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
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;