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Dmatrix refactor stage 1 (#3301)

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* Use sparse page as singular CSR matrix representation

* Simplify dmatrix methods

* Reduce statefullness of batch iterators

* BREAKING CHANGE: Remove prob_buffer_row parameter. Users are instead recommended to sample their dataset as a preprocessing step before using XGBoost.
This commit is contained in:
Rory Mitchell
2018-06-07 10:25:58 +12:00
committed by GitHub
parent 286dccb8e8
commit a96039141a
47 changed files with 650 additions and 1036 deletions
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20
src/gbm/gblinear.cc
View File

@@ -86,7 +86,7 @@ class GBLinear : public GradientBooster {
if (!p_fmat->HaveColAccess(false)) {
monitor_.Start("InitColAccess");
std::vector<bool> enabled(p_fmat->Info().num_col_, true);
p_fmat->InitColAccess(enabled, 1.0f, param_.max_row_perbatch, false);
p_fmat->InitColAccess(param_.max_row_perbatch, false);
monitor_.Stop("InitColAccess");
}
@@ -120,7 +120,7 @@ class GBLinear : public GradientBooster {
monitor_.Stop("PredictBatch");
}
// add base margin
void PredictInstance(const SparseBatch::Inst &inst,
void PredictInstance(const SparsePage::Inst &inst,
std::vector<bst_float> *out_preds,
unsigned ntree_limit,
unsigned root_index) override {
@@ -152,15 +152,15 @@ class GBLinear : public GradientBooster {
// make sure contributions is zeroed, we could be reusing a previously allocated one
std::fill(contribs.begin(), contribs.end(), 0);
// start collecting the contributions
dmlc::DataIter<RowBatch>* iter = p_fmat->RowIterator();
auto iter = p_fmat->RowIterator();
iter->BeforeFirst();
while (iter->Next()) {
const RowBatch& batch = iter->Value();
auto batch = iter->Value();
// parallel over local batch
const auto nsize = static_cast<bst_omp_uint>(batch.size);
const auto nsize = static_cast<bst_omp_uint>(batch.Size());
#pragma omp parallel for schedule(static)
for (bst_omp_uint i = 0; i < nsize; ++i) {
const RowBatch::Inst &inst = batch[i];
auto inst = batch[i];
auto row_idx = static_cast<size_t>(batch.base_rowid + i);
// loop over output groups
for (int gid = 0; gid < ngroup; ++gid) {
@@ -203,15 +203,15 @@ class GBLinear : public GradientBooster {
std::vector<bst_float> &preds = *out_preds;
const std::vector<bst_float>& base_margin = p_fmat->Info().base_margin_;
// start collecting the prediction
dmlc::DataIter<RowBatch> *iter = p_fmat->RowIterator();
auto iter = p_fmat->RowIterator();
const int ngroup = model_.param.num_output_group;
preds.resize(p_fmat->Info().num_row_ * ngroup);
while (iter->Next()) {
const RowBatch &batch = iter->Value();
auto batch = iter->Value();
// output convention: nrow * k, where nrow is number of rows
// k is number of group
// parallel over local batch
const auto nsize = static_cast<omp_ulong>(batch.size);
const auto nsize = static_cast<omp_ulong>(batch.Size());
#pragma omp parallel for schedule(static)
for (omp_ulong i = 0; i < nsize; ++i) {
const size_t ridx = batch.base_rowid + i;
@@ -265,7 +265,7 @@ class GBLinear : public GradientBooster {
}
}
inline void Pred(const RowBatch::Inst &inst, bst_float *preds, int gid,
inline void Pred(const SparsePage::Inst &inst, bst_float *preds, int gid,
bst_float base) {
bst_float psum = model_.bias()[gid] + base;
for (bst_uint i = 0; i < inst.length; ++i) {

16
src/gbm/gbtree.cc
View File

@@ -221,7 +221,7 @@ class GBTree : public GradientBooster {
predictor_->PredictBatch(p_fmat, out_preds, model_, 0, ntree_limit);
}
void PredictInstance(const SparseBatch::Inst& inst,
void PredictInstance(const SparsePage::Inst& inst,
std::vector<bst_float>* out_preds,
unsigned ntree_limit,
unsigned root_index) override {
@@ -361,7 +361,7 @@ class Dart : public GBTree {
PredLoopInternal<Dart>(p_fmat, &out_preds->HostVector(), 0, ntree_limit, true);
}
void PredictInstance(const SparseBatch::Inst& inst,
void PredictInstance(const SparsePage::Inst& inst,
std::vector<bst_float>* out_preds,
unsigned ntree_limit,
unsigned root_index) override {
@@ -437,21 +437,21 @@ class Dart : public GBTree {
<< "size_leaf_vector is enforced to 0 so far";
CHECK_EQ(preds.size(), p_fmat->Info().num_row_ * num_group);
// start collecting the prediction
dmlc::DataIter<RowBatch>* iter = p_fmat->RowIterator();
auto iter = p_fmat->RowIterator();
auto* self = static_cast<Derived*>(this);
iter->BeforeFirst();
while (iter->Next()) {
const RowBatch &batch = iter->Value();
auto batch = iter->Value();
// parallel over local batch
constexpr int kUnroll = 8;
const auto nsize = static_cast<bst_omp_uint>(batch.size);
const auto nsize = static_cast<bst_omp_uint>(batch.Size());
const bst_omp_uint rest = nsize % kUnroll;
#pragma omp parallel for schedule(static)
for (bst_omp_uint i = 0; i < nsize - rest; i += kUnroll) {
const int tid = omp_get_thread_num();
RegTree::FVec& feats = thread_temp_[tid];
int64_t ridx[kUnroll];
RowBatch::Inst inst[kUnroll];
SparsePage::Inst inst[kUnroll];
for (int k = 0; k < kUnroll; ++k) {
ridx[k] = static_cast<int64_t>(batch.base_rowid + i + k);
}
@@ -470,7 +470,7 @@ class Dart : public GBTree {
for (bst_omp_uint i = nsize - rest; i < nsize; ++i) {
RegTree::FVec& feats = thread_temp_[0];
const auto ridx = static_cast<int64_t>(batch.base_rowid + i);
const RowBatch::Inst inst = batch[i];
const SparsePage::Inst inst = batch[i];
for (int gid = 0; gid < num_group; ++gid) {
const size_t offset = ridx * num_group + gid;
preds[offset] +=
@@ -497,7 +497,7 @@ class Dart : public GBTree {
}
// predict the leaf scores without dropped trees
inline bst_float PredValue(const RowBatch::Inst &inst,
inline bst_float PredValue(const SparsePage::Inst &inst,
int bst_group,
unsigned root_index,
RegTree::FVec *p_feats,