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263 lines
10 KiB
263 lines
10 KiB
/* 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 <atomic>
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#include <ctime>
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#include <map>
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#include <memory>
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#include <random>
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#include <string>
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#include <unordered_map>
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#include <vector>
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#include "paddle/fluid/distributed/communicator_common.h"
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#include "paddle/fluid/distributed/service/service.h"
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#include "paddle/fluid/framework/archive.h"
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#include "paddle/fluid/framework/io/fs.h"
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#include "paddle/fluid/framework/io/shell.h"
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#include "paddle/fluid/framework/program_desc.h"
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#include "paddle/fluid/framework/scope.h"
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#include "paddle/fluid/framework/tensor.h"
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#include "paddle/fluid/framework/variable_helper.h"
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#include "paddle/fluid/platform/macros.h" // for DISABLE_COPY_AND_ASSIGN
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namespace paddle {
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namespace framework {
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class LoDTensor;
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class Scope;
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class SelectedRows;
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class Variable;
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} // namespace framework
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} // namespace paddle
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namespace paddle {
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namespace distributed {
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class PSCore;
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using framework::LoDTensor;
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using framework::Scope;
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using framework::SelectedRows;
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using framework::Variable;
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using RpcCtxMap = std::unordered_map<std::string, CommContext>;
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class FleetWrapper {
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public:
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virtual ~FleetWrapper() {}
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FleetWrapper() {
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scale_sparse_gradient_with_batch_size_ = true;
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// trainer sleep some time for pserver core dump
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sleep_seconds_before_fail_exit_ = 300;
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// pserver request server timeout ms
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client2client_request_timeout_ms_ = 500000;
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// pserver connect server timeout_ms
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client2client_connect_timeout_ms_ = 10000;
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// pserver request max retry
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client2client_max_retry_ = 3;
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}
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// set client to client communication config
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void SetClient2ClientConfig(int request_timeout_ms, int connect_timeout_ms,
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int max_retry);
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// Pull sparse variables from server in sync mode
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// Param<in>: scope, table_id, var_names, fea_keys, fea_dim, var_emb_names
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// Param<out>: fea_values
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void PullSparseVarsSync(const Scope& scope, const uint64_t table_id,
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const std::vector<std::string>& var_names,
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std::vector<uint64_t>* fea_keys,
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std::vector<std::vector<float>>* fea_values,
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int fea_dim,
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const std::vector<std::string>& var_emb_names);
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// Pull sparse variables from server in async mode
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// Param<in>: scope, table_id, var_names, fea_keys, fea_dim
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// Param<out>: fea_values std::future
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std::future<int32_t> PullSparseVarsAsync(
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const Scope& scope, const uint64_t table_id,
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const std::vector<std::string>& var_names,
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std::vector<uint64_t>* fea_keys,
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std::vector<std::vector<float>>* fea_values, int fea_dim);
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// Pull sparse variables from server in sync mode
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// pull immediately to tensors
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void PullSparseToTensorSync(const uint64_t table_id, int fea_dim,
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uint64_t padding_id, platform::Place place,
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std::vector<const LoDTensor*>* inputs, // NOLINT
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std::vector<LoDTensor*>* outputs); // NOLINT
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// pull dense variables from server in sync mod
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// Param<in>: scope, table_id, var_names
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// Param<out>: void
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void PullDenseVarsSync(const Scope& scope, const uint64_t table_id,
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const std::vector<std::string>& var_names);
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// pull dense variables from server in async mod
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// Param<in>: scope, table_id, var_names
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// Param<out>: pull_dense_status
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void PullDenseVarsAsync(const Scope& scope, const uint64_t table_id,
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const std::vector<std::string>& var_names,
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std::vector<std::future<int32_t>>* pull_dense_status,
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bool in_cpu);
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// push dense parameters(not gradients) to server in sync mode
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void PushDenseParamSync(const Scope& scope, const uint64_t table_id,
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const std::vector<std::string>& var_names);
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void PushDenseVarsAsync(const Scope& scope, const uint64_t table_id,
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const std::vector<std::string>& var_names,
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std::vector<std::future<int32_t>>* push_sparse_status,
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float scale_datanorm, int batch_size);
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// push dense variables to server in sync mode
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void PushDenseVarsSync(Scope* scope, const uint64_t table_id,
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const std::vector<std::string>& var_names);
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void PushSparseVarsAsync(
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const Scope& scope, const uint64_t table_id, const std::string& grad,
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std::vector<std::future<int32_t>>* push_sparse_status);
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// This is specially designed for click/show stats in server
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// Param<in>: scope, table_id, fea_keys, fea_labels, sparse_key_names,
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// sparse_grad_names, batch_size, use_cvm, dump_slot
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// Param<out>: push_values, push_sparse_status
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void PushSparseVarsWithLabelAsync(
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const Scope& scope, const uint64_t table_id,
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const std::vector<uint64_t>& fea_keys,
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const std::vector<float>& fea_labels,
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const std::vector<std::string>& sparse_key_names,
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const std::vector<std::string>& sparse_grad_names, const int emb_dim,
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std::vector<std::vector<float>>* push_values,
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std::vector<std::future<int32_t>>* push_sparse_status,
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const int batch_size, const bool use_cvm, const bool dump_slot,
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std::vector<uint64_t>* sparse_push_keys, const bool no_cvm);
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// Push sparse variables to server in async mode
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void PushSparseFromTensorWithLabelAsync(
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const Scope& scope, const uint64_t table_id, int fea_dim,
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uint64_t padding_id, bool scale_sparse, const std::string& accesor,
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const std::string& click_name, platform::Place place,
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const std::vector<std::string>& input_names,
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std::vector<const LoDTensor*>* inputs, // NOLINT
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std::vector<const LoDTensor*>* outputs); // NOLINT
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// Push sparse variables to server in Async mode
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// Param<In>: scope, table_id, fea_keys, sparse_grad_names
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// Param<Out>: push_values, push_sparse_status
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// init server
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void LoadSparseOnServer(const std::string& path, const std::string& meta,
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uint32_t table_id);
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// init server
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// void InitServer(const std::string& dist_desc,
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// const std::vector<uint64_t>& host_sign_list, int index);
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void InitServer(
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const std::string& dist_desc,
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const std::vector<std::string>& host_sign_list, int index, int trainers,
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const std::vector<framework::ProgramDesc>& server_sub_program = {});
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// init trainer
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void InitWorker(const std::string& dist_desc,
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const std::vector<std::string>& host_sign_list, Scope* scope,
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const RpcCtxMap& send_ctx,
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const std::unordered_map<uint64_t, std::vector<std::string>>&
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dense_varnames,
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const std::map<std::string, std::string>& envs, int node_num,
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int index);
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// stop server
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void StopServer();
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// finalize worker to make worker can be stop
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void FinalizeWorker();
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// run server with ip port
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uint64_t RunServer(const std::string& ip, uint32_t port);
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// get client info
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std::vector<uint64_t> GetClientsInfo();
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// create client to client connection
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void CreateClient2ClientConnection();
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// flush all push requests
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void ClientFlush();
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// barrier with barrier table
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void BarrierWithTable(uint32_t barrier_type);
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void PrintTableStat(const uint64_t table_id);
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// mode = 0, load all feature
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// mode = 1, load delta feature, which means load diff
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void LoadModel(const std::string& path, const int mode);
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// mode = 0, load all feature
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// mode = 1, load delta feature, which means load diff
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void LoadModelOneTable(const uint64_t table_id, const std::string& path,
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const int mode);
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// mode = 0, save all feature
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// mode = 1, save delta feature, which means save diff
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void SaveModel(const std::string& path, const int mode);
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// mode = 0, save all feature
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// mode = 1, save delta feature, which means save diff
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void SaveModelOneTable(const uint64_t table_id, const std::string& path,
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const int mode);
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// recv table from server and save it in LodTensor
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void RecvAndSaveTable(const uint64_t table_id, const std::string& path);
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// clear all models, release their memory
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void ClearModel();
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// clear one table
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void ClearOneTable(const uint64_t table_id);
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// shrink sparse table
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void ShrinkSparseTable(int table_id);
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// shrink dense table
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void ShrinkDenseTable(int table_id, Scope* scope,
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std::vector<std::string> var_list, float decay,
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int emb_dim);
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typedef std::function<int32_t(int, int, const std::string&)> MsgHandlerFunc;
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// register client to client communication
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int RegisterClientToClientMsgHandler(int msg_type, MsgHandlerFunc handler);
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// send client to client message
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std::future<int32_t> SendClientToClientMsg(int msg_type, int to_client_id,
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const std::string& msg);
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// FleetWrapper singleton
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static std::shared_ptr<FleetWrapper> GetInstance() {
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if (NULL == s_instance_) {
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s_instance_.reset(new paddle::distributed::FleetWrapper());
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}
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return s_instance_;
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}
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// this performs better than rand_r, especially large data
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std::default_random_engine& LocalRandomEngine();
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static std::shared_ptr<paddle::distributed::PSCore> pserver_ptr_;
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private:
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static std::shared_ptr<FleetWrapper> s_instance_;
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size_t GetAbsoluteSum(size_t start, size_t end, size_t level,
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const framework::LoD& lod);
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protected:
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static bool is_initialized_;
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std::map<uint64_t, std::vector<paddle::distributed::Region>> _regions;
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bool scale_sparse_gradient_with_batch_size_;
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int32_t sleep_seconds_before_fail_exit_;
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int client2client_request_timeout_ms_;
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int client2client_connect_timeout_ms_;
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int client2client_max_retry_;
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DISABLE_COPY_AND_ASSIGN(FleetWrapper);
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};
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} // end namespace distributed
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} // end namespace paddle
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