Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Move ellpack page construction into DMatrix (#4833)

Browse Source
This commit is contained in:
Rong Ou
2019-09-16 20:50:55 -07:00
committed by Jiaming Yuan
parent 512f037e55
commit 125bcec62e
17 changed files with 761 additions and 513 deletions
Download Patch File Download Diff File Expand all files Collapse all files

43
src/common/hist_util.cu
View File

@@ -99,15 +99,15 @@ struct SketchContainer {
std::vector<std::mutex> col_locks_; // NOLINT
static constexpr int kOmpNumColsParallelizeLimit = 1000;
SketchContainer(const tree::TrainParam &param, DMatrix *dmat) :
SketchContainer(int max_bin, DMatrix *dmat) :
col_locks_(dmat->Info().num_col_) {
const MetaInfo &info = dmat->Info();
// Initialize Sketches for this dmatrix
sketches_.resize(info.num_col_);
#pragma omp parallel for default(none) shared(info, param) schedule(static) \
#pragma omp parallel for default(none) shared(info, max_bin) schedule(static) \
if (info.num_col_ > kOmpNumColsParallelizeLimit) // NOLINT
for (int icol = 0; icol < info.num_col_; ++icol) { // NOLINT
sketches_[icol].Init(info.num_row_, 1.0 / (8 * param.max_bin));
sketches_[icol].Init(info.num_row_, 1.0 / (8 * max_bin));
}
}
@@ -130,7 +130,7 @@ struct GPUSketcher {
bool has_weights_{false};
size_t row_stride_{0};
tree::TrainParam param_;
const int max_bin_;
SketchContainer *sketch_container_;
dh::device_vector<size_t> row_ptrs_{};
dh::device_vector<Entry> entries_{};
@@ -148,11 +148,11 @@ struct GPUSketcher {
public:
DeviceShard(int device,
bst_uint n_rows,
tree::TrainParam param,
int max_bin,
SketchContainer* sketch_container) :
device_(device),
n_rows_(n_rows),
param_(std::move(param)),
max_bin_(max_bin),
sketch_container_(sketch_container) {
}
@@ -183,7 +183,7 @@ struct GPUSketcher {
}
constexpr int kFactor = 8;
double eps = 1.0 / (kFactor * param_.max_bin);
double eps = 1.0 / (kFactor * max_bin_);
size_t dummy_nlevel;
WXQSketch::LimitSizeLevel(gpu_batch_nrows_, eps, &dummy_nlevel, &n_cuts_);
@@ -362,7 +362,7 @@ struct GPUSketcher {
// add cuts into sketches
thrust::copy(cuts_d_.begin(), cuts_d_.end(), cuts_h_.begin());
#pragma omp parallel for default(none) schedule(static) \
if (num_cols_ > SketchContainer::kOmpNumColsParallelizeLimit) // NOLINT
if (num_cols_ > SketchContainer::kOmpNumColsParallelizeLimit) // NOLINT
for (int icol = 0; icol < num_cols_; ++icol) {
WXQSketch::SummaryContainer summary;
summary.Reserve(n_cuts_);
@@ -403,10 +403,8 @@ struct GPUSketcher {
};
void SketchBatch(const SparsePage &batch, const MetaInfo &info) {
auto device = generic_param_.gpu_id;
// create device shard
shard_.reset(new DeviceShard(device, batch.Size(), param_, sketch_container_.get()));
shard_.reset(new DeviceShard(device_, batch.Size(), max_bin_, sketch_container_.get()));
// compute sketches for the shard
shard_->Init(batch, info, gpu_batch_nrows_);
@@ -417,9 +415,8 @@ struct GPUSketcher {
row_stride_ = shard_->GetRowStride();
}
GPUSketcher(const tree::TrainParam &param, const GenericParameter &generic_param, int gpu_nrows)
: param_(param), generic_param_(generic_param), gpu_batch_nrows_(gpu_nrows), row_stride_(0) {
}
GPUSketcher(int device, int max_bin, int gpu_nrows)
: device_(device), max_bin_(max_bin), gpu_batch_nrows_(gpu_nrows), row_stride_(0) {}
/* Builds the sketches on the GPU for the dmatrix and returns the row stride
* for the entire dataset */
@@ -427,29 +424,31 @@ struct GPUSketcher {
const MetaInfo &info = dmat->Info();
row_stride_ = 0;
sketch_container_.reset(new SketchContainer(param_, dmat));
sketch_container_.reset(new SketchContainer(max_bin_, dmat));
for (const auto &batch : dmat->GetBatches<SparsePage>()) {
this->SketchBatch(batch, info);
}
hmat->Init(&sketch_container_->sketches_, param_.max_bin);
hmat->Init(&sketch_container_->sketches_, max_bin_);
return row_stride_;
}
private:
std::unique_ptr<DeviceShard> shard_;
const tree::TrainParam &param_;
const GenericParameter &generic_param_;
const int device_;
const int max_bin_;
int gpu_batch_nrows_;
size_t row_stride_;
std::unique_ptr<SketchContainer> sketch_container_;
};
size_t DeviceSketch
(const tree::TrainParam &param, const GenericParameter &learner_param, int gpu_batch_nrows,
DMatrix *dmat, HistogramCuts *hmat) {
GPUSketcher sketcher(param, learner_param, gpu_batch_nrows);
size_t DeviceSketch(int device,
int max_bin,
int gpu_batch_nrows,
DMatrix* dmat,
HistogramCuts* hmat) {
GPUSketcher sketcher(device, max_bin, gpu_batch_nrows);
// 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);

9
src/common/hist_util.h
View File

@@ -290,10 +290,11 @@ class DenseCuts : public CutsBuilder {
*
* \return The row stride across the entire dataset.
*/
size_t DeviceSketch
(const tree::TrainParam& param, const GenericParameter &learner_param, int gpu_batch_nrows,
DMatrix* dmat, HistogramCuts* hmat);
size_t DeviceSketch(int device,
int max_bin,
int gpu_batch_nrows,
DMatrix* dmat,
HistogramCuts* hmat);
/*!
* \brief preprocessed global index matrix, in CSR format

25
src/data/ellpack_page.cc Normal file
View File

@@ -0,0 +1,25 @@
/*!
* Copyright 2019 XGBoost contributors
*
* \file ellpack_page.cc
*/
#ifndef XGBOOST_USE_CUDA
#include <xgboost/data.h>
// dummy implementation of ELlpackPage in case CUDA is not used
namespace xgboost {
class EllpackPageImpl {};
EllpackPage::EllpackPage(DMatrix* dmat) {
LOG(FATAL) << "Internal Error: XGBoost is not compiled with CUDA but EllpackPage is required";
}
EllpackPage::~EllpackPage() {
LOG(FATAL) << "Internal Error: XGBoost is not compiled with CUDA but EllpackPage is required";
}
} // namespace xgboost
#endif // XGBOOST_USE_CUDA

197
src/data/ellpack_page.cu Normal file
View File

@@ -0,0 +1,197 @@
/*!
* Copyright 2019 XGBoost contributors
*
* \file ellpack_page.cu
*/
#include <xgboost/data.h>
#include "./ellpack_page.cuh"
#include "../common/hist_util.h"
#include "../common/random.h"
namespace xgboost {
EllpackPage::EllpackPage(DMatrix* dmat) : impl_{new EllpackPageImpl(dmat)} {}
EllpackPage::~EllpackPage() = default;
EllpackPageImpl::EllpackPageImpl(DMatrix* dmat) : dmat_{dmat} {}
// Bin each input data entry, store the bin indices in compressed form.
template<typename std::enable_if<true, int>::type = 0>
__global__ void CompressBinEllpackKernel(
common::CompressedBufferWriter wr,
common::CompressedByteT* __restrict__ buffer, // gidx_buffer
const size_t* __restrict__ row_ptrs, // row offset of input data
const Entry* __restrict__ entries, // One batch of input data
const float* __restrict__ cuts, // HistogramCuts::cut
const uint32_t* __restrict__ cut_rows, // HistogramCuts::row_ptrs
size_t base_row, // batch_row_begin
size_t n_rows,
size_t row_stride,
unsigned int null_gidx_value) {
size_t irow = threadIdx.x + blockIdx.x * blockDim.x;
int ifeature = threadIdx.y + blockIdx.y * blockDim.y;
if (irow >= n_rows || ifeature >= row_stride) {
return;
}
int row_length = static_cast<int>(row_ptrs[irow + 1] - row_ptrs[irow]);
unsigned int bin = null_gidx_value;
if (ifeature < row_length) {
Entry entry = entries[row_ptrs[irow] - row_ptrs[0] + ifeature];
int feature = entry.index;
float fvalue = entry.fvalue;
// {feature_cuts, ncuts} forms the array of cuts of `feature'.
const float *feature_cuts = &cuts[cut_rows[feature]];
int ncuts = cut_rows[feature + 1] - cut_rows[feature];
// Assigning the bin in current entry.
// S.t.: fvalue < feature_cuts[bin]
bin = dh::UpperBound(feature_cuts, ncuts, fvalue);
if (bin >= ncuts) {
bin = ncuts - 1;
}
// Add the number of bins in previous features.
bin += cut_rows[feature];
}
// Write to gidx buffer.
wr.AtomicWriteSymbol(buffer, bin, (irow + base_row) * row_stride + ifeature);
}
void EllpackPageImpl::Init(int device, int max_bin, int gpu_batch_nrows) {
if (initialised_) return;
monitor_.Init("ellpack_page");
dh::safe_cuda(cudaSetDevice(device));
monitor_.StartCuda("Quantiles");
// Create the quantile sketches for the dmatrix and initialize HistogramCuts.
common::HistogramCuts hmat;
size_t row_stride = common::DeviceSketch(device, max_bin, gpu_batch_nrows, dmat_, &hmat);
monitor_.StopCuda("Quantiles");
const auto& info = dmat_->Info();
auto is_dense = info.num_nonzero_ == info.num_row_ * info.num_col_;
// Init global data for each shard
monitor_.StartCuda("InitCompressedData");
InitCompressedData(device, hmat, row_stride, is_dense);
monitor_.StopCuda("InitCompressedData");
monitor_.StartCuda("BinningCompression");
DeviceHistogramBuilderState hist_builder_row_state(info.num_row_);
for (const auto& batch : dmat_->GetBatches<SparsePage>()) {
hist_builder_row_state.BeginBatch(batch);
CreateHistIndices(device, batch, hist_builder_row_state.GetRowStateOnDevice());
hist_builder_row_state.EndBatch();
}
monitor_.StopCuda("BinningCompression");
initialised_ = true;
}
void EllpackPageImpl::InitCompressedData(int device,
const common::HistogramCuts& hmat,
size_t row_stride,
bool is_dense) {
n_bins = hmat.Ptrs().back();
int null_gidx_value = hmat.Ptrs().back();
int num_symbols = n_bins + 1;
// minimum value for each feature.
common::Span<bst_float> min_fvalue;
// Required buffer size for storing data matrix in ELLPack format.
size_t compressed_size_bytes = common::CompressedBufferWriter::CalculateBufferSize(
row_stride * dmat_->Info().num_row_, num_symbols);
ba.Allocate(device,
&feature_segments, hmat.Ptrs().size(),
&gidx_fvalue_map, hmat.Values().size(),
&min_fvalue, hmat.MinValues().size(),
&gidx_buffer, compressed_size_bytes);
dh::CopyVectorToDeviceSpan(gidx_fvalue_map, hmat.Values());
dh::CopyVectorToDeviceSpan(min_fvalue, hmat.MinValues());
dh::CopyVectorToDeviceSpan(feature_segments, hmat.Ptrs());
thrust::fill(
thrust::device_pointer_cast(gidx_buffer.data()),
thrust::device_pointer_cast(gidx_buffer.data() + gidx_buffer.size()), 0);
ellpack_matrix.Init(feature_segments,
min_fvalue,
gidx_fvalue_map,
row_stride,
common::CompressedIterator<uint32_t>(gidx_buffer.data(), num_symbols),
is_dense,
null_gidx_value);
}
void EllpackPageImpl::CreateHistIndices(int device,
const SparsePage& row_batch,
const RowStateOnDevice& device_row_state) {
// Has any been allocated for me in this batch?
if (!device_row_state.rows_to_process_from_batch) return;
unsigned int null_gidx_value = n_bins;
size_t row_stride = this->ellpack_matrix.row_stride;
const auto &offset_vec = row_batch.offset.ConstHostVector();
int num_symbols = n_bins + 1;
// bin and compress entries in batches of rows
size_t gpu_batch_nrows = std::min(
dh::TotalMemory(device) / (16 * row_stride * sizeof(Entry)),
static_cast<size_t>(device_row_state.rows_to_process_from_batch));
const std::vector<Entry>& data_vec = row_batch.data.ConstHostVector();
size_t gpu_nbatches = common::DivRoundUp(device_row_state.rows_to_process_from_batch,
gpu_batch_nrows);
for (size_t gpu_batch = 0; gpu_batch < gpu_nbatches; ++gpu_batch) {
size_t batch_row_begin = gpu_batch * gpu_batch_nrows;
size_t batch_row_end = (gpu_batch + 1) * gpu_batch_nrows;
if (batch_row_end > device_row_state.rows_to_process_from_batch) {
batch_row_end = device_row_state.rows_to_process_from_batch;
}
size_t batch_nrows = batch_row_end - batch_row_begin;
const auto ent_cnt_begin =
offset_vec[device_row_state.row_offset_in_current_batch + batch_row_begin];
const auto ent_cnt_end =
offset_vec[device_row_state.row_offset_in_current_batch + batch_row_end];
/*! \brief row offset in SparsePage (the input data). */
dh::device_vector<size_t> row_ptrs(batch_nrows+1);
thrust::copy(
offset_vec.data() + device_row_state.row_offset_in_current_batch + batch_row_begin,
offset_vec.data() + device_row_state.row_offset_in_current_batch + batch_row_end + 1,
row_ptrs.begin());
// number of entries in this batch.
size_t n_entries = ent_cnt_end - ent_cnt_begin;
dh::device_vector<Entry> entries_d(n_entries);
// copy data entries to device.
dh::safe_cuda(cudaMemcpy(entries_d.data().get(),
data_vec.data() + ent_cnt_begin,
n_entries * sizeof(Entry),
cudaMemcpyDefault));
const dim3 block3(32, 8, 1); // 256 threads
const dim3 grid3(common::DivRoundUp(batch_nrows, block3.x),
common::DivRoundUp(row_stride, block3.y),
1);
CompressBinEllpackKernel<<<grid3, block3>>>(
common::CompressedBufferWriter(num_symbols),
gidx_buffer.data(),
row_ptrs.data().get(),
entries_d.data().get(),
gidx_fvalue_map.data(),
feature_segments.data(),
device_row_state.total_rows_processed + batch_row_begin,
batch_nrows,
row_stride,
null_gidx_value);
}
}
} // namespace xgboost

203
src/data/ellpack_page.cuh Normal file
View File

@@ -0,0 +1,203 @@
/*!
* Copyright 2019 by XGBoost Contributors
*
* \file ellpack_page.cuh
*/
#ifndef XGBOOST_DATA_ELLPACK_PAGE_H_
#define XGBOOST_DATA_ELLPACK_PAGE_H_
#include <xgboost/data.h>
#include "../common/compressed_iterator.h"
#include "../common/device_helpers.cuh"
#include "../common/hist_util.h"
namespace xgboost {
// Find a gidx value for a given feature otherwise return -1 if not found
__forceinline__ __device__ int BinarySearchRow(
bst_uint begin, bst_uint end,
common::CompressedIterator<uint32_t> data,
int const fidx_begin, int const fidx_end) {
bst_uint previous_middle = UINT32_MAX;
while (end != begin) {
auto middle = begin + (end - begin) / 2;
if (middle == previous_middle) {
break;
}
previous_middle = middle;
auto gidx = data[middle];
if (gidx >= fidx_begin && gidx < fidx_end) {
return gidx;
} else if (gidx < fidx_begin) {
begin = middle;
} else {
end = middle;
}
}
// Value is missing
return -1;
}
/** \brief Struct for accessing and manipulating an ellpack matrix on the
* device. Does not own underlying memory and may be trivially copied into
* kernels.*/
struct ELLPackMatrix {
common::Span<uint32_t> feature_segments;
/*! \brief minimum value for each feature. */
common::Span<bst_float> min_fvalue;
/*! \brief Cut. */
common::Span<bst_float> gidx_fvalue_map;
/*! \brief row length for ELLPack. */
size_t row_stride{0};
common::CompressedIterator<uint32_t> gidx_iter;
int null_gidx_value;
XGBOOST_DEVICE size_t BinCount() const { return gidx_fvalue_map.size(); }
// Get a matrix element, uses binary search for look up Return NaN if missing
// Given a row index and a feature index, returns the corresponding cut value
__device__ bst_float GetElement(size_t ridx, size_t fidx) const {
auto row_begin = row_stride * ridx;
auto row_end = row_begin + row_stride;
auto gidx = -1;
if (is_dense) {
gidx = gidx_iter[row_begin + fidx];
} else {
gidx =
BinarySearchRow(row_begin, row_end, gidx_iter, feature_segments[fidx],
feature_segments[fidx + 1]);
}
if (gidx == -1) {
return nan("");
}
return gidx_fvalue_map[gidx];
}
void Init(common::Span<uint32_t> feature_segments,
common::Span<bst_float> min_fvalue,
common::Span<bst_float> gidx_fvalue_map, size_t row_stride,
common::CompressedIterator<uint32_t> gidx_iter, bool is_dense,
int null_gidx_value) {
this->feature_segments = feature_segments;
this->min_fvalue = min_fvalue;
this->gidx_fvalue_map = gidx_fvalue_map;
this->row_stride = row_stride;
this->gidx_iter = gidx_iter;
this->is_dense = is_dense;
this->null_gidx_value = null_gidx_value;
}
private:
bool is_dense;
};
// Instances of this type are created while creating the histogram bins for the
// entire dataset across multiple sparse page batches. This keeps track of the number
// of rows to process from a batch and the position from which to process on each device.
struct RowStateOnDevice {
// Number of rows assigned to this device
size_t total_rows_assigned_to_device;
// Number of rows processed thus far
size_t total_rows_processed;
// Number of rows to process from the current sparse page batch
size_t rows_to_process_from_batch;
// Offset from the current sparse page batch to begin processing
size_t row_offset_in_current_batch;
explicit RowStateOnDevice(size_t total_rows)
: total_rows_assigned_to_device(total_rows), total_rows_processed(0),
rows_to_process_from_batch(0), row_offset_in_current_batch(0) {
}
explicit RowStateOnDevice(size_t total_rows, size_t batch_rows)
: total_rows_assigned_to_device(total_rows), total_rows_processed(0),
rows_to_process_from_batch(batch_rows), row_offset_in_current_batch(0) {
}
// Advance the row state by the number of rows processed
void Advance() {
total_rows_processed += rows_to_process_from_batch;
CHECK_LE(total_rows_processed, total_rows_assigned_to_device);
rows_to_process_from_batch = row_offset_in_current_batch = 0;
}
};
// An instance of this type is created which keeps track of total number of rows to process,
// rows processed thus far, rows to process and the offset from the current sparse page batch
// to begin processing on each device
class DeviceHistogramBuilderState {
public:
explicit DeviceHistogramBuilderState(int n_rows) : device_row_state_(n_rows) {}
const RowStateOnDevice& GetRowStateOnDevice() const {
return device_row_state_;
}
// This method is invoked at the beginning of each sparse page batch. This distributes
// the rows in the sparse page to the device.
// TODO(sriramch): Think of a way to utilize *all* the GPUs to build the compressed bins.
void BeginBatch(const SparsePage &batch) {
size_t rem_rows = batch.Size();
size_t row_offset_in_current_batch = 0;
// Do we have anymore left to process from this batch on this device?
if (device_row_state_.total_rows_assigned_to_device > device_row_state_.total_rows_processed) {
// There are still some rows that needs to be assigned to this device
device_row_state_.rows_to_process_from_batch =
std::min(
device_row_state_.total_rows_assigned_to_device - device_row_state_.total_rows_processed,
rem_rows);
} else {
// All rows have been assigned to this device
device_row_state_.rows_to_process_from_batch = 0;
}
device_row_state_.row_offset_in_current_batch = row_offset_in_current_batch;
row_offset_in_current_batch += device_row_state_.rows_to_process_from_batch;
rem_rows -= device_row_state_.rows_to_process_from_batch;
}
// This method is invoked after completion of each sparse page batch
void EndBatch() {
device_row_state_.Advance();
}
private:
RowStateOnDevice device_row_state_{0};
};
class EllpackPageImpl {
public:
ELLPackMatrix ellpack_matrix;
int n_bins{};
/*! \brief global index of histogram, which is stored in ELLPack format. */
common::Span<common::CompressedByteT> gidx_buffer;
explicit EllpackPageImpl(DMatrix* dmat);
void Init(int device, int max_bin, int gpu_batch_nrows);
void InitCompressedData(int device,
const common::HistogramCuts& hmat,
size_t row_stride,
bool is_dense);
void CreateHistIndices(int device,
const SparsePage& row_batch,
const RowStateOnDevice& device_row_state);
private:
bool initialised_{false};
DMatrix* dmat_;
common::Monitor monitor_;
dh::BulkAllocator ba;
/*! \brief Cut. */
common::Span<bst_float> gidx_fvalue_map;
/*! \brief row_ptr form HistogramCuts. */
common::Span<uint32_t> feature_segments;
};
} // namespace xgboost
#endif // XGBOOST_DATA_ELLPACK_PAGE_H_

33
src/data/simple_batch_iterator.h Normal file
View File

@@ -0,0 +1,33 @@
/*!
* Copyright 2019 XGBoost contributors
*/
#ifndef XGBOOST_DATA_SIMPLE_BATCH_ITERATOR_H_
#define XGBOOST_DATA_SIMPLE_BATCH_ITERATOR_H_
#include <xgboost/data.h>
namespace xgboost {
namespace data {
template<typename T>
class SimpleBatchIteratorImpl : public BatchIteratorImpl<T> {
public:
explicit SimpleBatchIteratorImpl(T* page) : page_(page) {}
T& operator*() override {
CHECK(page_ != nullptr);
return *page_;
}
const T& operator*() const override {
CHECK(page_ != nullptr);
return *page_;
}
void operator++() override { page_ = nullptr; }
bool AtEnd() const override { return page_ == nullptr; }
private:
T* page_{nullptr};
};
} // namespace data
} // namespace xgboost
#endif // XGBOOST_DATA_SIMPLE_BATCH_ITERATOR_H_

30
src/data/simple_dmatrix.cc
View File

@@ -6,6 +6,7 @@
*/
#include "./simple_dmatrix.h"
#include <xgboost/data.h>
#include "./simple_batch_iterator.h"
#include "../common/random.h"
namespace xgboost {
@@ -29,25 +30,6 @@ float SimpleDMatrix::GetColDensity(size_t cidx) {
return 1.0f - (static_cast<float>(nmiss)) / this->Info().num_row_;
}
template<typename T>
class SimpleBatchIteratorImpl : public BatchIteratorImpl<T> {
public:
explicit SimpleBatchIteratorImpl(T* page) : page_(page) {}
T& operator*() override {
CHECK(page_ != nullptr);
return *page_;
}
const T& operator*() const override {
CHECK(page_ != nullptr);
return *page_;
}
void operator++() override { page_ = nullptr; }
bool AtEnd() const override { return page_ == nullptr; }
private:
T* page_{nullptr};
};
BatchSet<SparsePage> SimpleDMatrix::GetRowBatches() {
// since csr is the default data structure so `source_` is always available.
auto cast = dynamic_cast<SimpleCSRSource*>(source_.get());
@@ -80,6 +62,16 @@ BatchSet<SortedCSCPage> SimpleDMatrix::GetSortedColumnBatches() {
return BatchSet<SortedCSCPage>(begin_iter);
}
BatchSet<EllpackPage> SimpleDMatrix::GetEllpackBatches() {
// ELLPACK page doesn't exist, generate it
if (!ellpack_page_) {
ellpack_page_.reset(new EllpackPage(this));
}
auto begin_iter =
BatchIterator<EllpackPage>(new SimpleBatchIteratorImpl<EllpackPage>(ellpack_page_.get()));
return BatchSet<EllpackPage>(begin_iter);
}
bool SimpleDMatrix::SingleColBlock() const { return true; }
} // namespace data
} // namespace xgboost

2
src/data/simple_dmatrix.h
View File

@@ -38,12 +38,14 @@ class SimpleDMatrix : public DMatrix {
BatchSet<SparsePage> GetRowBatches() override;
BatchSet<CSCPage> GetColumnBatches() override;
BatchSet<SortedCSCPage> GetSortedColumnBatches() override;
BatchSet<EllpackPage> GetEllpackBatches() override;
// source data pointer.
std::unique_ptr<DataSource<SparsePage>> source_;
std::unique_ptr<CSCPage> column_page_;
std::unique_ptr<SortedCSCPage> sorted_column_page_;
std::unique_ptr<EllpackPage> ellpack_page_;
};
} // namespace data
} // namespace xgboost

12
src/data/sparse_page_dmatrix.cc
View File

@@ -10,6 +10,8 @@
#if DMLC_ENABLE_STD_THREAD
#include "./sparse_page_dmatrix.h"
#include "./simple_batch_iterator.h"
namespace xgboost {
namespace data {
@@ -72,6 +74,16 @@ BatchSet<SortedCSCPage> SparsePageDMatrix::GetSortedColumnBatches() {
return BatchSet<SortedCSCPage>(begin_iter);
}
BatchSet<EllpackPage> SparsePageDMatrix::GetEllpackBatches() {
// ELLPACK page doesn't exist, generate it
if (!ellpack_page_) {
ellpack_page_.reset(new EllpackPage(this));
}
auto begin_iter =
BatchIterator<EllpackPage>(new SimpleBatchIteratorImpl<EllpackPage>(ellpack_page_.get()));
return BatchSet<EllpackPage>(begin_iter);
}
float SparsePageDMatrix::GetColDensity(size_t cidx) {
// Finds densities if we don't already have them
if (col_density_.empty()) {

4
src/data/sparse_page_dmatrix.h
View File

@@ -24,7 +24,7 @@ class SparsePageDMatrix : public DMatrix {
explicit SparsePageDMatrix(std::unique_ptr<DataSource<SparsePage>>&& source,
std::string cache_info)
: row_source_(std::move(source)), cache_info_(std::move(cache_info)) {}
virtual ~SparsePageDMatrix() = default;
~SparsePageDMatrix() override = default;
MetaInfo& Info() override;
@@ -38,11 +38,13 @@ class SparsePageDMatrix : public DMatrix {
BatchSet<SparsePage> GetRowBatches() override;
BatchSet<CSCPage> GetColumnBatches() override;
BatchSet<SortedCSCPage> GetSortedColumnBatches() override;
BatchSet<EllpackPage> GetEllpackBatches() override;
// source data pointers.
std::unique_ptr<DataSource<SparsePage>> row_source_;
std::unique_ptr<SparsePageSource<CSCPage>> column_source_;
std::unique_ptr<SparsePageSource<SortedCSCPage>> sorted_column_source_;
std::unique_ptr<EllpackPage> ellpack_page_;
// the cache prefix
std::string cache_info_;
// Store column densities to avoid recalculating

390
src/tree/updater_gpu_hist.cu
View File

@@ -21,6 +21,7 @@
#include "../common/host_device_vector.h"
#include "../common/timer.h"
#include "../common/span.h"
#include "../data/ellpack_page.cuh"
#include "param.h"
#include "updater_gpu_common.cuh"
#include "constraints.cuh"
@@ -108,83 +109,6 @@ inline static bool LossGuide(const ExpandEntry& lhs, const ExpandEntry& rhs) {
}
}
// Find a gidx value for a given feature otherwise return -1 if not found
__forceinline__ __device__ int BinarySearchRow(
bst_uint begin, bst_uint end,
common::CompressedIterator<uint32_t> data,
int const fidx_begin, int const fidx_end) {
bst_uint previous_middle = UINT32_MAX;
while (end != begin) {
auto middle = begin + (end - begin) / 2;
if (middle == previous_middle) {
break;
}
previous_middle = middle;
auto gidx = data[middle];
if (gidx >= fidx_begin && gidx < fidx_end) {
return gidx;
} else if (gidx < fidx_begin) {
begin = middle;
} else {
end = middle;
}
}
// Value is missing
return -1;
}
/** \brief Struct for accessing and manipulating an ellpack matrix on the
* device. Does not own underlying memory and may be trivially copied into
* kernels.*/
struct ELLPackMatrix {
common::Span<uint32_t> feature_segments;
/*! \brief minimum value for each feature. */
common::Span<bst_float> min_fvalue;
/*! \brief Cut. */
common::Span<bst_float> gidx_fvalue_map;
/*! \brief row length for ELLPack. */
size_t row_stride{0};
common::CompressedIterator<uint32_t> gidx_iter;
bool is_dense;
int null_gidx_value;
XGBOOST_DEVICE size_t BinCount() const { return gidx_fvalue_map.size(); }
// Get a matrix element, uses binary search for look up Return NaN if missing
// Given a row index and a feature index, returns the corresponding cut value
__device__ bst_float GetElement(size_t ridx, size_t fidx) const {
auto row_begin = row_stride * ridx;
auto row_end = row_begin + row_stride;
auto gidx = -1;
if (is_dense) {
gidx = gidx_iter[row_begin + fidx];
} else {
gidx =
BinarySearchRow(row_begin, row_end, gidx_iter, feature_segments[fidx],
feature_segments[fidx + 1]);
}
if (gidx == -1) {
return nan("");
}
return gidx_fvalue_map[gidx];
}
void Init(common::Span<uint32_t> feature_segments,
common::Span<bst_float> min_fvalue,
common::Span<bst_float> gidx_fvalue_map, size_t row_stride,
common::CompressedIterator<uint32_t> gidx_iter, bool is_dense,
int null_gidx_value) {
this->feature_segments = feature_segments;
this->min_fvalue = min_fvalue;
this->gidx_fvalue_map = gidx_fvalue_map;
this->row_stride = row_stride;
this->gidx_iter = gidx_iter;
this->is_dense = is_dense;
this->null_gidx_value = null_gidx_value;
}
};
// With constraints
template <typename GradientPairT>
XGBOOST_DEVICE float inline LossChangeMissing(
@@ -247,7 +171,7 @@ template <int BLOCK_THREADS, typename ReduceT, typename ScanT,
typename MaxReduceT, typename TempStorageT, typename GradientSumT>
__device__ void EvaluateFeature(
int fidx, common::Span<const GradientSumT> node_histogram,
const ELLPackMatrix& matrix,
const xgboost::ELLPackMatrix& matrix,
DeviceSplitCandidate* best_split, // shared memory storing best split
const DeviceNodeStats& node, const GPUTrainingParam& param,
TempStorageT* temp_storage, // temp memory for cub operations
@@ -322,7 +246,7 @@ __global__ void EvaluateSplitKernel(
common::Span<const GradientSumT> node_histogram, // histogram for gradients
common::Span<const int> feature_set, // Selected features
DeviceNodeStats node,
ELLPackMatrix matrix,
xgboost::ELLPackMatrix matrix,
GPUTrainingParam gpu_param,
common::Span<DeviceSplitCandidate> split_candidates, // resulting split
ValueConstraint value_constraint,
@@ -473,48 +397,8 @@ struct CalcWeightTrainParam {
learning_rate(p.learning_rate) {}
};
// Bin each input data entry, store the bin indices in compressed form.
template<typename std::enable_if<true, int>::type = 0>
__global__ void CompressBinEllpackKernel(
common::CompressedBufferWriter wr,
common::CompressedByteT* __restrict__ buffer, // gidx_buffer
const size_t* __restrict__ row_ptrs, // row offset of input data
const Entry* __restrict__ entries, // One batch of input data
const float* __restrict__ cuts, // HistogramCuts::cut
const uint32_t* __restrict__ cut_rows, // HistogramCuts::row_ptrs
size_t base_row, // batch_row_begin
size_t n_rows,
size_t row_stride,
unsigned int null_gidx_value) {
size_t irow = threadIdx.x + blockIdx.x * blockDim.x;
int ifeature = threadIdx.y + blockIdx.y * blockDim.y;
if (irow >= n_rows || ifeature >= row_stride) {
return;
}
int row_length = static_cast<int>(row_ptrs[irow + 1] - row_ptrs[irow]);
unsigned int bin = null_gidx_value;
if (ifeature < row_length) {
Entry entry = entries[row_ptrs[irow] - row_ptrs[0] + ifeature];
int feature = entry.index;
float fvalue = entry.fvalue;
// {feature_cuts, ncuts} forms the array of cuts of `feature'.
const float *feature_cuts = &cuts[cut_rows[feature]];
int ncuts = cut_rows[feature + 1] - cut_rows[feature];
// Assigning the bin in current entry.
// S.t.: fvalue < feature_cuts[bin]
bin = dh::UpperBound(feature_cuts, ncuts, fvalue);
if (bin >= ncuts) {
bin = ncuts - 1;
}
// Add the number of bins in previous features.
bin += cut_rows[feature];
}
// Write to gidx buffer.
wr.AtomicWriteSymbol(buffer, bin, (irow + base_row) * row_stride + ifeature);
}
template <typename GradientSumT>
__global__ void SharedMemHistKernel(ELLPackMatrix matrix,
__global__ void SharedMemHistKernel(xgboost::ELLPackMatrix matrix,
common::Span<const RowPartitioner::RowIndexT> d_ridx,
GradientSumT* d_node_hist,
const GradientPair* d_gpair, size_t n_elements,
@@ -548,59 +432,17 @@ __global__ void SharedMemHistKernel(ELLPackMatrix matrix,
}
}
// Instances of this type are created while creating the histogram bins for the
// entire dataset across multiple sparse page batches. This keeps track of the number
// of rows to process from a batch and the position from which to process on each device.
struct RowStateOnDevice {
// Number of rows assigned to this device
size_t total_rows_assigned_to_device;
// Number of rows processed thus far
size_t total_rows_processed;
// Number of rows to process from the current sparse page batch
size_t rows_to_process_from_batch;
// Offset from the current sparse page batch to begin processing
size_t row_offset_in_current_batch;
explicit RowStateOnDevice(size_t total_rows)
: total_rows_assigned_to_device(total_rows), total_rows_processed(0),
rows_to_process_from_batch(0), row_offset_in_current_batch(0) {
}
explicit RowStateOnDevice(size_t total_rows, size_t batch_rows)
: total_rows_assigned_to_device(total_rows), total_rows_processed(0),
rows_to_process_from_batch(batch_rows), row_offset_in_current_batch(0) {
}
// Advance the row state by the number of rows processed
void Advance() {
total_rows_processed += rows_to_process_from_batch;
CHECK_LE(total_rows_processed, total_rows_assigned_to_device);
rows_to_process_from_batch = row_offset_in_current_batch = 0;
}
};
// Manage memory for a single GPU
template <typename GradientSumT>
struct DeviceShard {
int n_bins;
int device_id;
EllpackPageImpl* page;
dh::BulkAllocator ba;
ELLPackMatrix ellpack_matrix;
std::unique_ptr<RowPartitioner> row_partitioner;
DeviceHistogram<GradientSumT> hist{};
/*! \brief row_ptr form HistogramCuts. */
common::Span<uint32_t> feature_segments;
/*! \brief minimum value for each feature. */
common::Span<bst_float> min_fvalue;
/*! \brief Cut. */
common::Span<bst_float> gidx_fvalue_map;
/*! \brief global index of histogram, which is stored in ELLPack format. */
common::Span<common::CompressedByteT> gidx_buffer;
/*! \brief Gradient pair for each row. */
common::Span<GradientPair> gpair;
@@ -631,11 +473,15 @@ struct DeviceShard {
std::function<bool(ExpandEntry, ExpandEntry)>>;
std::unique_ptr<ExpandQueue> qexpand;
DeviceShard(int _device_id, bst_uint _n_rows, TrainParam _param, uint32_t column_sampler_seed,
DeviceShard(int _device_id,
EllpackPageImpl* _page,
bst_uint _n_rows,
TrainParam _param,
uint32_t column_sampler_seed,
uint32_t n_features)
: device_id(_device_id),
page(_page),
n_rows(_n_rows),
n_bins(0),
param(std::move(_param)),
prediction_cache_initialised(false),
column_sampler(column_sampler_seed),
@@ -643,12 +489,7 @@ struct DeviceShard {
monitor.Init(std::string("DeviceShard") + std::to_string(device_id));
}
void InitCompressedData(
const common::HistogramCuts& hmat, size_t row_stride, bool is_dense);
void CreateHistIndices(
const SparsePage &row_batch, const common::HistogramCuts &hmat,
const RowStateOnDevice &device_row_state, int rows_per_batch);
void InitHistogram();
~DeviceShard() { // NOLINT
dh::safe_cuda(cudaSetDevice(device_id));
@@ -762,7 +603,7 @@ struct DeviceShard {
int constexpr kBlockThreads = 256;
EvaluateSplitKernel<kBlockThreads, GradientSumT>
<<<uint32_t(d_feature_set.size()), kBlockThreads, 0, streams[i]>>>(
hist.GetNodeHistogram(nidx), d_feature_set, node, ellpack_matrix,
hist.GetNodeHistogram(nidx), d_feature_set, node, page->ellpack_matrix,
gpu_param, d_split_candidates, node_value_constraints[nidx],
monotone_constraints);
@@ -788,11 +629,11 @@ struct DeviceShard {
auto d_ridx = row_partitioner->GetRows(nidx);
auto d_gpair = gpair.data();
auto n_elements = d_ridx.size() * ellpack_matrix.row_stride;
auto n_elements = d_ridx.size() * page->ellpack_matrix.row_stride;
const size_t smem_size =
use_shared_memory_histograms
? sizeof(GradientSumT) * ellpack_matrix.BinCount()
? sizeof(GradientSumT) * page->ellpack_matrix.BinCount()
: 0;
const int items_per_thread = 8;
const int block_threads = 256;
@@ -802,7 +643,7 @@ struct DeviceShard {
return;
}
SharedMemHistKernel<<<grid_size, block_threads, smem_size>>>(
ellpack_matrix, d_ridx, d_node_hist.data(), d_gpair, n_elements,
page->ellpack_matrix, d_ridx, d_node_hist.data(), d_gpair, n_elements,
use_shared_memory_histograms);
}
@@ -812,7 +653,7 @@ struct DeviceShard {
auto d_node_hist_histogram = hist.GetNodeHistogram(nidx_histogram);
auto d_node_hist_subtraction = hist.GetNodeHistogram(nidx_subtraction);
dh::LaunchN(device_id, n_bins, [=] __device__(size_t idx) {
dh::LaunchN(device_id, page->n_bins, [=] __device__(size_t idx) {
d_node_hist_subtraction[idx] =
d_node_hist_parent[idx] - d_node_hist_histogram[idx];
});
@@ -827,7 +668,7 @@ struct DeviceShard {
}
void UpdatePosition(int nidx, RegTree::Node split_node) {
auto d_matrix = ellpack_matrix;
auto d_matrix = page->ellpack_matrix;
row_partitioner->UpdatePosition(
nidx, split_node.LeftChild(), split_node.RightChild(),
@@ -859,7 +700,7 @@ struct DeviceShard {
dh::safe_cuda(cudaMemcpy(d_nodes.data(), p_tree->GetNodes().data(),
d_nodes.size() * sizeof(RegTree::Node),
cudaMemcpyHostToDevice));
auto d_matrix = ellpack_matrix;
auto d_matrix = page->ellpack_matrix;
row_partitioner->FinalisePosition(
[=] __device__(bst_uint ridx, int position) {
auto node = d_nodes[position];
@@ -922,7 +763,7 @@ struct DeviceShard {
reducer->AllReduceSum(
reinterpret_cast<typename GradientSumT::ValueT*>(d_node_hist),
reinterpret_cast<typename GradientSumT::ValueT*>(d_node_hist),
ellpack_matrix.BinCount() *
page->ellpack_matrix.BinCount() *
(sizeof(GradientSumT) / sizeof(typename GradientSumT::ValueT)));
reducer->Synchronize();
@@ -1097,11 +938,7 @@ struct DeviceShard {
};
template <typename GradientSumT>
inline void DeviceShard<GradientSumT>::InitCompressedData(
const common::HistogramCuts &hmat, size_t row_stride, bool is_dense) {
n_bins = hmat.Ptrs().back();
int null_gidx_value = hmat.Ptrs().back();
inline void DeviceShard<GradientSumT>::InitHistogram() {
CHECK(!(param.max_leaves == 0 && param.max_depth == 0))
<< "Max leaves and max depth cannot both be unconstrained for "
"gpu_hist.";
@@ -1113,163 +950,25 @@ inline void DeviceShard<GradientSumT>::InitCompressedData(
&gpair, n_rows,
&prediction_cache, n_rows,
&node_sum_gradients_d, max_nodes,
&feature_segments, hmat.Ptrs().size(),
&gidx_fvalue_map, hmat.Values().size(),
&min_fvalue, hmat.MinValues().size(),
&monotone_constraints, param.monotone_constraints.size());
dh::CopyVectorToDeviceSpan(gidx_fvalue_map, hmat.Values());
dh::CopyVectorToDeviceSpan(min_fvalue, hmat.MinValues());
dh::CopyVectorToDeviceSpan(feature_segments, hmat.Ptrs());
dh::CopyVectorToDeviceSpan(monotone_constraints, param.monotone_constraints);
node_sum_gradients.resize(max_nodes);
// allocate compressed bin data
int num_symbols = n_bins + 1;
// Required buffer size for storing data matrix in ELLPack format.
size_t compressed_size_bytes =
common::CompressedBufferWriter::CalculateBufferSize(row_stride * n_rows,
num_symbols);
ba.Allocate(device_id, &gidx_buffer, compressed_size_bytes);
thrust::fill(
thrust::device_pointer_cast(gidx_buffer.data()),
thrust::device_pointer_cast(gidx_buffer.data() + gidx_buffer.size()), 0);
ellpack_matrix.Init(
feature_segments, min_fvalue,
gidx_fvalue_map, row_stride,
common::CompressedIterator<uint32_t>(gidx_buffer.data(), num_symbols),
is_dense, null_gidx_value);
// check if we can use shared memory for building histograms
// (assuming atleast we need 2 CTAs per SM to maintain decent latency
// hiding)
auto histogram_size = sizeof(GradientSumT) * hmat.Ptrs().back();
auto histogram_size = sizeof(GradientSumT) * page->n_bins;
auto max_smem = dh::MaxSharedMemory(device_id);
if (histogram_size <= max_smem) {
use_shared_memory_histograms = true;
}
// Init histogram
hist.Init(device_id, hmat.Ptrs().back());
hist.Init(device_id, page->n_bins);
}
template <typename GradientSumT>
inline void DeviceShard<GradientSumT>::CreateHistIndices(
const SparsePage &row_batch,
const common::HistogramCuts &hmat,
const RowStateOnDevice &device_row_state,
int rows_per_batch) {
// Has any been allocated for me in this batch?
if (!device_row_state.rows_to_process_from_batch) return;
unsigned int null_gidx_value = hmat.Ptrs().back();
size_t row_stride = this->ellpack_matrix.row_stride;
const auto &offset_vec = row_batch.offset.ConstHostVector();
int num_symbols = n_bins + 1;
// bin and compress entries in batches of rows
size_t gpu_batch_nrows = std::min(
dh::TotalMemory(device_id) / (16 * row_stride * sizeof(Entry)),
static_cast<size_t>(device_row_state.rows_to_process_from_batch));
const std::vector<Entry>& data_vec = row_batch.data.ConstHostVector();
size_t gpu_nbatches = common::DivRoundUp(device_row_state.rows_to_process_from_batch,
gpu_batch_nrows);
for (size_t gpu_batch = 0; gpu_batch < gpu_nbatches; ++gpu_batch) {
size_t batch_row_begin = gpu_batch * gpu_batch_nrows;
size_t batch_row_end = (gpu_batch + 1) * gpu_batch_nrows;
if (batch_row_end > device_row_state.rows_to_process_from_batch) {
batch_row_end = device_row_state.rows_to_process_from_batch;
}
size_t batch_nrows = batch_row_end - batch_row_begin;
const auto ent_cnt_begin =
offset_vec[device_row_state.row_offset_in_current_batch + batch_row_begin];
const auto ent_cnt_end =
offset_vec[device_row_state.row_offset_in_current_batch + batch_row_end];
/*! \brief row offset in SparsePage (the input data). */
dh::device_vector<size_t> row_ptrs(batch_nrows+1);
thrust::copy(
offset_vec.data() + device_row_state.row_offset_in_current_batch + batch_row_begin,
offset_vec.data() + device_row_state.row_offset_in_current_batch + batch_row_end + 1,
row_ptrs.begin());
// number of entries in this batch.
size_t n_entries = ent_cnt_end - ent_cnt_begin;
dh::device_vector<Entry> entries_d(n_entries);
// copy data entries to device.
dh::safe_cuda
(cudaMemcpy
(entries_d.data().get(), data_vec.data() + ent_cnt_begin,
n_entries * sizeof(Entry), cudaMemcpyDefault));
const dim3 block3(32, 8, 1); // 256 threads
const dim3 grid3(common::DivRoundUp(batch_nrows, block3.x),
common::DivRoundUp(row_stride, block3.y), 1);
CompressBinEllpackKernel<<<grid3, block3>>>
(common::CompressedBufferWriter(num_symbols),
gidx_buffer.data(),
row_ptrs.data().get(),
entries_d.data().get(),
gidx_fvalue_map.data(),
feature_segments.data(),
device_row_state.total_rows_processed + batch_row_begin,
batch_nrows,
row_stride,
null_gidx_value);
}
}
// An instance of this type is created which keeps track of total number of rows to process,
// rows processed thus far, rows to process and the offset from the current sparse page batch
// to begin processing on each device
class DeviceHistogramBuilderState {
public:
template <typename GradientSumT>
explicit DeviceHistogramBuilderState(const std::unique_ptr<DeviceShard<GradientSumT>>& shard)
: device_row_state_(shard->n_rows) {}
const RowStateOnDevice& GetRowStateOnDevice() const {
return device_row_state_;
}
// This method is invoked at the beginning of each sparse page batch. This distributes
// the rows in the sparse page to the device.
// TODO(sriramch): Think of a way to utilize *all* the GPUs to build the compressed bins.
void BeginBatch(const SparsePage &batch) {
size_t rem_rows = batch.Size();
size_t row_offset_in_current_batch = 0;
// Do we have anymore left to process from this batch on this device?
if (device_row_state_.total_rows_assigned_to_device > device_row_state_.total_rows_processed) {
// There are still some rows that needs to be assigned to this device
device_row_state_.rows_to_process_from_batch =
std::min(
device_row_state_.total_rows_assigned_to_device - device_row_state_.total_rows_processed,
rem_rows);
} else {
// All rows have been assigned to this device
device_row_state_.rows_to_process_from_batch = 0;
}
device_row_state_.row_offset_in_current_batch = row_offset_in_current_batch;
row_offset_in_current_batch += device_row_state_.rows_to_process_from_batch;
rem_rows -= device_row_state_.rows_to_process_from_batch;
}
// This method is invoked after completion of each sparse page batch
void EndBatch() {
device_row_state_.Advance();
}
private:
RowStateOnDevice device_row_state_{0};
};
template <typename GradientSumT>
class GPUHistMakerSpecialised {
public:
@@ -1319,47 +1018,33 @@ class GPUHistMakerSpecialised {
uint32_t column_sampling_seed = common::GlobalRandom()();
rabit::Broadcast(&column_sampling_seed, sizeof(column_sampling_seed), 0);
// TODO(rongou): support multiple Ellpack pages.
EllpackPageImpl* page{};
for (auto& batch : dmat->GetBatches<EllpackPage>()) {
page = batch.Impl();
page->Init(device_, param_.max_bin, hist_maker_param_.gpu_batch_nrows);
}
// Create device shard
dh::safe_cuda(cudaSetDevice(device_));
shard_.reset(new DeviceShard<GradientSumT>(device_,
page,
info_->num_row_,
param_,
column_sampling_seed,
info_->num_col_));
monitor_.StartCuda("Quantiles");
// Create the quantile sketches for the dmatrix and initialize HistogramCuts
size_t row_stride = common::DeviceSketch(param_, *generic_param_,
hist_maker_param_.gpu_batch_nrows,
dmat, &hmat_);
monitor_.StopCuda("Quantiles");
auto is_dense = info_->num_nonzero_ == info_->num_row_ * info_->num_col_;
// Init global data for each shard
monitor_.StartCuda("InitCompressedData");
dh::safe_cuda(cudaSetDevice(shard_->device_id));
shard_->InitCompressedData(hmat_, row_stride, is_dense);
monitor_.StopCuda("InitCompressedData");
monitor_.StartCuda("BinningCompression");
DeviceHistogramBuilderState hist_builder_row_state(shard_);
for (const auto &batch : dmat->GetBatches<SparsePage>()) {
hist_builder_row_state.BeginBatch(batch);
dh::safe_cuda(cudaSetDevice(shard_->device_id));
shard_->CreateHistIndices(batch, hmat_, hist_builder_row_state.GetRowStateOnDevice(),
hist_maker_param_.gpu_batch_nrows);
hist_builder_row_state.EndBatch();
}
monitor_.StopCuda("BinningCompression");
monitor_.StartCuda("InitHistogram");
dh::safe_cuda(cudaSetDevice(device_));
shard_->InitHistogram();
monitor_.StopCuda("InitHistogram");
p_last_fmat_ = dmat;
initialised_ = true;
}
void InitData(HostDeviceVector<GradientPair>* gpair, DMatrix* dmat) {
void InitData(DMatrix* dmat) {
if (!initialised_) {
monitor_.StartCuda("InitDataOnce");
this->InitDataOnce(dmat);
@@ -1387,7 +1072,7 @@ class GPUHistMakerSpecialised {
void UpdateTree(HostDeviceVector<GradientPair>* gpair, DMatrix* p_fmat,
RegTree* p_tree) {
monitor_.StartCuda("InitData");
this->InitData(gpair, p_fmat);
this->InitData(p_fmat);
monitor_.StopCuda("InitData");
gpair->SetDevice(device_);
@@ -1408,7 +1093,6 @@ class GPUHistMakerSpecialised {
}
TrainParam param_; // NOLINT
common::HistogramCuts hmat_; // NOLINT
MetaInfo* info_{}; // NOLINT
std::unique_ptr<DeviceShard<GradientSumT>> shard_; // NOLINT