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change allreduce lib to rabit library, xgboost now run with rabit

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This commit is contained in:
tqchen
2014-12-20 00:17:09 -08:00
parent 5ae99372d6
commit 8e16cc4617
28 changed files with 105 additions and 1206 deletions
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44
src/learner/learner-inl.hpp
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@@ -10,7 +10,8 @@
#include <utility>
#include <string>
#include <limits>
#include "../sync/sync.h"
// rabit library for synchronization
#include <rabit.h>
#include "./objective.h"
#include "./evaluation.h"
#include "../gbm/gbm.h"
@@ -31,7 +32,6 @@ class BoostLearner {
name_gbm_ = "gbtree";
silent= 0;
prob_buffer_row = 1.0f;
part_load_col = 0;
distributed_mode = 0;
pred_buffer_size = 0;
}
@@ -65,7 +65,7 @@ class BoostLearner {
buffer_size += mats[i]->info.num_row();
num_feature = std::max(num_feature, static_cast<unsigned>(mats[i]->info.num_col()));
}
sync::AllReduce(&num_feature, 1, sync::kMax);
rabit::Allreduce<rabit::op::Max>(&num_feature, 1);
char str_temp[25];
if (num_feature > mparam.num_feature) {
utils::SPrintf(str_temp, sizeof(str_temp), "%u", num_feature);
@@ -103,7 +103,6 @@ class BoostLearner {
utils::Error("%s is invalid value for dsplit, should be row or col", val);
}
}
if (!strcmp(name, "part_load_col")) part_load_col = atoi(val);
if (!strcmp(name, "prob_buffer_row")) {
prob_buffer_row = static_cast<float>(atof(val));
utils::Check(distributed_mode == 0,
@@ -153,7 +152,7 @@ class BoostLearner {
if (gbm_ != NULL) delete gbm_;
this->InitObjGBM();
gbm_->LoadModel(fi);
if (keep_predbuffer && distributed_mode == 2 && sync::GetRank() != 0) {
if (keep_predbuffer && distributed_mode == 2 && rabit::GetRank() != 0) {
gbm_->ResetPredBuffer(pred_buffer_size);
}
}
@@ -188,38 +187,7 @@ class BoostLearner {
*/
inline void CheckInit(DMatrix *p_train) {
int ncol = static_cast<int>(p_train->info.info.num_col);
std::vector<bool> enabled(ncol, true);
if (part_load_col != 0) {
std::vector<unsigned> col_index;
for (int i = 0; i < ncol; ++i) {
col_index.push_back(i);
}
random::Shuffle(col_index);
std::string s_model;
utils::MemoryBufferStream ms(&s_model);
utils::IStream &fs = ms;
if (sync::GetRank() == 0) {
fs.Write(col_index);
sync::Bcast(&s_model, 0);
} else {
sync::Bcast(&s_model, 0);
fs.Read(&col_index);
}
int nsize = sync::GetWorldSize();
int step = (ncol + nsize -1) / nsize;
int pid = sync::GetRank();
std::fill(enabled.begin(), enabled.end(), false);
int start = step * pid;
int end = std::min(step * (pid + 1), ncol);
std::string name = sync::GetProcessorName();
utils::Printf("rank %d of %s idset:", pid, name.c_str());
for (int i = start; i < end; ++i) {
enabled[col_index[i]] = true;
utils::Printf(" %u", col_index[i]);
}
utils::Printf("\n");
}
std::vector<bool> enabled(ncol, true);
// initialize column access
p_train->fmat()->InitColAccess(enabled, prob_buffer_row);
}
@@ -380,8 +348,6 @@ class BoostLearner {
int silent;
// distributed learning mode, if any, 0:none, 1:col, 2:row
int distributed_mode;
// randomly load part of data
int part_load_col;
// cached size of predict buffer
size_t pred_buffer_size;
// maximum buffred row value