You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
133 lines
5.2 KiB
133 lines
5.2 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License");
|
|
you may not use this file except in compliance with the License.
|
|
You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software
|
|
distributed under the License is distributed on an "AS IS" BASIS,
|
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
See the License for the specific language governing permissions and
|
|
limitations under the License. */
|
|
|
|
#include "paddle/operators/adamax_op.h"
|
|
|
|
namespace paddle {
|
|
namespace operators {
|
|
|
|
class AdamaxOp : public framework::OperatorWithKernel {
|
|
public:
|
|
using framework::OperatorWithKernel::OperatorWithKernel;
|
|
|
|
void InferShape(framework::InferShapeContext *ctx) const override {
|
|
PADDLE_ENFORCE(ctx->HasInput("Param"),
|
|
"Input(Param) of AdamaxOp should not be null.");
|
|
PADDLE_ENFORCE(ctx->HasInput("Grad"),
|
|
"Input(Grad) of AdamaxOp should not be null.");
|
|
PADDLE_ENFORCE(ctx->HasInput("Moment"),
|
|
"Input(Moment) of AdamaxOp should not be null.");
|
|
PADDLE_ENFORCE(ctx->HasInput("InfNorm"),
|
|
"Input(InfNorm) of AdamaxOp should not be null.");
|
|
PADDLE_ENFORCE(ctx->HasInput("LearningRate"),
|
|
"Input(LearningRate) of AdamaxOp should not be null.");
|
|
PADDLE_ENFORCE(ctx->HasInput("Beta1Pow"),
|
|
"Input(Beta1Pow) of AdamaxOp should not be null.");
|
|
|
|
PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
|
|
"Output(ParamOut) of AdamaxOp should not be null.");
|
|
PADDLE_ENFORCE(ctx->HasOutput("MomentOut"),
|
|
"Output(MomentOut) of AdamaxOp should not be null.");
|
|
PADDLE_ENFORCE(ctx->HasOutput("InfNormOut"),
|
|
"Output(InfNormOut) of AdamaxOp should not be null.");
|
|
|
|
auto lr_dims = ctx->GetInputDim("LearningRate");
|
|
PADDLE_ENFORCE_EQ(framework::product(lr_dims), 1,
|
|
"Learning rate should have 1 dimension");
|
|
auto beta1_pow_dims = ctx->GetInputDim("Beta1Pow");
|
|
PADDLE_ENFORCE_EQ(framework::product(beta1_pow_dims), 1,
|
|
"Beta1 power accumulator should have 1 dimension");
|
|
auto param_dims = ctx->GetInputDim("Param");
|
|
PADDLE_ENFORCE_EQ(
|
|
param_dims, ctx->GetInputDim("Grad"),
|
|
"Param and Grad input of AdamaxOp should have same dimension");
|
|
PADDLE_ENFORCE_EQ(
|
|
param_dims, ctx->GetInputDim("Moment"),
|
|
"Param and Moment input of AdamaxOp should have same dimension");
|
|
PADDLE_ENFORCE_EQ(
|
|
param_dims, ctx->GetInputDim("InfNorm"),
|
|
"Param and InfNorm input of AdamaxOp should have same dimension");
|
|
|
|
ctx->SetOutputDim("ParamOut", param_dims);
|
|
ctx->SetOutputDim("MomentOut", param_dims);
|
|
ctx->SetOutputDim("InfNormOut", param_dims);
|
|
}
|
|
};
|
|
|
|
class AdamaxOpMaker : public framework::OpProtoAndCheckerMaker {
|
|
public:
|
|
AdamaxOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
|
|
: OpProtoAndCheckerMaker(proto, op_checker) {
|
|
AddInput("Param", "(Tensor) Input parameter");
|
|
AddInput("Grad", "(Tensor) Input gradient");
|
|
AddInput("LearningRate", "(Tensor) Learning rate");
|
|
AddInput("Moment", "(Tensor) First moment");
|
|
AddInput("InfNorm",
|
|
"(Tensor) "
|
|
"Input exponentially weighted infinity norm");
|
|
AddInput("Beta1Pow", "(Tensor) Input beta1 power accumulator");
|
|
|
|
AddOutput("ParamOut", "(Tensor) Output parameter");
|
|
AddOutput("MomentOut", "(Tensor) Output first moment");
|
|
AddOutput("InfNormOut",
|
|
"(Tensor) "
|
|
"Output exponentially weighted infinity norm");
|
|
|
|
AddAttr<float>("beta1",
|
|
"(float, default 0.9) "
|
|
"Exponential decay rate for the "
|
|
"1st moment estimates.")
|
|
.SetDefault(0.9f);
|
|
AddAttr<float>("beta2",
|
|
"(float, default 0.999) "
|
|
"exponential decay rate for the weighted "
|
|
"infinity norm estimates.")
|
|
.SetDefault(0.999f);
|
|
AddAttr<float>("epsilon",
|
|
"(float, default 1.0e-8) "
|
|
"Constant for numerical stability")
|
|
.SetDefault(1.0e-8f);
|
|
AddComment(R"DOC(
|
|
Adamax Optimizer.
|
|
|
|
We implement the Adamax optimizer from Section 7 of the Adam
|
|
paper: https://arxiv.org/abs/1412.6980. Adamax is a variant of the
|
|
Adam algorithm based on the infinity norm.
|
|
|
|
Adamax updates:
|
|
|
|
$$
|
|
moment\_out = \beta_1 * moment + (1 - \beta_1) * grad \\
|
|
inf\_norm\_out = max(\beta_2 * inf\_norm + \epsilon, |grad|) \\
|
|
learning\_rate = \frac{learning\_rate}{1 - \beta_{1\_pow}} \\
|
|
param\_out = param - learning\_rate * \frac{moment\_out}{inf\_norm\_out}
|
|
$$
|
|
|
|
The original paper does not have an epsilon attribute.
|
|
However, it is added here for numerical stability to prevent the
|
|
division by 0 error.
|
|
|
|
)DOC");
|
|
}
|
|
};
|
|
|
|
} // namespace operators
|
|
} // namespace paddle
|
|
|
|
namespace ops = paddle::operators;
|
|
REGISTER_OP_WITHOUT_GRADIENT(adamax, ops::AdamaxOp, ops::AdamaxOpMaker);
|
|
REGISTER_OP_CPU_KERNEL(adamax,
|
|
ops::AdamaxOpKernel<paddle::platform::CPUPlace, float>,
|
|
ops::AdamaxOpKernel<paddle::platform::CPUPlace, double>);
|