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186 lines
8.0 KiB
186 lines
8.0 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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namespace paddle {
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namespace operators {
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class LambOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE_EQ(ctx->HasInput("Param"), true,
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platform::errors::NotFound(
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"Input(Param) of LambOp should not be null."));
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PADDLE_ENFORCE_EQ(ctx->HasInput("Grad"), true,
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platform::errors::NotFound(
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"Input(Grad) of LambOp should not be null."));
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PADDLE_ENFORCE_EQ(ctx->HasInput("Moment1"), true,
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platform::errors::NotFound(
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"Input(Moment1) of LambOp should not be null."));
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PADDLE_ENFORCE_EQ(ctx->HasInput("Moment2"), true,
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platform::errors::NotFound(
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"Input(Moment2) of LambOp should not be null."));
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PADDLE_ENFORCE_EQ(ctx->HasInput("LearningRate"), true,
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platform::errors::NotFound(
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"Input(LearningRate) of LambOp should not be null."));
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PADDLE_ENFORCE_EQ(ctx->HasInput("Beta1Pow"), true,
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platform::errors::NotFound(
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"Input(Beta1Pow) of LambOp should not be null."));
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PADDLE_ENFORCE_EQ(ctx->HasInput("Beta2Pow"), true,
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platform::errors::NotFound(
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"Input(Beta2Pow) of LambOp should not be null."));
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PADDLE_ENFORCE_EQ(ctx->HasOutput("ParamOut"), true,
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platform::errors::NotFound(
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"Output(ParamOut) of LambOp should not be null."));
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PADDLE_ENFORCE_EQ(ctx->HasOutput("Moment1Out"), true,
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platform::errors::NotFound(
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"Output(Moment1Out) of LambOp should not be null."));
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PADDLE_ENFORCE_EQ(ctx->HasOutput("Moment2Out"), true,
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platform::errors::NotFound(
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"Output(Moment2Out) of LambOp should not be null."));
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auto lr_dims = ctx->GetInputDim("LearningRate");
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PADDLE_ENFORCE_NE(
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framework::product(lr_dims), 0,
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platform::errors::InvalidArgument(
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"The number of LearningRate shall not be 0, but received %d. Maybe "
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"the Input variable LearningRate has not "
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"been initialized. You may need to confirm "
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"if you put exe.run(startup_program) "
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"after optimizer.minimize function.",
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framework::product(lr_dims)));
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PADDLE_ENFORCE_EQ(
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framework::product(lr_dims), 1,
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platform::errors::InvalidArgument(
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"Learning rate should have 1 dimension, but received %d.",
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framework::product(lr_dims)));
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auto beta1_pow_dims = ctx->GetInputDim("Beta1Pow");
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PADDLE_ENFORCE_GE(framework::product(beta1_pow_dims), 1,
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platform::errors::InvalidArgument(
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"The size of Beta1 power accumulator should be "
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"greater than 0, but received %d.",
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framework::product(beta1_pow_dims)));
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auto beta2_pow_dims = ctx->GetInputDim("Beta2Pow");
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PADDLE_ENFORCE_GE(framework::product(beta2_pow_dims), 1,
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platform::errors::InvalidArgument(
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"The size of Beta2 power accumulator should be "
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"greater than 0, but received %d.",
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framework::product(beta2_pow_dims)));
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auto param_dims = ctx->GetInputDim("Param");
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if (ctx->GetInputsVarType("Grad")[0] ==
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framework::proto::VarType::LOD_TENSOR) {
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PADDLE_ENFORCE_EQ(
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param_dims, ctx->GetInputDim("Grad"),
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platform::errors::InvalidArgument(
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"Param and Grad input of LambOp should have same dimension. But "
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"received Param dims: [%s], Grad dims: [%s].",
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param_dims, ctx->GetInputDim("Grad")));
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}
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PADDLE_ENFORCE_EQ(
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param_dims, ctx->GetInputDim("Moment1"),
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platform::errors::InvalidArgument(
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"Param and Moment1 input of LambOp should have same dimension. But "
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"received Param dims: [%s], Moment1 dims: [%s].",
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param_dims, ctx->GetInputDim("Moment1")));
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PADDLE_ENFORCE_EQ(
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param_dims, ctx->GetInputDim("Moment2"),
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platform::errors::InvalidArgument(
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"Param and Moment2 input of LambOp should have same dimension. But "
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"received Param dims: [%s], Moment2 dims: [%s].",
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param_dims, ctx->GetInputDim("Moment2")));
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ctx->SetOutputDim("ParamOut", param_dims);
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ctx->SetOutputDim("Moment1Out", param_dims);
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ctx->SetOutputDim("Moment2Out", param_dims);
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}
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const {
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auto input_data_type =
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OperatorWithKernel::IndicateVarDataType(ctx, "Param");
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return framework::OpKernelType(input_data_type, ctx.GetPlace());
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}
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};
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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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$$
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m_t &= \beta_1 m_{t - 1}+ (1 - \beta_1)g_t \\
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v_t &= \beta_2 v_{t - 1} + (1 - \beta_2)g_t^2 \\
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r_t &= \frac{m_t}{\sqrt{v_t}+\epsilon} \\
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w_t &= w_{t-1} -\eta_t \frac{\left \| w_{t-1}\right \|}{\left \| r_t + \lambda w_{t-1}\right \|} (r_t + \lambda w_{t-1})
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$$
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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::LambOp, 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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