fix shuffler bug

paddle/fluid/train/custom_trainer/feed/common/runtime_environment.cc

@@ -161,7 +161,12 @@ public:
 protected:
     virtual void print_log(EnvironmentRole role, EnvironmentLogType type, 
         EnvironmentLogLevel level,  const std::string& log_str) {
-        if (type == EnvironmentLogType::MASTER_LOG && !is_master_node(role)) {
+        if (type == EnvironmentLogType::MASTER_LOG) {
+            if (is_master_node(role)) {
+                fprintf(stdout, log_str.c_str());
+                fprintf(stdout, "\n");
+                fflush(stdout);
+            }
             return;
         }
         VLOG(static_cast<int>(level)) << log_str;

paddle/fluid/train/custom_trainer/feed/common/runtime_environment.h

@@ -75,8 +75,8 @@ public:
     // 环境定制化log
     template<class... ARGS>
     void log(EnvironmentRole role, EnvironmentLogType type, 
-        EnvironmentLogLevel level, const char* fmt, ARGS && ... args) {
-        print_log(role, type, level, paddle::string::format_string(fmt, args...));
+        EnvironmentLogLevel level, ARGS && ... args) {
+        print_log(role, type, level, paddle::string::format_string(args...));
     }
     // 多线程可调用接口      End
 
@@ -106,14 +106,14 @@ protected:
 };
 REGIST_REGISTERER(RuntimeEnvironment);
 
-#define ENVLOG_WORKER_ALL_NOTICE \
-environment->log(EnvironmentRole::WORKER, EnvironmentLogType::ALL_LOG, EnvironmentLogType::NOTICE, 
-#define ENVLOG_WORKER_MASTER_NOTICE \
-environment->log(EnvironmentRole::WORKER, EnvironmentLogType::MASTER_LOG, EnvironmentLogType::NOTICE, 
-#define ENVLOG_WORKER_ALL_ERROR \
-environment->log(EnvironmentRole::WORKER, EnvironmentLogType::ALL_LOG, EnvironmentLogType::ERROR, 
-#define ENVLOG_WORKER_MASTER_ERROR \
-environment->log(EnvironmentRole::WORKER, EnvironmentLogType::MASTER_LOG, EnvironmentLogType::ERROR, 
+#define ENVLOG_WORKER_ALL_NOTICE(...) \
+environment->log(EnvironmentRole::WORKER, EnvironmentLogType::ALL_LOG, EnvironmentLogLevel::NOTICE, __VA_ARGS__);
+#define ENVLOG_WORKER_MASTER_NOTICE(...) \
+environment->log(EnvironmentRole::WORKER, EnvironmentLogType::MASTER_LOG, EnvironmentLogLevel::NOTICE, __VA_ARGS__);
+#define ENVLOG_WORKER_ALL_ERROR(...) \
+environment->log(EnvironmentRole::WORKER, EnvironmentLogType::ALL_LOG, EnvironmentLogLevel::ERROR, __VA_ARGS__);
+#define ENVLOG_WORKER_MASTER_ERROR(...) \
+environment->log(EnvironmentRole::WORKER, EnvironmentLogType::MASTER_LOG, EnvironmentLogLevel::ERROR, __VA_ARGS__);
 
 std::string format_timestamp(time_t time, const char* format);
 inline std::string format_timestamp(time_t time, const std::string& format) {

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

@@ -137,7 +137,7 @@ paddle::framework::Channel<DataItem> MultiThreadExecutor::run(
     paddle::framework::Channel<DataItem> input, const DataParser* parser) {
 
     uint64_t epoch_id = _trainer_context->epoch_accessor->current_epoch_id();
-
+    auto* environment = _trainer_context->environment.get();
     // 输入流
     PipelineOptions input_pipe_option;
     input_pipe_option.need_hold_input_data = true;
@@ -243,8 +243,8 @@ paddle::framework::Channel<DataItem> MultiThreadExecutor::run(
     for (auto& monitor : _monitors) {
         if (monitor->need_compute_result(epoch_id)) {
             monitor->compute_result();
-            VLOG(2) << "[Monitor]" << _train_exe_name << ", monitor:" << monitor->get_name()
-                << ", result:" << monitor->format_result();
+            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() << ","; 
             monitor->reset();

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

@@ -5,6 +5,7 @@
 #include "paddle/fluid/train/custom_trainer/feed/trainer_context.h"
 #include "paddle/fluid/train/custom_trainer/feed/executor/executor.h"
 #include "paddle/fluid/train/custom_trainer/feed/dataset/data_reader.h"
+#include "paddle/fluid/train/custom_trainer/feed/common/runtime_environment.h"
 
 namespace paddle {
 namespace custom_trainer {

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

@@ -155,10 +155,11 @@ int LearnerProcess::load_model(uint64_t epoch_id) {
     if (!environment->is_master_node(EnvironmentRole::WORKER)) {
         return 0;
     }
-    VLOG(2) << "Start Load Model";
     auto* fs = _context_ptr->file_system.get();
     std::set<uint32_t> loaded_table_set;
     auto model_dir = _context_ptr->epoch_accessor->checkpoint_path();
+    paddle::platform::Timer timer;
+    timer.Start();
     for (auto& executor : _executors) {
         const auto& table_accessors = executor->table_accessors();
         for (auto& itr : table_accessors) {
@@ -172,6 +173,7 @@ int LearnerProcess::load_model(uint64_t epoch_id) {
                 auto scope = std::move(executor->fetch_scope());
                 CHECK(itr.second[0]->create(scope.get()) == 0);
             } else {
+                ENVLOG_WORKER_MASTER_NOTICE("Loading model %s", model_dir.c_str());
                 auto status = _context_ptr->ps_client()->load(itr.first, 
                     model_dir, std::to_string((int)ModelSaveWay::ModelSaveTrainCheckpoint));
                 CHECK(status.get() == 0) << "table load failed, id:" << itr.first;
@@ -179,7 +181,8 @@ int LearnerProcess::load_model(uint64_t epoch_id) {
             loaded_table_set.insert(itr.first);
         }
     }
-    VLOG(2) << "Finish Load Model";
+    timer.Pause();
+    ENVLOG_WORKER_MASTER_NOTICE("Finished loading model, cost:%f", timer.ElapsedSec());
     return 0;
 }
 
@@ -189,9 +192,7 @@ int LearnerProcess::run() {
     auto* epoch_accessor = _context_ptr->epoch_accessor.get(); 
     uint64_t epoch_id = epoch_accessor->current_epoch_id();
 
-    environment->log(EnvironmentRole::WORKER, EnvironmentLogType::MASTER_LOG, EnvironmentLogLevel::NOTICE, 
-        "Resume train with epoch_id:%d %s", epoch_id, _context_ptr->epoch_accessor->text(epoch_id).c_str());
-    
+    ENVLOG_WORKER_MASTER_NOTICE("Resume train with epoch_id:%d %s", epoch_id, _context_ptr->epoch_accessor->text(epoch_id).c_str());
     //尝试加载模型 or 初始化
     CHECK(load_model(epoch_id) == 0);
     environment->barrier(EnvironmentRole::WORKER); 
@@ -208,19 +209,16 @@ int LearnerProcess::run() {
         std::string epoch_log_title = paddle::string::format_string(
             "train epoch_id:%d label:%s", epoch_id, epoch_accessor->text(epoch_id).c_str());
         std::string data_path = paddle::string::to_string<std::string>(dataset->epoch_data_path(epoch_id));
-        
+        ENVLOG_WORKER_MASTER_NOTICE("    ==== begin %s ====", epoch_accessor->text(epoch_id).c_str());
         //Step1. 等待样本ready
         {
-            environment->log(EnvironmentRole::WORKER, EnvironmentLogType::MASTER_LOG, EnvironmentLogLevel::NOTICE, 
-                "%s, wait data ready:%s", epoch_log_title.c_str(), data_path.c_str());
+            ENVLOG_WORKER_MASTER_NOTICE("      %s, wait data ready:%s", epoch_log_title.c_str(), data_path.c_str());
             while (dataset->epoch_data_status(epoch_id) != DatasetStatus::Ready) {
                 sleep(30);  
                 dataset->pre_detect_data(epoch_id);
-                environment->log(EnvironmentRole::WORKER, EnvironmentLogType::MASTER_LOG, EnvironmentLogLevel::NOTICE, 
-                "data not ready, wait 30s");
+                ENVLOG_WORKER_MASTER_NOTICE("      epoch_id:%d data not ready, wait 30s", epoch_id);
             } 
-            environment->log(EnvironmentRole::WORKER, EnvironmentLogType::MASTER_LOG, EnvironmentLogLevel::NOTICE, 
-                "Start %s, data is ready", epoch_log_title.c_str());
+            ENVLOG_WORKER_MASTER_NOTICE("      Start %s, data is ready", epoch_log_title.c_str());
             environment->barrier(EnvironmentRole::WORKER); 
         }
     
@@ -232,7 +230,7 @@ int LearnerProcess::run() {
                 environment->barrier(EnvironmentRole::WORKER); 
                 paddle::platform::Timer timer;
                 timer.Start();
-                VLOG(2) << "Start executor:" << executor->train_exe_name();
+                ENVLOG_WORKER_MASTER_NOTICE("Start executor:%s", executor->train_exe_name().c_str());
                 auto data_name = executor->train_data_name();
                 paddle::framework::Channel<DataItem> input_channel;
                 if (backup_input_map.count(data_name)) {
@@ -242,7 +240,7 @@ int LearnerProcess::run() {
                 }
                 input_channel = executor->run(input_channel, dataset->data_parser(data_name));
                 timer.Pause();
-                VLOG(2) << "End executor:" << executor->train_exe_name() << ", cost:" << timer.ElapsedSec();
+                ENVLOG_WORKER_MASTER_NOTICE("End executor:%s, cost:%f", executor->train_exe_name().c_str(), timer.ElapsedSec());
 
                 // 等待异步梯度完成
                 _context_ptr->ps_client()->flush();
@@ -273,21 +271,22 @@ int LearnerProcess::run() {
                 environment->is_master_node(EnvironmentRole::WORKER)) {
                 paddle::platform::Timer timer;
                 timer.Start();
-                VLOG(2) << "Start shrink table"; 
+                ENVLOG_WORKER_MASTER_NOTICE("Start shrink table");
                 for (auto& executor : _executors) {
                     const auto& table_accessors = executor->table_accessors();
                     for (auto& itr : table_accessors) {
                         CHECK(itr.second[0]->shrink() == 0);
                     }
                 } 
-                VLOG(2) << "End shrink table, cost:" << timer.ElapsedSec();
+                timer.Pause();
+                ENVLOG_WORKER_MASTER_NOTICE("End shrink table, cost:%f", timer.ElapsedSec());
             }
             environment->barrier(EnvironmentRole::WORKER); 
 
             epoch_accessor->epoch_done(epoch_id);
             environment->barrier(EnvironmentRole::WORKER); 
         }
-    
+        ENVLOG_WORKER_MASTER_NOTICE("    ==== end %s ====", epoch_accessor->text(epoch_id).c_str());
         //Step4. Output Monitor && RunStatus
         //TODO
     }