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Fix CPU hist init for sparse dataset. (#4625)

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* Fix CPU hist init for sparse dataset.

* Implement sparse histogram cut.
* Allow empty features.

* Fix windows build, don't use sparse in distributed environment.

* Comments.

* Smaller threshold.

* Fix windows omp.

* Fix msvc lambda capture.

* Fix MSVC macro.

* Fix MSVC initialization list.

* Fix MSVC initialization list x2.

* Preserve categorical feature behavior.

* Rename matrix to sparse cuts.
* Reuse UseGroup.
* Check for categorical data when adding cut.

Co-Authored-By: Philip Hyunsu Cho <chohyu01@cs.washington.edu>

* Sanity check.

* Fix comments.

* Fix comment.
This commit is contained in:
Jiaming Yuan
2019-07-04 19:27:03 -04:00
committed by Philip Hyunsu Cho
parent b7a1f22d24
commit d9a47794a5
33 changed files with 681 additions and 299 deletions
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23
src/common/hist_util.cu
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@@ -3,6 +3,7 @@
*/
#include "./hist_util.h"
#include <xgboost/logging.h>
#include <thrust/copy.h>
#include <thrust/functional.h>
@@ -24,7 +25,7 @@
namespace xgboost {
namespace common {
using WXQSketch = HistCutMatrix::WXQSketch;
using WXQSketch = DenseCuts::WXQSketch;
__global__ void FindCutsK
(WXQSketch::Entry* __restrict__ cuts, const bst_float* __restrict__ data,
@@ -92,7 +93,7 @@ __global__ void UnpackFeaturesK
* across distinct rows.
*/
struct SketchContainer {
std::vector<HistCutMatrix::WXQSketch> sketches_; // NOLINT
std::vector<DenseCuts::WXQSketch> sketches_; // NOLINT
std::vector<std::mutex> col_locks_; // NOLINT
static constexpr int kOmpNumColsParallelizeLimit = 1000;
@@ -300,7 +301,7 @@ struct GPUSketcher {
} else if (n_cuts_cur_[icol] > 0) {
// if more elements than cuts: use binary search on cumulative weights
int block = 256;
FindCutsK<<<dh::DivRoundUp(n_cuts_cur_[icol], block), block>>>
FindCutsK<<<common::DivRoundUp(n_cuts_cur_[icol], block), block>>>
(cuts_d_.data().get() + icol * n_cuts_, fvalues_cur_.data().get(),
weights2_.data().get(), n_unique, n_cuts_cur_[icol]);
dh::safe_cuda(cudaGetLastError()); // NOLINT
@@ -342,8 +343,8 @@ struct GPUSketcher {
dim3 block3(16, 64, 1);
// NOTE: This will typically support ~ 4M features - 64K*64
dim3 grid3(dh::DivRoundUp(batch_nrows, block3.x),
dh::DivRoundUp(num_cols_, block3.y), 1);
dim3 grid3(common::DivRoundUp(batch_nrows, block3.x),
common::DivRoundUp(num_cols_, block3.y), 1);
UnpackFeaturesK<<<grid3, block3>>>
(fvalues_.data().get(), has_weights_ ? feature_weights_.data().get() : nullptr,
row_ptrs_.data().get() + batch_row_begin,
@@ -392,7 +393,7 @@ struct GPUSketcher {
row_ptrs_.resize(n_rows_ + 1);
thrust::copy(offset_vec.data() + row_begin_,
offset_vec.data() + row_end_ + 1, row_ptrs_.begin());
size_t gpu_nbatches = dh::DivRoundUp(n_rows_, gpu_batch_nrows_);
size_t gpu_nbatches = common::DivRoundUp(n_rows_, gpu_batch_nrows_);
for (size_t gpu_batch = 0; gpu_batch < gpu_nbatches; ++gpu_batch) {
SketchBatch(row_batch, info, gpu_batch);
}
@@ -434,7 +435,7 @@ struct GPUSketcher {
/* Builds the sketches on the GPU for the dmatrix and returns the row stride
* for the entire dataset */
size_t Sketch(DMatrix *dmat, HistCutMatrix *hmat) {
size_t Sketch(DMatrix *dmat, DenseCuts *hmat) {
const MetaInfo &info = dmat->Info();
row_stride_ = 0;
@@ -459,9 +460,13 @@ struct GPUSketcher {
size_t DeviceSketch
(const tree::TrainParam &param, const LearnerTrainParam &learner_param, int gpu_batch_nrows,
DMatrix *dmat, HistCutMatrix *hmat) {
DMatrix *dmat, HistogramCuts *hmat) {
GPUSketcher sketcher(param, learner_param, gpu_batch_nrows);
return sketcher.Sketch(dmat, hmat);
// We only need to return the result in HistogramCuts container, so it is safe to
// use a pointer of local HistogramCutsDense
DenseCuts dense_cuts(hmat);
auto res = sketcher.Sketch(dmat, &dense_cuts);
return res;
}
} // namespace common