fix code style

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

@@ -23,6 +23,10 @@ public:
     // 执行训练
     virtual int run(::paddle::framework::Scope* scope) = 0;
 
+    // cost time millisecond
+    virtual uint64_t epoch_cost() const {
+        return 0;
+    }
 };
 REGIST_REGISTERER(Executor);
 

paddle/fluid/train/custom_trainer/feed/monitor/auc_monitor.cc

@@ -13,11 +13,8 @@ int AucMonitor::initialize(const YAML::Node& config, std::shared_ptr<TrainerCont
         _table_size = config["table_size"].as<int>();
     }
     set_table_size(_table_size);
-    _compute_interval = 3600;
-    if (config["compute_interval"]) {
-        _compute_interval = config["compute_interval"].as<uint32_t>();
-        CHECK(_compute_interval % 60 == 0);
-    }
+    _compute_interval = config["compute_interval"].as<uint32_t>();
+    CHECK(_compute_interval % 60 == 0);
     return 0;
 }
 
@@ -36,7 +33,6 @@ bool AucMonitor::need_compute_result(int epoch_id) {
     uint64_t epoch_time = _epoch_accessor->epoch_timestamp(epoch_id);
     return epoch_time % _compute_interval == 0;
 }
-
 void AucMonitor::compute_result() {
     auto* environment = Monitor::_context_ptr->environment.get();
     double* table[2] = {&_table[0][0], &_table[1][0]};
@@ -59,7 +55,6 @@ void AucMonitor::compute_result() {
         ReduceOperator::SUM, EnvironmentRole::WORKER) / (fp + tp);
     _rmse = sqrt(environment->all_reduce(_local_sqrerr,
         ReduceOperator::SUM, EnvironmentRole::WORKER) / (fp + tp));
-    _rmse = sqrt(_rmse / (fp + tp));
     _actual_ctr = tp / (fp + tp);
     _predicted_ctr = environment->all_reduce(_local_pred,
         ReduceOperator::SUM, EnvironmentRole::WORKER) / (fp + tp);
@@ -72,8 +67,7 @@ std::string AucMonitor::format_result() {
     if (fabs(_predicted_ctr) > 1e-6) {
         copc = _actual_ctr / _predicted_ctr;
     }
-    char buf[10240];
-    snprintf(buf, 10240 * sizeof(char), "AUC=%.6f BUCKET_ERROR=%.6f MAE=%.6f RMSE=%.6f "
+    return paddle::string::format_string("AUC=%.6f BUCKET_ERROR=%.6f MAE=%.6f RMSE=%.6f "
         "Actual CTR=%.6f Predicted CTR=%.6f COPC=%.6f INS Count=%.0f",
         _auc,
         _bucket_error,
@@ -83,8 +77,6 @@ std::string AucMonitor::format_result() {
         _predicted_ctr, 
         copc,
         _size);
-
-    return std::string(buf);
 }
 
 void AucMonitor::add_unlocked(double pred, int label) {

paddle/fluid/train/custom_trainer/feed/monitor/cost_monitor.cc

+#include "paddle/fluid/train/custom_trainer/feed/monitor/cost_monitor.h"
+
+namespace paddle {
+namespace custom_trainer {
+namespace feed {
+
+int CostMonitor::initialize(const YAML::Node& config, std::shared_ptr<TrainerContext> context_ptr) {
+    Monitor::initialize(config, context_ptr);
+    if (config["compute_interval"]) {
+        _compute_interval = config["compute_interval"].as<uint32_t>();
+    }
+}
+
+void CostMonitor::add_data(int epoch_id,
+        const MultiThreadExecutor* executor,
+        SampleInstance* samples, size_t num) {
+    CHECK(executor != nullptr);
+    //TODO use paddle time
+    _total_time_ms += 1;
+    _total_cnt ++;
+}
+
+bool CostMonitor::need_compute_result(int epoch_id) {
+    uint64_t epoch_time = _epoch_accessor->epoch_timestamp(epoch_id);
+    return epoch_time % _compute_interval == 0;
+}
+
+}  // namespace feed
+}  // namespace custom_trainer
+}  // namespace paddle

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

+#pragma once
+#include <string>
+#include <cmath> //std::lround
+#include "paddle/fluid/train/custom_trainer/feed/monitor/monitor.h"
+
+namespace paddle {
+namespace custom_trainer {
+namespace feed {
+
+// cost time profile
+class CostMonitor : public Monitor {
+public:
+    CostMonitor() : _total_time_ms(0), _total_cnt(0), _avg_time_ms(0), _compute_interval(0) {}
+    virtual ~CostMonitor() {}
+
+    virtual int initialize(const YAML::Node& config,
+        std::shared_ptr<TrainerContext> context_ptr) override;
+
+    //添加一项记录，统计内容Monitor自行从Executor按需获取
+    virtual void add_data(int epoch_id, 
+            const MultiThreadExecutor* executor, 
+            SampleInstance* samples, 
+            size_t num);
+    
+    //是否开始结果统计
+    virtual bool need_compute_result(int epoch_id);
+    //统计当前结果
+    virtual void compute_result() {
+        CHECK(_total_cnt != 0);
+        _avg_time_ms = _total_time_ms / _total_cnt;
+    }
+    //基于现有结果，输出格式化的统计信息
+    virtual std::string format_result() {
+        return paddle::string::format_string(
+                "Monitor %s: Cost Time=%lu", Monitor::_name.c_str(), _avg_time_ms);
+    }
+    
+    virtual void reset() {
+        _total_time_ms = 0;
+        _total_cnt = 0;
+        _avg_time_ms = 0;
+    }
+
+protected:
+    std::string _name;
+
+private:
+    uint64_t _total_time_ms;
+    uint64_t _total_cnt;
+    uint64_t _avg_time_ms;
+    uint32_t  _compute_interval;
+};
+
+}  // namespace feed
+}  // namespace custom_trainer
+}  // namespace paddle