[CUDA] Add binary objective for cuda_exp

CMakeLists.txt

@@ -398,6 +398,8 @@ endif()
 if(USE_CUDA_EXP)
       src/boosting/cuda/*.cpp
       src/boosting/cuda/*.cu
+      src/objective/cuda/*.cpp
+      src/objective/cuda/*.cu
       src/treelearner/cuda/*.cpp
       src/treelearner/cuda/*.cu
       src/io/cuda/*.cu

include/LightGBM/cuda/cuda_objective_function.hpp

@@ -0,0 +1,27 @@
+/*!
+ * Copyright (c) 2021 Microsoft Corporation. All rights reserved.
+ * Licensed under the MIT License. See LICENSE file in the project root for
+ * license information.
+ */
+
+#ifndef LIGHTGBM_OBJECTIVE_CUDA_CUDA_OBJECTIVE_HPP_
+#define LIGHTGBM_OBJECTIVE_CUDA_CUDA_OBJECTIVE_HPP_
+
+#ifdef USE_CUDA_EXP
+
+#include <LightGBM/cuda/cuda_utils.h>
+#include <LightGBM/objective_function.h>
+#include <LightGBM/meta.h>
+
+namespace LightGBM {
+
+class CUDAObjectiveInterface {
+ public:
+  virtual void ConvertOutputCUDA(const data_size_t /*num_data*/, const double* /*input*/, double* /*output*/) const {}
+};
+
+}  // namespace LightGBM
+
+#endif  // USE_CUDA_EXP
+
+#endif  // LIGHTGBM_OBJECTIVE_CUDA_CUDA_OBJECTIVE_HPP_

include/LightGBM/objective_function.h

@@ -93,6 +93,15 @@ class ObjectiveFunction {
   * \brief Whether boosting is done on CUDA
   */
   virtual bool IsCUDAObjective() const { return false; }
+
+  #ifdef USE_CUDA_EXP
+  /*!
+  * \brief Get output convert function for CUDA version
+  */
+  virtual std::function<void(data_size_t, const double*, double*)> GetCUDAConvertOutputFunc() const {
+    return [] (data_size_t, const double*, double*) {};
+  }
+  #endif  // USE_CUDA_EXP
 };
 
 }  // namespace LightGBM

src/boosting/gbdt.cpp

@@ -607,22 +607,26 @@ void GBDT::UpdateScore(const Tree* tree, const int cur_tree_id) {
   }
 }
 
-std::vector<double> GBDT::EvalOneMetric(const Metric* metric, const double* score) const {
+#ifdef USE_CUDA_EXP
+std::vector<double> GBDT::EvalOneMetric(const Metric* metric, const double* score, const data_size_t num_data) const {
+#else
+std::vector<double> GBDT::EvalOneMetric(const Metric* metric, const double* score, const data_size_t /*num_data*/) const {
+#endif  // USE_CUDA_EXP
   #ifdef USE_CUDA_EXP
   const bool evaluation_on_cuda = metric->IsCUDAMetric();
   if ((boosting_on_gpu_ && evaluation_on_cuda) || (!boosting_on_gpu_ && !evaluation_on_cuda)) {
   #endif  // USE_CUDA_EXP
     return metric->Eval(score, objective_function_);
   #ifdef USE_CUDA_EXP
   } else if (boosting_on_gpu_ && !evaluation_on_cuda) {
-    const size_t total_size = static_cast<size_t>(num_data_) * static_cast<size_t>(num_tree_per_iteration_);
+    const size_t total_size = static_cast<size_t>(num_data) * static_cast<size_t>(num_tree_per_iteration_);
     if (total_size > host_score_.size()) {
       host_score_.resize(total_size, 0.0f);
     }
     CopyFromCUDADeviceToHost<double>(host_score_.data(), score, total_size, __FILE__, __LINE__);
     return metric->Eval(host_score_.data(), objective_function_);
   } else {
-    const size_t total_size = static_cast<size_t>(num_data_) * static_cast<size_t>(num_tree_per_iteration_);
+    const size_t total_size = static_cast<size_t>(num_data) * static_cast<size_t>(num_tree_per_iteration_);
     if (total_size > cuda_score_.Size()) {
       cuda_score_.Resize(total_size);
     }
@@ -641,7 +645,7 @@ std::string GBDT::OutputMetric(int iter) {
   if (need_output) {
     for (auto& sub_metric : training_metrics_) {
       auto name = sub_metric->GetName();
-      auto scores = EvalOneMetric(sub_metric, train_score_updater_->score());
+      auto scores = EvalOneMetric(sub_metric, train_score_updater_->score(), train_score_updater_->num_data());
       for (size_t k = 0; k < name.size(); ++k) {
         std::stringstream tmp_buf;
         tmp_buf << "Iteration:" << iter
@@ -658,7 +662,7 @@ std::string GBDT::OutputMetric(int iter) {
   if (need_output || early_stopping_round_ > 0) {
     for (size_t i = 0; i < valid_metrics_.size(); ++i) {
       for (size_t j = 0; j < valid_metrics_[i].size(); ++j) {
-        auto test_scores = EvalOneMetric(valid_metrics_[i][j], valid_score_updater_[i]->score());
+        auto test_scores = EvalOneMetric(valid_metrics_[i][j], valid_score_updater_[i]->score(), valid_score_updater_[i]->num_data());
         auto name = valid_metrics_[i][j]->GetName();
         for (size_t k = 0; k < name.size(); ++k) {
           std::stringstream tmp_buf;
@@ -698,15 +702,15 @@ std::vector<double> GBDT::GetEvalAt(int data_idx) const {
   std::vector<double> ret;
   if (data_idx == 0) {
     for (auto& sub_metric : training_metrics_) {
-      auto scores = EvalOneMetric(sub_metric, train_score_updater_->score());
+      auto scores = EvalOneMetric(sub_metric, train_score_updater_->score(), train_score_updater_->num_data());
       for (auto score : scores) {
         ret.push_back(score);
       }
     }
   } else {
     auto used_idx = data_idx - 1;
     for (size_t j = 0; j < valid_metrics_[used_idx].size(); ++j) {
-      auto test_scores = EvalOneMetric(valid_metrics_[used_idx][j], valid_score_updater_[used_idx]->score());
+      auto test_scores = EvalOneMetric(valid_metrics_[used_idx][j], valid_score_updater_[used_idx]->score(), valid_score_updater_[used_idx]->num_data());
       for (auto score : test_scores) {
         ret.push_back(score);
       }
@@ -760,6 +764,14 @@ void GBDT::GetPredictAt(int data_idx, double* out_result, int64_t* out_len) {
     num_data = valid_score_updater_[used_idx]->num_data();
     *out_len = static_cast<int64_t>(num_data) * num_class_;
   }
+  #ifdef USE_CUDA_EXP
+  std::vector<double> host_raw_scores;
+  if (boosting_on_gpu_) {
+    host_raw_scores.resize(static_cast<size_t>(*out_len), 0.0);
+    CopyFromCUDADeviceToHost<double>(host_raw_scores.data(), raw_scores, static_cast<size_t>(*out_len), __FILE__, __LINE__);
+    raw_scores = host_raw_scores.data();
+  }
+  #endif  // USE_CUDA_EXP
   if (objective_function_ != nullptr) {
     #pragma omp parallel for schedule(static)
     for (data_size_t i = 0; i < num_data; ++i) {

src/boosting/gbdt.h

@@ -443,7 +443,7 @@ class GBDT : public GBDTBase {
   * \brief eval results for one metric
 
   */
-  virtual std::vector<double> EvalOneMetric(const Metric* metric, const double* score) const;
+  virtual std::vector<double> EvalOneMetric(const Metric* metric, const double* score, const data_size_t num_data) const;
 
   /*!
   * \brief Print metric result of current iteration

src/objective/binary_objective.hpp

@@ -189,7 +189,7 @@ class BinaryLogloss: public ObjectiveFunction {
 
   data_size_t NumPositiveData() const override { return num_pos_data_; }
 
- private:
+ protected:
   /*! \brief Number of data */
   data_size_t num_data_;
   /*! \brief Number of positive samples */

src/objective/cuda/cuda_binary_objective.cpp

@@ -0,0 +1,82 @@
+/*!
+ * Copyright (c) 2021 Microsoft Corporation. All rights reserved.
+ * Licensed under the MIT License. See LICENSE file in the project root for
+ * license information.
+ */
+
+#ifdef USE_CUDA_EXP
+
+#include "cuda_binary_objective.hpp"
+
+#include <string>
+#include <vector>
+
+namespace LightGBM {
+
+CUDABinaryLogloss::CUDABinaryLogloss(const Config& config):
+BinaryLogloss(config), ova_class_id_(-1) {
+  cuda_label_ = nullptr;
+  cuda_ova_label_ = nullptr;
+  cuda_weights_ = nullptr;
+  cuda_boost_from_score_ = nullptr;
+  cuda_sum_weights_ = nullptr;
+  cuda_label_weights_ = nullptr;
+}
+
+CUDABinaryLogloss::CUDABinaryLogloss(const Config& config, const int ova_class_id):
+BinaryLogloss(config, [ova_class_id](label_t label) { return static_cast<int>(label) == ova_class_id; }), ova_class_id_(ova_class_id) {}
+
+CUDABinaryLogloss::CUDABinaryLogloss(const std::vector<std::string>& strs): BinaryLogloss(strs) {}
+
+CUDABinaryLogloss::~CUDABinaryLogloss() {
+  DeallocateCUDAMemory<label_t>(&cuda_ova_label_, __FILE__, __LINE__);
+  DeallocateCUDAMemory<double>(&cuda_label_weights_, __FILE__, __LINE__);
+  DeallocateCUDAMemory<double>(&cuda_boost_from_score_, __FILE__, __LINE__);
+  DeallocateCUDAMemory<double>(&cuda_sum_weights_, __FILE__, __LINE__);
+}
+
+void CUDABinaryLogloss::Init(const Metadata& metadata, data_size_t num_data) {
+  BinaryLogloss::Init(metadata, num_data);
+  if (ova_class_id_ == -1) {
+    cuda_label_ = metadata.cuda_metadata()->cuda_label();
+    cuda_ova_label_ = nullptr;
+  } else {
+    InitCUDAMemoryFromHostMemory<label_t>(&cuda_ova_label_, metadata.cuda_metadata()->cuda_label(), static_cast<size_t>(num_data), __FILE__, __LINE__);
+    LaunchResetOVACUDALableKernel();
+    cuda_label_ = cuda_ova_label_;
+  }
+  cuda_weights_ = metadata.cuda_metadata()->cuda_weights();
+  AllocateCUDAMemory<double>(&cuda_boost_from_score_, 1, __FILE__, __LINE__);
+  SetCUDAMemory<double>(cuda_boost_from_score_, 0, 1, __FILE__, __LINE__);
+  AllocateCUDAMemory<double>(&cuda_sum_weights_, 1, __FILE__, __LINE__);
+  SetCUDAMemory<double>(cuda_sum_weights_, 0, 1, __FILE__, __LINE__);
+  if (label_weights_[0] != 1.0f || label_weights_[1] != 1.0f) {
+    InitCUDAMemoryFromHostMemory<double>(&cuda_label_weights_, label_weights_, 2, __FILE__, __LINE__);
+  } else {
+    cuda_label_weights_ = nullptr;
+  }
+}
+
+void CUDABinaryLogloss::GetGradients(const double* scores, score_t* gradients, score_t* hessians) const {
+  LaunchGetGradientsKernel(scores, gradients, hessians);
+  SynchronizeCUDADevice(__FILE__, __LINE__);
+}
+
+double CUDABinaryLogloss::BoostFromScore(int) const {
+  LaunchBoostFromScoreKernel();
+  SynchronizeCUDADevice(__FILE__, __LINE__);
+  double boost_from_score = 0.0f;
+  CopyFromCUDADeviceToHost<double>(&boost_from_score, cuda_boost_from_score_, 1, __FILE__, __LINE__);
+  double pavg = 0.0f;
+  CopyFromCUDADeviceToHost<double>(&pavg, cuda_sum_weights_, 1, __FILE__, __LINE__);
+  Log::Info("[%s:%s]: pavg=%f -> initscore=%f",  GetName(), __func__, pavg, boost_from_score);
+  return boost_from_score;
+}
+
+void CUDABinaryLogloss::ConvertOutputCUDA(const data_size_t num_data, const double* input, double* output) const {
+  LaunchConvertOutputCUDAKernel(num_data, input, output);
+}
+
+}  // namespace LightGBM
+
+#endif  // USE_CUDA_EXP