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77 lines
2.8 KiB
77 lines
2.8 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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#pragma once
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#include "paddle/framework/eigen.h"
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#include "paddle/framework/op_registry.h"
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#include "paddle/framework/selected_rows.h"
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namespace paddle {
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namespace operators {
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template <typename T>
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class SGDOpKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* param = ctx.Input<framework::Tensor>("Param");
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auto* param_out = ctx.Output<framework::Tensor>("ParamOut");
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auto* learning_rate = ctx.Input<framework::Tensor>("LearningRate");
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auto* grad_var = ctx.InputVar("Grad");
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// Actually, all tensors are LoDTensor except SelectedRows.
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if (grad_var->IsType<framework::LoDTensor>()) {
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param_out->mutable_data<T>(ctx.GetPlace());
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auto* grad = ctx.Input<framework::Tensor>("Grad");
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auto p = framework::EigenVector<T>::Flatten(*param);
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auto g = framework::EigenVector<T>::Flatten(*grad);
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auto o = framework::EigenVector<T>::Flatten(*param_out);
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auto* lr = learning_rate->data<T>();
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o = p - lr[0] * g;
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} else if (grad_var->IsType<framework::SelectedRows>()) {
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// TODO(qijun): In Sparse SGD operator, in-place update is enforced.
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// This manual optimization brings difficulty to track data dependency.
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// It's better to find a more elegant solution.
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PADDLE_ENFORCE_EQ(param, param_out);
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auto* grad = ctx.Input<framework::SelectedRows>("Grad");
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auto in_height = grad->height();
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auto out_dims = param_out->dims();
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PADDLE_ENFORCE_EQ(in_height, out_dims[0]);
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auto& in_value = grad->value();
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auto& in_rows = grad->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, param_out->numel() / in_height);
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auto* in_data = in_value.data<T>();
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auto* out_data = param_out->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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} else {
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PADDLE_THROW("Unsupported Variable Type of Grad");
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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