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103 lines
3.4 KiB
103 lines
3.4 KiB
5 years ago
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/* Copyright (c) 2020 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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#pragma once
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#include <string>
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#include <utility>
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#include <vector>
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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template <typename DeviceContext, typename T>
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class PartialSumKernel : 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 ins = ctx.MultiInput<Tensor>("X");
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Tensor* out = ctx.Output<Tensor>("Out");
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PADDLE_ENFORCE_EQ(
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ins[0] != nullptr, true,
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platform::errors::InvalidArgument("The input should not be null."));
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auto place = ctx.GetPlace(); // CPUPlace only now
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auto* out_t = out->mutable_data<T>(place);
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auto start_index = ctx.Attr<int>("start_index");
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auto length = ctx.Attr<int>("length");
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auto batch_size = ins[0]->dims()[0];
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if (length == -1) {
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length = ins[0]->dims()[1] - start_index;
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}
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memset(out_t, 0, sizeof(T) * batch_size * length);
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for (size_t i = 0; i < ins.size(); ++i) {
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auto* in_t = ins[i]->data<T>();
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auto total_len = ins[i]->dims()[1];
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for (auto bs_id = 0; bs_id < batch_size; ++bs_id) {
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for (auto k = 0; k < length; ++k) {
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out_t[bs_id * length + k] +=
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in_t[bs_id * total_len + start_index + k];
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}
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}
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}
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}
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};
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template <typename T>
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class PartialSumGradientOpKernel : 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* out_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
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auto ins = ctx.MultiInput<framework::LoDTensor>("X");
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auto outs =
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ctx.MultiOutput<framework::LoDTensor>(framework::GradVarName("X"));
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PADDLE_ENFORCE_EQ(
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ins[0] != nullptr, true,
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platform::errors::InvalidArgument("The input should not be null."));
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auto start_index = ctx.Attr<int>("start_index");
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auto length = ctx.Attr<int>("length");
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auto batch_size = ins[0]->dims()[0];
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if (length == -1) {
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length = ins[0]->dims()[1] - start_index;
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}
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// initialize
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auto& place = *ctx.template device_context<platform::CPUDeviceContext>()
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.eigen_device();
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for (size_t i = 0; i < outs.size(); ++i) {
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outs[i]->mutable_data<T>(ctx.GetPlace());
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auto dxt = framework::EigenVector<T>::Flatten(*outs[i]);
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dxt.device(place) = dxt.constant(static_cast<T>(0));
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}
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auto* out_grad_t = out_grad->data<T>();
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for (size_t i = 0; i < outs.size(); ++i) {
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auto* out_t = outs[i]->data<T>();
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auto total_len = ins[i]->dims()[1];
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for (auto bs_id = 0; bs_id < batch_size; ++bs_id) {
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for (int len = 0; len < length; ++len) {
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out_t[start_index + bs_id * total_len + len] =
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out_grad_t[bs_id * length + len] * static_cast<T>(1);
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}
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}
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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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