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103 lines
3.6 KiB
103 lines
3.6 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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/operators/sgd_op.h"
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namespace paddle {
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namespace operators {
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class SGDOp : 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(ctx->HasInput("Param"),
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"Input(Param) of SGDOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Grad"),
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"Input(Grad) of SGDOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("LearningRate"),
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"Input(LearningRate) of SGDOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
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"Output(ParamOut) of SGDOp should not be null.");
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auto lr_dims = ctx->GetInputDim("LearningRate");
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PADDLE_ENFORCE_EQ(framework::product(lr_dims), 1,
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"Learning rate should have 1 element");
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auto param_dim = ctx->GetInputDim("Param");
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// TODO(qijun): check dimensions of Param and Grad at complie
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// and run time.
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ctx->SetOutputDim("ParamOut", param_dim);
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}
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};
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class SGDOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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SGDOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("Param", "(Tensor) Input parameter");
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AddInput("LearningRate", "(Tensor) Learning rate of SGD");
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AddInput("Grad", "(Tensor) Input gradient");
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AddOutput("ParamOut", "(Tensor) Output parameter");
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AddComment(R"DOC(
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SGD operator
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This operator implements one step of the stochastic gradient descent algorithm.
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$$param_out = param - learning_rate * grad$$
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)DOC");
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}
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};
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template <typename T>
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struct SparseSGDFunctor<platform::CPUPlace, T> {
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void operator()(const platform::DeviceContext& context,
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const framework::SelectedRows& input,
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const framework::Tensor& learning_rate,
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framework::Tensor* output) {
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auto in_height = input.height();
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auto out_dims = output->dims();
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PADDLE_ENFORCE_EQ(in_height, out_dims[0]);
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auto& in_value = input.value();
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auto& in_rows = input.rows();
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int64_t in_row_numel = in_value.numel() / in_rows.size();
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PADDLE_ENFORCE_EQ(in_row_numel, output->numel() / in_height);
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auto* in_data = in_value.data<T>();
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auto* out_data = output->data<T>();
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auto* lr = learning_rate.data<T>();
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for (size_t i = 0; i < in_rows.size(); i++) {
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for (int64_t j = 0; j < in_row_numel; j++) {
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out_data[in_rows[i] * in_row_numel + j] -=
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lr[0] * in_data[i * in_row_numel + j];
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}
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}
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}
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};
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template struct SparseSGDFunctor<platform::CPUPlace, float>;
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template struct SparseSGDFunctor<platform::CPUPlace, double>;
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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(sgd, ops::SGDOp, ops::SGDOpMaker);
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REGISTER_OP_CPU_KERNEL(sgd, ops::SGDOpKernel<paddle::platform::CPUPlace, float>,
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ops::SGDOpKernel<paddle::platform::CPUPlace, double>);
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