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96 lines
3.5 KiB
96 lines
3.5 KiB
/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include "paddle/fluid/operators/optimizers/lamb_op.h"
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#include "paddle/fluid/operators/optimizers/adam_op.h"
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namespace paddle {
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namespace operators {
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class LambOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("Param",
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"(LoDTensor, default LoDTensor<float>) "
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"Input parameter that has to be updated.");
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AddInput("Grad",
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"(LoDTensor, default LoDTensor<float>) "
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"Input gradient of the parameter.");
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AddInput("LearningRate", "(Tensor) Learning rate.");
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AddInput("Moment1", "(Tensor) Input first moment.");
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AddInput("Moment2", "(Tensor) Input second moment.");
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AddInput("Beta1Pow", "(Tensor) Input beta1 power accumulator.");
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AddInput("Beta2Pow", "(Tensor) Input beta2 power accumulator.");
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AddOutput("ParamOut", "(Tensor) Output parameter.");
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AddOutput("Moment1Out", "(Tensor) Output first moment.");
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AddOutput("Moment2Out", "(Tensor) Output second moment.");
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AddAttr<float>("weight_decay", "(float) Weight decay rate.");
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AddAttr<float>("beta1",
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"(float, default 0.9) The exponential decay rate for the "
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"1st moment estimates.")
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.SetDefault(0.9);
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AddAttr<float>("beta2",
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"(float, default 0.999) The exponential decay rate for the "
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"2nd moment estimates.")
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.SetDefault(0.999);
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AddAttr<float>("epsilon",
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"(float, default 1.0e-6) "
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"Constant for numerical stability.")
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.SetDefault(1.0e-6f);
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AddComment(R"DOC(
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LAMB (Layer-wise Adaptive Moments optimizer for Batching training) Optimizer.
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LAMB Optimizer is designed to scale up the batch size of training without losing
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accuracy, which supports adaptive element-wise updating and accurate layer-wise
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correction. For more information, please refer to https://arxiv.org/abs/1904.00962.
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The updating of parameters follows:
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$$
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m_t^l &= \beta_1 m_{t - 1}^l + (1 - \beta_1)g_t^l \\
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v_t^l &= \beta_2 v_{t - 1}^l + (1 - \beta_2)g_t^l \odot g_t^l \\
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\widehat{m}_t^l &= m_t^l/(1 - \beta_1^t) \\
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\widehat{v}_t^l &= v_t^l/(1 - \beta_2^t) \\
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r_1 &= \left \| w_{t-1}^l \right \|_2 \\
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r_2 &= \left \| \frac{\widehat{m}_t^l}{\sqrt{\widehat{v}_t^l+\epsilon}} + \lambda w_{t-1}^l \right \|_2 \\
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r &= r_1 / r_2 \\
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\eta^l &= r \times \eta \\
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w_t^l &= w_{t-1}^l -\eta ^l \times (\frac{\widehat{m}_t^l}{\sqrt{\widehat{v}_t^l+\epsilon}} + \lambda w_{t-1}^l)
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$$
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where $m$ is the 1st moment, and $v$ the 2nd moment, $\eta$ the
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learning rate, $\lambda$ the weight decay rate.
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)DOC");
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP_WITHOUT_GRADIENT(lamb, ops::AdamOp, ops::LambOpMaker);
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REGISTER_OP_CPU_KERNEL(
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lamb, ops::LambOpKernel<paddle::platform::CPUDeviceContext, float>,
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ops::LambOpKernel<paddle::platform::CPUDeviceContext, double>);
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