Merge branch 'master' of ssh://icode.baidu.com:8235/baidu/feed-mlarch/paddle-trainer

Change-Id: If03dc2ea6e1a8b9bcedb9d5c9fa9dbcc44d41396

BCLOUD

@@ -36,6 +36,7 @@ CONFIGS('baidu/third-party/pybind11@v2.2.4@git_branch')
 CONFIGS('baidu/third-party/python@gcc482output@git_branch')
 CONFIGS('baidu/third-party/yaml-cpp@yaml-cpp_0-6-2-0_GEN_PD_BL@git_tag')
 CONFIGS('baidu/third-party/openmpi@openmpi_1-4-5-0-feed_mlarch@git_branch')
+CONFIGS('baidu/feed-mlarch/hopscotch-map@stable')
 CONFIGS('baidu/paddlepaddle/pslib@stable')
 CONFIGS('third-64/gtest@gtest_1-7-0-100_PD_BL')
 HEADERS('paddle/fluid/memory/*.h', '$INC/paddle/fluid/memory/')

paddle/fluid/train/custom_trainer/feed/dataset/data_reader.h

@@ -68,6 +68,16 @@ public:
     std::string data;//样本数据， maybe压缩格式
 };
 
+template <class AR>
+paddle::framework::Archive<AR>& operator>>(paddle::framework::Archive<AR>& ar, DataItem& x) {
+    return ar >> x.id >> x.data;
+}
+
+template <class AR>
+paddle::framework::Archive<AR>& operator<<(paddle::framework::Archive<AR>& ar, const DataItem& x) {
+    return ar << x.id << x.data;
+}
+
 typedef std::shared_ptr<Pipeline<DataItem, SampleInstance>> SampleInstancePipe;
 inline SampleInstancePipe make_sample_instance_channel() {
     return std::make_shared<Pipeline<DataItem, SampleInstance>>();

paddle/fluid/train/custom_trainer/feed/executor/multi_thread_executor.cc

@@ -243,7 +243,7 @@ paddle::framework::Channel<DataItem> MultiThreadExecutor::run(
     for (auto& monitor : _monitors) {
         if (monitor->need_compute_result(epoch_id)) {
             monitor->compute_result();
-            ENVLOG_WORKER_MASTER_NOTICE("[Monitor]%s, monitor:%s, , result:%s",
+            ENVLOG_WORKER_MASTER_NOTICE("[Monitor]%s, monitor:%s, result:%s",
                 _train_exe_name.c_str(), monitor->get_name().c_str(), monitor->format_result().c_str());
             _trainer_context->monitor_ssm << _train_exe_name << ":" << 
                 monitor->get_name() << ":" << monitor->format_result() << ","; 

paddle/fluid/train/custom_trainer/feed/main.cc

@@ -21,6 +21,7 @@ int main(int argc, char* argv[]) {
 
     //load trainer config
     auto trainer_context_ptr = std::make_shared<TrainerContext>();
+    trainer_context_ptr->cache_dict.reset(new SignCacheDict);
     trainer_context_ptr->trainer_config = YAML::LoadFile(FLAGS_feed_trainer_conf_path);    
 
     //environment

paddle/fluid/train/custom_trainer/feed/process/learner_process.cc

@@ -16,6 +16,8 @@ namespace feed {
 int LearnerProcess::initialize(std::shared_ptr<TrainerContext> context_ptr) {
     int ret = Process::initialize(context_ptr);
     auto& config = _context_ptr->trainer_config;
+    _is_dump_cache_model = config["dump_cache_model"].as<bool>(false);
+    _cache_load_converter = config["load_cache_converter"].as<std::string>("");
     _startup_dump_inference_base = config["startup_dump_inference_base"].as<bool>(false);
     if (config["executor"]) {
         _executors.resize(config["executor"].size());
@@ -27,6 +29,53 @@ int LearnerProcess::initialize(std::shared_ptr<TrainerContext> context_ptr) {
     return 0;
 }
 
+// 更新各节点存储的CacheModel
+int LearnerProcess::update_cache_model(uint64_t epoch_id, ModelSaveWay way) {
+    auto fs = _context_ptr->file_system;
+    auto* ps_client = _context_ptr->pslib->ps_client();
+    auto* environment = _context_ptr->environment.get();
+    auto* epoch_accessor = _context_ptr->epoch_accessor.get();
+    if (!epoch_accessor->need_save_model(epoch_id, way)) {
+        return 0;
+    }
+    auto* ps_param = _context_ptr->pslib->get_param();
+    if (_is_dump_cache_model && way == ModelSaveWay::ModelSaveInferenceBase) {
+        auto model_dir = epoch_accessor->model_save_path(epoch_id, way);
+        auto& table_param = ps_param->server_param().downpour_server_param().downpour_table_param();
+        for (auto& param : table_param) {
+            if (param.type() != paddle::PS_SPARSE_TABLE) {
+                continue;
+            }
+            auto cache_model_path = fs->path_join(
+                model_dir, string::format_string("%03d_cache/", param.table_id()));
+            if (!fs->exists(cache_model_path)) {
+                continue;
+            }
+            auto& cache_dict = *(_context_ptr->cache_dict.get());
+            cache_dict.clear();
+            cache_dict.reserve(_cache_sign_max_num);
+            auto cache_file_list = fs->list(fs->path_join(cache_model_path, "part*"));
+            for (auto& cache_path : cache_file_list) {
+                auto cache_file = fs->open_read(cache_path, _cache_load_converter);
+                char *buffer = nullptr;
+                size_t buffer_size = 0;
+                ssize_t line_len = 0;
+                while ((line_len = getline(&buffer, &buffer_size, cache_file.get())) != -1) {
+                    if (line_len <= 1) {
+                        continue;
+                    }
+                    char* data_ptr = NULL;
+                    cache_dict.append(strtoul(buffer, &data_ptr, 10));
+                }
+                if (buffer != nullptr) {
+                    free(buffer);
+                } 
+            }
+            break;
+        }
+    }
+    return 0;
+}
 int LearnerProcess::wait_save_model(uint64_t epoch_id, ModelSaveWay way, bool is_force_dump) {
     auto fs = _context_ptr->file_system;
     auto* ps_client = _context_ptr->pslib->ps_client();
@@ -65,7 +114,38 @@ int LearnerProcess::wait_save_model(uint64_t epoch_id, ModelSaveWay way, bool is
     }
     timer.Pause();
     VLOG(2) << "Save Model Cost(s):" << timer.ElapsedSec();
+    
+    // save cache model, 只有inference需要cache_model
+    auto* ps_param = _context_ptr->pslib->get_param();
+    if (_is_dump_cache_model && (way == ModelSaveWay::ModelSaveInferenceBase ||
+        way == ModelSaveWay::ModelSaveInferenceDelta)) {
+        auto& table_param = ps_param->server_param().downpour_server_param().downpour_table_param();
+        for (auto& param : table_param) {
+            if (param.type() != paddle::PS_SPARSE_TABLE) {
+                continue;
+            } 
+            double cache_threshold = 0.0;
+            auto status = ps_client->get_cache_threshold(param.table_id(), cache_threshold);
+            CHECK(status.get() == 0) << "CacheThreshold Get failed!";
+            status = ps_client->cache_shuffle(param.table_id(), model_dir, std::to_string((int)way),
+                std::to_string(cache_threshold));
+            CHECK(status.get() == 0) << "Cache Shuffler Failed";
+            status = ps_client->save_cache(param.table_id(), model_dir, std::to_string((int)way));
+            auto feature_size = status.get();
+            CHECK(feature_size >= 0) << "Cache Save Failed";
+            auto cache_model_path = fs->path_join(
+                model_dir, string::format_string("%03d_cache/sparse_cache.meta", param.table_id()));
+            auto cache_meta_file = fs->open_write(cache_model_path, "");
+            auto meta = string::format_string("file_prefix:part\npart_num:%d\nkey_num:%d\n", 
+                param.sparse_table_cache_file_num(), feature_size);
+            CHECK(fwrite(meta.c_str(), meta.size(), 1, cache_meta_file.get()) == 1) << "Cache Meta Failed";
+            if (feature_size > _cache_sign_max_num) {
+                _cache_sign_max_num = feature_size;
+            }
+        }
+    }
     _context_ptr->epoch_accessor->update_model_donefile(epoch_id, way);
+
     return all_ret;
 }
 
@@ -176,6 +256,9 @@ int LearnerProcess::run() {
         {
             wait_save_model(epoch_id, ModelSaveWay::ModelSaveInferenceBase);
             environment->barrier(EnvironmentRole::WORKER); 
+            update_cache_model(epoch_id, ModelSaveWay::ModelSaveInferenceBase);
+            environment->barrier(EnvironmentRole::WORKER); 
+
             if (epoch_accessor->is_last_epoch(epoch_id)) {
                 wait_save_model(epoch_id, ModelSaveWay::ModelSaveTrainCheckpointBase);
             } else {

paddle/fluid/train/custom_trainer/feed/process/learner_process.h

@@ -22,9 +22,13 @@ protected:
 virtual int load_model(uint64_t epoch_id);
 // 同步保存所有模型, is_force_dump:不判断dump条件,强制dump出模型
 virtual int wait_save_model(uint64_t epoch_id, ModelSaveWay way, bool is_force_dump = false);
+virtual int update_cache_model(uint64_t epoch_id, ModelSaveWay way);
 
 private:
-    bool _startup_dump_inference_base;  //启动立即dump base
+    bool _is_dump_cache_model;          // 是否进行cache dump
+    uint32_t _cache_sign_max_num = 0;   // cache sign最大个数
+    std::string _cache_load_converter;  // cache加载的前置转换脚本
+    bool _startup_dump_inference_base;  // 启动立即dump base
     std::vector<std::shared_ptr<MultiThreadExecutor>> _executors;
 };
 

paddle/fluid/train/custom_trainer/feed/shuffler/shuffler.cc

 #include "paddle/fluid/framework/archive.h"
 #include "paddle/fluid/train/custom_trainer/feed/trainer_context.h"
 #include "paddle/fluid/train/custom_trainer/feed/shuffler/shuffler.h"
+#include <bthread/butex.h>
 
 namespace paddle {
 namespace custom_trainer {
@@ -40,73 +41,195 @@ public:
         Shuffler::initialize(config, context_ptr);
         _max_concurrent_num = config["max_concurrent_num"].as<int>(4); // 最大并发发送数
         _max_package_size = config["max_package_size"].as<int>(1024);  // 最大包个数，一次发送package个数据
+        _shuffle_data_msg_type = config["shuffle_data_msg_type"].as<int>(3);  // c2c msg type
+        _finish_msg_type = config["finish_msg_type"].as<int>(4);  // c2c msg type
+
+        reset_channel();
+        auto binded = std::bind(&GlobalShuffler::get_client2client_msg, this, std::placeholders::_1, std::placeholders::_2, std::placeholders::_3);
+        _trainer_context->pslib->ps_client()->registe_client2client_msg_handler(_shuffle_data_msg_type,
+            binded);
+        _trainer_context->pslib->ps_client()->registe_client2client_msg_handler(_finish_msg_type,
+            binded);
         return 0;
     }
+
+    // 所有worker必须都调用shuffle，并且shuffler同时只能有一个shuffle任务
     virtual int shuffle(::paddle::framework::Channel<DataItem>& data_channel) {
         uint32_t send_count = 0;
         uint32_t package_size = _max_package_size;
         uint32_t concurrent_num = _max_concurrent_num;
-        uint32_t current_wait_idx = 0;
+        ::paddle::framework::Channel<DataItem> input_channel = ::paddle::framework::MakeChannel<DataItem>(data_channel);
+        data_channel.swap(input_channel);
+        set_channel(data_channel);
+
         auto* environment = _trainer_context->environment.get();
         auto worker_num = environment->node_num(EnvironmentRole::WORKER);
         std::vector<std::vector<std::future<int>>> waits(concurrent_num);
-        std::vector<DataItem> send_buffer(concurrent_num * package_size);
-        std::vector<paddle::framework::BinaryArchive> request_data_buffer(worker_num);
-        while (true) {
-            auto read_size = data_channel->Read(concurrent_num * package_size, &send_buffer[0]);
-            if (read_size == 0) {
-                break;
+        std::vector<DataItem> send_buffer(package_size);
+        std::vector<std::vector<DataItem>> send_buffer_worker(worker_num);
+
+        int status = 0;// >0: finish; =0: running; <0: fail
+        while (status == 0) {
+            // update status
+            // 如果在训练期，则限速shuffle
+            // 如果在wait状态，全速shuffle
+            if (_trainer_context->is_status(TrainerStatus::Training)) {
+                concurrent_num = 1;
+                package_size = _max_concurrent_num / 2;
+            } else {
+                package_size = _max_package_size;
+                concurrent_num = _max_concurrent_num;
             }
-            for (size_t idx = 0; idx < read_size; idx += package_size) {
-                // data shard && seriliaze
-                for (size_t i = 0; i < worker_num; ++i) {
-                    request_data_buffer[i].Clear();
+            for (uint32_t current_wait_idx = 0; status == 0 && current_wait_idx < concurrent_num; ++current_wait_idx) {
+                auto read_size = input_channel->Read(package_size, send_buffer.data());
+                if (read_size == 0) {
+                    status = 1;
+                    break;
                 }
-                for (size_t i = idx; i < package_size && i < read_size; ++i) {
-                    auto worker_idx = _shuffle_key_func(send_buffer[i].id) % worker_num;
-                    // TODO Serialize To Arcive
-                    //request_data_buffer[worker_idx] << send_buffer[i];
+                for (int i = 0; i < worker_num; ++i) {
+                    send_buffer_worker.clear();
                 }
-                std::string data_vec[worker_num];
-                for (size_t i = 0; i < worker_num; ++i) {
-                    auto& buffer = request_data_buffer[i];
-                    data_vec[i].assign(buffer.Buffer(), buffer.Length()); 
+                for (int i = 0; i < read_size; ++i) {
+                    auto worker_idx = _shuffle_key_func(send_buffer[i].id) % worker_num;
+                    send_buffer_worker[worker_idx].push_back(std::move(send_buffer[i]));
                 }
-
-                // wait async done
                 for (auto& wait_s : waits[current_wait_idx]) {
-                    if (!wait_s.valid()) {
+                    if (wait_s.get() != 0) {
+                        LOG(WARNING) << "fail to send shuffle data";
+                        status = -1;
                         break;
                     }
-                    CHECK(wait_s.get() == 0);
                 }
-
-                // send shuffle data
-                for (size_t i = 0; i < worker_num; ++i) {
-                    waits[current_wait_idx][i] = _trainer_context->pslib->ps_client()->send_client2client_msg(3, i * 2, data_vec[i]);
+                if (status != 0) {
+                    break;
+                }
+                waits[current_wait_idx].clear();
+                for (int i = 0; i < worker_num; ++i) {
+                    if (!send_buffer_worker[i].empty()) {
+                        waits[current_wait_idx].push_back(send_shuffle_data(i, send_buffer_worker[i]));
+                    }
                 }
-                
-                // update status
-                // 如果在训练期，则限速shuffle     
-                // 如果在wait状态，全速shuffle
-                if (_trainer_context->is_status(TrainerStatus::Training)) {
-                    concurrent_num = 1;
-                    package_size = _max_concurrent_num / 2;
-                } else {
-                    package_size = _max_package_size;
-                    concurrent_num = _max_concurrent_num;
+            }
+        }
+        for (auto& waits_s : waits) {
+            for (auto& wait_s : waits_s) {
+                if (wait_s.get() != 0) {
+                    LOG(WARNING) << "fail to send shuffle data";
+                    status = -1;
                 }
-                ++current_wait_idx;
-                current_wait_idx = current_wait_idx >= concurrent_num ? 0 : current_wait_idx;
             }
         }
-        return 0;
+        VLOG(5) << "start send finish, worker_num: " << worker_num;
+        waits[0].clear();
+        for (int i = 0; i < worker_num; ++i) {
+             waits[0].push_back(send_finish(i));
+        }
+        VLOG(5) << "wait all finish";
+        for (int i = 0; i < worker_num; ++i) {
+            if (waits[0][i].get() != 0) {
+                LOG(WARNING) << "fail to send finish " << i;
+                status = -1;
+            }
+        }
+        VLOG(5) << "finish shuffler, status: " << status;
+        return status < 0 ? status : 0;
     }
 
 private:
+    /*
+    1. 部分c2c send_shuffle_data先到, 此时channel未设置, 等待wait_channel
+    2. shuffle中调用set_channel, 先reset_wait_num, 再解锁channel
+    3. 当接收到所有worker的finish请求后，先reset_channel, 再同时返回
+    */
+    bool wait_channel() {
+        VLOG(5) << "wait_channel";
+        std::lock_guard<bthread::Mutex> lock(_channel_mutex);
+        return _out_channel != nullptr;
+    }
+    void reset_channel() {
+        VLOG(5) << "reset_channel";
+        _channel_mutex.lock();
+        if (_out_channel != nullptr) {
+            _out_channel->Close();
+        }
+        _out_channel = nullptr;
+    }
+    void reset_wait_num() {
+        _wait_num_mutex.lock();
+        _wait_num = _trainer_context->environment->node_num(EnvironmentRole::WORKER);
+        VLOG(5) << "reset_wait_num: " << _wait_num;
+    }
+    void set_channel(paddle::framework::Channel<DataItem>& channel) {
+        VLOG(5) << "set_channel";
+        // 在节点开始写入channel之前，重置wait_num
+        CHECK(_out_channel == nullptr);
+        _out_channel = channel;
+        reset_wait_num();
+        _channel_mutex.unlock();
+    }
+
+    int32_t finish_write_channel() {
+        int wait_num = --_wait_num;
+        VLOG(5) << "finish_write_channel, wait_num: " << wait_num;
+        // 同步所有worker，在所有写入完成后，c2c_msg返回前，重置channel
+        if (wait_num == 0) {
+            reset_channel();
+            _wait_num_mutex.unlock();
+        } else {
+            std::lock_guard<bthread::Mutex> lock(_wait_num_mutex);
+        }
+        return 0;
+    }
+    int32_t write_to_channel(std::vector<DataItem>&& items) {
+        size_t items_size = items.size();
+        VLOG(5) << "write_to_channel, items_size: " << items_size;
+        return _out_channel->Write(std::move(items)) == items_size ? 0 : -1;
+    }
+
+    int32_t get_client2client_msg(int msg_type, int from_client, const std::string& msg) {
+        // wait channel
+        if (!wait_channel()) {
+            LOG(FATAL) << "out_channel is null";
+            return -1;
+        }
+        VLOG(5) << "get c2c msg, type: " << msg_type << ", from_client: " << from_client << ", msg_size: " << msg.size();
+        if (msg_type == _shuffle_data_msg_type) {
+            paddle::framework::BinaryArchive ar;
+            ar.SetReadBuffer(const_cast<char*>(msg.data()), msg.size(), [](char*){});
+            std::vector<DataItem> items;
+            ar >> items;
+            return write_to_channel(std::move(items));
+        } else if (msg_type == _finish_msg_type) {
+            return finish_write_channel();
+        }
+        LOG(FATAL) << "no such msg type: " << msg_type;
+        return -1;
+    }
+
+    std::future<int32_t> send_shuffle_data(int to_client_id, std::vector<DataItem>& items) {
+        VLOG(5) << "send_shuffle_data, to_client_id: " << to_client_id << ", items_size: " << items.size();
+        paddle::framework::BinaryArchive ar;
+        ar << items;
+        return _trainer_context->pslib->ps_client()->send_client2client_msg(_shuffle_data_msg_type, to_client_id,
+            std::string(ar.Buffer(), ar.Length()));
+    }
+
+    std::future<int32_t> send_finish(int to_client_id) {
+        VLOG(5) << "send_finish, to_client_id: " << to_client_id;
+        static const std::string empty_str;
+        return _trainer_context->pslib->ps_client()->send_client2client_msg(_finish_msg_type, to_client_id, empty_str);
+    }
+
     uint32_t _max_package_size = 0;
     uint32_t _max_concurrent_num  = 0;
+    int _shuffle_data_msg_type = 3;
+    int _finish_msg_type = 4;
     
+    bthread::Mutex _channel_mutex;
+    paddle::framework::Channel<DataItem> _out_channel = nullptr;
+
+    bthread::Mutex _wait_num_mutex;
+    std::atomic<int> _wait_num;
 };
 REGIST_CLASS(Shuffler, GlobalShuffler);
 

paddle/fluid/train/custom_trainer/feed/trainer_context.h

@@ -3,6 +3,7 @@
 #include <memory>
 #include <vector>
 #include <sstream>
+#include <tsl/bhopscotch_map.h>
 #include "paddle/fluid/platform/place.h"
 #include "paddle/fluid/train/custom_trainer/feed/common/yaml_helper.h"
 #include "paddle/fluid/train/custom_trainer/feed/common/pslib_warpper.h"
@@ -35,15 +36,61 @@ enum class TrainerStatus {
     Saving    = 1  // 模型存储状态
 };
 
+const uint32_t SignCacheMaxValueNum = 13;
+struct SignCacheData {
+    SignCacheData() {
+        memset(cache_value, 0, sizeof(float) * SignCacheMaxValueNum);
+    }
+    uint32_t idx;
+    float cache_value[SignCacheMaxValueNum];
+};
 class SignCacheDict {
 public:
-    int32_t sign2index(uint64_t sign) {
-        return -1;
+    inline int32_t sign2index(uint64_t sign) {
+        auto itr = _sign2data_map.find(sign);
+        if (itr == _sign2data_map.end()) {  
+            return -1;
+        } 
+        return itr->second.idx;
+    }
+
+    inline uint64_t index2sign(int32_t index) {
+        if (index >= _sign_list.size()) {
+            return 0;
+        } 
+        return _sign_list[index];
+    }
+
+    inline void reserve(uint32_t size) {
+        _sign_list.reserve(size);
+        _sign2data_map.reserve(size);
+    }
+
+    inline void clear() {
+        _sign_list.clear();
+        _sign2data_map.clear();
+    }
+
+    inline void append(uint64_t sign) {
+        if (_sign2data_map.find(sign) != _sign2data_map.end()) {
+            return;
+        }
+        SignCacheData data;
+        data.idx = _sign_list.size();
+        _sign_list.push_back(sign);
+        _sign2data_map.emplace(sign, std::move(data));
     }
 
-    uint64_t index2sign(int32_t index) {
-        return 0;
+    inline SignCacheData* data(uint64_t sign) {
+        tsl::bhopscotch_pg_map<uint64_t, SignCacheData>::iterator itr = _sign2data_map.find(sign);
+        if (itr == _sign2data_map.end()) {
+            return nullptr;
+        }
+        return const_cast<SignCacheData*>(&(itr->second));
     }
+private:
+    std::vector<uint64_t> _sign_list;
+    tsl::bhopscotch_pg_map<uint64_t, SignCacheData> _sign2data_map;
 };
 
 class TrainerContext {

paddle/fluid/train/custom_trainer/feed/unit_test/test_archive_dataitem.cc

+#include <gtest/gtest.h>
+#include "paddle/fluid/train/custom_trainer/feed/dataset/data_reader.h"
+
+TEST(Archive, DataItem) {
+    paddle::custom_trainer::feed::DataItem item;
+    paddle::custom_trainer::feed::DataItem item2;
+    item.id = "123";
+    item.data = "name";
+
+    paddle::framework::BinaryArchive ar;
+    ar << item;
+    ar >> item2;
+
+    ASSERT_EQ(item.id, item2.id);
+    ASSERT_EQ(item.data, item2.data);
+    item.id += "~";
+    item.data += "~";
+    ASSERT_NE(item.id, item2.id);
+    ASSERT_NE(item.data, item2.data);
+}
\ No newline at end of file