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Fix loading GPU pickle with a CPU-only xgboost distribution. #8632

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Jan 4, 2023
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Fix loading GPU pickle with a CPU-only xgboost distribution. #8632

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30 changes: 22 additions & 8 deletions src/gbm/gbtree.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -28,6 +28,7 @@
#include "xgboost/logging.h"
#include "xgboost/objective.h"
#include "xgboost/predictor.h"
#include "xgboost/string_view.h"
#include "xgboost/tree_updater.h"

namespace xgboost {
Expand Down Expand Up @@ -394,23 +395,36 @@ void GBTree::LoadConfig(Json const& in) {
tparam_.process_type = TreeProcessType::kDefault;
int32_t const n_gpus = xgboost::common::AllVisibleGPUs();
if (n_gpus == 0 && tparam_.predictor == PredictorType::kGPUPredictor) {
LOG(WARNING)
<< "Loading from a raw memory buffer on CPU only machine. "
"Changing predictor to auto.";
LOG(WARNING) << "Loading from a raw memory buffer on CPU only machine. "
"Changing predictor to auto.";
tparam_.UpdateAllowUnknown(Args{{"predictor", "auto"}});
}

auto msg = StringView{
R"(
Loading from a raw memory buffer (like pickle in Python, RDS in R) on a CPU-only
machine. Consider using `save_model/load_model` instead. See:

https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html

for more details about differences between saving model and serializing.)"};

if (n_gpus == 0 && tparam_.tree_method == TreeMethod::kGPUHist) {
tparam_.UpdateAllowUnknown(Args{{"tree_method", "hist"}});
LOG(WARNING)
<< "Loading from a raw memory buffer on CPU only machine. "
"Changing tree_method to hist.";
LOG(WARNING) << msg << " Changing `tree_method` to `hist`.";
}

auto const& j_updaters = get<Object const>(in["updater"]);
updaters_.clear();

for (auto const& kv : j_updaters) {
std::unique_ptr<TreeUpdater> up(
TreeUpdater::Create(kv.first, ctx_, model_.learner_model_param->task));
auto name = kv.first;
if (n_gpus == 0 && name == "grow_gpu_hist") {
name = "grow_quantile_histmaker";
LOG(WARNING) << "Changing updater from `grow_gpu_hist` to `grow_quantile_histmaker`.";
}
std::unique_ptr<TreeUpdater> up{
TreeUpdater::Create(name, ctx_, model_.learner_model_param->task)};
up->LoadConfig(kv.second);
updaters_.push_back(std::move(up));
}
Expand Down