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82 lines
3.0 KiB
82 lines
3.0 KiB
/* Copyright (c) 2016 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 "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/memory/memcpy.h"
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namespace paddle {
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namespace operators {
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template <typename DeviceContext, typename T>
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class MultiplexCPUKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const {
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auto ins = ctx.MultiInput<framework::Tensor>("X");
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auto ids = ctx.Input<framework::Tensor>("Ids");
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auto* out = ctx.Output<framework::Tensor>("Out");
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out->mutable_data<T>(ctx.GetPlace());
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auto rows = ins[0]->dims()[0];
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auto cols = ins[0]->numel() / rows;
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auto index = ids->data<int32_t>();
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platform::CPUPlace place = boost::get<platform::CPUPlace>(ctx.GetPlace());
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for (auto i = 0; i < rows; i++) {
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int32_t k = index[i];
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PADDLE_ENFORCE_GE(k, 0, "index must be nonnegative.");
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PADDLE_ENFORCE_LT(static_cast<size_t>(k), ins.size(),
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"index exceeds the number of candidate tensors.");
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memory::Copy(place, out->data<T>() + i * cols, place,
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ins[k]->data<T>() + i * cols, cols * sizeof(T));
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}
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}
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};
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template <typename DeviceContext, typename T>
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class MultiplexGradCPUKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const {
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auto* d_out = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
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auto* ids = ctx.Input<framework::Tensor>("Ids");
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auto ins = ctx.MultiInput<framework::Tensor>("X");
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auto d_ins =
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ctx.MultiOutput<framework::Tensor>(framework::GradVarName("X"));
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for (size_t i = 0; i < d_ins.size(); i++) {
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if (d_ins[i]) {
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d_ins[i]->mutable_data<T>(ctx.GetPlace());
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auto t = framework::EigenVector<T>::Flatten(*d_ins[i]);
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t.device(*ctx.template device_context<DeviceContext>().eigen_device()) =
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t.constant(static_cast<T>(0));
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}
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}
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auto rows = ins[0]->dims()[0];
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auto cols = ins[0]->numel() / rows;
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auto* index = ids->data<int32_t>();
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platform::CPUPlace place = boost::get<platform::CPUPlace>(ctx.GetPlace());
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for (auto i = 0; i < rows; i++) {
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size_t k = static_cast<size_t>(index[i]);
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if (d_ins[k]) {
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memory::Copy(place, d_ins[k]->data<T>() + i * cols, place,
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d_out->data<T>() + i * cols, cols * sizeof(T));
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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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