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Remove single_precision_histogram for gpu_hist (#7828)

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This commit is contained in:
Rory Mitchell
2022-05-03 14:53:19 +02:00
committed by GitHub
parent 50d854e02e
commit 90cce38236
17 changed files with 97 additions and 155 deletions
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140
src/tree/updater_gpu_hist.cu
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@@ -50,12 +50,9 @@ DMLC_REGISTRY_FILE_TAG(updater_gpu_hist);
// training parameters specific to this algorithm
struct GPUHistMakerTrainParam
: public XGBoostParameter<GPUHistMakerTrainParam> {
bool single_precision_histogram;
bool debug_synchronize;
// declare parameters
DMLC_DECLARE_PARAMETER(GPUHistMakerTrainParam) {
DMLC_DECLARE_FIELD(single_precision_histogram).set_default(false).describe(
"Use single precision to build histograms.");
DMLC_DECLARE_FIELD(debug_synchronize).set_default(false).describe(
"Check if all distributed tree are identical after tree construction.");
}
@@ -557,6 +554,13 @@ struct GPUHistMakerDevice {
void ApplySplit(const GPUExpandEntry& candidate, RegTree* p_tree) {
RegTree& tree = *p_tree;
// Sanity check - have we created a leaf with no training instances?
if (!rabit::IsDistributed() && row_partitioner) {
CHECK(row_partitioner->GetRows(candidate.nid).size() > 0)
<< "No training instances in this leaf!";
}
auto parent_sum = candidate.split.left_sum + candidate.split.right_sum;
auto base_weight = candidate.base_weight;
auto left_weight = candidate.left_weight * param.learning_rate;
@@ -702,26 +706,42 @@ struct GPUHistMakerDevice {
}
};
template <typename GradientSumT>
class GPUHistMakerSpecialised {
class GPUHistMaker : public TreeUpdater {
using GradientSumT = GradientPairPrecise;
public:
explicit GPUHistMakerSpecialised(ObjInfo task) : task_{task} {};
void Configure(const Args& args, GenericParameter const* generic_param) {
explicit GPUHistMaker(GenericParameter const* ctx, ObjInfo task)
: TreeUpdater(ctx), task_{task} {};
void Configure(const Args& args) override {
// Used in test to count how many configurations are performed
LOG(DEBUG) << "[GPU Hist]: Configure";
param_.UpdateAllowUnknown(args);
ctx_ = generic_param;
hist_maker_param_.UpdateAllowUnknown(args);
dh::CheckComputeCapability();
initialised_ = false;
monitor_.Init("updater_gpu_hist");
}
~GPUHistMakerSpecialised() { // NOLINT
void LoadConfig(Json const& in) override {
auto const& config = get<Object const>(in);
FromJson(config.at("gpu_hist_train_param"), &this->hist_maker_param_);
initialised_ = false;
FromJson(config.at("train_param"), &param_);
}
void SaveConfig(Json* p_out) const override {
auto& out = *p_out;
out["gpu_hist_train_param"] = ToJson(hist_maker_param_);
out["train_param"] = ToJson(param_);
}
~GPUHistMaker() { // NOLINT
dh::GlobalMemoryLogger().Log();
}
void Update(HostDeviceVector<GradientPair>* gpair, DMatrix* dmat,
common::Span<HostDeviceVector<bst_node_t>> out_position,
const std::vector<RegTree*>& trees) {
const std::vector<RegTree*>& trees) override {
monitor_.Start("Update");
// rescale learning rate according to size of trees
@@ -791,7 +811,7 @@ class GPUHistMakerSpecialised {
}
fs.Seek(0);
rabit::Broadcast(&s_model, 0);
RegTree reference_tree {}; // rank 0 tree
RegTree reference_tree{}; // rank 0 tree
reference_tree.Load(&fs);
CHECK(*local_tree == reference_tree);
}
@@ -806,8 +826,8 @@ class GPUHistMakerSpecialised {
maker->UpdateTree(gpair, p_fmat, task_, p_tree, &reducer_, p_out_position);
}
bool UpdatePredictionCache(const DMatrix *data,
linalg::VectorView<bst_float> p_out_preds) {
bool UpdatePredictionCache(const DMatrix* data,
linalg::VectorView<bst_float> p_out_preds) override {
if (maker == nullptr || p_last_fmat_ == nullptr || p_last_fmat_ != data) {
return false;
}
@@ -817,109 +837,33 @@ class GPUHistMakerSpecialised {
return true;
}
TrainParam param_; // NOLINT
MetaInfo* info_{}; // NOLINT
TrainParam param_; // NOLINT
MetaInfo* info_{}; // NOLINT
std::unique_ptr<GPUHistMakerDevice<GradientSumT>> maker; // NOLINT
char const* Name() const override { return "grow_gpu_hist"; }
private:
bool initialised_ { false };
bool initialised_{false};
GPUHistMakerTrainParam hist_maker_param_;
Context const* ctx_;
dh::AllReducer reducer_;
DMatrix* p_last_fmat_ { nullptr };
DMatrix* p_last_fmat_{nullptr};
RegTree const* p_last_tree_{nullptr};
ObjInfo task_;
common::Monitor monitor_;
};
class GPUHistMaker : public TreeUpdater {
public:
explicit GPUHistMaker(ObjInfo task) : task_{task} {}
void Configure(const Args& args) override {
// Used in test to count how many configurations are performed
LOG(DEBUG) << "[GPU Hist]: Configure";
hist_maker_param_.UpdateAllowUnknown(args);
// The passed in args can be empty, if we simply purge the old maker without
// preserving parameters then we can't do Update on it.
TrainParam param;
if (float_maker_) {
param = float_maker_->param_;
} else if (double_maker_) {
param = double_maker_->param_;
}
if (hist_maker_param_.single_precision_histogram) {
float_maker_.reset(new GPUHistMakerSpecialised<GradientPair>(task_));
float_maker_->param_ = param;
float_maker_->Configure(args, ctx_);
} else {
double_maker_.reset(new GPUHistMakerSpecialised<GradientPairPrecise>(task_));
double_maker_->param_ = param;
double_maker_->Configure(args, ctx_);
}
}
void LoadConfig(Json const& in) override {
auto const& config = get<Object const>(in);
FromJson(config.at("gpu_hist_train_param"), &this->hist_maker_param_);
if (hist_maker_param_.single_precision_histogram) {
float_maker_.reset(new GPUHistMakerSpecialised<GradientPair>(task_));
FromJson(config.at("train_param"), &float_maker_->param_);
} else {
double_maker_.reset(new GPUHistMakerSpecialised<GradientPairPrecise>(task_));
FromJson(config.at("train_param"), &double_maker_->param_);
}
}
void SaveConfig(Json* p_out) const override {
auto& out = *p_out;
out["gpu_hist_train_param"] = ToJson(hist_maker_param_);
if (hist_maker_param_.single_precision_histogram) {
out["train_param"] = ToJson(float_maker_->param_);
} else {
out["train_param"] = ToJson(double_maker_->param_);
}
}
void Update(HostDeviceVector<GradientPair>* gpair, DMatrix* dmat,
common::Span<HostDeviceVector<bst_node_t>> out_position,
const std::vector<RegTree*>& trees) override {
if (hist_maker_param_.single_precision_histogram) {
float_maker_->Update(gpair, dmat, out_position, trees);
} else {
double_maker_->Update(gpair, dmat, out_position, trees);
}
}
bool UpdatePredictionCache(const DMatrix* data,
linalg::VectorView<bst_float> p_out_preds) override {
if (hist_maker_param_.single_precision_histogram) {
return float_maker_->UpdatePredictionCache(data, p_out_preds);
} else {
return double_maker_->UpdatePredictionCache(data, p_out_preds);
}
}
char const* Name() const override {
return "grow_gpu_hist";
}
bool HasNodePosition() const override { return true; }
private:
GPUHistMakerTrainParam hist_maker_param_;
ObjInfo task_;
std::unique_ptr<GPUHistMakerSpecialised<GradientPair>> float_maker_;
std::unique_ptr<GPUHistMakerSpecialised<GradientPairPrecise>> double_maker_;
};
#if !defined(GTEST_TEST)
XGBOOST_REGISTER_TREE_UPDATER(GPUHistMaker, "grow_gpu_hist")
.describe("Grow tree with GPU.")
.set_body([](ObjInfo task) { return new GPUHistMaker(task); });
.set_body([](GenericParameter const* tparam, ObjInfo task) {
return new GPUHistMaker(tparam, task);
});
#endif // !defined(GTEST_TEST)
} // namespace tree