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gpu_predictor.cu
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gpu_predictor.cu
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/*!
* Copyright 2017-2020 by Contributors
*/
#include <thrust/copy.h>
#include <thrust/device_ptr.h>
#include <thrust/device_vector.h>
#include <thrust/fill.h>
#include <memory>
#include "xgboost/data.h"
#include "xgboost/predictor.h"
#include "xgboost/tree_model.h"
#include "xgboost/tree_updater.h"
#include "xgboost/host_device_vector.h"
#include "../gbm/gbtree_model.h"
#include "../data/ellpack_page.cuh"
#include "../data/device_adapter.cuh"
#include "../common/common.h"
#include "../common/device_helpers.cuh"
namespace xgboost {
namespace predictor {
DMLC_REGISTRY_FILE_TAG(gpu_predictor);
struct SparsePageView {
common::Span<const Entry> d_data;
common::Span<const bst_row_t> d_row_ptr;
XGBOOST_DEVICE SparsePageView(common::Span<const Entry> data,
common::Span<const bst_row_t> row_ptr) :
d_data{data}, d_row_ptr{row_ptr} {}
};
struct SparsePageLoader {
bool use_shared;
common::Span<const bst_row_t> d_row_ptr;
common::Span<const Entry> d_data;
bst_feature_t num_features;
float* smem;
size_t entry_start;
__device__ SparsePageLoader(SparsePageView data, bool use_shared, bst_feature_t num_features,
bst_row_t num_rows, size_t entry_start)
: use_shared(use_shared),
d_row_ptr(data.d_row_ptr),
d_data(data.d_data),
num_features(num_features),
entry_start(entry_start) {
extern __shared__ float _smem[];
smem = _smem;
// Copy instances
if (use_shared) {
bst_uint global_idx = blockDim.x * blockIdx.x + threadIdx.x;
int shared_elements = blockDim.x * num_features;
dh::BlockFill(smem, shared_elements, nanf(""));
__syncthreads();
if (global_idx < num_rows) {
bst_uint elem_begin = d_row_ptr[global_idx];
bst_uint elem_end = d_row_ptr[global_idx + 1];
for (bst_uint elem_idx = elem_begin; elem_idx < elem_end; elem_idx++) {
Entry elem = d_data[elem_idx - entry_start];
smem[threadIdx.x * num_features + elem.index] = elem.fvalue;
}
}
__syncthreads();
}
}
__device__ float GetFvalue(int ridx, int fidx) const {
if (use_shared) {
return smem[threadIdx.x * num_features + fidx];
} else {
// Binary search
auto begin_ptr = d_data.begin() + (d_row_ptr[ridx] - entry_start);
auto end_ptr = d_data.begin() + (d_row_ptr[ridx + 1] - entry_start);
common::Span<const Entry>::iterator previous_middle;
while (end_ptr != begin_ptr) {
auto middle = begin_ptr + (end_ptr - begin_ptr) / 2;
if (middle == previous_middle) {
break;
} else {
previous_middle = middle;
}
if (middle->index == fidx) {
return middle->fvalue;
} else if (middle->index < fidx) {
begin_ptr = middle;
} else {
end_ptr = middle;
}
}
// Value is missing
return nanf("");
}
}
};
struct EllpackLoader {
EllpackDeviceAccessor const& matrix;
XGBOOST_DEVICE EllpackLoader(EllpackDeviceAccessor const& m, bool use_shared,
bst_feature_t num_features, bst_row_t num_rows,
size_t entry_start)
: matrix{m} {}
__device__ __forceinline__ float GetFvalue(int ridx, int fidx) const {
auto gidx = matrix.GetBinIndex(ridx, fidx);
if (gidx == -1) {
return nan("");
}
// The gradient index needs to be shifted by one as min values are not included in the
// cuts.
if (gidx == matrix.feature_segments[fidx]) {
return matrix.min_fvalue[fidx];
}
return matrix.gidx_fvalue_map[gidx - 1];
}
};
template <typename Batch>
struct DeviceAdapterLoader {
Batch batch;
bst_feature_t columns;
float* smem;
bool use_shared;
using BatchT = Batch;
DEV_INLINE DeviceAdapterLoader(Batch const batch, bool use_shared,
bst_feature_t num_features, bst_row_t num_rows,
size_t entry_start) :
batch{batch},
columns{num_features},
use_shared{use_shared} {
extern __shared__ float _smem[];
smem = _smem;
if (use_shared) {
uint32_t global_idx = blockDim.x * blockIdx.x + threadIdx.x;
size_t shared_elements = blockDim.x * num_features;
dh::BlockFill(smem, shared_elements, nanf(""));
__syncthreads();
if (global_idx < num_rows) {
auto beg = global_idx * columns;
auto end = (global_idx + 1) * columns;
for (size_t i = beg; i < end; ++i) {
smem[threadIdx.x * num_features + (i - beg)] = batch.GetElement(i).value;
}
}
}
__syncthreads();
}
DEV_INLINE float GetFvalue(bst_row_t ridx, bst_feature_t fidx) const {
if (use_shared) {
return smem[threadIdx.x * columns + fidx];
}
return batch.GetElement(ridx * columns + fidx).value;
}
};
template <typename Loader>
__device__ float GetLeafWeight(bst_uint ridx, const RegTree::Node* tree,
Loader* loader) {
RegTree::Node n = tree[0];
while (!n.IsLeaf()) {
float fvalue = loader->GetFvalue(ridx, n.SplitIndex());
// Missing value
if (isnan(fvalue)) {
n = tree[n.DefaultChild()];
} else {
if (fvalue < n.SplitCond()) {
n = tree[n.LeftChild()];
} else {
n = tree[n.RightChild()];
}
}
}
return n.LeafValue();
}
template <typename Loader, typename Data>
__global__ void PredictKernel(Data data,
common::Span<const RegTree::Node> d_nodes,
common::Span<float> d_out_predictions,
common::Span<size_t> d_tree_segments,
common::Span<int> d_tree_group,
size_t tree_begin, size_t tree_end, size_t num_features,
size_t num_rows, size_t entry_start,
bool use_shared, int num_group) {
bst_uint global_idx = blockDim.x * blockIdx.x + threadIdx.x;
Loader loader(data, use_shared, num_features, num_rows, entry_start);
if (global_idx >= num_rows) return;
if (num_group == 1) {
float sum = 0;
for (int tree_idx = tree_begin; tree_idx < tree_end; tree_idx++) {
const RegTree::Node* d_tree =
&d_nodes[d_tree_segments[tree_idx - tree_begin]];
float leaf = GetLeafWeight(global_idx, d_tree, &loader);
sum += leaf;
}
d_out_predictions[global_idx] += sum;
} else {
for (int tree_idx = tree_begin; tree_idx < tree_end; tree_idx++) {
int tree_group = d_tree_group[tree_idx];
const RegTree::Node* d_tree =
&d_nodes[d_tree_segments[tree_idx - tree_begin]];
bst_uint out_prediction_idx = global_idx * num_group + tree_group;
d_out_predictions[out_prediction_idx] +=
GetLeafWeight(global_idx, d_tree, &loader);
}
}
}
class DeviceModel {
public:
dh::device_vector<RegTree::Node> nodes;
dh::device_vector<size_t> tree_segments;
dh::device_vector<int> tree_group;
size_t tree_beg_; // NOLINT
size_t tree_end_; // NOLINT
int num_group;
void CopyModel(const gbm::GBTreeModel& model,
const thrust::host_vector<size_t>& h_tree_segments,
const thrust::host_vector<RegTree::Node>& h_nodes,
size_t tree_begin, size_t tree_end) {
nodes.resize(h_nodes.size());
dh::safe_cuda(cudaMemcpyAsync(nodes.data().get(), h_nodes.data(),
sizeof(RegTree::Node) * h_nodes.size(),
cudaMemcpyHostToDevice));
tree_segments.resize(h_tree_segments.size());
dh::safe_cuda(cudaMemcpyAsync(tree_segments.data().get(), h_tree_segments.data(),
sizeof(size_t) * h_tree_segments.size(),
cudaMemcpyHostToDevice));
tree_group.resize(model.tree_info.size());
dh::safe_cuda(cudaMemcpyAsync(tree_group.data().get(), model.tree_info.data(),
sizeof(int) * model.tree_info.size(),
cudaMemcpyHostToDevice));
this->tree_beg_ = tree_begin;
this->tree_end_ = tree_end;
this->num_group = model.learner_model_param->num_output_group;
}
void Init(const gbm::GBTreeModel& model, size_t tree_begin, size_t tree_end, int32_t gpu_id) {
dh::safe_cuda(cudaSetDevice(gpu_id));
CHECK_EQ(model.param.size_leaf_vector, 0);
// Copy decision trees to device
thrust::host_vector<size_t> h_tree_segments{};
h_tree_segments.reserve((tree_end - tree_begin) + 1);
size_t sum = 0;
h_tree_segments.push_back(sum);
for (auto tree_idx = tree_begin; tree_idx < tree_end; tree_idx++) {
sum += model.trees.at(tree_idx)->GetNodes().size();
h_tree_segments.push_back(sum);
}
thrust::host_vector<RegTree::Node> h_nodes(h_tree_segments.back());
for (auto tree_idx = tree_begin; tree_idx < tree_end; tree_idx++) {
auto& src_nodes = model.trees.at(tree_idx)->GetNodes();
std::copy(src_nodes.begin(), src_nodes.end(),
h_nodes.begin() + h_tree_segments[tree_idx - tree_begin]);
}
CopyModel(model, h_tree_segments, h_nodes, tree_begin, tree_end);
}
};
class GPUPredictor : public xgboost::Predictor {
private:
void PredictInternal(const SparsePage& batch, size_t num_features,
HostDeviceVector<bst_float>* predictions,
size_t batch_offset) {
batch.offset.SetDevice(generic_param_->gpu_id);
batch.data.SetDevice(generic_param_->gpu_id);
const uint32_t BLOCK_THREADS = 128;
size_t num_rows = batch.Size();
auto GRID_SIZE = static_cast<uint32_t>(common::DivRoundUp(num_rows, BLOCK_THREADS));
auto shared_memory_bytes =
static_cast<size_t>(sizeof(float) * num_features * BLOCK_THREADS);
bool use_shared = true;
if (shared_memory_bytes > max_shared_memory_bytes_) {
shared_memory_bytes = 0;
use_shared = false;
}
size_t entry_start = 0;
SparsePageView data{batch.data.DeviceSpan(), batch.offset.DeviceSpan()};
dh::LaunchKernel {GRID_SIZE, BLOCK_THREADS, shared_memory_bytes} (
PredictKernel<SparsePageLoader, SparsePageView>,
data,
dh::ToSpan(model_.nodes), predictions->DeviceSpan().subspan(batch_offset),
dh::ToSpan(model_.tree_segments), dh::ToSpan(model_.tree_group),
model_.tree_beg_, model_.tree_end_, num_features, num_rows,
entry_start, use_shared, model_.num_group);
}
void PredictInternal(EllpackDeviceAccessor const& batch, HostDeviceVector<bst_float>* out_preds,
size_t batch_offset) {
const uint32_t BLOCK_THREADS = 256;
size_t num_rows = batch.n_rows;
auto GRID_SIZE = static_cast<uint32_t>(common::DivRoundUp(num_rows, BLOCK_THREADS));
bool use_shared = false;
size_t entry_start = 0;
dh::LaunchKernel {GRID_SIZE, BLOCK_THREADS} (
PredictKernel<EllpackLoader, EllpackDeviceAccessor>,
batch,
dh::ToSpan(model_.nodes), out_preds->DeviceSpan().subspan(batch_offset),
dh::ToSpan(model_.tree_segments), dh::ToSpan(model_.tree_group),
model_.tree_beg_, model_.tree_end_, batch.NumFeatures(), num_rows,
entry_start, use_shared, model_.num_group);
}
void DevicePredictInternal(DMatrix* dmat, HostDeviceVector<float>* out_preds,
const gbm::GBTreeModel& model, size_t tree_begin,
size_t tree_end) {
dh::safe_cuda(cudaSetDevice(generic_param_->gpu_id));
if (tree_end - tree_begin == 0) {
return;
}
model_.Init(model, tree_begin, tree_end, generic_param_->gpu_id);
out_preds->SetDevice(generic_param_->gpu_id);
if (dmat->PageExists<SparsePage>()) {
size_t batch_offset = 0;
for (auto &batch : dmat->GetBatches<SparsePage>()) {
this->PredictInternal(batch, model.learner_model_param->num_feature,
out_preds, batch_offset);
batch_offset += batch.Size() * model.learner_model_param->num_output_group;
}
} else {
size_t batch_offset = 0;
for (auto const& page : dmat->GetBatches<EllpackPage>()) {
this->PredictInternal(
page.Impl()->GetDeviceAccessor(generic_param_->gpu_id), out_preds,
batch_offset);
batch_offset += page.Impl()->n_rows;
}
}
}
public:
explicit GPUPredictor(GenericParameter const* generic_param) :
Predictor::Predictor{generic_param} {}
~GPUPredictor() override {
if (generic_param_->gpu_id >= 0) {
dh::safe_cuda(cudaSetDevice(generic_param_->gpu_id));
}
}
void PredictBatch(DMatrix* dmat, PredictionCacheEntry* predts,
const gbm::GBTreeModel& model, int tree_begin,
unsigned ntree_limit = 0) override {
// This function is duplicated with CPU predictor PredictBatch, see comments in there.
// FIXME(trivialfis): Remove the duplication.
std::lock_guard<std::mutex> const guard(lock_);
int device = generic_param_->gpu_id;
CHECK_GE(device, 0) << "Set `gpu_id' to positive value for processing GPU data.";
ConfigureDevice(device);
CHECK_EQ(tree_begin, 0);
auto* out_preds = &predts->predictions;
CHECK_GE(predts->version, tree_begin);
if (out_preds->Size() == 0 && dmat->Info().num_row_ != 0) {
CHECK_EQ(predts->version, 0);
}
if (predts->version == 0) {
this->InitOutPredictions(dmat->Info(), out_preds, model);
}
uint32_t const output_groups = model.learner_model_param->num_output_group;
CHECK_NE(output_groups, 0);
uint32_t real_ntree_limit = ntree_limit * output_groups;
if (real_ntree_limit == 0 || real_ntree_limit > model.trees.size()) {
real_ntree_limit = static_cast<uint32_t>(model.trees.size());
}
uint32_t const end_version = (tree_begin + real_ntree_limit) / output_groups;
if (predts->version > end_version) {
CHECK_NE(ntree_limit, 0);
this->InitOutPredictions(dmat->Info(), out_preds, model);
predts->version = 0;
}
uint32_t const beg_version = predts->version;
CHECK_LE(beg_version, end_version);
if (beg_version < end_version) {
this->DevicePredictInternal(dmat, out_preds, model,
beg_version * output_groups,
end_version * output_groups);
}
uint32_t delta = end_version - beg_version;
CHECK_LE(delta, model.trees.size());
predts->Update(delta);
CHECK(out_preds->Size() == output_groups * dmat->Info().num_row_ ||
out_preds->Size() == dmat->Info().num_row_);
}
template <typename Adapter, typename Loader>
void DispatchedInplacePredict(dmlc::any const &x,
const gbm::GBTreeModel &model, float missing,
PredictionCacheEntry *out_preds,
uint32_t tree_begin, uint32_t tree_end) const {
auto max_shared_memory_bytes = dh::MaxSharedMemory(this->generic_param_->gpu_id);
uint32_t const output_groups = model.learner_model_param->num_output_group;
DeviceModel d_model;
d_model.Init(model, tree_begin, tree_end, this->generic_param_->gpu_id);
auto m = dmlc::get<std::shared_ptr<Adapter>>(x);
CHECK_EQ(m->NumColumns(), model.learner_model_param->num_feature)
<< "Number of columns in data must equal to trained model.";
CHECK_EQ(this->generic_param_->gpu_id, m->DeviceIdx())
<< "XGBoost is running on device: " << this->generic_param_->gpu_id << ", "
<< "but data is on: " << m->DeviceIdx();
MetaInfo info;
info.num_col_ = m->NumColumns();
info.num_row_ = m->NumRows();
this->InitOutPredictions(info, &(out_preds->predictions), model);
const uint32_t BLOCK_THREADS = 128;
auto GRID_SIZE = static_cast<uint32_t>(common::DivRoundUp(info.num_row_, BLOCK_THREADS));
auto shared_memory_bytes =
static_cast<size_t>(sizeof(float) * m->NumColumns() * BLOCK_THREADS);
bool use_shared = true;
if (shared_memory_bytes > max_shared_memory_bytes) {
shared_memory_bytes = 0;
use_shared = false;
}
size_t entry_start = 0;
dh::LaunchKernel {GRID_SIZE, BLOCK_THREADS, shared_memory_bytes} (
PredictKernel<Loader, typename Loader::BatchT>,
m->Value(),
dh::ToSpan(d_model.nodes), out_preds->predictions.DeviceSpan(),
dh::ToSpan(d_model.tree_segments), dh::ToSpan(d_model.tree_group),
tree_begin, tree_end, m->NumColumns(), info.num_row_,
entry_start, use_shared, output_groups);
}
void InplacePredict(dmlc::any const &x, const gbm::GBTreeModel &model,
float missing, PredictionCacheEntry *out_preds,
uint32_t tree_begin, unsigned tree_end) const override {
if (x.type() == typeid(std::shared_ptr<data::CupyAdapter>)) {
this->DispatchedInplacePredict<
data::CupyAdapter, DeviceAdapterLoader<data::CupyAdapterBatch>>(
x, model, missing, out_preds, tree_begin, tree_end);
} else if (x.type() == typeid(std::shared_ptr<data::CudfAdapter>)) {
this->DispatchedInplacePredict<
data::CudfAdapter, DeviceAdapterLoader<data::CudfAdapterBatch>>(
x, model, missing, out_preds, tree_begin, tree_end);
} else {
LOG(FATAL) << "Only CuPy and CuDF are supported by GPU Predictor.";
}
}
protected:
void InitOutPredictions(const MetaInfo& info,
HostDeviceVector<bst_float>* out_preds,
const gbm::GBTreeModel& model) const {
size_t n_classes = model.learner_model_param->num_output_group;
size_t n = n_classes * info.num_row_;
const HostDeviceVector<bst_float>& base_margin = info.base_margin_;
out_preds->SetDevice(generic_param_->gpu_id);
out_preds->Resize(n);
if (base_margin.Size() != 0) {
CHECK_EQ(base_margin.Size(), n);
out_preds->Copy(base_margin);
} else {
out_preds->Fill(model.learner_model_param->base_score);
}
}
void PredictInstance(const SparsePage::Inst& inst,
std::vector<bst_float>* out_preds,
const gbm::GBTreeModel& model, unsigned ntree_limit) override {
LOG(FATAL) << "[Internal error]: " << __func__
<< " is not implemented in GPU Predictor.";
}
void PredictLeaf(DMatrix* p_fmat, std::vector<bst_float>* out_preds,
const gbm::GBTreeModel& model,
unsigned ntree_limit) override {
LOG(FATAL) << "[Internal error]: " << __func__
<< " is not implemented in GPU Predictor.";
}
void PredictContribution(DMatrix* p_fmat,
std::vector<bst_float>* out_contribs,
const gbm::GBTreeModel& model, unsigned ntree_limit,
std::vector<bst_float>* tree_weights,
bool approximate, int condition,
unsigned condition_feature) override {
LOG(FATAL) << "[Internal error]: " << __func__
<< " is not implemented in GPU Predictor.";
}
void PredictInteractionContributions(DMatrix* p_fmat,
std::vector<bst_float>* out_contribs,
const gbm::GBTreeModel& model,
unsigned ntree_limit,
std::vector<bst_float>* tree_weights,
bool approximate) override {
LOG(FATAL) << "[Internal error]: " << __func__
<< " is not implemented in GPU Predictor.";
}
void Configure(const std::vector<std::pair<std::string, std::string>>& cfg) override {
Predictor::Configure(cfg);
}
private:
/*! \brief Reconfigure the device when GPU is changed. */
void ConfigureDevice(int device) {
if (device >= 0) {
max_shared_memory_bytes_ = dh::MaxSharedMemory(device);
}
}
std::mutex lock_;
DeviceModel model_;
size_t max_shared_memory_bytes_;
};
XGBOOST_REGISTER_PREDICTOR(GPUPredictor, "gpu_predictor")
.describe("Make predictions using GPU.")
.set_body([](GenericParameter const* generic_param) {
return new GPUPredictor(generic_param);
});
} // namespace predictor
} // namespace xgboost