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[GPU-Plugin] Unify gpu_gpair/bst_gpair. Refactor. (#2477)

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This commit is contained in:
Rory Mitchell
2017-07-01 17:31:13 +12:00
committed by GitHub
parent d535340459
commit 5f1b0bb386
14 changed files with 230 additions and 426 deletions
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17
src/tree/param.h
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@@ -14,11 +14,6 @@
#include <limits>
#include <vector>
#ifdef __NVCC__
#define XGB_DEVICE __host__ __device__
#else
#define XGB_DEVICE
#endif
namespace xgboost {
namespace tree {
@@ -234,7 +229,7 @@ struct TrainParam : public dmlc::Parameter<TrainParam> {
// functions for L1 cost
template <typename T1, typename T2>
XGB_DEVICE inline static T1 ThresholdL1(T1 w, T2 lambda) {
XGBOOST_DEVICE inline static T1 ThresholdL1(T1 w, T2 lambda) {
if (w > +lambda)
return w - lambda;
if (w < -lambda)
@@ -243,18 +238,18 @@ XGB_DEVICE inline static T1 ThresholdL1(T1 w, T2 lambda) {
}
template <typename T>
XGB_DEVICE inline static T Sqr(T a) { return a * a; }
XGBOOST_DEVICE inline static T Sqr(T a) { return a * a; }
// calculate the cost of loss function
template <typename TrainingParams, typename T>
XGB_DEVICE inline T CalcGainGivenWeight(const TrainingParams &p, T sum_grad,
XGBOOST_DEVICE inline T CalcGainGivenWeight(const TrainingParams &p, T sum_grad,
T sum_hess, T w) {
return -(2.0 * sum_grad * w + (sum_hess + p.reg_lambda) * Sqr(w));
}
// calculate the cost of loss function
template <typename TrainingParams, typename T>
XGB_DEVICE inline T CalcGain(const TrainingParams &p, T sum_grad, T sum_hess) {
XGBOOST_DEVICE inline T CalcGain(const TrainingParams &p, T sum_grad, T sum_hess) {
if (sum_hess < p.min_child_weight)
return 0.0;
if (p.max_delta_step == 0.0f) {
@@ -276,7 +271,7 @@ XGB_DEVICE inline T CalcGain(const TrainingParams &p, T sum_grad, T sum_hess) {
}
// calculate cost of loss function with four statistics
template <typename TrainingParams, typename T>
XGB_DEVICE inline T CalcGain(const TrainingParams &p, T sum_grad, T sum_hess,
XGBOOST_DEVICE inline T CalcGain(const TrainingParams &p, T sum_grad, T sum_hess,
T test_grad, T test_hess) {
T w = CalcWeight(sum_grad, sum_hess);
T ret = test_grad * w + 0.5 * (test_hess + p.reg_lambda) * Sqr(w);
@@ -288,7 +283,7 @@ XGB_DEVICE inline T CalcGain(const TrainingParams &p, T sum_grad, T sum_hess,
}
// calculate weight given the statistics
template <typename TrainingParams, typename T>
XGB_DEVICE inline T CalcWeight(const TrainingParams &p, T sum_grad,
XGBOOST_DEVICE inline T CalcWeight(const TrainingParams &p, T sum_grad,
T sum_hess) {
if (sum_hess < p.min_child_weight)
return 0.0;