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

Replaced std::vector with HostDeviceVector in MetaInfo and SparsePage. (#3446)

Browse Source 
* Replaced std::vector with HostDeviceVector in MetaInfo and SparsePage.

- added distributions to HostDeviceVector
- using HostDeviceVector for labels, weights and base margings in MetaInfo
- using HostDeviceVector for offset and data in SparsePage
- other necessary refactoring

* Added const version of HostDeviceVector API calls.

- const versions added to calls that can trigger data transfers, e.g. DevicePointer()
- updated the code that uses HostDeviceVector
- objective functions now accept const HostDeviceVector<bst_float>& for predictions

* Updated src/linear/updater_gpu_coordinate.cu.

* Added read-only state for HostDeviceVector sync.

- this means no copies are performed if both host and devices access
  the HostDeviceVector read-only

* Fixed linter and test errors.

- updated the lz4 plugin
- added ConstDeviceSpan to HostDeviceVector
- using device % dh::NVisibleDevices() for the physical device number,
  e.g. in calls to cudaSetDevice()

* Fixed explicit template instantiation errors for HostDeviceVector.

- replaced HostDeviceVector<unsigned int> with HostDeviceVector<int>

* Fixed HostDeviceVector tests that require multiple GPUs.

- added a mock set device handler; when set, it is called instead of cudaSetDevice()
This commit is contained in:
Andy Adinets 2018-08-30 04:28:47 +02:00 committed by Rory Mitchell
parent 58d783df16
commit 72cd1517d6
45 changed files with 1141 additions and 560 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

84
include/xgboost/data.h
View File

@ -17,6 +17,8 @@
#include "./base.h"
#include "../../src/common/span.h"
#include "../../src/common/host_device_vector.h"
namespace xgboost {
// forward declare learner.
class LearnerImpl;
@ -41,7 +43,7 @@ class MetaInfo {
/*! \brief number of nonzero entries in the data */
uint64_t num_nonzero_{0};
/*! \brief label of each instance */
std::vector<bst_float> labels_;
HostDeviceVector<bst_float> labels_;
/*!
* \brief specified root index of each instance,
* can be used for multi task setting
@ -53,7 +55,7 @@ class MetaInfo {
*/
std::vector<bst_uint> group_ptr_;
/*! \brief weights of each instance, optional */
std::vector<bst_float> weights_;
HostDeviceVector<bst_float> weights_;
/*! \brief session-id of each instance, optional */
std::vector<uint64_t> qids_;
/*!
@ -61,7 +63,7 @@ class MetaInfo {
* if specified, xgboost will start from this init margin
* can be used to specify initial prediction to boost from.
*/
std::vector<bst_float> base_margin_;
HostDeviceVector<bst_float> base_margin_;
/*! \brief version flag, used to check version of this info */
static const int kVersion = 2;
/*! \brief version that introduced qid field */
@ -74,7 +76,7 @@ class MetaInfo {
* \return The weight.
*/
inline bst_float GetWeight(size_t i) const {
return weights_.size() != 0 ? weights_[i] : 1.0f;
return weights_.Size() != 0 ? weights_.HostVector()[i] : 1.0f;
}
/*!
* \brief Get the root index of i-th instance.
@ -86,12 +88,12 @@ class MetaInfo {
}
/*! \brief get sorted indexes (argsort) of labels by absolute value (used by cox loss) */
inline const std::vector<size_t>& LabelAbsSort() const {
if (label_order_cache_.size() == labels_.size()) {
if (label_order_cache_.size() == labels_.Size()) {
return label_order_cache_;
}
label_order_cache_.resize(labels_.size());
label_order_cache_.resize(labels_.Size());
std::iota(label_order_cache_.begin(), label_order_cache_.end(), 0);
const auto l = labels_;
const auto& l = labels_.HostVector();
XGBOOST_PARALLEL_SORT(label_order_cache_.begin(), label_order_cache_.end(),
[&l](size_t i1, size_t i2) {return std::abs(l[i1]) < std::abs(l[i2]);});
@ -151,9 +153,9 @@ struct Entry {
*/
class SparsePage {
public:
std::vector<size_t> offset;
HostDeviceVector<size_t> offset;
/*! \brief the data of the segments */
std::vector<Entry> data;
HostDeviceVector<Entry> data;
size_t base_rowid;
@ -162,8 +164,10 @@ class SparsePage {
/*! \brief get i-th row from the batch */
inline Inst operator[](size_t i) const {
return {data.data() + offset[i],
static_cast<Inst::index_type>(offset[i + 1] - offset[i])};
const auto& data_vec = data.HostVector();
const auto& offset_vec = offset.HostVector();
return {data_vec.data() + offset_vec[i],
static_cast<Inst::index_type>(offset_vec[i + 1] - offset_vec[i])};
}
/*! \brief constructor */
@ -172,18 +176,19 @@ class SparsePage {
}
/*! \return number of instance in the page */
inline size_t Size() const {
return offset.size() - 1;
return offset.Size() - 1;
}
/*! \return estimation of memory cost of this page */
inline size_t MemCostBytes() const {
return offset.size() * sizeof(size_t) + data.size() * sizeof(Entry);
return offset.Size() * sizeof(size_t) + data.Size() * sizeof(Entry);
}
/*! \brief clear the page */
inline void Clear() {
base_rowid = 0;
offset.clear();
offset.push_back(0);
data.clear();
auto& offset_vec = offset.HostVector();
offset_vec.clear();
offset_vec.push_back(0);
data.HostVector().clear();
}
/*!
@ -191,33 +196,39 @@ class SparsePage {
* \param batch the row batch.
*/
inline void Push(const dmlc::RowBlock<uint32_t>& batch) {
data.reserve(data.size() + batch.offset[batch.size] - batch.offset[0]);
offset.reserve(offset.size() + batch.size);
auto& data_vec = data.HostVector();
auto& offset_vec = offset.HostVector();
data_vec.reserve(data.Size() + batch.offset[batch.size] - batch.offset[0]);
offset_vec.reserve(offset.Size() + batch.size);
CHECK(batch.index != nullptr);
for (size_t i = 0; i < batch.size; ++i) {
offset.push_back(offset.back() + batch.offset[i + 1] - batch.offset[i]);
offset_vec.push_back(offset_vec.back() + batch.offset[i + 1] - batch.offset[i]);
}
for (size_t i = batch.offset[0]; i < batch.offset[batch.size]; ++i) {
uint32_t index = batch.index[i];
bst_float fvalue = batch.value == nullptr ? 1.0f : batch.value[i];
data.emplace_back(index, fvalue);
data_vec.emplace_back(index, fvalue);
}
CHECK_EQ(offset.back(), data.size());
CHECK_EQ(offset_vec.back(), data.Size());
}
/*!
* \brief Push a sparse page
* \param batch the row page
*/
inline void Push(const SparsePage &batch) {
size_t top = offset.back();
data.resize(top + batch.data.size());
std::memcpy(dmlc::BeginPtr(data) + top,
dmlc::BeginPtr(batch.data),
sizeof(Entry) * batch.data.size());
size_t begin = offset.size();
offset.resize(begin + batch.Size());
auto& data_vec = data.HostVector();
auto& offset_vec = offset.HostVector();
const auto& batch_offset_vec = batch.offset.HostVector();
const auto& batch_data_vec = batch.data.HostVector();
size_t top = offset_vec.back();
data_vec.resize(top + batch.data.Size());
std::memcpy(dmlc::BeginPtr(data_vec) + top,
dmlc::BeginPtr(batch_data_vec),
sizeof(Entry) * batch.data.Size());
size_t begin = offset.Size();
offset_vec.resize(begin + batch.Size());
for (size_t i = 0; i < batch.Size(); ++i) {
offset[i + begin] = top + batch.offset[i + 1];
offset_vec[i + begin] = top + batch_offset_vec[i + 1];
}
}
/*!
@ -225,20 +236,21 @@ class SparsePage {
* \param inst an instance row
*/
inline void Push(const Inst &inst) {
offset.push_back(offset.back() + inst.size());
size_t begin = data.size();
data.resize(begin + inst.size());
auto& data_vec = data.HostVector();
auto& offset_vec = offset.HostVector();
offset_vec.push_back(offset_vec.back() + inst.size());
size_t begin = data_vec.size();
data_vec.resize(begin + inst.size());
if (inst.size() != 0) {
std::memcpy(dmlc::BeginPtr(data) + begin, inst.data(),
std::memcpy(dmlc::BeginPtr(data_vec) + begin, inst.data(),
sizeof(Entry) * inst.size());
}
}
size_t Size() { return offset.size() - 1; }
size_t Size() { return offset.Size() - 1; }
};
/*!
* \brief This is data structure that user can pass to DMatrix::Create
* to create a DMatrix for training, user can create this data structure

2
include/xgboost/objective.h
View File

@ -44,7 +44,7 @@ class ObjFunction {
* \param iteration current iteration number.
* \param out_gpair output of get gradient, saves gradient and second order gradient in
*/
virtual void GetGradient(HostDeviceVector<bst_float>* preds,
virtual void GetGradient(const HostDeviceVector<bst_float>& preds,
const MetaInfo& info,
int iteration,
HostDeviceVector<GradientPair>* out_gpair) = 0;

11
plugin/example/custom_obj.cc
View File

@ -33,21 +33,22 @@ class MyLogistic : public ObjFunction {
void Configure(const std::vector<std::pair<std::string, std::string> >& args) override {
param_.InitAllowUnknown(args);
}
void GetGradient(HostDeviceVector<bst_float> *preds,
void GetGradient(const HostDeviceVector<bst_float> &preds,
const MetaInfo &info,
int iter,
HostDeviceVector<GradientPair> *out_gpair) override {
out_gpair->Resize(preds->Size());
std::vector<bst_float>& preds_h = preds->HostVector();
out_gpair->Resize(preds.Size());
const std::vector<bst_float>& preds_h = preds.HostVector();
std::vector<GradientPair>& out_gpair_h = out_gpair->HostVector();
const std::vector<bst_float>& labels_h = info.labels_.HostVector();
for (size_t i = 0; i < preds_h.size(); ++i) {
bst_float w = info.GetWeight(i);
// scale the negative examples!
if (info.labels_[i] == 0.0f) w *= param_.scale_neg_weight;
if (labels_h[i] == 0.0f) w *= param_.scale_neg_weight;
// logistic transformation
bst_float p = 1.0f / (1.0f + std::exp(-preds_h[i]));
// this is the gradient
bst_float grad = (p - info.labels_[i]) * w;
bst_float grad = (p - labels_h[i]) * w;
// this is the second order gradient
bst_float hess = p * (1.0f - p) * w;
out_gpair_h.at(i) = GradientPair(grad, hess);

51
plugin/lz4/sparse_page_lz4_format.cc
View File

@ -177,15 +177,17 @@ class SparsePageLZ4Format : public SparsePageFormat {
}
bool Read(SparsePage* page, dmlc::SeekStream* fi) override {
if (!fi->Read(&(page->offset))) return false;
CHECK_NE(page->offset.size(), 0) << "Invalid SparsePage file";
auto& offset_vec = page->offset.HostVector();
auto& data_vec = page->data.HostVector();
if (!fi->Read(&(offset_vec))) return false;
CHECK_NE(offset_vec.size(), 0) << "Invalid SparsePage file";
this->LoadIndexValue(fi);
page->data.resize(page->offset.back());
data_vec.resize(offset_vec.back());
CHECK_EQ(index_.data.size(), value_.data.size());
CHECK_EQ(index_.data.size(), page->data.size());
for (size_t i = 0; i < page->data.size(); ++i) {
page->data[i] = Entry(index_.data[i] + min_index_, value_.data[i]);
CHECK_EQ(index_.data.size(), data_vec.size());
for (size_t i = 0; i < data_vec.size(); ++i) {
data_vec[i] = Entry(index_.data[i] + min_index_, value_.data[i]);
}
return true;
}
@ -195,24 +197,25 @@ class SparsePageLZ4Format : public SparsePageFormat {
const std::vector<bst_uint>& sorted_index_set) override {
if (!fi->Read(&disk_offset_)) return false;
this->LoadIndexValue(fi);
page->offset.clear();
page->offset.push_back(0);
auto& offset_vec = page->offset.HostVector();
auto& data_vec = page->data.HostVector();
offset_vec.clear();
offset_vec.push_back(0);
for (bst_uint cid : sorted_index_set) {
page->offset.push_back(
page->offset.back() + disk_offset_[cid + 1] - disk_offset_[cid]);
offset_vec.push_back(
offset_vec.back() + disk_offset_[cid + 1] - disk_offset_[cid]);
}
page->data.resize(page->offset.back());
data_vec.resize(offset_vec.back());
CHECK_EQ(index_.data.size(), value_.data.size());
CHECK_EQ(index_.data.size(), disk_offset_.back());
for (size_t i = 0; i < sorted_index_set.size(); ++i) {
bst_uint cid = sorted_index_set[i];
size_t dst_begin = page->offset[i];
size_t dst_begin = offset_vec[i];
size_t src_begin = disk_offset_[cid];
size_t num = disk_offset_[cid + 1] - disk_offset_[cid];
for (size_t j = 0; j < num; ++j) {
page->data[dst_begin + j] = Entry(
data_vec[dst_begin + j] = Entry(
index_.data[src_begin + j] + min_index_, value_.data[src_begin + j]);
}
}
@ -220,22 +223,24 @@ class SparsePageLZ4Format : public SparsePageFormat {
}
void Write(const SparsePage& page, dmlc::Stream* fo) override {
CHECK(page.offset.size() != 0 && page.offset[0] == 0);
CHECK_EQ(page.offset.back(), page.data.size());
fo->Write(page.offset);
const auto& offset_vec = page.offset.HostVector();
const auto& data_vec = page.data.HostVector();
CHECK(offset_vec.size() != 0 && offset_vec[0] == 0);
CHECK_EQ(offset_vec.back(), data_vec.size());
fo->Write(offset_vec);
min_index_ = page.base_rowid;
fo->Write(&min_index_, sizeof(min_index_));
index_.data.resize(page.data.size());
value_.data.resize(page.data.size());
index_.data.resize(data_vec.size());
value_.data.resize(data_vec.size());
for (size_t i = 0; i < page.data.size(); ++i) {
bst_uint idx = page.data[i].index - min_index_;
for (size_t i = 0; i < data_vec.size(); ++i) {
bst_uint idx = data_vec[i].index - min_index_;
CHECK_LE(idx, static_cast<bst_uint>(std::numeric_limits<StorageIndex>::max()))
<< "The storage index is chosen to limited to smaller equal than "
<< std::numeric_limits<StorageIndex>::max()
<< "min_index=" << min_index_;
index_.data[i] = static_cast<StorageIndex>(idx);
value_.data[i] = page.data[i].fvalue;
value_.data[i] = data_vec[i].fvalue;
}
index_.InitCompressChunks(kChunkSize, kMaxChunk);
@ -259,7 +264,7 @@ class SparsePageLZ4Format : public SparsePageFormat {
raw_bytes_value_ += value_.RawBytes();
encoded_bytes_index_ += index_.EncodedBytes();
encoded_bytes_value_ += value_.EncodedBytes();
raw_bytes_ += page.offset.size() * sizeof(size_t);
raw_bytes_ += offset_vec.size() * sizeof(size_t);
}
inline void LoadIndexValue(dmlc::SeekStream* fi) {

99
src/c_api/c_api.cc
View File

@ -250,20 +250,22 @@ XGB_DLL int XGDMatrixCreateFromCSREx(const size_t* indptr,
API_BEGIN();
data::SimpleCSRSource& mat = *source;
mat.page_.offset.reserve(nindptr);
mat.page_.data.reserve(nelem);
mat.page_.offset.resize(1);
mat.page_.offset[0] = 0;
auto& offset_vec = mat.page_.offset.HostVector();
auto& data_vec = mat.page_.data.HostVector();
offset_vec.reserve(nindptr);
data_vec.reserve(nelem);
offset_vec.resize(1);
offset_vec[0] = 0;
size_t num_column = 0;
for (size_t i = 1; i < nindptr; ++i) {
for (size_t j = indptr[i - 1]; j < indptr[i]; ++j) {
if (!common::CheckNAN(data[j])) {
// automatically skip nan.
mat.page_.data.emplace_back(Entry(indices[j], data[j]));
data_vec.emplace_back(Entry(indices[j], data[j]));
num_column = std::max(num_column, static_cast<size_t>(indices[j] + 1));
}
}
mat.page_.offset.push_back(mat.page_.data.size());
offset_vec.push_back(mat.page_.data.Size());
}
mat.info.num_col_ = num_column;
@ -273,7 +275,7 @@ XGB_DLL int XGDMatrixCreateFromCSREx(const size_t* indptr,
mat.info.num_col_ = num_col;
}
mat.info.num_row_ = nindptr - 1;
mat.info.num_nonzero_ = mat.page_.data.size();
mat.info.num_nonzero_ = mat.page_.data.Size();
*out = new std::shared_ptr<DMatrix>(DMatrix::Create(std::move(source)));
API_END();
}
@ -305,7 +307,9 @@ XGB_DLL int XGDMatrixCreateFromCSCEx(const size_t* col_ptr,
// FIXME: User should be able to control number of threads
const int nthread = omp_get_max_threads();
data::SimpleCSRSource& mat = *source;
common::ParallelGroupBuilder<Entry> builder(&mat.page_.offset, &mat.page_.data);
auto& offset_vec = mat.page_.offset.HostVector();
auto& data_vec = mat.page_.data.HostVector();
common::ParallelGroupBuilder<Entry> builder(&offset_vec, &data_vec);
builder.InitBudget(0, nthread);
size_t ncol = nindptr - 1; // NOLINT(*)
#pragma omp parallel for schedule(static)
@ -329,15 +333,16 @@ XGB_DLL int XGDMatrixCreateFromCSCEx(const size_t* col_ptr,
}
}
}
mat.info.num_row_ = mat.page_.offset.size() - 1;
mat.info.num_row_ = mat.page_.offset.Size() - 1;
if (num_row > 0) {
CHECK_LE(mat.info.num_row_, num_row);
// provision for empty rows at the bottom of matrix
auto& offset_vec = mat.page_.offset.HostVector();
for (uint64_t i = mat.info.num_row_; i < static_cast<uint64_t>(num_row); ++i) {
mat.page_.offset.push_back(mat.page_.offset.back());
offset_vec.push_back(offset_vec.back());
}
mat.info.num_row_ = num_row;
CHECK_EQ(mat.info.num_row_, mat.page_.offset.size() - 1); // sanity check
CHECK_EQ(mat.info.num_row_, offset_vec.size() - 1); // sanity check
}
mat.info.num_col_ = ncol;
mat.info.num_nonzero_ = nelem;
@ -368,7 +373,9 @@ XGB_DLL int XGDMatrixCreateFromMat(const bst_float* data,
API_BEGIN();
data::SimpleCSRSource& mat = *source;
mat.page_.offset.resize(1+nrow);
auto& offset_vec = mat.page_.offset.HostVector();
auto& data_vec = mat.page_.data.HostVector();
offset_vec.resize(1+nrow);
bool nan_missing = common::CheckNAN(missing);
mat.info.num_row_ = nrow;
mat.info.num_col_ = ncol;
@ -388,9 +395,9 @@ XGB_DLL int XGDMatrixCreateFromMat(const bst_float* data,
}
}
}
mat.page_.offset[i+1] = mat.page_.offset[i] + nelem;
offset_vec[i+1] = offset_vec[i] + nelem;
}
mat.page_.data.resize(mat.page_.data.size() + mat.page_.offset.back());
data_vec.resize(mat.page_.data.Size() + offset_vec.back());
data = data0;
for (xgboost::bst_ulong i = 0; i < nrow; ++i, data += ncol) {
@ -399,14 +406,14 @@ XGB_DLL int XGDMatrixCreateFromMat(const bst_float* data,
if (common::CheckNAN(data[j])) {
} else {
if (nan_missing || data[j] != missing) {
mat.page_.data[mat.page_.offset[i] + matj] = Entry(j, data[j]);
data_vec[offset_vec[i] + matj] = Entry(j, data[j]);
++matj;
}
}
}
}
mat.info.num_nonzero_ = mat.page_.data.size();
mat.info.num_nonzero_ = mat.page_.data.Size();
*out = new std::shared_ptr<DMatrix>(DMatrix::Create(std::move(source)));
API_END();
}
@ -461,7 +468,9 @@ XGB_DLL int XGDMatrixCreateFromMat_omp(const bst_float* data, // NOLINT
std::unique_ptr<data::SimpleCSRSource> source(new data::SimpleCSRSource());
data::SimpleCSRSource& mat = *source;
mat.page_.offset.resize(1+nrow);
auto& offset_vec = mat.page_.offset.HostVector();
auto& data_vec = mat.page_.data.HostVector();
offset_vec.resize(1+nrow);
mat.info.num_row_ = nrow;
mat.info.num_col_ = ncol;
@ -487,7 +496,7 @@ XGB_DLL int XGDMatrixCreateFromMat_omp(const bst_float* data, // NOLINT
++nelem;
}
}
mat.page_.offset[i+1] = nelem;
offset_vec[i+1] = nelem;
}
}
// Inform about any NaNs and resize data matrix
@ -496,8 +505,8 @@ XGB_DLL int XGDMatrixCreateFromMat_omp(const bst_float* data, // NOLINT
}
// do cumulative sum (to avoid otherwise need to copy)
PrefixSum(&mat.page_.offset[0], mat.page_.offset.size());
mat.page_.data.resize(mat.page_.data.size() + mat.page_.offset.back());
PrefixSum(&offset_vec[0], offset_vec.size());
data_vec.resize(mat.page_.data.Size() + offset_vec.back());
// Fill data matrix (now that know size, no need for slow push_back())
#pragma omp parallel num_threads(nthread)
@ -508,7 +517,7 @@ XGB_DLL int XGDMatrixCreateFromMat_omp(const bst_float* data, // NOLINT
for (xgboost::bst_ulong j = 0; j < ncol; ++j) {
if (common::CheckNAN(data[ncol * i + j])) {
} else if (nan_missing || data[ncol * i + j] != missing) {
mat.page_.data[mat.page_.offset[i] + matj] =
data_vec[offset_vec[i] + matj] =
Entry(j, data[ncol * i + j]);
++matj;
}
@ -518,7 +527,7 @@ XGB_DLL int XGDMatrixCreateFromMat_omp(const bst_float* data, // NOLINT
// restore omp state
omp_set_num_threads(nthread_orig);
mat.info.num_nonzero_ = mat.page_.data.size();
mat.info.num_nonzero_ = mat.page_.data.Size();
*out = new std::shared_ptr<DMatrix>(DMatrix::Create(std::move(source)));
API_END();
}
@ -611,10 +620,11 @@ XGB_DLL int XGDMatrixCreateFromDT(void** data, const char** feature_stypes,
std::unique_ptr<data::SimpleCSRSource> source(new data::SimpleCSRSource());
data::SimpleCSRSource& mat = *source;
mat.page_.offset.resize(1 + nrow);
mat.page_.offset.Resize(1 + nrow);
mat.info.num_row_ = nrow;
mat.info.num_col_ = ncol;
auto& page_offset = mat.page_.offset.HostVector();
#pragma omp parallel num_threads(nthread)
{
// Count elements per row, column by column
@ -624,15 +634,17 @@ XGB_DLL int XGDMatrixCreateFromDT(void** data, const char** feature_stypes,
for (omp_ulong i = 0; i < nrow; ++i) {
float val = DTGetValue(data[j], dtype, i);
if (!std::isnan(val)) {
mat.page_.offset[i + 1]++;
page_offset[i + 1]++;
}
}
}
}
// do cumulative sum (to avoid otherwise need to copy)
PrefixSum(&mat.page_.offset[0], mat.page_.offset.size());
PrefixSum(&page_offset[0], page_offset.size());
mat.page_.data.resize(mat.page_.data.size() + mat.page_.offset.back());
mat.page_.data.Resize(mat.page_.data.Size() + page_offset.back());
auto& page_data = mat.page_.data.HostVector();
// Fill data matrix (now that know size, no need for slow push_back())
std::vector<size_t> position(nrow);
@ -644,7 +656,7 @@ XGB_DLL int XGDMatrixCreateFromDT(void** data, const char** feature_stypes,
for (omp_ulong i = 0; i < nrow; ++i) {
float val = DTGetValue(data[j], dtype, i);
if (!std::isnan(val)) {
mat.page_.data[mat.page_.offset[i] + position[i]] = Entry(j, val);
page_data[page_offset[i] + position[i]] = Entry(j, val);
position[i]++;
}
}
@ -654,7 +666,7 @@ XGB_DLL int XGDMatrixCreateFromDT(void** data, const char** feature_stypes,
// restore omp state
omp_set_num_threads(nthread_orig);
mat.info.num_nonzero_ = mat.page_.data.size();
mat.info.num_nonzero_ = mat.page_.data.Size();
*out = new std::shared_ptr<DMatrix>(DMatrix::Create(std::move(source)));
API_END();
}
@ -683,23 +695,32 @@ XGB_DLL int XGDMatrixSliceDMatrix(DMatrixHandle handle,
CHECK(iter->Next());
const auto& batch = iter->Value();
const auto& src_labels = src.info.labels_.ConstHostVector();
const auto& src_weights = src.info.weights_.ConstHostVector();
const auto& src_base_margin = src.info.base_margin_.ConstHostVector();
auto& ret_labels = ret.info.labels_.HostVector();
auto& ret_weights = ret.info.weights_.HostVector();
auto& ret_base_margin = ret.info.base_margin_.HostVector();
auto& offset_vec = ret.page_.offset.HostVector();
auto& data_vec = ret.page_.data.HostVector();
for (xgboost::bst_ulong i = 0; i < len; ++i) {
const int ridx = idxset[i];
auto inst = batch[ridx];
CHECK_LT(static_cast<xgboost::bst_ulong>(ridx), batch.Size());
ret.page_.data.insert(ret.page_.data.end(), inst.data(),
data_vec.insert(data_vec.end(), inst.data(),
inst.data() + inst.size());
ret.page_.offset.push_back(ret.page_.offset.back() + inst.size());
offset_vec.push_back(offset_vec.back() + inst.size());
ret.info.num_nonzero_ += inst.size();
if (src.info.labels_.size() != 0) {
ret.info.labels_.push_back(src.info.labels_[ridx]);
if (src_labels.size() != 0) {
ret_labels.push_back(src_labels[ridx]);
}
if (src.info.weights_.size() != 0) {
ret.info.weights_.push_back(src.info.weights_[ridx]);
if (src_weights.size() != 0) {
ret_weights.push_back(src_weights[ridx]);
}
if (src.info.base_margin_.size() != 0) {
ret.info.base_margin_.push_back(src.info.base_margin_[ridx]);
if (src_base_margin.size() != 0) {
ret_base_margin.push_back(src_base_margin[ridx]);
}
if (src.info.root_index_.size() != 0) {
ret.info.root_index_.push_back(src.info.root_index_[ridx]);
@ -771,11 +792,11 @@ XGB_DLL int XGDMatrixGetFloatInfo(const DMatrixHandle handle,
const MetaInfo& info = static_cast<std::shared_ptr<DMatrix>*>(handle)->get()->Info();
const std::vector<bst_float>* vec = nullptr;
if (!std::strcmp(field, "label")) {
vec = &info.labels_;
vec = &info.labels_.HostVector();
} else if (!std::strcmp(field, "weight")) {
vec = &info.weights_;
vec = &info.weights_.HostVector();
} else if (!std::strcmp(field, "base_margin")) {
vec = &info.base_margin_;
vec = &info.base_margin_.HostVector();
} else {
LOG(FATAL) << "Unknown float field name " << field;
}

2
src/cli_main.cc
View File

@ -332,7 +332,7 @@ void CLIPredict(const CLIParam& param) {
std::unique_ptr<dmlc::Stream> fo(
dmlc::Stream::Create(param.name_pred.c_str(), "w"));
dmlc::ostream os(fo.get());
for (bst_float p : preds.HostVector()) {
for (bst_float p : preds.ConstHostVector()) {
os << std::setprecision(std::numeric_limits<bst_float>::max_digits10 + 2)
<< p << '\n';
}

4
src/common/hist_util.cc
View File

@ -35,6 +35,7 @@ void HistCutMatrix::Init(DMatrix* p_fmat, uint32_t max_num_bins) {
auto iter = p_fmat->RowIterator();
iter->BeforeFirst();
const auto& weights = info.weights_.HostVector();
while (iter->Next()) {
auto &batch = iter->Value();
#pragma omp parallel num_threads(nthread)
@ -50,7 +51,8 @@ void HistCutMatrix::Init(DMatrix* p_fmat, uint32_t max_num_bins) {
SparsePage::Inst inst = batch[i];
for (auto& ins : inst) {
if (ins.index >= begin && ins.index < end) {
sketchs[ins.index].Push(ins.fvalue, info.GetWeight(ridx));
sketchs[ins.index].Push(ins.fvalue,
weights.size() > 0 ? weights[ridx] : 1.0f);
}
}
}

26
src/common/hist_util.cu
View File

@ -118,7 +118,7 @@ struct GPUSketcher {
void Init(const SparsePage& row_batch, const MetaInfo& info) {
num_cols_ = info.num_col_;
has_weights_ = info.weights_.size() > 0;
has_weights_ = info.weights_.Size() > 0;
// find the batch size
if (param_.gpu_batch_nrows == 0) {
@ -282,19 +282,23 @@ struct GPUSketcher {
size_t batch_row_end = std::min((gpu_batch + 1) * gpu_batch_nrows_,
static_cast<size_t>(n_rows_));
size_t batch_nrows = batch_row_end - batch_row_begin;
size_t n_entries =
row_batch.offset[row_begin_ + batch_row_end] -
row_batch.offset[row_begin_ + batch_row_begin];
const auto& offset_vec = row_batch.offset.HostVector();
const auto& data_vec = row_batch.data.HostVector();
size_t n_entries = offset_vec[row_begin_ + batch_row_end] -
offset_vec[row_begin_ + batch_row_begin];
// copy the batch to the GPU
dh::safe_cuda
(cudaMemcpy(entries_.data().get(),
&row_batch.data[row_batch.offset[row_begin_ + batch_row_begin]],
data_vec.data() + offset_vec[row_begin_ + batch_row_begin],
n_entries * sizeof(Entry), cudaMemcpyDefault));
// copy the weights if necessary
if (has_weights_) {
const auto& weights_vec = info.weights_.HostVector();
dh::safe_cuda
(cudaMemcpy(weights_.data().get(),
info.weights_.data() + row_begin_ + batch_row_begin,
weights_vec.data() + row_begin_ + batch_row_begin,
batch_nrows * sizeof(bst_float), cudaMemcpyDefault));
}
@ -310,7 +314,7 @@ struct GPUSketcher {
row_ptrs_.data().get() + batch_row_begin,
has_weights_ ? weights_.data().get() : nullptr, entries_.data().get(),
gpu_batch_nrows_, num_cols_,
row_batch.offset[row_begin_ + batch_row_begin], batch_nrows);
offset_vec[row_begin_ + batch_row_begin], batch_nrows);
dh::safe_cuda(cudaGetLastError()); // NOLINT
dh::safe_cuda(cudaDeviceSynchronize()); // NOLINT
@ -331,13 +335,11 @@ struct GPUSketcher {
void Sketch(const SparsePage& row_batch, const MetaInfo& info) {
// copy rows to the device
dh::safe_cuda(cudaSetDevice(device_));
const auto& offset_vec = row_batch.offset.HostVector();
row_ptrs_.resize(n_rows_ + 1);
thrust::copy(row_batch.offset.data() + row_begin_,
row_batch.offset.data() + row_end_ + 1,
row_ptrs_.begin());
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_);
for (size_t gpu_batch = 0; gpu_batch < gpu_nbatches; ++gpu_batch) {
SketchBatch(row_batch, info, gpu_batch);
}

81
src/common/host_device_vector.cc
View File

@ -6,7 +6,8 @@
// dummy implementation of HostDeviceVector in case CUDA is not used
#include <xgboost/base.h>
#include <xgboost/data.h>
#include <cstdint>
#include <utility>
#include "./host_device_vector.h"
@ -14,25 +15,27 @@ namespace xgboost {
template <typename T>
struct HostDeviceVectorImpl {
explicit HostDeviceVectorImpl(size_t size, T v) : data_h_(size, v) {}
HostDeviceVectorImpl(std::initializer_list<T> init) : data_h_(init) {}
explicit HostDeviceVectorImpl(std::vector<T> init) : data_h_(std::move(init)) {}
explicit HostDeviceVectorImpl(size_t size, T v) : data_h_(size, v), distribution_() {}
HostDeviceVectorImpl(std::initializer_list<T> init) : data_h_(init), distribution_() {}
explicit HostDeviceVectorImpl(std::vector<T> init) : data_h_(std::move(init)), distribution_() {}
std::vector<T> data_h_;
GPUDistribution distribution_;
};
template <typename T>
HostDeviceVector<T>::HostDeviceVector(size_t size, T v, GPUSet devices) : impl_(nullptr) {
HostDeviceVector<T>::HostDeviceVector(size_t size, T v, GPUDistribution distribution)
: impl_(nullptr) {
impl_ = new HostDeviceVectorImpl<T>(size, v);
}
template <typename T>
HostDeviceVector<T>::HostDeviceVector(std::initializer_list<T> init, GPUSet devices)
HostDeviceVector<T>::HostDeviceVector(std::initializer_list<T> init, GPUDistribution distribution)
: impl_(nullptr) {
impl_ = new HostDeviceVectorImpl<T>(init);
}
template <typename T>
HostDeviceVector<T>::HostDeviceVector(const std::vector<T>& init, GPUSet devices)
HostDeviceVector<T>::HostDeviceVector(const std::vector<T>& init, GPUDistribution distribution)
: impl_(nullptr) {
impl_ = new HostDeviceVectorImpl<T>(init);
}
@ -44,33 +47,69 @@ HostDeviceVector<T>::~HostDeviceVector() {
delete tmp;
}
template <typename T>
HostDeviceVector<T>::HostDeviceVector(const HostDeviceVector<T>& other)
: impl_(nullptr) {
impl_ = new HostDeviceVectorImpl<T>(*other.impl_);
}
template <typename T>
HostDeviceVector<T>& HostDeviceVector<T>::operator=(const HostDeviceVector<T>& other) {
if (this == &other) {
return *this;
}
delete impl_;
impl_ = new HostDeviceVectorImpl<T>(*other.impl_);
return *this;
}
template <typename T>
size_t HostDeviceVector<T>::Size() const { return impl_->data_h_.size(); }
template <typename T>
GPUSet HostDeviceVector<T>::Devices() const { return GPUSet::Empty(); }
template <typename T>
const GPUDistribution& HostDeviceVector<T>::Distribution() const {
return impl_->distribution_;
}
template <typename T>
T* HostDeviceVector<T>::DevicePointer(int device) { return nullptr; }
template <typename T>
const T* HostDeviceVector<T>::ConstDevicePointer(int device) const {
return nullptr;
}
template <typename T>
common::Span<T> HostDeviceVector<T>::DeviceSpan(int device) {
return common::Span<T>();
}
template <typename T>
common::Span<const T> HostDeviceVector<T>::ConstDeviceSpan(int device) const {
return common::Span<const T>();
}
template <typename T>
std::vector<T>& HostDeviceVector<T>::HostVector() { return impl_->data_h_; }
template <typename T>
const std::vector<T>& HostDeviceVector<T>::ConstHostVector() const {
return impl_->data_h_;
}
template <typename T>
void HostDeviceVector<T>::Resize(size_t new_size, T v) {
impl_->data_h_.resize(new_size, v);
}
template <typename T>
size_t HostDeviceVector<T>::DeviceStart(int device) { return 0; }
size_t HostDeviceVector<T>::DeviceStart(int device) const { return 0; }
template <typename T>
size_t HostDeviceVector<T>::DeviceSize(int device) { return 0; }
size_t HostDeviceVector<T>::DeviceSize(int device) const { return 0; }
template <typename T>
void HostDeviceVector<T>::Fill(T v) {
@ -78,9 +117,9 @@ void HostDeviceVector<T>::Fill(T v) {
}
template <typename T>
void HostDeviceVector<T>::Copy(HostDeviceVector<T>* other) {
CHECK_EQ(Size(), other->Size());
std::copy(other->HostVector().begin(), other->HostVector().end(), HostVector().begin());
void HostDeviceVector<T>::Copy(const HostDeviceVector<T>& other) {
CHECK_EQ(Size(), other.Size());
std::copy(other.HostVector().begin(), other.HostVector().end(), HostVector().begin());
}
template <typename T>
@ -96,13 +135,27 @@ void HostDeviceVector<T>::Copy(std::initializer_list<T> other) {
}
template <typename T>
void HostDeviceVector<T>::Reshard(GPUSet devices) { }
bool HostDeviceVector<T>::HostCanAccess(GPUAccess access) const {
return true;
}
template <typename T>
bool HostDeviceVector<T>::DeviceCanAccess(int device, GPUAccess access) const {
return false;
}
template <typename T>
void HostDeviceVector<T>::Reshard(const GPUDistribution& distribution) const { }
template <typename T>
void HostDeviceVector<T>::Reshard(GPUSet devices) const { }
// explicit instantiations are required, as HostDeviceVector isn't header-only
template class HostDeviceVector<bst_float>;
template class HostDeviceVector<GradientPair>;
template class HostDeviceVector<unsigned int>;
template class HostDeviceVector<int>;
template class HostDeviceVector<Entry>;
template class HostDeviceVector<size_t>;
} // namespace xgboost

424
src/common/host_device_vector.cu
View File

@ -2,119 +2,159 @@
* Copyright 2017 XGBoost contributors
*/
#include <thrust/fill.h>
#include "./host_device_vector.h"
#include <thrust/fill.h>
#include <xgboost/data.h>
#include <algorithm>
#include <cstdint>
#include <mutex>
#include "./device_helpers.cuh"
namespace xgboost {
// the handler to call instead of cudaSetDevice; only used for testing
static void (*cudaSetDeviceHandler)(int) = nullptr; // NOLINT
void SetCudaSetDeviceHandler(void (*handler)(int)) {
cudaSetDeviceHandler = handler;
}
// wrapper over access with useful methods
class Permissions {
GPUAccess access_;
explicit Permissions(GPUAccess access) : access_(access) {}
public:
Permissions() : access_(GPUAccess::kNone) {}
explicit Permissions(bool perm)
: access_(perm ? GPUAccess::kWrite : GPUAccess::kNone) {}
bool CanRead() const { return access_ >= kRead; }
bool CanWrite() const { return access_ == kWrite; }
bool CanAccess(GPUAccess access) const { return access_ >= access; }
void Grant(GPUAccess access) { access_ = std::max(access_, access); }
void DenyComplementary(GPUAccess compl_access) {
access_ = std::min(access_, GPUAccess::kWrite - compl_access);
}
Permissions Complementary() const {
return Permissions(GPUAccess::kWrite - access_);
}
};
template <typename T>
struct HostDeviceVectorImpl {
struct DeviceShard {
DeviceShard() : index_(-1), device_(-1), start_(0), on_d_(false), vec_(nullptr) {}
static size_t ShardStart(size_t size, int ndevices, int index) {
size_t portion = dh::DivRoundUp(size, ndevices);
size_t begin = index * portion;
begin = begin > size ? size : begin;
return begin;
}
static size_t ShardSize(size_t size, int ndevices, int index) {
size_t portion = dh::DivRoundUp(size, ndevices);
size_t begin = index * portion, end = (index + 1) * portion;
begin = begin > size ? size : begin;
end = end > size ? size : end;
return end - begin;
}
DeviceShard()
: index_(-1), proper_size_(0), device_(-1), start_(0), perm_d_(false),
cached_size_(~0), vec_(nullptr) {}
void Init(HostDeviceVectorImpl<T>* vec, int device) {
if (vec_ == nullptr) { vec_ = vec; }
CHECK_EQ(vec, vec_);
device_ = device;
index_ = vec_->devices_.Index(device);
size_t size_h = vec_->Size();
int ndevices = vec_->devices_.Size();
start_ = ShardStart(size_h, ndevices, index_);
size_t size_d = ShardSize(size_h, ndevices, index_);
dh::safe_cuda(cudaSetDevice(device_));
data_.resize(size_d);
on_d_ = !vec_->on_h_;
index_ = vec_->distribution_.devices_.Index(device);
LazyResize(vec_->Size());
perm_d_ = vec_->perm_h_.Complementary();
}
void ScatterFrom(const T* begin) {
// TODO(canonizer): avoid full copy of host data
LazySyncDevice();
dh::safe_cuda(cudaSetDevice(device_));
LazySyncDevice(GPUAccess::kWrite);
SetDevice();
dh::safe_cuda(cudaMemcpy(data_.data().get(), begin + start_,
data_.size() * sizeof(T), cudaMemcpyDefault));
}
void GatherTo(thrust::device_ptr<T> begin) {
LazySyncDevice();
dh::safe_cuda(cudaSetDevice(device_));
LazySyncDevice(GPUAccess::kRead);
SetDevice();
dh::safe_cuda(cudaMemcpy(begin.get() + start_, data_.data().get(),
data_.size() * sizeof(T), cudaMemcpyDefault));
proper_size_ * sizeof(T), cudaMemcpyDefault));
}
void Fill(T v) {
// TODO(canonizer): avoid full copy of host data
LazySyncDevice();
dh::safe_cuda(cudaSetDevice(device_));
LazySyncDevice(GPUAccess::kWrite);
SetDevice();
thrust::fill(data_.begin(), data_.end(), v);
}
void Copy(DeviceShard* other) {
// TODO(canonizer): avoid full copy of host data for this (but not for other)
LazySyncDevice();
other->LazySyncDevice();
dh::safe_cuda(cudaSetDevice(device_));
LazySyncDevice(GPUAccess::kWrite);
other->LazySyncDevice(GPUAccess::kRead);
SetDevice();
dh::safe_cuda(cudaMemcpy(data_.data().get(), other->data_.data().get(),
data_.size() * sizeof(T), cudaMemcpyDefault));
}
void LazySyncHost() {
dh::safe_cuda(cudaSetDevice(device_));
void LazySyncHost(GPUAccess access) {
SetDevice();
dh::safe_cuda(cudaMemcpy(vec_->data_h_.data() + start_,
data_.data().get(), data_.size() * sizeof(T),
data_.data().get(), proper_size_ * sizeof(T),
cudaMemcpyDeviceToHost));
on_d_ = false;
perm_d_.DenyComplementary(access);
}
void LazySyncDevice() {
if (on_d_) { return; }
void LazyResize(size_t new_size) {
if (new_size == cached_size_) { return; }
// resize is required
int ndevices = vec_->distribution_.devices_.Size();
start_ = vec_->distribution_.ShardStart(new_size, index_);
proper_size_ = vec_->distribution_.ShardProperSize(new_size, index_);
size_t size_d = vec_->distribution_.ShardSize(new_size, index_);
SetDevice();
data_.resize(size_d);
cached_size_ = new_size;
}
void LazySyncDevice(GPUAccess access) {
if (perm_d_.CanAccess(access)) { return; }
if (perm_d_.CanRead()) {
// deny read to the host
perm_d_.Grant(access);
std::lock_guard<std::mutex> lock(vec_->mutex_);
vec_->perm_h_.DenyComplementary(access);
return;
}
// data is on the host
size_t size_h = vec_->data_h_.size();
int ndevices = vec_->devices_.Size();
start_ = ShardStart(size_h, ndevices, index_);
size_t size_d = ShardSize(size_h, ndevices, index_);
LazyResize(size_h);
SetDevice();
dh::safe_cuda(
cudaMemcpy(data_.data().get(), vec_->data_h_.data() + start_,
data_.size() * sizeof(T), cudaMemcpyHostToDevice));
perm_d_.Grant(access);
std::lock_guard<std::mutex> lock(vec_->mutex_);
vec_->perm_h_.DenyComplementary(access);
vec_->size_d_ = size_h;
}
void SetDevice() {
if (cudaSetDeviceHandler == nullptr) {
dh::safe_cuda(cudaSetDevice(device_));
data_.resize(size_d);
dh::safe_cuda(cudaMemcpy(data_.data().get(),
vec_->data_h_.data() + start_,
size_d * sizeof(T), cudaMemcpyHostToDevice));
on_d_ = true;
// this may cause a race condition if LazySyncDevice() is called
// from multiple threads in parallel;
// however, the race condition is benign, and will not cause problems
vec_->on_h_ = false;
vec_->size_d_ = vec_->data_h_.size();
} else {
(*cudaSetDeviceHandler)(device_);
}
}
int index_;
int device_;
thrust::device_vector<T> data_;
// cached vector size
size_t cached_size_;
size_t start_;
// true if there is an up-to-date copy of data on device, false otherwise
bool on_d_;
// size of the portion to copy back to the host
size_t proper_size_;
Permissions perm_d_;
HostDeviceVectorImpl<T>* vec_;
};
HostDeviceVectorImpl(size_t size, T v, GPUSet devices)
: devices_(devices), on_h_(devices.IsEmpty()), size_d_(0) {
if (!devices.IsEmpty()) {
HostDeviceVectorImpl(size_t size, T v, GPUDistribution distribution)
: distribution_(distribution), perm_h_(distribution.IsEmpty()), size_d_(0) {
if (!distribution_.IsEmpty()) {
size_d_ = size;
InitShards();
Fill(v);
@ -123,11 +163,16 @@ struct HostDeviceVectorImpl {
}
}
// required, as a new std::mutex has to be created
HostDeviceVectorImpl(const HostDeviceVectorImpl<T>& other)
: data_h_(other.data_h_), perm_h_(other.perm_h_), size_d_(other.size_d_),
distribution_(other.distribution_), mutex_(), shards_(other.shards_) {}
// Init can be std::vector<T> or std::initializer_list<T>
template <class Init>
HostDeviceVectorImpl(const Init& init, GPUSet devices)
: devices_(devices), on_h_(devices.IsEmpty()), size_d_(0) {
if (!devices.IsEmpty()) {
HostDeviceVectorImpl(const Init& init, GPUDistribution distribution)
: distribution_(distribution), perm_h_(distribution.IsEmpty()), size_d_(0) {
if (!distribution_.IsEmpty()) {
size_d_ = init.size();
InitShards();
Copy(init);
@ -137,58 +182,78 @@ struct HostDeviceVectorImpl {
}
void InitShards() {
int ndevices = devices_.Size();
int ndevices = distribution_.devices_.Size();
shards_.resize(ndevices);
dh::ExecuteIndexShards(&shards_, [&](int i, DeviceShard& shard) {
shard.Init(this, devices_[i]);
shard.Init(this, distribution_.devices_[i]);
});
}
HostDeviceVectorImpl(const HostDeviceVectorImpl<T>&) = delete;
HostDeviceVectorImpl(HostDeviceVectorImpl<T>&&) = delete;
void operator=(const HostDeviceVectorImpl<T>&) = delete;
void operator=(HostDeviceVectorImpl<T>&&) = delete;
size_t Size() const { return perm_h_.CanRead() ? data_h_.size() : size_d_; }
size_t Size() const { return on_h_ ? data_h_.size() : size_d_; }
GPUSet Devices() const { return distribution_.devices_; }
GPUSet Devices() const { return devices_; }
const GPUDistribution& Distribution() const { return distribution_; }
T* DevicePointer(int device) {
CHECK(devices_.Contains(device));
LazySyncDevice(device);
return shards_[devices_.Index(device)].data_.data().get();
CHECK(distribution_.devices_.Contains(device));
LazySyncDevice(device, GPUAccess::kWrite);
return shards_[distribution_.devices_.Index(device)].data_.data().get();
}
const T* ConstDevicePointer(int device) {
CHECK(distribution_.devices_.Contains(device));
LazySyncDevice(device, GPUAccess::kRead);
return shards_[distribution_.devices_.Index(device)].data_.data().get();
}
common::Span<T> DeviceSpan(int device) {
CHECK(devices_.Contains(device));
LazySyncDevice(device);
return { shards_[devices_.Index(device)].data_.data().get(),
static_cast<typename common::Span<T>::index_type>(Size()) };
GPUSet devices = distribution_.devices_;
CHECK(devices.Contains(device));
LazySyncDevice(device, GPUAccess::kWrite);
return {shards_[devices.Index(device)].data_.data().get(),
static_cast<typename common::Span<T>::index_type>(Size())};
}
common::Span<const T> ConstDeviceSpan(int device) {
GPUSet devices = distribution_.devices_;
CHECK(devices.Contains(device));
LazySyncDevice(device, GPUAccess::kRead);
return {shards_[devices.Index(device)].data_.data().get(),
static_cast<typename common::Span<const T>::index_type>(Size())};
}
size_t DeviceSize(int device) {
CHECK(devices_.Contains(device));
LazySyncDevice(device);
return shards_[devices_.Index(device)].data_.size();
CHECK(distribution_.devices_.Contains(device));
LazySyncDevice(device, GPUAccess::kRead);
return shards_[distribution_.devices_.Index(device)].data_.size();
}
size_t DeviceStart(int device) {
CHECK(devices_.Contains(device));
LazySyncDevice(device);
return shards_[devices_.Index(device)].start_;
CHECK(distribution_.devices_.Contains(device));
LazySyncDevice(device, GPUAccess::kRead);
return shards_[distribution_.devices_.Index(device)].start_;
}
thrust::device_ptr<T> tbegin(int device) { // NOLINT
return thrust::device_ptr<T>(DevicePointer(device));
}
thrust::device_ptr<const T> tcbegin(int device) { // NOLINT
return thrust::device_ptr<const T>(ConstDevicePointer(device));
}
thrust::device_ptr<T> tend(int device) { // NOLINT
return tbegin(device) + DeviceSize(device);
}
void ScatterFrom(thrust::device_ptr<T> begin, thrust::device_ptr<T> end) {
thrust::device_ptr<const T> tcend(int device) { // NOLINT
return tcbegin(device) + DeviceSize(device);
}
void ScatterFrom(thrust::device_ptr<const T> begin, thrust::device_ptr<const T> end) {
CHECK_EQ(end - begin, Size());
if (on_h_) {
if (perm_h_.CanWrite()) {
dh::safe_cuda(cudaMemcpy(data_h_.data(), begin.get(),
(end - begin) * sizeof(T),
cudaMemcpyDeviceToHost));
@ -201,7 +266,7 @@ struct HostDeviceVectorImpl {
void GatherTo(thrust::device_ptr<T> begin, thrust::device_ptr<T> end) {
CHECK_EQ(end - begin, Size());
if (on_h_) {
if (perm_h_.CanWrite()) {
dh::safe_cuda(cudaMemcpy(begin.get(), data_h_.data(),
data_h_.size() * sizeof(T),
cudaMemcpyHostToDevice));
@ -211,7 +276,7 @@ struct HostDeviceVectorImpl {
}
void Fill(T v) {
if (on_h_) {
if (perm_h_.CanWrite()) {
std::fill(data_h_.begin(), data_h_.end(), v);
} else {
dh::ExecuteShards(&shards_, [&](DeviceShard& shard) { shard.Fill(v); });
@ -220,10 +285,10 @@ struct HostDeviceVectorImpl {
void Copy(HostDeviceVectorImpl<T>* other) {
CHECK_EQ(Size(), other->Size());
if (on_h_ && other->on_h_) {
if (perm_h_.CanWrite() && other->perm_h_.CanWrite()) {
std::copy(other->data_h_.begin(), other->data_h_.end(), data_h_.begin());
} else {
CHECK(devices_ == other->devices_);
CHECK(distribution_ == other->distribution_);
dh::ExecuteIndexShards(&shards_, [&](int i, DeviceShard& shard) {
shard.Copy(&other->shards_[i]);
});
@ -232,7 +297,7 @@ struct HostDeviceVectorImpl {
void Copy(const std::vector<T>& other) {
CHECK_EQ(Size(), other.size());
if (on_h_) {
if (perm_h_.CanWrite()) {
std::copy(other.begin(), other.end(), data_h_.begin());
} else {
dh::ExecuteShards(&shards_, [&](DeviceShard& shard) {
@ -243,7 +308,7 @@ struct HostDeviceVectorImpl {
void Copy(std::initializer_list<T> other) {
CHECK_EQ(Size(), other.size());
if (on_h_) {
if (perm_h_.CanWrite()) {
std::copy(other.begin(), other.end(), data_h_.begin());
} else {
dh::ExecuteShards(&shards_, [&](DeviceShard& shard) {
@ -253,72 +318,117 @@ struct HostDeviceVectorImpl {
}
std::vector<T>& HostVector() {
LazySyncHost();
LazySyncHost(GPUAccess::kWrite);
return data_h_;
}
void Reshard(GPUSet new_devices) {
if (devices_ == new_devices)
return;
CHECK(devices_.IsEmpty());
devices_ = new_devices;
const std::vector<T>& ConstHostVector() {
LazySyncHost(GPUAccess::kRead);
return data_h_;
}
void Reshard(const GPUDistribution& distribution) {
if (distribution_ == distribution) { return; }
CHECK(distribution_.IsEmpty());
distribution_ = distribution;
InitShards();
}
void Reshard(GPUSet new_devices) {
if (distribution_.Devices() == new_devices) { return; }
Reshard(GPUDistribution::Block(new_devices));
}
void Resize(size_t new_size, T v) {
if (new_size == Size())
return;
if (Size() == 0 && !devices_.IsEmpty()) {
if (new_size == Size()) { return; }
if (distribution_.IsFixedSize()) {
CHECK_EQ(new_size, distribution_.offsets_.back());
}
if (Size() == 0 && !distribution_.IsEmpty()) {
// fast on-device resize
on_h_ = false;
perm_h_ = Permissions(false);
size_d_ = new_size;
InitShards();
Fill(v);
} else {
// resize on host
LazySyncHost();
LazySyncHost(GPUAccess::kWrite);
data_h_.resize(new_size, v);
}
}
void LazySyncHost() {
if (on_h_)
void LazySyncHost(GPUAccess access) {
if (perm_h_.CanAccess(access)) { return; }
if (perm_h_.CanRead()) {
// data is present, just need to deny access to the device
dh::ExecuteShards(&shards_, [&](DeviceShard& shard) {
shard.perm_d_.DenyComplementary(access);
});
perm_h_.Grant(access);
return;
if (data_h_.size() != size_d_)
data_h_.resize(size_d_);
dh::ExecuteShards(&shards_, [&](DeviceShard& shard) { shard.LazySyncHost(); });
on_h_ = true;
}
if (data_h_.size() != size_d_) { data_h_.resize(size_d_); }
dh::ExecuteShards(&shards_, [&](DeviceShard& shard) {
shard.LazySyncHost(access);
});
perm_h_.Grant(access);
}
void LazySyncDevice(int device) {
CHECK(devices_.Contains(device));
shards_[devices_.Index(device)].LazySyncDevice();
void LazySyncDevice(int device, GPUAccess access) {
GPUSet devices = distribution_.Devices();
CHECK(devices.Contains(device));
shards_[devices.Index(device)].LazySyncDevice(access);
}
bool HostCanAccess(GPUAccess access) { return perm_h_.CanAccess(access); }
bool DeviceCanAccess(int device, GPUAccess access) {
GPUSet devices = distribution_.Devices();
if (!devices.Contains(device)) { return false; }
return shards_[devices.Index(device)].perm_d_.CanAccess(access);
}
std::vector<T> data_h_;
bool on_h_;
Permissions perm_h_;
// the total size of the data stored on the devices
size_t size_d_;
GPUSet devices_;
GPUDistribution distribution_;
// protects size_d_ and perm_h_ when updated from multiple threads
std::mutex mutex_;
std::vector<DeviceShard> shards_;
};
template <typename T>
HostDeviceVector<T>::HostDeviceVector(size_t size, T v, GPUSet devices)
: impl_(nullptr) {
impl_ = new HostDeviceVectorImpl<T>(size, v, devices);
HostDeviceVector<T>::HostDeviceVector
(size_t size, T v, GPUDistribution distribution) : impl_(nullptr) {
impl_ = new HostDeviceVectorImpl<T>(size, v, distribution);
}
template <typename T>
HostDeviceVector<T>::HostDeviceVector(std::initializer_list<T> init, GPUSet devices)
: impl_(nullptr) {
impl_ = new HostDeviceVectorImpl<T>(init, devices);
HostDeviceVector<T>::HostDeviceVector
(std::initializer_list<T> init, GPUDistribution distribution) : impl_(nullptr) {
impl_ = new HostDeviceVectorImpl<T>(init, distribution);
}
template <typename T>
HostDeviceVector<T>::HostDeviceVector(const std::vector<T>& init, GPUSet devices)
HostDeviceVector<T>::HostDeviceVector
(const std::vector<T>& init, GPUDistribution distribution) : impl_(nullptr) {
impl_ = new HostDeviceVectorImpl<T>(init, distribution);
}
template <typename T>
HostDeviceVector<T>::HostDeviceVector(const HostDeviceVector<T>& other)
: impl_(nullptr) {
impl_ = new HostDeviceVectorImpl<T>(init, devices);
impl_ = new HostDeviceVectorImpl<T>(*other.impl_);
}
template <typename T>
HostDeviceVector<T>& HostDeviceVector<T>::operator=
(const HostDeviceVector<T>& other) {
if (this == &other) { return *this; }
delete impl_;
impl_ = new HostDeviceVectorImpl<T>(*other.impl_);
return *this;
}
template <typename T>
@ -335,7 +445,19 @@ template <typename T>
GPUSet HostDeviceVector<T>::Devices() const { return impl_->Devices(); }
template <typename T>
T* HostDeviceVector<T>::DevicePointer(int device) { return impl_->DevicePointer(device); }
const GPUDistribution& HostDeviceVector<T>::Distribution() const {
return impl_->Distribution();
}
template <typename T>
T* HostDeviceVector<T>::DevicePointer(int device) {
return impl_->DevicePointer(device);
}
template <typename T>
const T* HostDeviceVector<T>::ConstDevicePointer(int device) const {
return impl_->ConstDevicePointer(device);
}
template <typename T>
common::Span<T> HostDeviceVector<T>::DeviceSpan(int device) {
@ -343,30 +465,49 @@ common::Span<T> HostDeviceVector<T>::DeviceSpan(int device) {
}
template <typename T>
size_t HostDeviceVector<T>::DeviceStart(int device) { return impl_->DeviceStart(device); }
common::Span<const T> HostDeviceVector<T>::ConstDeviceSpan(int device) const {
return impl_->ConstDeviceSpan(device);
}
template <typename T>
size_t HostDeviceVector<T>::DeviceSize(int device) { return impl_->DeviceSize(device); }
size_t HostDeviceVector<T>::DeviceStart(int device) const {
return impl_->DeviceStart(device);
}
template <typename T>
size_t HostDeviceVector<T>::DeviceSize(int device) const {
return impl_->DeviceSize(device);
}
template <typename T>
thrust::device_ptr<T> HostDeviceVector<T>::tbegin(int device) { // NOLINT
return impl_->tbegin(device);
}
template <typename T>
thrust::device_ptr<const T> HostDeviceVector<T>::tcbegin(int device) const { // NOLINT
return impl_->tcbegin(device);
}
template <typename T>
thrust::device_ptr<T> HostDeviceVector<T>::tend(int device) { // NOLINT
return impl_->tend(device);
}
template <typename T>
thrust::device_ptr<const T> HostDeviceVector<T>::tcend(int device) const { // NOLINT
return impl_->tcend(device);
}
template <typename T>
void HostDeviceVector<T>::ScatterFrom
(thrust::device_ptr<T> begin, thrust::device_ptr<T> end) {
(thrust::device_ptr<const T> begin, thrust::device_ptr<const T> end) {
impl_->ScatterFrom(begin, end);
}
template <typename T>
void HostDeviceVector<T>::GatherTo
(thrust::device_ptr<T> begin, thrust::device_ptr<T> end) {
(thrust::device_ptr<T> begin, thrust::device_ptr<T> end) const {
impl_->GatherTo(begin, end);
}
@ -376,8 +517,8 @@ void HostDeviceVector<T>::Fill(T v) {
}
template <typename T>
void HostDeviceVector<T>::Copy(HostDeviceVector<T>* other) {
impl_->Copy(other->impl_);
void HostDeviceVector<T>::Copy(const HostDeviceVector<T>& other) {
impl_->Copy(other.impl_);
}
template <typename T>
@ -394,10 +535,30 @@ template <typename T>
std::vector<T>& HostDeviceVector<T>::HostVector() { return impl_->HostVector(); }
template <typename T>
void HostDeviceVector<T>::Reshard(GPUSet new_devices) {
const std::vector<T>& HostDeviceVector<T>::ConstHostVector() const {
return impl_->ConstHostVector();
}
template <typename T>
bool HostDeviceVector<T>::HostCanAccess(GPUAccess access) const {
return impl_->HostCanAccess(access);
}
template <typename T>
bool HostDeviceVector<T>::DeviceCanAccess(int device, GPUAccess access) const {
return impl_->DeviceCanAccess(device, access);
}
template <typename T>
void HostDeviceVector<T>::Reshard(GPUSet new_devices) const {
impl_->Reshard(new_devices);
}
template <typename T>
void HostDeviceVector<T>::Reshard(const GPUDistribution& distribution) const {
impl_->Reshard(distribution);
}
template <typename T>
void HostDeviceVector<T>::Resize(size_t new_size, T v) {
impl_->Resize(new_size, v);
@ -406,7 +567,8 @@ void HostDeviceVector<T>::Resize(size_t new_size, T v) {
// explicit instantiations are required, as HostDeviceVector isn't header-only
template class HostDeviceVector<bst_float>;
template class HostDeviceVector<GradientPair>;
template class HostDeviceVector<unsigned int>;
template class HostDeviceVector<int>;
template class HostDeviceVector<Entry>;
template class HostDeviceVector<size_t>;
} // namespace xgboost

209
src/common/host_device_vector.h
View File

@ -1,28 +1,6 @@
/*!
* Copyright 2017 XGBoost contributors
*/
#ifndef XGBOOST_COMMON_HOST_DEVICE_VECTOR_H_
#define XGBOOST_COMMON_HOST_DEVICE_VECTOR_H_
#include <dmlc/logging.h>
#include <algorithm>
#include <cstdlib>
#include <initializer_list>
#include <vector>
#include "gpu_set.h"
#include "span.h"
// only include thrust-related files if host_device_vector.h
// is included from a .cu file
#ifdef __CUDACC__
#include <thrust/device_ptr.h>
#endif
namespace xgboost {
template <typename T> struct HostDeviceVectorImpl;
/**
* @file host_device_vector.h
@ -70,44 +48,203 @@ template <typename T> struct HostDeviceVectorImpl;
* if different threads call these methods with different values of the device argument.
* All other methods are not thread safe.
*/
#ifndef XGBOOST_COMMON_HOST_DEVICE_VECTOR_H_
#define XGBOOST_COMMON_HOST_DEVICE_VECTOR_H_
#include <dmlc/logging.h>
#include <algorithm>
#include <cstdlib>
#include <initializer_list>
#include <vector>
#include "gpu_set.h"
#include "span.h"
// only include thrust-related files if host_device_vector.h
// is included from a .cu file
#ifdef __CUDACC__
#include <thrust/device_ptr.h>
#endif
namespace xgboost {
#ifdef __CUDACC__
// Sets a function to call instead of cudaSetDevice();
// only added for testing
void SetCudaSetDeviceHandler(void (*handler)(int));
#endif
template <typename T> struct HostDeviceVectorImpl;
// Distribution for the HostDeviceVector; it specifies such aspects as the devices it is
// distributed on, whether there are copies of elements from other GPUs as well as the granularity
// of splitting. It may also specify explicit boundaries for devices, in which case the size of the
// array cannot be changed.
class GPUDistribution {
template<typename T> friend struct HostDeviceVectorImpl;
public:
explicit GPUDistribution(GPUSet devices = GPUSet::Empty())
: devices_(devices), granularity_(1), overlap_(0) {}
private:
GPUDistribution(GPUSet devices, int granularity, int overlap,
std::vector<size_t> offsets)
: devices_(devices), granularity_(granularity), overlap_(overlap),
offsets_(std::move(offsets)) {}
public:
static GPUDistribution Block(GPUSet devices) { return GPUDistribution(devices); }
static GPUDistribution Overlap(GPUSet devices, int overlap) {
return GPUDistribution(devices, 1, overlap, std::vector<size_t>());
}
static GPUDistribution Granular(GPUSet devices, int granularity) {
return GPUDistribution(devices, granularity, 0, std::vector<size_t>());
}
static GPUDistribution Explicit(GPUSet devices, std::vector<size_t> offsets) {
return GPUDistribution(devices, 1, 0, offsets);
}
friend bool operator==(const GPUDistribution& a, const GPUDistribution& b) {
return a.devices_ == b.devices_ && a.granularity_ == b.granularity_ &&
a.overlap_ == b.overlap_ && a.offsets_ == b.offsets_;
}
friend bool operator!=(const GPUDistribution& a, const GPUDistribution& b) {
return !(a == b);
}
GPUSet Devices() const { return devices_; }
bool IsEmpty() const { return devices_.IsEmpty(); }
size_t ShardStart(size_t size, int index) const {
if (size == 0) { return 0; }
if (offsets_.size() > 0) {
// explicit offsets are provided
CHECK_EQ(offsets_.back(), size);
return offsets_.at(index);
}
// no explicit offsets
size_t begin = std::min(index * Portion(size), size);
begin = begin > size ? size : begin;
return begin;
}
size_t ShardSize(size_t size, int index) const {
if (size == 0) { return 0; }
if (offsets_.size() > 0) {
// explicit offsets are provided
CHECK_EQ(offsets_.back(), size);
return offsets_.at(index + 1) - offsets_.at(index) +
(index == devices_.Size() - 1 ? overlap_ : 0);
}
size_t portion = Portion(size);
size_t begin = std::min(index * portion, size);
size_t end = std::min((index + 1) * portion + overlap_ * granularity_, size);
return end - begin;
}
size_t ShardProperSize(size_t size, int index) const {
if (size == 0) { return 0; }
return ShardSize(size, index) - (devices_.Size() - 1 > index ? overlap_ : 0);
}
bool IsFixedSize() const { return !offsets_.empty(); }
private:
static size_t DivRoundUp(size_t a, size_t b) { return (a + b - 1) / b; }
static size_t RoundUp(size_t a, size_t b) { return DivRoundUp(a, b) * b; }
size_t Portion(size_t size) const {
return RoundUp
(DivRoundUp
(std::max(static_cast<int64_t>(size - overlap_ * granularity_),
static_cast<int64_t>(1)),
devices_.Size()), granularity_);
}
GPUSet devices_;
int granularity_;
int overlap_;
// explicit offsets for the GPU parts, if any
std::vector<size_t> offsets_;
};
enum GPUAccess {
kNone, kRead,
// write implies read
kWrite
};
inline GPUAccess operator-(GPUAccess a, GPUAccess b) {
return static_cast<GPUAccess>(static_cast<int>(a) - static_cast<int>(b));
}
template <typename T>
class HostDeviceVector {
public:
explicit HostDeviceVector(size_t size = 0, T v = T(),
GPUSet devices = GPUSet::Empty());
HostDeviceVector(std::initializer_list<T> init, GPUSet devices = GPUSet::Empty());
GPUDistribution distribution = GPUDistribution());
HostDeviceVector(std::initializer_list<T> init,
GPUDistribution distribution = GPUDistribution());
explicit HostDeviceVector(const std::vector<T>& init,
GPUSet devices = GPUSet::Empty());
GPUDistribution distribution = GPUDistribution());
~HostDeviceVector();
HostDeviceVector(const HostDeviceVector<T>&) = delete;
HostDeviceVector(HostDeviceVector<T>&&) = delete;
void operator=(const HostDeviceVector<T>&) = delete;
void operator=(HostDeviceVector<T>&&) = delete;
HostDeviceVector(const HostDeviceVector<T>&);
HostDeviceVector<T>& operator=(const HostDeviceVector<T>&);
size_t Size() const;
GPUSet Devices() const;
T* DevicePointer(int device);
const GPUDistribution& Distribution() const;
common::Span<T> DeviceSpan(int device);
common::Span<const T> ConstDeviceSpan(int device) const;
common::Span<const T> DeviceSpan(int device) const { return ConstDeviceSpan(device); }
T* DevicePointer(int device);
const T* ConstDevicePointer(int device) const;
const T* DevicePointer(int device) const { return ConstDevicePointer(device); }
T* HostPointer() { return HostVector().data(); }
size_t DeviceStart(int device);
size_t DeviceSize(int device);
const T* ConstHostPointer() const { return ConstHostVector().data(); }
const T* HostPointer() const { return ConstHostPointer(); }
size_t DeviceStart(int device) const;
size_t DeviceSize(int device) const;
// only define functions returning device_ptr
// if HostDeviceVector.h is included from a .cu file
#ifdef __CUDACC__
thrust::device_ptr<T> tbegin(int device); // NOLINT
thrust::device_ptr<T> tend(int device); // NOLINT
void ScatterFrom(thrust::device_ptr<T> begin, thrust::device_ptr<T> end);
void GatherTo(thrust::device_ptr<T> begin, thrust::device_ptr<T> end);
thrust::device_ptr<const T> tcbegin(int device) const; // NOLINT
thrust::device_ptr<const T> tcend(int device) const; // NOLINT
thrust::device_ptr<const T> tbegin(int device) const { // NOLINT
return tcbegin(device);
}
thrust::device_ptr<const T> tend(int device) const { return tcend(device); } // NOLINT
void ScatterFrom(thrust::device_ptr<const T> begin, thrust::device_ptr<const T> end);
void GatherTo(thrust::device_ptr<T> begin, thrust::device_ptr<T> end) const;
#endif
void Fill(T v);
void Copy(HostDeviceVector<T>* other);
void Copy(const HostDeviceVector<T>& other);
void Copy(const std::vector<T>& other);
void Copy(std::initializer_list<T> other);
std::vector<T>& HostVector();
void Reshard(GPUSet devices);
const std::vector<T>& ConstHostVector() const;
const std::vector<T>& HostVector() const {return ConstHostVector(); }
bool HostCanAccess(GPUAccess access) const;
bool DeviceCanAccess(int device, GPUAccess access) const;
void Reshard(const GPUDistribution& distribution) const;
void Reshard(GPUSet devices) const;
void Resize(size_t new_size, T v = T());
private:

43
src/data/data.cc
View File

@ -25,12 +25,12 @@ namespace xgboost {
// implementation of inline functions
void MetaInfo::Clear() {
num_row_ = num_col_ = num_nonzero_ = 0;
labels_.clear();
labels_.HostVector().clear();
root_index_.clear();
group_ptr_.clear();
qids_.clear();
weights_.clear();
base_margin_.clear();
weights_.HostVector().clear();
base_margin_.HostVector().clear();
}
void MetaInfo::SaveBinary(dmlc::Stream *fo) const {
@ -39,12 +39,12 @@ void MetaInfo::SaveBinary(dmlc::Stream *fo) const {
fo->Write(&num_row_, sizeof(num_row_));
fo->Write(&num_col_, sizeof(num_col_));
fo->Write(&num_nonzero_, sizeof(num_nonzero_));
fo->Write(labels_);
fo->Write(labels_.HostVector());
fo->Write(group_ptr_);
fo->Write(qids_);
fo->Write(weights_);
fo->Write(weights_.HostVector());
fo->Write(root_index_);
fo->Write(base_margin_);
fo->Write(base_margin_.HostVector());
}
void MetaInfo::LoadBinary(dmlc::Stream *fi) {
@ -55,16 +55,16 @@ void MetaInfo::LoadBinary(dmlc::Stream *fi) {
CHECK(fi->Read(&num_col_, sizeof(num_col_)) == sizeof(num_col_)) << "MetaInfo: invalid format";
CHECK(fi->Read(&num_nonzero_, sizeof(num_nonzero_)) == sizeof(num_nonzero_))
<< "MetaInfo: invalid format";
CHECK(fi->Read(&labels_)) << "MetaInfo: invalid format";
CHECK(fi->Read(&labels_.HostVector())) << "MetaInfo: invalid format";
CHECK(fi->Read(&group_ptr_)) << "MetaInfo: invalid format";
if (version >= kVersionQidAdded) {
CHECK(fi->Read(&qids_)) << "MetaInfo: invalid format";
} else { // old format doesn't contain qid field
qids_.clear();
}
CHECK(fi->Read(&weights_)) << "MetaInfo: invalid format";
CHECK(fi->Read(&weights_.HostVector())) << "MetaInfo: invalid format";
CHECK(fi->Read(&root_index_)) << "MetaInfo: invalid format";
CHECK(fi->Read(&base_margin_)) << "MetaInfo: invalid format";
CHECK(fi->Read(&base_margin_.HostVector())) << "MetaInfo: invalid format";
}
// try to load group information from file, if exists
@ -121,17 +121,20 @@ void MetaInfo::SetInfo(const char* key, const void* dptr, DataType dtype, size_t
DISPATCH_CONST_PTR(dtype, dptr, cast_dptr,
std::copy(cast_dptr, cast_dptr + num, root_index_.begin()));
} else if (!std::strcmp(key, "label")) {
labels_.resize(num);
auto& labels = labels_.HostVector();
labels.resize(num);
DISPATCH_CONST_PTR(dtype, dptr, cast_dptr,
std::copy(cast_dptr, cast_dptr + num, labels_.begin()));
std::copy(cast_dptr, cast_dptr + num, labels.begin()));
} else if (!std::strcmp(key, "weight")) {
weights_.resize(num);
auto& weights = weights_.HostVector();
weights.resize(num);
DISPATCH_CONST_PTR(dtype, dptr, cast_dptr,
std::copy(cast_dptr, cast_dptr + num, weights_.begin()));
std::copy(cast_dptr, cast_dptr + num, weights.begin()));
} else if (!std::strcmp(key, "base_margin")) {
base_margin_.resize(num);
auto& base_margin = base_margin_.HostVector();
base_margin.resize(num);
DISPATCH_CONST_PTR(dtype, dptr, cast_dptr,
std::copy(cast_dptr, cast_dptr + num, base_margin_.begin()));
std::copy(cast_dptr, cast_dptr + num, base_margin.begin()));
} else if (!std::strcmp(key, "group")) {
group_ptr_.resize(num + 1);
DISPATCH_CONST_PTR(dtype, dptr, cast_dptr,
@ -230,12 +233,14 @@ DMatrix* DMatrix::Load(const std::string& uri,
LOG(CONSOLE) << info.group_ptr_.size() - 1
<< " groups are loaded from " << fname << ".group";
}
if (MetaTryLoadFloatInfo(fname + ".base_margin", &info.base_margin_) && !silent) {
LOG(CONSOLE) << info.base_margin_.size()
if (MetaTryLoadFloatInfo
(fname + ".base_margin", &info.base_margin_.HostVector()) && !silent) {
LOG(CONSOLE) << info.base_margin_.Size()
<< " base_margin are loaded from " << fname << ".base_margin";
}
if (MetaTryLoadFloatInfo(fname + ".weight", &info.weights_) && !silent) {
LOG(CONSOLE) << info.weights_.size()
if (MetaTryLoadFloatInfo
(fname + ".weight", &info.weights_.HostVector()) && !silent) {
LOG(CONSOLE) << info.weights_.Size()
<< " weights are loaded from " << fname << ".weight";
}
}

24
src/data/simple_csr_source.cc
View File

@ -35,10 +35,12 @@ void SimpleCSRSource::CopyFrom(dmlc::Parser<uint32_t>* parser) {
while (parser->Next()) {
const dmlc::RowBlock<uint32_t>& batch = parser->Value();
if (batch.label != nullptr) {
info.labels_.insert(info.labels_.end(), batch.label, batch.label + batch.size);
auto& labels = info.labels_.HostVector();
labels.insert(labels.end(), batch.label, batch.label + batch.size);
}
if (batch.weight != nullptr) {
info.weights_.insert(info.weights_.end(), batch.weight, batch.weight + batch.size);
auto& weights = info.weights_.HostVector();
weights.insert(weights.end(), batch.weight, batch.weight + batch.size);
}
if (batch.qid != nullptr) {
info.qids_.insert(info.qids_.end(), batch.qid, batch.qid + batch.size);
@ -62,16 +64,18 @@ void SimpleCSRSource::CopyFrom(dmlc::Parser<uint32_t>* parser) {
// update information
this->info.num_row_ += batch.size;
// copy the data over
auto& data_vec = page_.data.HostVector();
auto& offset_vec = page_.offset.HostVector();
for (size_t i = batch.offset[0]; i < batch.offset[batch.size]; ++i) {
uint32_t index = batch.index[i];
bst_float fvalue = batch.value == nullptr ? 1.0f : batch.value[i];
page_.data.emplace_back(index, fvalue);
data_vec.emplace_back(index, fvalue);
this->info.num_col_ = std::max(this->info.num_col_,
static_cast<uint64_t>(index + 1));
}
size_t top = page_.offset.size();
size_t top = page_.offset.Size();
for (size_t i = 0; i < batch.size; ++i) {
page_.offset.push_back(page_.offset[top - 1] + batch.offset[i + 1] - batch.offset[0]);
offset_vec.push_back(offset_vec[top - 1] + batch.offset[i + 1] - batch.offset[0]);
}
}
if (last_group_id != default_max) {
@ -79,7 +83,7 @@ void SimpleCSRSource::CopyFrom(dmlc::Parser<uint32_t>* parser) {
info.group_ptr_.push_back(group_size);
}
}
this->info.num_nonzero_ = static_cast<uint64_t>(page_.data.size());
this->info.num_nonzero_ = static_cast<uint64_t>(page_.data.Size());
// Either every row has query ID or none at all
CHECK(info.qids_.empty() || info.qids_.size() == info.num_row_);
}
@ -89,16 +93,16 @@ void SimpleCSRSource::LoadBinary(dmlc::Stream* fi) {
CHECK(fi->Read(&tmagic, sizeof(tmagic)) == sizeof(tmagic)) << "invalid input file format";
CHECK_EQ(tmagic, kMagic) << "invalid format, magic number mismatch";
info.LoadBinary(fi);
fi->Read(&page_.offset);
fi->Read(&page_.data);
fi->Read(&page_.offset.HostVector());
fi->Read(&page_.data.HostVector());
}
void SimpleCSRSource::SaveBinary(dmlc::Stream* fo) const {
int tmagic = kMagic;
fo->Write(&tmagic, sizeof(tmagic));
info.SaveBinary(fo);
fo->Write(page_.offset);
fo->Write(page_.data);
fo->Write(page_.offset.HostVector());
fo->Write(page_.data.HostVector());
}
void SimpleCSRSource::BeforeFirst() {

10
src/data/simple_dmatrix.cc
View File

@ -41,8 +41,10 @@ void SimpleDMatrix::MakeOneBatch(SparsePage* pcol, bool sorted) {
// bit map
const int nthread = omp_get_max_threads();
pcol->Clear();
auto& pcol_offset_vec = pcol->offset.HostVector();
auto& pcol_data_vec = pcol->data.HostVector();
common::ParallelGroupBuilder<Entry>
builder(&pcol->offset, &pcol->data);
builder(&pcol_offset_vec, &pcol_data_vec);
builder.InitBudget(Info().num_col_, nthread);
// start working
auto iter = this->RowIterator();
@ -88,9 +90,9 @@ void SimpleDMatrix::MakeOneBatch(SparsePage* pcol, bool sorted) {
auto ncol = static_cast<bst_omp_uint>(pcol->Size());
#pragma omp parallel for schedule(dynamic, 1) num_threads(nthread)
for (bst_omp_uint i = 0; i < ncol; ++i) {
if (pcol->offset[i] < pcol->offset[i + 1]) {
std::sort(dmlc::BeginPtr(pcol->data) + pcol->offset[i],
dmlc::BeginPtr(pcol->data) + pcol->offset[i + 1],
if (pcol_offset_vec[i] < pcol_offset_vec[i + 1]) {
std::sort(dmlc::BeginPtr(pcol_data_vec) + pcol_offset_vec[i],
dmlc::BeginPtr(pcol_data_vec) + pcol_offset_vec[i + 1],
Entry::CmpValue);
}
}

23
src/data/sparse_page_dmatrix.cc
View File

@ -141,15 +141,19 @@ void SparsePageDMatrix::InitColAccess(
pcol->Clear();
pcol->base_rowid = buffered_rowset_[begin];
const int nthread = std::max(omp_get_max_threads(), std::max(omp_get_num_procs() / 2 - 1, 1));
auto& offset_vec = pcol->offset.HostVector();
auto& data_vec = pcol->data.HostVector();
common::ParallelGroupBuilder<Entry>
builder(&pcol->offset, &pcol->data);
builder(&offset_vec, &data_vec);
builder.InitBudget(info.num_col_, nthread);
bst_omp_uint ndata = static_cast<bst_uint>(prow.Size());
const auto& prow_offset_vec = prow.offset.HostVector();
const auto& prow_data_vec = prow.data.HostVector();
#pragma omp parallel for schedule(static) num_threads(nthread)
for (bst_omp_uint i = 0; i < ndata; ++i) {
int tid = omp_get_thread_num();
for (size_t j = prow.offset[i]; j < prow.offset[i+1]; ++j) {
const auto e = prow.data[j];
for (size_t j = prow_offset_vec[i]; j < prow_offset_vec[i+1]; ++j) {
const auto e = prow_data_vec[j];
builder.AddBudget(e.index, tid);
}
}
@ -157,8 +161,8 @@ void SparsePageDMatrix::InitColAccess(
#pragma omp parallel for schedule(static) num_threads(nthread)
for (bst_omp_uint i = 0; i < ndata; ++i) {
int tid = omp_get_thread_num();
for (size_t j = prow.offset[i]; j < prow.offset[i+1]; ++j) {
const Entry &e = prow.data[j];
for (size_t j = prow_offset_vec[i]; j < prow_offset_vec[i+1]; ++j) {
const Entry &e = prow_data_vec[j];
builder.Push(e.index,
Entry(buffered_rowset_[i + begin], e.fvalue),
tid);
@ -170,9 +174,9 @@ void SparsePageDMatrix::InitColAccess(
auto ncol = static_cast<bst_omp_uint>(pcol->Size());
#pragma omp parallel for schedule(dynamic, 1) num_threads(nthread)
for (bst_omp_uint i = 0; i < ncol; ++i) {
if (pcol->offset[i] < pcol->offset[i + 1]) {
std::sort(dmlc::BeginPtr(pcol->data) + pcol->offset[i],
dmlc::BeginPtr(pcol->data) + pcol->offset[i + 1],
if (offset_vec[i] < offset_vec[i + 1]) {
std::sort(dmlc::BeginPtr(data_vec) + offset_vec[i],
dmlc::BeginPtr(data_vec) + offset_vec[i + 1],
Entry::CmpValue);
}
}
@ -233,8 +237,9 @@ void SparsePageDMatrix::InitColAccess(
size_t tick_expected = kStep;
while (make_next_col(page.get())) {
const auto& page_offset_vec = page->offset.ConstHostVector();
for (size_t i = 0; i < page->Size(); ++i) {
col_size_[i] += page->offset[i + 1] - page->offset[i];
col_size_[i] += page_offset_vec[i + 1] - page_offset_vec[i];
}
bytes_write += page->MemCostBytes();

42
src/data/sparse_page_raw_format.cc
View File

@ -15,13 +15,15 @@ DMLC_REGISTRY_FILE_TAG(sparse_page_raw_format);
class SparsePageRawFormat : public SparsePageFormat {
public:
bool Read(SparsePage* page, dmlc::SeekStream* fi) override {
if (!fi->Read(&(page->offset))) return false;
CHECK_NE(page->offset.size(), 0U) << "Invalid SparsePage file";
page->data.resize(page->offset.back());
if (page->data.size() != 0) {
CHECK_EQ(fi->Read(dmlc::BeginPtr(page->data),
(page->data).size() * sizeof(Entry)),
(page->data).size() * sizeof(Entry))
auto& offset_vec = page->offset.HostVector();
if (!fi->Read(&offset_vec)) return false;
auto& data_vec = page->data.HostVector();
CHECK_NE(page->offset.Size(), 0U) << "Invalid SparsePage file";
data_vec.resize(offset_vec.back());
if (page->data.Size() != 0) {
CHECK_EQ(fi->Read(dmlc::BeginPtr(data_vec),
(page->data).Size() * sizeof(Entry)),
(page->data).Size() * sizeof(Entry))
<< "Invalid SparsePage file";
}
return true;
@ -31,15 +33,17 @@ class SparsePageRawFormat : public SparsePageFormat {
dmlc::SeekStream* fi,
const std::vector<bst_uint>& sorted_index_set) override {
if (!fi->Read(&disk_offset_)) return false;
auto& offset_vec = page->offset.HostVector();
auto& data_vec = page->data.HostVector();
// setup the offset
page->offset.clear();
page->offset.push_back(0);
offset_vec.clear();
offset_vec.push_back(0);
for (unsigned int fid : sorted_index_set) {
CHECK_LT(fid + 1, disk_offset_.size());
size_t size = disk_offset_[fid + 1] - disk_offset_[fid];
page->offset.push_back(page->offset.back() + size);
offset_vec.push_back(offset_vec.back() + size);
}
page->data.resize(page->offset.back());
data_vec.resize(offset_vec.back());
// read in the data
size_t begin = fi->Tell();
size_t curr_offset = 0;
@ -53,14 +57,14 @@ class SparsePageRawFormat : public SparsePageFormat {
size_t j, size_to_read = 0;
for (j = i; j < sorted_index_set.size(); ++j) {
if (disk_offset_[sorted_index_set[j]] == disk_offset_[fid] + size_to_read) {
size_to_read += page->offset[j + 1] - page->offset[j];
size_to_read += offset_vec[j + 1] - offset_vec[j];
} else {
break;
}
}
if (size_to_read != 0) {
CHECK_EQ(fi->Read(dmlc::BeginPtr(page->data) + page->offset[i],
CHECK_EQ(fi->Read(dmlc::BeginPtr(data_vec) + offset_vec[i],
size_to_read * sizeof(Entry)),
size_to_read * sizeof(Entry))
<< "Invalid SparsePage file";
@ -76,11 +80,13 @@ class SparsePageRawFormat : public SparsePageFormat {
}
void Write(const SparsePage& page, dmlc::Stream* fo) override {
CHECK(page.offset.size() != 0 && page.offset[0] == 0);
CHECK_EQ(page.offset.back(), page.data.size());
fo->Write(page.offset);
if (page.data.size() != 0) {
fo->Write(dmlc::BeginPtr(page.data), page.data.size() * sizeof(Entry));
const auto& offset_vec = page.offset.HostVector();
const auto& data_vec = page.data.HostVector();
CHECK(page.offset.Size() != 0 && offset_vec[0] == 0);
CHECK_EQ(offset_vec.back(), page.data.Size());
fo->Write(offset_vec);
if (page.data.Size() != 0) {
fo->Write(dmlc::BeginPtr(data_vec), page.data.Size() * sizeof(Entry));
}
}

10
src/data/sparse_page_source.cc
View File

@ -129,10 +129,12 @@ void SparsePageSource::Create(dmlc::Parser<uint32_t>* src,
while (src->Next()) {
const dmlc::RowBlock<uint32_t>& batch = src->Value();
if (batch.label != nullptr) {
info.labels_.insert(info.labels_.end(), batch.label, batch.label + batch.size);
auto& labels = info.labels_.HostVector();
labels.insert(labels.end(), batch.label, batch.label + batch.size);
}
if (batch.weight != nullptr) {
info.weights_.insert(info.weights_.end(), batch.weight, batch.weight + batch.size);
auto& weights = info.weights_.HostVector();
weights.insert(weights.end(), batch.weight, batch.weight + batch.size);
}
if (batch.qid != nullptr) {
info.qids_.insert(info.qids_.end(), batch.qid, batch.qid + batch.size);
@ -175,7 +177,7 @@ void SparsePageSource::Create(dmlc::Parser<uint32_t>* src,
}
}
if (page->data.size() != 0) {
if (page->data.Size() != 0) {
writer.PushWrite(std::move(page));
}
@ -224,7 +226,7 @@ void SparsePageSource::Create(DMatrix* src,
<< (bytes_write >> 20UL) << " written";
}
}
if (page->data.size() != 0) {
if (page->data.Size() != 0) {
writer.PushWrite(std::move(page));
}

4
src/gbm/gblinear.cc
View File

@ -143,7 +143,7 @@ class GBLinear : public GradientBooster {
model_.LazyInitModel();
CHECK_EQ(ntree_limit, 0U)
<< "GBLinear::PredictContribution: ntrees is only valid for gbtree predictor";
const std::vector<bst_float>& base_margin = p_fmat->Info().base_margin_;
const auto& base_margin = p_fmat->Info().base_margin_.ConstHostVector();
const int ngroup = model_.param.num_output_group;
const size_t ncolumns = model_.param.num_feature + 1;
// allocate space for (#features + bias) times #groups times #rows
@ -201,7 +201,7 @@ class GBLinear : public GradientBooster {
monitor_.Start("PredictBatchInternal");
model_.LazyInitModel();
std::vector<bst_float> &preds = *out_preds;
const std::vector<bst_float>& base_margin = p_fmat->Info().base_margin_;
const auto& base_margin = p_fmat->Info().base_margin_.ConstHostVector();
// start collecting the prediction
auto iter = p_fmat->RowIterator();
const int ngroup = model_.param.num_output_group;

7
src/gbm/gbtree.cc
View File

@ -195,8 +195,8 @@ class GBTree : public GradientBooster {
<< "must have exactly ngroup*nrow gpairs";
// TODO(canonizer): perform this on GPU if HostDeviceVector has device set.
HostDeviceVector<GradientPair> tmp(in_gpair->Size() / ngroup,
GradientPair(), in_gpair->Devices());
std::vector<GradientPair>& gpair_h = in_gpair->HostVector();
GradientPair(), in_gpair->Distribution());
const auto& gpair_h = in_gpair->ConstHostVector();
auto nsize = static_cast<bst_omp_uint>(tmp.Size());
for (int gid = 0; gid < ngroup; ++gid) {
std::vector<GradientPair>& tmp_h = tmp.HostVector();
@ -402,7 +402,8 @@ class Dart : public GBTree {
if (init_out_preds) {
size_t n = num_group * p_fmat->Info().num_row_;
const std::vector<bst_float>& base_margin = p_fmat->Info().base_margin_;
const auto& base_margin =
p_fmat->Info().base_margin_.ConstHostVector();
out_preds->resize(n);
if (base_margin.size() != 0) {
CHECK_EQ(out_preds->size(), n);

8
src/learner.cc
View File

@ -386,7 +386,7 @@ class LearnerImpl : public Learner {
this->PredictRaw(train, &preds_);
monitor_.Stop("PredictRaw");
monitor_.Start("GetGradient");
obj_->GetGradient(&preds_, train->Info(), iter, &gpair_);
obj_->GetGradient(preds_, train->Info(), iter, &gpair_);
monitor_.Stop("GetGradient");
gbm_->DoBoost(train, &gpair_, obj_.get());
monitor_.Stop("UpdateOneIter");
@ -416,7 +416,8 @@ class LearnerImpl : public Learner {
obj_->EvalTransform(&preds_);
for (auto& ev : metrics_) {
os << '\t' << data_names[i] << '-' << ev->Name() << ':'
<< ev->Eval(preds_.HostVector(), data_sets[i]->Info(), tparam_.dsplit == 2);
<< ev->Eval(preds_.ConstHostVector(), data_sets[i]->Info(),
tparam_.dsplit == 2);
}
}
@ -459,7 +460,8 @@ class LearnerImpl : public Learner {
this->PredictRaw(data, &preds_);
obj_->EvalTransform(&preds_);
return std::make_pair(metric,
ev->Eval(preds_.HostVector(), data->Info(), tparam_.dsplit == 2));
ev->Eval(preds_.ConstHostVector(), data->Info(),
tparam_.dsplit == 2));
}
void Predict(DMatrix* data, bool output_margin,

8
src/linear/updater_coordinate.cc
View File

@ -90,7 +90,8 @@ class CoordinateUpdater : public LinearUpdater {
const int ngroup = model->param.num_output_group;
// update bias
for (int group_idx = 0; group_idx < ngroup; ++group_idx) {
auto grad = GetBiasGradientParallel(group_idx, ngroup, in_gpair->HostVector(), p_fmat);
auto grad = GetBiasGradientParallel(group_idx, ngroup,
in_gpair->ConstHostVector(), p_fmat);
auto dbias = static_cast<float>(param.learning_rate *
CoordinateDeltaBias(grad.first, grad.second));
model->bias()[group_idx] += dbias;
@ -98,12 +99,13 @@ class CoordinateUpdater : public LinearUpdater {
dbias, &in_gpair->HostVector(), p_fmat);
}
// prepare for updating the weights
selector->Setup(*model, in_gpair->HostVector(), p_fmat, param.reg_alpha_denorm,
selector->Setup(*model, in_gpair->ConstHostVector(), p_fmat, param.reg_alpha_denorm,
param.reg_lambda_denorm, param.top_k);
// update weights
for (int group_idx = 0; group_idx < ngroup; ++group_idx) {
for (unsigned i = 0U; i < model->param.num_feature; i++) {
int fidx = selector->NextFeature(i, *model, group_idx, in_gpair->HostVector(), p_fmat,
int fidx = selector->NextFeature
(i, *model, group_idx, in_gpair->ConstHostVector(), p_fmat,
param.reg_alpha_denorm, param.reg_lambda_denorm);
if (fidx < 0) break;
this->UpdateFeature(fidx, group_idx, &in_gpair->HostVector(), p_fmat, model);

6
src/linear/updater_gpu_coordinate.cu
View File

@ -259,7 +259,7 @@ class GPUCoordinateUpdater : public LinearUpdater {
monitor.Start("UpdateGpair");
// Update gpair
dh::ExecuteShards(&shards, [&](std::unique_ptr<DeviceShard> &shard) {
shard->UpdateGpair(in_gpair->HostVector(), model->param);
shard->UpdateGpair(in_gpair->ConstHostVector(), model->param);
});
monitor.Stop("UpdateGpair");
@ -267,7 +267,7 @@ class GPUCoordinateUpdater : public LinearUpdater {
this->UpdateBias(p_fmat, model);
monitor.Stop("UpdateBias");
// prepare for updating the weights
selector->Setup(*model, in_gpair->HostVector(), p_fmat,
selector->Setup(*model, in_gpair->ConstHostVector(), p_fmat,
param.reg_alpha_denorm, param.reg_lambda_denorm,
param.top_k);
monitor.Start("UpdateFeature");
@ -275,7 +275,7 @@ class GPUCoordinateUpdater : public LinearUpdater {
++group_idx) {
for (auto i = 0U; i < model->param.num_feature; i++) {
auto fidx = selector->NextFeature(
i, *model, group_idx, in_gpair->HostVector(), p_fmat,
i, *model, group_idx, in_gpair->ConstHostVector(), p_fmat,
param.reg_alpha_denorm, param.reg_lambda_denorm);
if (fidx < 0) break;
this->UpdateFeature(fidx, group_idx, &in_gpair->HostVector(), model);

14
src/linear/updater_shotgun.cc
View File

@ -63,13 +63,14 @@ class ShotgunUpdater : public LinearUpdater {
}
void Update(HostDeviceVector<GradientPair> *in_gpair, DMatrix *p_fmat,
gbm::GBLinearModel *model, double sum_instance_weight) override {
std::vector<GradientPair> &gpair = in_gpair->HostVector();
auto &gpair = in_gpair->HostVector();
param_.DenormalizePenalties(sum_instance_weight);
const int ngroup = model->param.num_output_group;
// update bias
for (int gid = 0; gid < ngroup; ++gid) {
auto grad = GetBiasGradientParallel(gid, ngroup, in_gpair->HostVector(), p_fmat);
auto grad = GetBiasGradientParallel(gid, ngroup,
in_gpair->ConstHostVector(), p_fmat);
auto dbias = static_cast<bst_float>(param_.learning_rate *
CoordinateDeltaBias(grad.first, grad.second));
model->bias()[gid] += dbias;
@ -77,7 +78,7 @@ class ShotgunUpdater : public LinearUpdater {
}
// lock-free parallel updates of weights
selector_->Setup(*model, in_gpair->HostVector(), p_fmat,
selector_->Setup(*model, in_gpair->ConstHostVector(), p_fmat,
param_.reg_alpha_denorm, param_.reg_lambda_denorm, 0);
auto iter = p_fmat->ColIterator();
while (iter->Next()) {
@ -85,15 +86,16 @@ class ShotgunUpdater : public LinearUpdater {
const auto nfeat = static_cast<bst_omp_uint>(batch.Size());
#pragma omp parallel for schedule(static)
for (bst_omp_uint i = 0; i < nfeat; ++i) {
int ii = selector_->NextFeature(i, *model, 0, in_gpair->HostVector(), p_fmat,
param_.reg_alpha_denorm, param_.reg_lambda_denorm);
int ii = selector_->NextFeature
(i, *model, 0, in_gpair->ConstHostVector(), p_fmat, param_.reg_alpha_denorm,
param_.reg_lambda_denorm);
if (ii < 0) continue;
const bst_uint fid = ii;
auto col = batch[ii];
for (int gid = 0; gid < ngroup; ++gid) {
double sum_grad = 0.0, sum_hess = 0.0;
for (auto& c : col) {
GradientPair &p = gpair[c.index * ngroup + gid];
const GradientPair &p = gpair[c.index * ngroup + gid];
if (p.GetHess() < 0.0f) continue;
const bst_float v = c.fvalue;
sum_grad += p.GetGrad() * v;

12
src/metric/elementwise_metric.cc
View File

@ -24,16 +24,18 @@ struct EvalEWiseBase : public Metric {
bst_float Eval(const std::vector<bst_float>& preds,
const MetaInfo& info,
bool distributed) const override {
CHECK_NE(info.labels_.size(), 0U) << "label set cannot be empty";
CHECK_EQ(preds.size(), info.labels_.size())
CHECK_NE(info.labels_.Size(), 0U) << "label set cannot be empty";
CHECK_EQ(preds.size(), info.labels_.Size())
<< "label and prediction size not match, "
<< "hint: use merror or mlogloss for multi-class classification";
const auto ndata = static_cast<omp_ulong>(info.labels_.size());
const auto ndata = static_cast<omp_ulong>(info.labels_.Size());
double sum = 0.0, wsum = 0.0;
const auto& labels = info.labels_.HostVector();
const auto& weights = info.weights_.HostVector();
#pragma omp parallel for reduction(+: sum, wsum) schedule(static)
for (omp_ulong i = 0; i < ndata; ++i) {
const bst_float wt = info.GetWeight(i);
sum += static_cast<const Derived*>(this)->EvalRow(info.labels_[i], preds[i]) * wt;
const bst_float wt = weights.size() > 0 ? weights[i] : 1.0f;
sum += static_cast<const Derived*>(this)->EvalRow(labels[i], preds[i]) * wt;
wsum += wt;
}
double dat[2]; dat[0] = sum, dat[1] = wsum;

16
src/metric/multiclass_metric.cc
View File

@ -23,20 +23,24 @@ struct EvalMClassBase : public Metric {
bst_float Eval(const std::vector<bst_float> &preds,
const MetaInfo &info,
bool distributed) const override {
CHECK_NE(info.labels_.size(), 0U) << "label set cannot be empty";
CHECK(preds.size() % info.labels_.size() == 0)
CHECK_NE(info.labels_.Size(), 0U) << "label set cannot be empty";
CHECK(preds.size() % info.labels_.Size() == 0)
<< "label and prediction size not match";
const size_t nclass = preds.size() / info.labels_.size();
const size_t nclass = preds.size() / info.labels_.Size();
CHECK_GE(nclass, 1U)
<< "mlogloss and merror are only used for multi-class classification,"
<< " use logloss for binary classification";
const auto ndata = static_cast<bst_omp_uint>(info.labels_.size());
const auto ndata = static_cast<bst_omp_uint>(info.labels_.Size());
double sum = 0.0, wsum = 0.0;
int label_error = 0;
const auto& labels = info.labels_.HostVector();
const auto& weights = info.weights_.HostVector();
#pragma omp parallel for reduction(+: sum, wsum) schedule(static)
for (bst_omp_uint i = 0; i < ndata; ++i) {
const bst_float wt = info.GetWeight(i);
auto label = static_cast<int>(info.labels_[i]);
const bst_float wt = weights.size() > 0 ? weights[i] : 1.0f;
auto label = static_cast<int>(labels[i]);
if (label >= 0 && label < static_cast<int>(nclass)) {
sum += Derived::EvalRow(label,
preds.data() + i * nclass,

45
src/metric/rank_metric.cc
View File

@ -32,7 +32,7 @@ struct EvalAMS : public Metric {
CHECK(!distributed) << "metric AMS do not support distributed evaluation";
using namespace std; // NOLINT(*)
const auto ndata = static_cast<bst_omp_uint>(info.labels_.size());
const auto ndata = static_cast<bst_omp_uint>(info.labels_.Size());
std::vector<std::pair<bst_float, unsigned> > rec(ndata);
#pragma omp parallel for schedule(static)
@ -45,10 +45,11 @@ struct EvalAMS : public Metric {
const double br = 10.0;
unsigned thresindex = 0;
double s_tp = 0.0, b_fp = 0.0, tams = 0.0;
const auto& labels = info.labels_.HostVector();
for (unsigned i = 0; i < static_cast<unsigned>(ndata-1) && i < ntop; ++i) {
const unsigned ridx = rec[i].second;
const bst_float wt = info.GetWeight(ridx);
if (info.labels_[ridx] > 0.5f) {
if (labels[ridx] > 0.5f) {
s_tp += wt;
} else {
b_fp += wt;
@ -84,14 +85,14 @@ struct EvalAuc : public Metric {
bst_float Eval(const std::vector<bst_float> &preds,
const MetaInfo &info,
bool distributed) const override {
CHECK_NE(info.labels_.size(), 0U) << "label set cannot be empty";
CHECK_EQ(preds.size(), info.labels_.size())
CHECK_NE(info.labels_.Size(), 0U) << "label set cannot be empty";
CHECK_EQ(preds.size(), info.labels_.Size())
<< "label size predict size not match";
std::vector<unsigned> tgptr(2, 0);
tgptr[1] = static_cast<unsigned>(info.labels_.size());
tgptr[1] = static_cast<unsigned>(info.labels_.Size());
const std::vector<unsigned> &gptr = info.group_ptr_.size() == 0 ? tgptr : info.group_ptr_;
CHECK_EQ(gptr.back(), info.labels_.size())
CHECK_EQ(gptr.back(), info.labels_.Size())
<< "EvalAuc: group structure must match number of prediction";
const auto ngroup = static_cast<bst_omp_uint>(gptr.size() - 1);
// sum statistics
@ -99,6 +100,7 @@ struct EvalAuc : public Metric {
int auc_error = 0;
// each thread takes a local rec
std::vector< std::pair<bst_float, unsigned> > rec;
const auto& labels = info.labels_.HostVector();
for (bst_omp_uint k = 0; k < ngroup; ++k) {
rec.clear();
for (unsigned j = gptr[k]; j < gptr[k + 1]; ++j) {
@ -110,7 +112,7 @@ struct EvalAuc : public Metric {
double sum_npos = 0.0, sum_nneg = 0.0, buf_pos = 0.0, buf_neg = 0.0;
for (size_t j = 0; j < rec.size(); ++j) {
const bst_float wt = info.GetWeight(rec[j].second);
const bst_float ctr = info.labels_[rec[j].second];
const bst_float ctr = labels[rec[j].second];
// keep bucketing predictions in same bucket
if (j != 0 && rec[j].first != rec[j - 1].first) {
sum_pospair += buf_neg * (sum_npos + buf_pos *0.5);
@ -156,7 +158,7 @@ struct EvalRankList : public Metric {
bst_float Eval(const std::vector<bst_float> &preds,
const MetaInfo &info,
bool distributed) const override {
CHECK_EQ(preds.size(), info.labels_.size())
CHECK_EQ(preds.size(), info.labels_.Size())
<< "label size predict size not match";
// quick consistency when group is not available
std::vector<unsigned> tgptr(2, 0);
@ -168,6 +170,7 @@ struct EvalRankList : public Metric {
const auto ngroup = static_cast<bst_omp_uint>(gptr.size() - 1);
// sum statistics
double sum_metric = 0.0f;
const auto& labels = info.labels_.HostVector();
#pragma omp parallel reduction(+:sum_metric)
{
// each thread takes a local rec
@ -176,7 +179,7 @@ struct EvalRankList : public Metric {
for (bst_omp_uint k = 0; k < ngroup; ++k) {
rec.clear();
for (unsigned j = gptr[k]; j < gptr[k + 1]; ++j) {
rec.emplace_back(preds[j], static_cast<int>(info.labels_[j]));
rec.emplace_back(preds[j], static_cast<int>(labels[j]));
}
sum_metric += this->EvalMetric(rec);
}
@ -314,7 +317,7 @@ struct EvalCox : public Metric {
CHECK(!distributed) << "Cox metric does not support distributed evaluation";
using namespace std; // NOLINT(*)
const auto ndata = static_cast<bst_omp_uint>(info.labels_.size());
const auto ndata = static_cast<bst_omp_uint>(info.labels_.Size());
const std::vector<size_t> &label_order = info.LabelAbsSort();
// pre-compute a sum for the denominator
@ -326,9 +329,10 @@ struct EvalCox : public Metric {
double out = 0;
double accumulated_sum = 0;
bst_omp_uint num_events = 0;
const auto& labels = info.labels_.HostVector();
for (bst_omp_uint i = 0; i < ndata; ++i) {
const size_t ind = label_order[i];
const auto label = info.labels_[ind];
const auto label = labels[ind];
if (label > 0) {
out -= log(preds[ind]) - log(exp_p_sum);
++num_events;
@ -336,7 +340,7 @@ struct EvalCox : public Metric {
// only update the denominator after we move forward in time (labels are sorted)
accumulated_sum += preds[ind];
if (i == ndata - 1 || std::abs(label) < std::abs(info.labels_[label_order[i + 1]])) {
if (i == ndata - 1 || std::abs(label) < std::abs(labels[label_order[i + 1]])) {
exp_p_sum -= accumulated_sum;
accumulated_sum = 0;
}
@ -358,14 +362,14 @@ struct EvalAucPR : public Metric {
bst_float Eval(const std::vector<bst_float> &preds, const MetaInfo &info,
bool distributed) const override {
CHECK_NE(info.labels_.size(), 0U) << "label set cannot be empty";
CHECK_EQ(preds.size(), info.labels_.size())
CHECK_NE(info.labels_.Size(), 0U) << "label set cannot be empty";
CHECK_EQ(preds.size(), info.labels_.Size())
<< "label size predict size not match";
std::vector<unsigned> tgptr(2, 0);
tgptr[1] = static_cast<unsigned>(info.labels_.size());
tgptr[1] = static_cast<unsigned>(info.labels_.Size());
const std::vector<unsigned> &gptr =
info.group_ptr_.size() == 0 ? tgptr : info.group_ptr_;
CHECK_EQ(gptr.back(), info.labels_.size())
CHECK_EQ(gptr.back(), info.labels_.Size())
<< "EvalAucPR: group structure must match number of prediction";
const auto ngroup = static_cast<bst_omp_uint>(gptr.size() - 1);
// sum statistics
@ -373,13 +377,14 @@ struct EvalAucPR : public Metric {
int auc_error = 0, auc_gt_one = 0;
// each thread takes a local rec
std::vector<std::pair<bst_float, unsigned>> rec;
const auto& labels = info.labels_.HostVector();
for (bst_omp_uint k = 0; k < ngroup; ++k) {
double total_pos = 0.0;
double total_neg = 0.0;
rec.clear();
for (unsigned j = gptr[k]; j < gptr[k + 1]; ++j) {
total_pos += info.GetWeight(j) * info.labels_[j];
total_neg += info.GetWeight(j) * (1.0f - info.labels_[j]);
total_pos += info.GetWeight(j) * labels[j];
total_neg += info.GetWeight(j) * (1.0f - labels[j]);
rec.emplace_back(preds[j], j);
}
XGBOOST_PARALLEL_SORT(rec.begin(), rec.end(), common::CmpFirst);
@ -390,8 +395,8 @@ struct EvalAucPR : public Metric {
// calculate AUC
double tp = 0.0, prevtp = 0.0, fp = 0.0, prevfp = 0.0, h = 0.0, a = 0.0, b = 0.0;
for (size_t j = 0; j < rec.size(); ++j) {
tp += info.GetWeight(rec[j].second) * info.labels_[rec[j].second];
fp += info.GetWeight(rec[j].second) * (1.0f - info.labels_[rec[j].second]);
tp += info.GetWeight(rec[j].second) * labels[rec[j].second];
fp += info.GetWeight(rec[j].second) * (1.0f - labels[rec[j].second]);
if ((j < rec.size() - 1 && rec[j].first != rec[j + 1].first) || j == rec.size() - 1) {
if (tp == prevtp) {
a = 1.0;

18
src/objective/hinge.cc
View File

@ -21,24 +21,26 @@ class HingeObj : public ObjFunction {
// This objective does not take any parameters
}
void GetGradient(HostDeviceVector<bst_float> *preds,
void GetGradient(const HostDeviceVector<bst_float> &preds,
const MetaInfo &info,
int iter,
HostDeviceVector<GradientPair> *out_gpair) override {
CHECK_NE(info.labels_.size(), 0U) << "label set cannot be empty";
CHECK_EQ(preds->Size(), info.labels_.size())
CHECK_NE(info.labels_.Size(), 0U) << "label set cannot be empty";
CHECK_EQ(preds.Size(), info.labels_.Size())
<< "labels are not correctly provided"
<< "preds.size=" << preds->Size()
<< ", label.size=" << info.labels_.size();
auto& preds_h = preds->HostVector();
<< "preds.size=" << preds.Size()
<< ", label.size=" << info.labels_.Size();
const auto& preds_h = preds.HostVector();
const auto& labels_h = info.labels_.HostVector();
const auto& weights_h = info.weights_.HostVector();
out_gpair->Resize(preds_h.size());
auto& gpair = out_gpair->HostVector();
for (size_t i = 0; i < preds_h.size(); ++i) {
auto y = info.labels_[i] * 2.0 - 1.0;
auto y = labels_h[i] * 2.0 - 1.0;
bst_float p = preds_h[i];
bst_float w = info.GetWeight(i);
bst_float w = weights_h.size() > 0 ? weights_h[i] : 1.0f;
bst_float g, h;
if (p * y < 1.0) {
g = -y * w;

11
src/objective/multiclass_obj.cc
View File

@ -35,19 +35,20 @@ class SoftmaxMultiClassObj : public ObjFunction {
void Configure(const std::vector<std::pair<std::string, std::string> >& args) override {
param_.InitAllowUnknown(args);
}
void GetGradient(HostDeviceVector<bst_float>* preds,
void GetGradient(const HostDeviceVector<bst_float>& preds,
const MetaInfo& info,
int iter,
HostDeviceVector<GradientPair>* out_gpair) override {
CHECK_NE(info.labels_.size(), 0U) << "label set cannot be empty";
CHECK(preds->Size() == (static_cast<size_t>(param_.num_class) * info.labels_.size()))
CHECK_NE(info.labels_.Size(), 0U) << "label set cannot be empty";
CHECK(preds.Size() == (static_cast<size_t>(param_.num_class) * info.labels_.Size()))
<< "SoftmaxMultiClassObj: label size and pred size does not match";
std::vector<bst_float>& preds_h = preds->HostVector();
const std::vector<bst_float>& preds_h = preds.HostVector();
out_gpair->Resize(preds_h.size());
std::vector<GradientPair>& gpair = out_gpair->HostVector();
const int nclass = param_.num_class;
const auto ndata = static_cast<omp_ulong>(preds_h.size() / nclass);
const auto& labels = info.labels_.HostVector();
int label_error = 0;
#pragma omp parallel
{
@ -58,7 +59,7 @@ class SoftmaxMultiClassObj : public ObjFunction {
rec[k] = preds_h[i * nclass + k];
}
common::Softmax(&rec);
auto label = static_cast<int>(info.labels_[i]);
auto label = static_cast<int>(labels[i]);
if (label < 0 || label >= nclass) {
label_error = label; label = 0;
}

13
src/objective/rank_obj.cc
View File

@ -38,18 +38,18 @@ class LambdaRankObj : public ObjFunction {
param_.InitAllowUnknown(args);
}
void GetGradient(HostDeviceVector<bst_float>* preds,
void GetGradient(const HostDeviceVector<bst_float>& preds,
const MetaInfo& info,
int iter,
HostDeviceVector<GradientPair>* out_gpair) override {
CHECK_EQ(preds->Size(), info.labels_.size()) << "label size predict size not match";
auto& preds_h = preds->HostVector();
CHECK_EQ(preds.Size(), info.labels_.Size()) << "label size predict size not match";
const auto& preds_h = preds.HostVector();
out_gpair->Resize(preds_h.size());
std::vector<GradientPair>& gpair = out_gpair->HostVector();
// quick consistency when group is not available
std::vector<unsigned> tgptr(2, 0); tgptr[1] = static_cast<unsigned>(info.labels_.size());
std::vector<unsigned> tgptr(2, 0); tgptr[1] = static_cast<unsigned>(info.labels_.Size());
const std::vector<unsigned> &gptr = info.group_ptr_.size() == 0 ? tgptr : info.group_ptr_;
CHECK(gptr.size() != 0 && gptr.back() == info.labels_.size())
CHECK(gptr.size() != 0 && gptr.back() == info.labels_.Size())
<< "group structure not consistent with #rows";
const auto ngroup = static_cast<bst_omp_uint>(gptr.size() - 1);
@ -67,11 +67,12 @@ class LambdaRankObj : public ObjFunction {
sum_weights += info.GetWeight(k);
}
bst_float weight_normalization_factor = ngroup/sum_weights;
const auto& labels = info.labels_.HostVector();
#pragma omp for schedule(static)
for (bst_omp_uint k = 0; k < ngroup; ++k) {
lst.clear(); pairs.clear();
for (unsigned j = gptr[k]; j < gptr[k+1]; ++j) {
lst.emplace_back(preds_h[j], info.labels_[j], j);
lst.emplace_back(preds_h[j], labels[j], j);
gpair[j] = GradientPair(0.0f, 0.0f);
}
std::sort(lst.begin(), lst.end(), ListEntry::CmpPred);

74
src/objective/regression_obj.cc
View File

@ -38,16 +38,18 @@ class RegLossObj : public ObjFunction {
const std::vector<std::pair<std::string, std::string> > &args) override {
param_.InitAllowUnknown(args);
}
void GetGradient(HostDeviceVector<bst_float> *preds, const MetaInfo &info,
void GetGradient(const HostDeviceVector<bst_float> &preds, const MetaInfo &info,
int iter, HostDeviceVector<GradientPair> *out_gpair) override {
CHECK_NE(info.labels_.size(), 0U) << "label set cannot be empty";
CHECK_EQ(preds->Size(), info.labels_.size())
CHECK_NE(info.labels_.Size(), 0U) << "label set cannot be empty";
CHECK_EQ(preds.Size(), info.labels_.Size())
<< "labels are not correctly provided"
<< "preds.size=" << preds->Size()
<< ", label.size=" << info.labels_.size();
auto& preds_h = preds->HostVector();
<< "preds.size=" << preds.Size()
<< ", label.size=" << info.labels_.Size();
const auto& preds_h = preds.HostVector();
const auto& labels = info.labels_.HostVector();
const auto& weights = info.weights_.HostVector();
this->LazyCheckLabels(info.labels_);
this->LazyCheckLabels(labels);
out_gpair->Resize(preds_h.size());
auto& gpair = out_gpair->HostVector();
const auto n = static_cast<omp_ulong>(preds_h.size());
@ -57,10 +59,10 @@ class RegLossObj : public ObjFunction {
const omp_ulong remainder = n % 8;
#pragma omp parallel for schedule(static)
for (omp_ulong i = 0; i < n - remainder; i += 8) {
avx::Float8 y(&info.labels_[i]);
avx::Float8 y(&labels[i]);
avx::Float8 p = Loss::PredTransform(avx::Float8(&preds_h[i]));
avx::Float8 w = info.weights_.empty() ? avx::Float8(1.0f)
: avx::Float8(&info.weights_[i]);
avx::Float8 w = weights.empty() ? avx::Float8(1.0f)
: avx::Float8(&weights[i]);
// Adjust weight
w += y * (scale * w - w);
avx::Float8 grad = Loss::FirstOrderGradient(p, y);
@ -68,7 +70,7 @@ class RegLossObj : public ObjFunction {
avx::StoreGpair(gpair_ptr + i, grad * w, hess * w);
}
for (omp_ulong i = n - remainder; i < n; ++i) {
auto y = info.labels_[i];
auto y = labels[i];
bst_float p = Loss::PredTransform(preds_h[i]);
bst_float w = info.GetWeight(i);
w += y * ((param_.scale_pos_weight * w) - w);
@ -140,15 +142,16 @@ class PoissonRegression : public ObjFunction {
param_.InitAllowUnknown(args);
}
void GetGradient(HostDeviceVector<bst_float> *preds,
void GetGradient(const HostDeviceVector<bst_float> &preds,
const MetaInfo &info,
int iter,
HostDeviceVector<GradientPair> *out_gpair) override {
CHECK_NE(info.labels_.size(), 0U) << "label set cannot be empty";
CHECK_EQ(preds->Size(), info.labels_.size()) << "labels are not correctly provided";
auto& preds_h = preds->HostVector();
out_gpair->Resize(preds->Size());
CHECK_NE(info.labels_.Size(), 0U) << "label set cannot be empty";
CHECK_EQ(preds.Size(), info.labels_.Size()) << "labels are not correctly provided";
const auto& preds_h = preds.HostVector();
out_gpair->Resize(preds.Size());
auto& gpair = out_gpair->HostVector();
const auto& labels = info.labels_.HostVector();
// check if label in range
bool label_correct = true;
// start calculating gradient
@ -157,7 +160,7 @@ class PoissonRegression : public ObjFunction {
for (omp_ulong i = 0; i < ndata; ++i) { // NOLINT(*)
bst_float p = preds_h[i];
bst_float w = info.GetWeight(i);
bst_float y = info.labels_[i];
bst_float y = labels[i];
if (y >= 0.0f) {
gpair[i] = GradientPair((std::exp(p) - y) * w,
std::exp(p + param_.max_delta_step) * w);
@ -201,13 +204,13 @@ class CoxRegression : public ObjFunction {
public:
// declare functions
void Configure(const std::vector<std::pair<std::string, std::string> >& args) override {}
void GetGradient(HostDeviceVector<bst_float> *preds,
void GetGradient(const HostDeviceVector<bst_float> &preds,
const MetaInfo &info,
int iter,
HostDeviceVector<GradientPair> *out_gpair) override {
CHECK_NE(info.labels_.size(), 0U) << "label set cannot be empty";
CHECK_EQ(preds->Size(), info.labels_.size()) << "labels are not correctly provided";
auto& preds_h = preds->HostVector();
CHECK_NE(info.labels_.Size(), 0U) << "label set cannot be empty";
CHECK_EQ(preds.Size(), info.labels_.Size()) << "labels are not correctly provided";
const auto& preds_h = preds.HostVector();
out_gpair->Resize(preds_h.size());
auto& gpair = out_gpair->HostVector();
const std::vector<size_t> &label_order = info.LabelAbsSort();
@ -221,6 +224,7 @@ class CoxRegression : public ObjFunction {
}
// start calculating grad and hess
const auto& labels = info.labels_.HostVector();
double r_k = 0;
double s_k = 0;
double last_exp_p = 0.0;
@ -231,7 +235,7 @@ class CoxRegression : public ObjFunction {
const double p = preds_h[ind];
const double exp_p = std::exp(p);
const double w = info.GetWeight(ind);
const double y = info.labels_[ind];
const double y = labels[ind];
const double abs_y = std::abs(y);
// only update the denominator after we move forward in time (labels are sorted)
@ -289,15 +293,16 @@ class GammaRegression : public ObjFunction {
void Configure(const std::vector<std::pair<std::string, std::string> >& args) override {
}
void GetGradient(HostDeviceVector<bst_float> *preds,
void GetGradient(const HostDeviceVector<bst_float> &preds,
const MetaInfo &info,
int iter,
HostDeviceVector<GradientPair> *out_gpair) override {
CHECK_NE(info.labels_.size(), 0U) << "label set cannot be empty";
CHECK_EQ(preds->Size(), info.labels_.size()) << "labels are not correctly provided";
auto& preds_h = preds->HostVector();
CHECK_NE(info.labels_.Size(), 0U) << "label set cannot be empty";
CHECK_EQ(preds.Size(), info.labels_.Size()) << "labels are not correctly provided";
const auto& preds_h = preds.HostVector();
out_gpair->Resize(preds_h.size());
auto& gpair = out_gpair->HostVector();
const auto& labels = info.labels_.HostVector();
// check if label in range
bool label_correct = true;
// start calculating gradient
@ -306,7 +311,7 @@ class GammaRegression : public ObjFunction {
for (omp_ulong i = 0; i < ndata; ++i) { // NOLINT(*)
bst_float p = preds_h[i];
bst_float w = info.GetWeight(i);
bst_float y = info.labels_[i];
bst_float y = labels[i];
if (y >= 0.0f) {
gpair[i] = GradientPair((1 - y / std::exp(p)) * w, y / std::exp(p) * w);
} else {
@ -356,24 +361,25 @@ class TweedieRegression : public ObjFunction {
param_.InitAllowUnknown(args);
}
void GetGradient(HostDeviceVector<bst_float> *preds,
void GetGradient(const HostDeviceVector<bst_float> &preds,
const MetaInfo &info,
int iter,
HostDeviceVector<GradientPair> *out_gpair) override {
CHECK_NE(info.labels_.size(), 0U) << "label set cannot be empty";
CHECK_EQ(preds->Size(), info.labels_.size()) << "labels are not correctly provided";
auto& preds_h = preds->HostVector();
out_gpair->Resize(preds->Size());
CHECK_NE(info.labels_.Size(), 0U) << "label set cannot be empty";
CHECK_EQ(preds.Size(), info.labels_.Size()) << "labels are not correctly provided";
const auto& preds_h = preds.HostVector();
out_gpair->Resize(preds.Size());
auto& gpair = out_gpair->HostVector();
const auto& labels = info.labels_.HostVector();
// check if label in range
bool label_correct = true;
// start calculating gradient
const omp_ulong ndata = static_cast<omp_ulong>(preds->Size()); // NOLINT(*)
const omp_ulong ndata = static_cast<omp_ulong>(preds.Size()); // NOLINT(*)
#pragma omp parallel for schedule(static)
for (omp_ulong i = 0; i < ndata; ++i) { // NOLINT(*)
bst_float p = preds_h[i];
bst_float w = info.GetWeight(i);
bst_float y = info.labels_[i];
bst_float y = labels[i];
float rho = param_.tweedie_variance_power;
if (y >= 0.0f) {
bst_float grad = -y * std::exp((1 - rho) * p) + std::exp((2 - rho) * p);

60
src/objective/regression_obj_gpu.cu
View File

@ -45,7 +45,7 @@ struct GPURegLossParam : public dmlc::Parameter<GPURegLossParam> {
// GPU kernel for gradient computation
template<typename Loss>
__global__ void get_gradient_k
(common::Span<GradientPair> out_gpair, common::Span<unsigned int> label_correct,
(common::Span<GradientPair> out_gpair, common::Span<int> label_correct,
common::Span<const float> preds, common::Span<const float> labels,
const float * __restrict__ weights, int n, float scale_pos_weight) {
int i = threadIdx.x + blockIdx.x * blockDim.x;
@ -75,66 +75,46 @@ __global__ void pred_transform_k(common::Span<float> preds, int n) {
template<typename Loss>
class GPURegLossObj : public ObjFunction {
protected:
bool copied_;
HostDeviceVector<bst_float> labels_, weights_;
HostDeviceVector<unsigned int> label_correct_;
HostDeviceVector<int> label_correct_;
// allocate device data for n elements, do nothing if memory is allocated already
void LazyResize(size_t n, size_t n_weights) {
if (labels_.Size() == n && weights_.Size() == n_weights)
return;
copied_ = false;
labels_.Reshard(devices_);
weights_.Reshard(devices_);
label_correct_.Reshard(devices_);
if (labels_.Size() != n) {
labels_.Resize(n);
label_correct_.Resize(devices_.Size());
}
if (weights_.Size() != n_weights)
weights_.Resize(n_weights);
void LazyResize() {
}
public:
GPURegLossObj() : copied_(false) {}
GPURegLossObj() {}
void Configure(const std::vector<std::pair<std::string, std::string> >& args) override {
param_.InitAllowUnknown(args);
// CHECK(param_.n_gpus != 0) << "Must have at least one device";
CHECK(param_.n_gpus != 0) << "Must have at least one device";
devices_ = GPUSet::All(param_.n_gpus).Normalised(param_.gpu_id);
label_correct_.Reshard(devices_);
label_correct_.Resize(devices_.Size());
}
void GetGradient(HostDeviceVector<float>* preds,
void GetGradient(const HostDeviceVector<float> &preds,
const MetaInfo &info,
int iter,
HostDeviceVector<GradientPair>* out_gpair) override {
CHECK_NE(info.labels_.size(), 0U) << "label set cannot be empty";
CHECK_EQ(preds->Size(), info.labels_.size())
CHECK_NE(info.labels_.Size(), 0U) << "label set cannot be empty";
CHECK_EQ(preds.Size(), info.labels_.Size())
<< "labels are not correctly provided"
<< "preds.size=" << preds->Size() << ", label.size=" << info.labels_.size();
size_t ndata = preds->Size();
preds->Reshard(devices_);
<< "preds.size=" << preds.Size() << ", label.size=" << info.labels_.Size();
size_t ndata = preds.Size();
preds.Reshard(devices_);
info.labels_.Reshard(devices_);
info.weights_.Reshard(devices_);
out_gpair->Reshard(devices_);
out_gpair->Resize(ndata);
LazyResize(ndata, info.weights_.size());
GetGradientDevice(preds, info, iter, out_gpair);
}
private:
void GetGradientDevice(HostDeviceVector<float>* preds,
void GetGradientDevice(const HostDeviceVector<float>& preds,
const MetaInfo &info,
int iter,
HostDeviceVector<GradientPair>* out_gpair) {
label_correct_.Fill(1);
// only copy the labels and weights once, similar to how the data is copied
if (!copied_) {
labels_.Copy(info.labels_);
if (info.weights_.size() > 0)
weights_.Copy(info.weights_);
copied_ = true;
}
// run the kernel
#pragma omp parallel for schedule(static, 1) if (devices_.Size() > 1)
@ -142,12 +122,12 @@ class GPURegLossObj : public ObjFunction {
int d = devices_[i];
dh::safe_cuda(cudaSetDevice(d));
const int block = 256;
size_t n = preds->DeviceSize(d);
size_t n = preds.DeviceSize(d);
if (n > 0) {
get_gradient_k<Loss><<<dh::DivRoundUp(n, block), block>>>
(out_gpair->DeviceSpan(d), label_correct_.DeviceSpan(d),
preds->DeviceSpan(d), labels_.DeviceSpan(d),
info.weights_.size() > 0 ? weights_.DevicePointer(d) : nullptr,
preds.DeviceSpan(d), info.labels_.DeviceSpan(d),
info.weights_.Size() > 0 ? info.weights_.DevicePointer(d) : nullptr,
n, param_.scale_pos_weight);
dh::safe_cuda(cudaGetLastError());
}
@ -155,7 +135,7 @@ class GPURegLossObj : public ObjFunction {
}
// copy "label correct" flags back to host
std::vector<unsigned int>& label_correct_h = label_correct_.HostVector();
std::vector<int>& label_correct_h = label_correct_.HostVector();
for (int i = 0; i < devices_.Size(); ++i) {
if (label_correct_h[i] == 0)
LOG(FATAL) << Loss::LabelErrorMsg();

6
src/predictor/cpu_predictor.cc
View File

@ -112,7 +112,7 @@ class CPUPredictor : public Predictor {
ntree_limit * model.param.num_output_group >= model.trees.size()) {
auto it = cache_.find(dmat);
if (it != cache_.end()) {
HostDeviceVector<bst_float>& y = it->second.predictions;
const HostDeviceVector<bst_float>& y = it->second.predictions;
if (y.Size() != 0) {
out_preds->Resize(y.Size());
std::copy(y.HostVector().begin(), y.HostVector().end(),
@ -128,7 +128,7 @@ class CPUPredictor : public Predictor {
HostDeviceVector<bst_float>* out_preds,
const gbm::GBTreeModel& model) const {
size_t n = model.param.num_output_group * info.num_row_;
const std::vector<bst_float>& base_margin = info.base_margin_;
const auto& base_margin = info.base_margin_.HostVector();
out_preds->Resize(n);
std::vector<bst_float>& out_preds_h = out_preds->HostVector();
if (base_margin.size() == n) {
@ -282,7 +282,7 @@ class CPUPredictor : public Predictor {
}
// start collecting the contributions
auto iter = p_fmat->RowIterator();
const std::vector<bst_float>& base_margin = info.base_margin_;
const auto& base_margin = info.base_margin_.HostVector();
iter->BeforeFirst();
while (iter->Next()) {
auto &batch = iter->Value();

24
src/predictor/gpu_predictor.cu
View File

@ -58,28 +58,30 @@ struct DeviceMatrix {
DeviceMatrix(DMatrix* dmat, int device_idx, bool silent) : p_mat(dmat) {
dh::safe_cuda(cudaSetDevice(device_idx));
auto info = dmat->Info();
const auto& info = dmat->Info();
ba.Allocate(device_idx, silent, &row_ptr, info.num_row_ + 1, &data,
info.num_nonzero_);
auto iter = dmat->RowIterator();
iter->BeforeFirst();
size_t data_offset = 0;
while (iter->Next()) {
auto &batch = iter->Value();
const auto& batch = iter->Value();
const auto& offset_vec = batch.offset.HostVector();
const auto& data_vec = batch.data.HostVector();
// Copy row ptr
dh::safe_cuda(cudaMemcpy(
row_ptr.Data() + batch.base_rowid, batch.offset.data(),
sizeof(size_t) * batch.offset.size(), cudaMemcpyHostToDevice));
row_ptr.Data() + batch.base_rowid, offset_vec.data(),
sizeof(size_t) * offset_vec.size(), cudaMemcpyHostToDevice));
if (batch.base_rowid > 0) {
auto begin_itr = row_ptr.tbegin() + batch.base_rowid;
auto end_itr = begin_itr + batch.Size() + 1;
IncrementOffset(begin_itr, end_itr, batch.base_rowid);
}
dh::safe_cuda(cudaMemcpy(data.Data() + data_offset, batch.data.data(),
sizeof(Entry) * batch.data.size(),
dh::safe_cuda(cudaMemcpy(data.Data() + data_offset, data_vec.data(),
sizeof(Entry) * data_vec.size(),
cudaMemcpyHostToDevice));
// Copy data
data_offset += batch.data.size();
data_offset += batch.data.Size();
}
}
};
@ -374,10 +376,10 @@ class GPUPredictor : public xgboost::Predictor {
HostDeviceVector<bst_float>* out_preds,
const gbm::GBTreeModel& model) const {
size_t n = model.param.num_output_group * info.num_row_;
const std::vector<bst_float>& base_margin = info.base_margin_;
const HostDeviceVector<bst_float>& base_margin = info.base_margin_;
out_preds->Reshard(devices);
out_preds->Resize(n);
if (base_margin.size() != 0) {
if (base_margin.Size() != 0) {
CHECK_EQ(out_preds->Size(), n);
out_preds->Copy(base_margin);
} else {
@ -391,11 +393,11 @@ class GPUPredictor : public xgboost::Predictor {
ntree_limit * model.param.num_output_group >= model.trees.size()) {
auto it = cache_.find(dmat);
if (it != cache_.end()) {
HostDeviceVector<bst_float>& y = it->second.predictions;
const HostDeviceVector<bst_float>& y = it->second.predictions;
if (y.Size() != 0) {
out_preds->Reshard(devices);
out_preds->Resize(y.Size());
out_preds->Copy(&y);
out_preds->Copy(y);
return true;
}
}

4
src/tree/updater_colmaker.cc
View File

@ -41,7 +41,7 @@ class ColMaker: public TreeUpdater {
Builder builder(
param_,
std::unique_ptr<SplitEvaluator>(spliteval_->GetHostClone()));
builder.Update(gpair->HostVector(), dmat, tree);
builder.Update(gpair->ConstHostVector(), dmat, tree);
}
param_.learning_rate = lr;
}
@ -784,7 +784,7 @@ class DistColMaker : public ColMaker {
param_,
std::unique_ptr<SplitEvaluator>(spliteval_->GetHostClone()));
// build the tree
builder.Update(gpair->HostVector(), dmat, trees[0]);
builder.Update(gpair->ConstHostVector(), dmat, trees[0]);
//// prune the tree, note that pruner will sync the tree
pruner_->Update(gpair, dmat, trees);
// update position after the tree is pruned

2
src/tree/updater_fast_hist.cc
View File

@ -164,7 +164,7 @@ class FastHistMaker: public TreeUpdater {
double time_evaluate_split = 0;
double time_apply_split = 0;
std::vector<GradientPair>& gpair_h = gpair->HostVector();
const std::vector<GradientPair>& gpair_h = gpair->ConstHostVector();
spliteval_->Reset();

2
src/tree/updater_gpu.cu
View File

@ -650,7 +650,7 @@ class GPUMaker : public TreeUpdater {
void convertToCsc(DMatrix* dmat, std::vector<float>* fval,
std::vector<int>* fId, std::vector<size_t>* offset) {
MetaInfo info = dmat->Info();
const MetaInfo& info = dmat->Info();
CHECK(info.num_col_ < std::numeric_limits<int>::max());
CHECK(info.num_row_ < std::numeric_limits<int>::max());
nRows = static_cast<int>(info.num_row_);

21
src/tree/updater_gpu_hist.cu
View File

@ -387,11 +387,13 @@ struct DeviceShard {
void InitRowPtrs(const SparsePage& row_batch) {
dh::safe_cuda(cudaSetDevice(device_idx));
const auto& offset_vec = row_batch.offset.HostVector();
row_ptrs.resize(n_rows + 1);
thrust::copy(row_batch.offset.data() + row_begin_idx,
row_batch.offset.data() + row_end_idx + 1,
thrust::copy(offset_vec.data() + row_begin_idx,
offset_vec.data() + row_end_idx + 1,
row_ptrs.begin());
auto row_iter = row_ptrs.begin();
// find the maximum row size
auto get_size = [=] __device__(size_t row) {
return row_iter[row + 1] - row_iter[row];
}; // NOLINT
@ -432,9 +434,12 @@ struct DeviceShard {
(dh::TotalMemory(device_idx) / (16 * row_stride * sizeof(Entry)),
static_cast<size_t>(n_rows));
thrust::device_vector<Entry> entries_d(gpu_batch_nrows * row_stride);
const auto& offset_vec = row_batch.offset.HostVector();
const auto& data_vec = row_batch.data.HostVector();
thrust::device_vector<Entry> entries_d(gpu_batch_nrows * row_stride);
size_t gpu_nbatches = dh::DivRoundUp(n_rows, 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;
@ -443,12 +448,12 @@ struct DeviceShard {
}
size_t batch_nrows = batch_row_end - batch_row_begin;
size_t n_entries =
row_batch.offset[row_begin_idx + batch_row_end] -
row_batch.offset[row_begin_idx + batch_row_begin];
offset_vec[row_begin_idx + batch_row_end] -
offset_vec[row_begin_idx + batch_row_begin];
dh::safe_cuda
(cudaMemcpy
(entries_d.data().get(),
&row_batch.data[row_batch.offset[row_begin_idx + batch_row_begin]],
data_vec.data() + offset_vec[row_begin_idx + batch_row_begin],
n_entries * sizeof(Entry), cudaMemcpyDefault));
dim3 block3(32, 8, 1);
dim3 grid3(dh::DivRoundUp(n_rows, block3.x),
@ -458,7 +463,7 @@ struct DeviceShard {
row_ptrs.data().get() + batch_row_begin,
entries_d.data().get(), cuts_d.data().get(), cut_row_ptrs_d.data().get(),
batch_row_begin, batch_nrows,
row_batch.offset[row_begin_idx + batch_row_begin],
offset_vec[row_begin_idx + batch_row_begin],
row_stride, null_gidx_value);
dh::safe_cuda(cudaGetLastError());
@ -538,7 +543,7 @@ struct DeviceShard {
std::fill(ridx_segments.begin(), ridx_segments.end(), Segment(0, 0));
ridx_segments.front() = Segment(0, ridx.Size());
this->gpair.copy(dh_gpair->tbegin(device_idx), dh_gpair->tend(device_idx));
this->gpair.copy(dh_gpair->tcbegin(device_idx), dh_gpair->tcend(device_idx));
SubsampleGradientPair(&gpair, param.subsample, row_begin_idx);
hist.Reset();
}

2
src/tree/updater_histmaker.cc
View File

@ -30,7 +30,7 @@ class HistMaker: public BaseMaker {
param_.learning_rate = lr / trees.size();
// build tree
for (auto tree : trees) {
this->Update(gpair->HostVector(), p_fmat, tree);
this->Update(gpair->ConstHostVector(), p_fmat, tree);
}
param_.learning_rate = lr;
}

2
src/tree/updater_refresh.cc
View File

@ -29,7 +29,7 @@ class TreeRefresher: public TreeUpdater {
DMatrix *p_fmat,
const std::vector<RegTree*> &trees) override {
if (trees.size() == 0) return;
std::vector<GradientPair> &gpair_h = gpair->HostVector();
const std::vector<GradientPair> &gpair_h = gpair->ConstHostVector();
// number of threads
// thread temporal space
std::vector<std::vector<TStats> > stemp;

2
src/tree/updater_skmaker.cc
View File

@ -30,7 +30,7 @@ class SketchMaker: public BaseMaker {
param_.learning_rate = lr / trees.size();
// build tree
for (auto tree : trees) {
this->Update(gpair->HostVector(), p_fmat, tree);
this->Update(gpair->ConstHostVector(), p_fmat, tree);
}
param_.learning_rate = lr;
}

152
tests/cpp/common/test_host_device_vector.cu
View File

@ -3,20 +3,168 @@
*/
#include <gtest/gtest.h>
#include "../../../src/common/host_device_vector.h"
#include <thrust/equal.h>
#include <thrust/iterator/counting_iterator.h>
#include "../../../src/common/device_helpers.cuh"
#include "../../../src/common/host_device_vector.h"
namespace xgboost {
namespace common {
void SetDevice(int device) {
int n_devices;
dh::safe_cuda(cudaGetDeviceCount(&n_devices));
device %= n_devices;
dh::safe_cuda(cudaSetDevice(device));
}
void InitHostDeviceVector(size_t n, const GPUDistribution& distribution,
HostDeviceVector<int> *v) {
// create the vector
GPUSet devices = distribution.Devices();
v->Reshard(distribution);
v->Resize(n);
ASSERT_EQ(v->Size(), n);
ASSERT_TRUE(v->Distribution() == distribution);
ASSERT_TRUE(v->Devices() == devices);
// ensure that the devices have read-write access
for (int i = 0; i < devices.Size(); ++i) {
ASSERT_TRUE(v->DeviceCanAccess(i, GPUAccess::kRead));
ASSERT_TRUE(v->DeviceCanAccess(i, GPUAccess::kWrite));
}
// ensure that the host has no access
ASSERT_FALSE(v->HostCanAccess(GPUAccess::kWrite));
ASSERT_FALSE(v->HostCanAccess(GPUAccess::kRead));
// fill in the data on the host
std::vector<int>& data_h = v->HostVector();
// ensure that the host has full access, while the devices have none
ASSERT_TRUE(v->HostCanAccess(GPUAccess::kRead));
ASSERT_TRUE(v->HostCanAccess(GPUAccess::kWrite));
for (int i = 0; i < devices.Size(); ++i) {
ASSERT_FALSE(v->DeviceCanAccess(i, GPUAccess::kRead));
ASSERT_FALSE(v->DeviceCanAccess(i, GPUAccess::kWrite));
}
ASSERT_EQ(data_h.size(), n);
std::copy_n(thrust::make_counting_iterator(0), n, data_h.begin());
}
void PlusOne(HostDeviceVector<int> *v) {
int n_devices = v->Devices().Size();
for (int i = 0; i < n_devices; ++i) {
SetDevice(i);
thrust::transform(v->tbegin(i), v->tend(i), v->tbegin(i),
[=]__device__(unsigned int a){ return a + 1; });
}
}
void CheckDevice(HostDeviceVector<int> *v,
const std::vector<size_t>& starts,
const std::vector<size_t>& sizes,
unsigned int first, GPUAccess access) {
int n_devices = sizes.size();
ASSERT_EQ(v->Devices().Size(), n_devices);
for (int i = 0; i < n_devices; ++i) {
ASSERT_EQ(v->DeviceSize(i), sizes.at(i));
SetDevice(i);
ASSERT_TRUE(thrust::equal(v->tcbegin(i), v->tcend(i),
thrust::make_counting_iterator(first + starts[i])));
ASSERT_TRUE(v->DeviceCanAccess(i, GPUAccess::kRead));
// ensure that the device has at most the access specified by access
ASSERT_EQ(v->DeviceCanAccess(i, GPUAccess::kWrite), access == GPUAccess::kWrite);
}
ASSERT_EQ(v->HostCanAccess(GPUAccess::kRead), access == GPUAccess::kRead);
ASSERT_FALSE(v->HostCanAccess(GPUAccess::kWrite));
for (int i = 0; i < n_devices; ++i) {
SetDevice(i);
ASSERT_TRUE(thrust::equal(v->tbegin(i), v->tend(i),
thrust::make_counting_iterator(first + starts[i])));
ASSERT_TRUE(v->DeviceCanAccess(i, GPUAccess::kRead));
ASSERT_TRUE(v->DeviceCanAccess(i, GPUAccess::kWrite));
}
ASSERT_FALSE(v->HostCanAccess(GPUAccess::kRead));
ASSERT_FALSE(v->HostCanAccess(GPUAccess::kWrite));
}
void CheckHost(HostDeviceVector<int> *v, GPUAccess access) {
const std::vector<int>& data_h = access == GPUAccess::kWrite ?
v->HostVector() : v->ConstHostVector();
for (size_t i = 0; i < v->Size(); ++i) {
ASSERT_EQ(data_h.at(i), i + 1);
}
ASSERT_TRUE(v->HostCanAccess(GPUAccess::kRead));
ASSERT_EQ(v->HostCanAccess(GPUAccess::kWrite), access == GPUAccess::kWrite);
size_t n_devices = v->Devices().Size();
for (int i = 0; i < n_devices; ++i) {
ASSERT_EQ(v->DeviceCanAccess(i, GPUAccess::kRead), access == GPUAccess::kRead);
// the devices should have no write access
ASSERT_FALSE(v->DeviceCanAccess(i, GPUAccess::kWrite));
}
}
void TestHostDeviceVector
(size_t n, const GPUDistribution& distribution,
const std::vector<size_t>& starts, const std::vector<size_t>& sizes) {
SetCudaSetDeviceHandler(SetDevice);
HostDeviceVector<int> v;
InitHostDeviceVector(n, distribution, &v);
CheckDevice(&v, starts, sizes, 0, GPUAccess::kRead);
PlusOne(&v);
CheckDevice(&v, starts, sizes, 1, GPUAccess::kWrite);
CheckHost(&v, GPUAccess::kRead);
CheckHost(&v, GPUAccess::kWrite);
SetCudaSetDeviceHandler(nullptr);
}
TEST(HostDeviceVector, TestBlock) {
size_t n = 1001;
int n_devices = 2;
auto distribution = GPUDistribution::Block(GPUSet::Range(0, n_devices));
std::vector<size_t> starts{0, 501};
std::vector<size_t> sizes{501, 500};
TestHostDeviceVector(n, distribution, starts, sizes);
}
TEST(HostDeviceVector, TestGranular) {
size_t n = 3003;
int n_devices = 2;
auto distribution = GPUDistribution::Granular(GPUSet::Range(0, n_devices), 3);
std::vector<size_t> starts{0, 1503};
std::vector<size_t> sizes{1503, 1500};
TestHostDeviceVector(n, distribution, starts, sizes);
}
TEST(HostDeviceVector, TestOverlap) {
size_t n = 1001;
int n_devices = 2;
auto distribution = GPUDistribution::Overlap(GPUSet::Range(0, n_devices), 1);
std::vector<size_t> starts{0, 500};
std::vector<size_t> sizes{501, 501};
TestHostDeviceVector(n, distribution, starts, sizes);
}
TEST(HostDeviceVector, TestExplicit) {
size_t n = 1001;
int n_devices = 2;
std::vector<size_t> offsets{0, 550, 1001};
auto distribution = GPUDistribution::Explicit(GPUSet::Range(0, n_devices), offsets);
std::vector<size_t> starts{0, 550};
std::vector<size_t> sizes{550, 451};
TestHostDeviceVector(n, distribution, starts, sizes);
}
TEST(HostDeviceVector, Span) {
HostDeviceVector<float> vec {1.0f, 2.0f, 3.0f, 4.0f};
vec.Reshard(GPUSet{0, 1});
auto span = vec.DeviceSpan(0);
ASSERT_EQ(vec.Size(), span.size());
ASSERT_EQ(vec.DevicePointer(0), span.data());
auto const_span = vec.ConstDeviceSpan(0);
ASSERT_EQ(vec.Size(), span.size());
ASSERT_EQ(vec.ConstDevicePointer(0), span.data());
}
} // namespace common
} // namespace xgboost

14
tests/cpp/data/test_metainfo.cc
View File

@ -16,9 +16,9 @@ TEST(MetaInfo, GetSet) {
info.SetInfo("root_index", double2, xgboost::kDouble, 2);
EXPECT_EQ(info.GetRoot(1), 2.0f);
EXPECT_EQ(info.labels_.size(), 0);
EXPECT_EQ(info.labels_.Size(), 0);
info.SetInfo("label", double2, xgboost::kFloat32, 2);
EXPECT_EQ(info.labels_.size(), 2);
EXPECT_EQ(info.labels_.Size(), 2);
float float2[2] = {1.0f, 2.0f};
EXPECT_EQ(info.GetWeight(1), 1.0f)
@ -27,9 +27,9 @@ TEST(MetaInfo, GetSet) {
EXPECT_EQ(info.GetWeight(1), 2.0f);
uint32_t uint32_t2[2] = {1U, 2U};
EXPECT_EQ(info.base_margin_.size(), 0);
EXPECT_EQ(info.base_margin_.Size(), 0);
info.SetInfo("base_margin", uint32_t2, xgboost::kUInt32, 2);
EXPECT_EQ(info.base_margin_.size(), 2);
EXPECT_EQ(info.base_margin_.Size(), 2);
uint64_t uint64_t2[2] = {1U, 2U};
EXPECT_EQ(info.group_ptr_.size(), 0);
@ -59,7 +59,7 @@ TEST(MetaInfo, SaveLoadBinary) {
fs = dmlc::Stream::Create(tmp_file.c_str(), "r");
xgboost::MetaInfo inforead;
inforead.LoadBinary(fs);
EXPECT_EQ(inforead.labels_, info.labels_);
EXPECT_EQ(inforead.labels_.HostVector(), info.labels_.HostVector());
EXPECT_EQ(inforead.num_col_, info.num_col_);
EXPECT_EQ(inforead.num_row_, info.num_row_);
@ -128,7 +128,7 @@ TEST(MetaInfo, LoadQid) {
CHECK(iter->Next());
const xgboost::SparsePage& batch = iter->Value();
CHECK_EQ(batch.base_rowid, 0);
CHECK(batch.offset == expected_offset);
CHECK(batch.data == expected_data);
CHECK(batch.offset.HostVector() == expected_offset);
CHECK(batch.data.HostVector() == expected_data);
CHECK(!iter->Next());
}

2
tests/cpp/data/test_simple_dmatrix.cc
View File

@ -13,7 +13,7 @@ TEST(SimpleDMatrix, MetaInfo) {
EXPECT_EQ(dmat->Info().num_row_, 2);
EXPECT_EQ(dmat->Info().num_col_, 5);
EXPECT_EQ(dmat->Info().num_nonzero_, 6);
EXPECT_EQ(dmat->Info().labels_.size(), dmat->Info().num_row_);
EXPECT_EQ(dmat->Info().labels_.Size(), dmat->Info().num_row_);
delete dmat;
}

4
tests/cpp/data/test_sparse_page_dmatrix.cc
View File

@ -16,7 +16,7 @@ TEST(SparsePageDMatrix, MetaInfo) {
EXPECT_EQ(dmat->Info().num_row_, 2);
EXPECT_EQ(dmat->Info().num_col_, 5);
EXPECT_EQ(dmat->Info().num_nonzero_, 6);
EXPECT_EQ(dmat->Info().labels_.size(), dmat->Info().num_row_);
EXPECT_EQ(dmat->Info().labels_.Size(), dmat->Info().num_row_);
// Clean up of external memory files
std::remove((tmp_file + ".cache").c_str());
@ -54,7 +54,7 @@ TEST(SparsePageDMatrix, RowAccess) {
delete dmat;
}
TEST(SparsePageDMatrix, ColAcess) {
TEST(SparsePageDMatrix, ColAccess) {
std::string tmp_file = CreateSimpleTestData();
xgboost::DMatrix * dmat = xgboost::DMatrix::Load(
tmp_file + "#" + tmp_file + ".cache", true, false);

15
tests/cpp/helpers.cc
View File

@ -49,9 +49,8 @@ void _CheckObjFunction(xgboost::ObjFunction * obj,
std::vector<xgboost::bst_float> out_grad,
std::vector<xgboost::bst_float> out_hess) {
xgboost::HostDeviceVector<xgboost::bst_float> in_preds(preds);
xgboost::HostDeviceVector<xgboost::GradientPair> out_gpair;
obj->GetGradient(&in_preds, info, 1, &out_gpair);
obj->GetGradient(in_preds, info, 1, &out_gpair);
std::vector<xgboost::GradientPair>& gpair = out_gpair.HostVector();
ASSERT_EQ(gpair.size(), in_preds.Size());
@ -73,8 +72,8 @@ void CheckObjFunction(xgboost::ObjFunction * obj,
std::vector<xgboost::bst_float> out_hess) {
xgboost::MetaInfo info;
info.num_row_ = labels.size();
info.labels_ = labels;
info.weights_ = weights;
info.labels_.HostVector() = labels;
info.weights_.HostVector() = weights;
_CheckObjFunction(obj, preds, labels, weights, info, out_grad, out_hess);
}
@ -88,8 +87,8 @@ void CheckRankingObjFunction(xgboost::ObjFunction * obj,
std::vector<xgboost::bst_float> out_hess) {
xgboost::MetaInfo info;
info.num_row_ = labels.size();
info.labels_ = labels;
info.weights_ = weights;
info.labels_.HostVector() = labels;
info.weights_.HostVector() = weights;
info.group_ptr_ = groups;
_CheckObjFunction(obj, preds, labels, weights, info, out_grad, out_hess);
@ -102,8 +101,8 @@ xgboost::bst_float GetMetricEval(xgboost::Metric * metric,
std::vector<xgboost::bst_float> weights) {
xgboost::MetaInfo info;
info.num_row_ = labels.size();
info.labels_ = labels;
info.weights_ = weights;
info.labels_.HostVector() = labels;
info.weights_.HostVector() = weights;
return metric->Eval(preds, info, false);
}