 0.03, 0.05]).astype(np.float32))
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opt = Net(True)
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_ = opt(beta1, beta2, gradient, eps, weight_decay_tensor, lr)
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param_expect = np.array([0.09971199, 0.29950103, 0.4993557]).astype(np.float32)
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m_expect = np.array([0.091, 0.273, 0.45499998]).astype(np.float32)
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v_expect = np.array([0.0999001, 0.29970092, 0.4995025]).astype(np.float32)
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assert np.allclose(opt.param.data.asnumpy(), param_expect)
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assert np.allclose(opt.m.data.asnumpy(), m_expect)
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assert np.allclose(opt.v.data.asnumpy(), v_expect)
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