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106 lines
3.8 KiB
106 lines
3.8 KiB
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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 <memory>
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#include <vector>
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#include "paddle/fluid/framework/fleet/box_wrapper.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/framework/tensor.h"
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namespace paddle {
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namespace operators {
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template <typename T>
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static void PullBoxSparseFunctor(const framework::ExecutionContext &ctx) {
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auto inputs = ctx.MultiInput<framework::Tensor>("Ids");
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auto outputs = ctx.MultiOutput<framework::Tensor>("Out");
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const auto slot_size = inputs.size();
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std::vector<const uint64_t *> all_keys(slot_size);
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// BoxPS only supports float now
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std::vector<float *> all_values(slot_size);
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std::vector<int64_t> slot_lengths(slot_size);
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for (size_t i = 0; i < slot_size; i++) {
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const auto *slot = inputs[i];
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const uint64_t *single_slot_keys =
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reinterpret_cast<const uint64_t *>(slot->data<int64_t>());
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all_keys[i] = single_slot_keys;
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slot_lengths[i] = slot->numel();
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auto *output = outputs[i]->mutable_data<T>(ctx.GetPlace());
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all_values[i] = output;
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}
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#ifdef PADDLE_WITH_BOX_PS
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auto hidden_size = ctx.Attr<int>("size");
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auto box_ptr = paddle::framework::BoxWrapper::GetInstance();
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box_ptr->PullSparse(ctx.GetPlace(), all_keys, all_values, slot_lengths,
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hidden_size);
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#endif
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}
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template <typename T>
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static void PushBoxSparseFunctor(const framework::ExecutionContext &ctx) {
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auto inputs = ctx.MultiInput<framework::LoDTensor>("Ids");
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auto d_output =
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ctx.MultiInput<framework::Tensor>(framework::GradVarName("Out"));
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const auto slot_size = inputs.size();
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std::vector<const uint64_t *> all_keys(slot_size);
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std::vector<const float *> all_grad_values(slot_size);
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std::vector<int64_t> slot_lengths(slot_size);
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int batch_size = -1;
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for (size_t i = 0; i < slot_size; i++) {
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const auto *slot = inputs[i];
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const uint64_t *single_slot_keys =
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reinterpret_cast<const uint64_t *>(slot->data<int64_t>());
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all_keys[i] = single_slot_keys;
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slot_lengths[i] = slot->numel();
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int cur_batch_size =
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slot->lod().size() ? slot->lod()[0].size() - 1 : slot->dims()[0];
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if (batch_size == -1) {
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batch_size = cur_batch_size;
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} else {
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PADDLE_ENFORCE_EQ(batch_size, cur_batch_size,
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platform::errors::PreconditionNotMet(
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"The batch size of all input slots should be same, "
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"please cheack"));
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}
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const float *grad_value = d_output[i]->data<float>();
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all_grad_values[i] = grad_value;
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}
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#ifdef PADDLE_WITH_BOX_PS
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auto hidden_size = ctx.Attr<int>("size");
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auto box_ptr = paddle::framework::BoxWrapper::GetInstance();
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box_ptr->PushSparseGrad(ctx.GetPlace(), all_keys, all_grad_values,
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slot_lengths, hidden_size, batch_size);
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#endif
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}
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using LoDTensor = framework::LoDTensor;
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template <typename T>
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class PullBoxSparseCPUKernel : 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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PullBoxSparseFunctor<T>(ctx);
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}
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};
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template <typename T>
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class PushBoxSparseCPUKernel : 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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PushBoxSparseFunctor<T>(ctx);
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}
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};
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} // namespace operators
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} // namespace paddle
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