Skip to content

Commit

Permalink
new single_precision_histogram param was added, tests were added, doc…
Browse files Browse the repository at this point in the history 
… was updated
  • Loading branch information
SHVETS, KIRILL committed May 20, 2020
1 parent dd01e4b commit 0d19506
Show file tree
Hide file tree
Showing 10 changed files with 610 additions and 305 deletions.
7 changes: 5 additions & 2 deletions doc/parameter.rst
View file
Original file line number Diff line number Diff line change
Expand Up @@ -225,12 +225,15 @@ Parameters for Tree Booster
list is a group of indices of features that are allowed to interact with each other.
See tutorial for more information

Additional parameters for `gpu_hist` tree method
Additional parameters for `hist` tree method
================================================

* ``single_precision_histogram``, [default=``false``]

- Use single precision to build histograms. See document for GPU support for more details.
- Use single precision to build histograms instead of double precision.

Additional parameters for `gpu_hist` tree method
================================================

* ``deterministic_histogram``, [default=``true``]

Expand Down
9 changes: 9 additions & 0 deletions include/xgboost/base.h
View file
Original file line number Diff line number Diff line change
Expand Up @@ -141,6 +141,15 @@ class GradientPairInternal {
public:
using ValueT = T;

inline void Add(const ValueT& grad, const ValueT& hess) {
grad_ += grad;
hess_ += hess;
}

inline static void Reduce(GradientPairInternal<T>& a, const GradientPairInternal<T>& b) { // NOLINT(*)
a += b;
}

XGBOOST_DEVICE GradientPairInternal() : grad_(0), hess_(0) {}

XGBOOST_DEVICE GradientPairInternal(T grad, T hess) {
Expand Down
123 changes: 91 additions & 32 deletions src/common/hist_util.cc
View file
Original file line number Diff line number Diff line change
Expand Up @@ -830,54 +830,78 @@ void GHistIndexBlockMatrix::Init(const GHistIndexMatrix& gmat,
/*!
* \brief fill a histogram by zeros in range [begin, end)
*/
void InitilizeHistByZeroes(GHistRow hist, size_t begin, size_t end) {
template<typename GradientSumT>
void InitilizeHistByZeroes(GHistRow<GradientSumT> hist, size_t begin, size_t end) {
#if defined(XGBOOST_STRICT_R_MODE) && XGBOOST_STRICT_R_MODE == 1
std::fill(hist.begin() + begin, hist.begin() + end, tree::GradStats());
std::fill(hist.begin() + begin, hist.begin() + end,
xgboost::detail::GradientPairInternal<GradientSumT>());
#else // defined(XGBOOST_STRICT_R_MODE) && XGBOOST_STRICT_R_MODE == 1
memset(hist.data() + begin, '\0', (end-begin)*sizeof(tree::GradStats));
memset(hist.data() + begin, '\0', (end-begin)*
sizeof(xgboost::detail::GradientPairInternal<GradientSumT>));
#endif // defined(XGBOOST_STRICT_R_MODE) && XGBOOST_STRICT_R_MODE == 1
}
template void InitilizeHistByZeroes(GHistRow<float> hist, size_t begin,
size_t end);
template void InitilizeHistByZeroes(GHistRow<double> hist, size_t begin,
size_t end);

/*!
* \brief Increment hist as dst += add in range [begin, end)
*/
void IncrementHist(GHistRow dst, const GHistRow add, size_t begin, size_t end) {
using FPType = decltype(tree::GradStats::sum_grad);
FPType* pdst = reinterpret_cast<FPType*>(dst.data());
const FPType* padd = reinterpret_cast<const FPType*>(add.data());
template<typename GradientSumT>
void IncrementHist(GHistRow<GradientSumT> dst, const GHistRow<GradientSumT> add,
size_t begin, size_t end) {
GradientSumT* pdst = reinterpret_cast<GradientSumT*>(dst.data());
const GradientSumT* padd = reinterpret_cast<const GradientSumT*>(add.data());

for (size_t i = 2 * begin; i < 2 * end; ++i) {
pdst[i] += padd[i];
}
}
template void IncrementHist(GHistRow<float> dst, const GHistRow<float> add,
size_t begin, size_t end);
template void IncrementHist(GHistRow<double> dst, const GHistRow<double> add,
size_t begin, size_t end);

/*!
* \brief Copy hist from src to dst in range [begin, end)
*/
void CopyHist(GHistRow dst, const GHistRow src, size_t begin, size_t end) {
using FPType = decltype(tree::GradStats::sum_grad);
FPType* pdst = reinterpret_cast<FPType*>(dst.data());
const FPType* psrc = reinterpret_cast<const FPType*>(src.data());
template<typename GradientSumT>
void CopyHist(GHistRow<GradientSumT> dst, const GHistRow<GradientSumT> src,
size_t begin, size_t end) {
GradientSumT* pdst = reinterpret_cast<GradientSumT*>(dst.data());
const GradientSumT* psrc = reinterpret_cast<const GradientSumT*>(src.data());

for (size_t i = 2 * begin; i < 2 * end; ++i) {
pdst[i] = psrc[i];
}
}
template void CopyHist(GHistRow<float> dst, const GHistRow<float> src,
size_t begin, size_t end);
template void CopyHist(GHistRow<double> dst, const GHistRow<double> src,
size_t begin, size_t end);

/*!
* \brief Compute Subtraction: dst = src1 - src2 in range [begin, end)
*/
void SubtractionHist(GHistRow dst, const GHistRow src1, const GHistRow src2,
template<typename GradientSumT>
void SubtractionHist(GHistRow<GradientSumT> dst, const GHistRow<GradientSumT> src1,
const GHistRow<GradientSumT> src2,
size_t begin, size_t end) {
using FPType = decltype(tree::GradStats::sum_grad);
FPType* pdst = reinterpret_cast<FPType*>(dst.data());
const FPType* psrc1 = reinterpret_cast<const FPType*>(src1.data());
const FPType* psrc2 = reinterpret_cast<const FPType*>(src2.data());
GradientSumT* pdst = reinterpret_cast<GradientSumT*>(dst.data());
const GradientSumT* psrc1 = reinterpret_cast<const GradientSumT*>(src1.data());
const GradientSumT* psrc2 = reinterpret_cast<const GradientSumT*>(src2.data());

for (size_t i = 2 * begin; i < 2 * end; ++i) {
pdst[i] = psrc1[i] - psrc2[i];
}
}
template void SubtractionHist(GHistRow<float> dst, const GHistRow<float> src1,
const GHistRow<float> src2,
size_t begin, size_t end);
template void SubtractionHist(GHistRow<double> dst, const GHistRow<double> src1,
const GHistRow<double> src2,
size_t begin, size_t end);

struct Prefetch {
public:
Expand Down Expand Up @@ -908,7 +932,7 @@ void BuildHistDenseKernel(const std::vector<GradientPair>& gpair,
const RowSetCollection::Elem row_indices,
const GHistIndexMatrix& gmat,
const size_t n_features,
GHistRow hist) {
GHistRow<FPType> hist) {
const size_t size = row_indices.Size();
const size_t* rid = row_indices.begin;
const float* pgh = reinterpret_cast<const float*>(gpair.data());
Expand Down Expand Up @@ -948,7 +972,7 @@ template<typename FPType, bool do_prefetch>
void BuildHistSparseKernel(const std::vector<GradientPair>& gpair,
const RowSetCollection::Elem row_indices,
const GHistIndexMatrix& gmat,
GHistRow hist) {
GHistRow<FPType> hist) {
const size_t size = row_indices.Size();
const size_t* rid = row_indices.begin;
const float* pgh = reinterpret_cast<const float*>(gpair.data());
Expand Down Expand Up @@ -987,7 +1011,7 @@ void BuildHistSparseKernel(const std::vector<GradientPair>& gpair,
template<typename FPType, bool do_prefetch, typename BinIdxType>
void BuildHistDispatchKernel(const std::vector<GradientPair>& gpair,
const RowSetCollection::Elem row_indices,
const GHistIndexMatrix& gmat, GHistRow hist, bool isDense) {
const GHistIndexMatrix& gmat, GHistRow<FPType> hist, bool isDense) {
if (isDense) {
const size_t* row_ptr = gmat.row_ptr.data();
const size_t n_features = row_ptr[row_indices.begin[0]+1] - row_ptr[row_indices.begin[0]];
Expand All @@ -1002,7 +1026,7 @@ void BuildHistDispatchKernel(const std::vector<GradientPair>& gpair,
template<typename FPType, bool do_prefetch>
void BuildHistKernel(const std::vector<GradientPair>& gpair,
const RowSetCollection::Elem row_indices,
const GHistIndexMatrix& gmat, const bool isDense, GHistRow hist) {
const GHistIndexMatrix& gmat, const bool isDense, GHistRow<FPType> hist) {
const bool is_dense = row_indices.Size() && isDense;
switch (gmat.index.GetBinTypeSize()) {
case kUint8BinsTypeSize:
Expand All @@ -1022,12 +1046,12 @@ void BuildHistKernel(const std::vector<GradientPair>& gpair,
}
}

void GHistBuilder::BuildHist(const std::vector<GradientPair>& gpair,
template<typename GradientSumT>
void GHistBuilder<GradientSumT>::BuildHist(const std::vector<GradientPair>& gpair,
const RowSetCollection::Elem row_indices,
const GHistIndexMatrix& gmat,
GHistRow hist,
GHistRowT hist,
bool isDense) {
using FPType = decltype(tree::GradStats::sum_grad);
const size_t nrows = row_indices.Size();
const size_t no_prefetch_size = Prefetch::NoPrefetchSize(nrows);

Expand All @@ -1036,29 +1060,42 @@ void GHistBuilder::BuildHist(const std::vector<GradientPair>& gpair,

if (contiguousBlock) {
// contiguous memory access, built-in HW prefetching is enough
BuildHistKernel<FPType, false>(gpair, row_indices, gmat, isDense, hist);
BuildHistKernel<GradientSumT, false>(gpair, row_indices, gmat, isDense, hist);
} else {
const RowSetCollection::Elem span1(row_indices.begin, row_indices.end - no_prefetch_size);
const RowSetCollection::Elem span2(row_indices.end - no_prefetch_size, row_indices.end);

BuildHistKernel<FPType, true>(gpair, span1, gmat, isDense, hist);
BuildHistKernel<GradientSumT, true>(gpair, span1, gmat, isDense, hist);
// no prefetching to avoid loading extra memory
BuildHistKernel<FPType, false>(gpair, span2, gmat, isDense, hist);
BuildHistKernel<GradientSumT, false>(gpair, span2, gmat, isDense, hist);
}
}
template
void GHistBuilder<float>::BuildHist(const std::vector<GradientPair>& gpair,
const RowSetCollection::Elem row_indices,
const GHistIndexMatrix& gmat,
GHistRow<float> hist,
bool isDense);
template
void GHistBuilder<double>::BuildHist(const std::vector<GradientPair>& gpair,
const RowSetCollection::Elem row_indices,
const GHistIndexMatrix& gmat,
GHistRow<double> hist,
bool isDense);

void GHistBuilder::BuildBlockHist(const std::vector<GradientPair>& gpair,
template<typename GradientSumT>
void GHistBuilder<GradientSumT>::BuildBlockHist(const std::vector<GradientPair>& gpair,
const RowSetCollection::Elem row_indices,
const GHistIndexBlockMatrix& gmatb,
GHistRow hist) {
GHistRowT hist) {
constexpr int kUnroll = 8; // loop unrolling factor
const size_t nblock = gmatb.GetNumBlock();
const size_t nrows = row_indices.end - row_indices.begin;
const size_t rest = nrows % kUnroll;
#if defined(_OPENMP)
const auto nthread = static_cast<bst_omp_uint>(this->nthread_); // NOLINT
#endif // defined(_OPENMP)
tree::GradStats* p_hist = hist.data();
xgboost::detail::GradientPairInternal<GradientSumT>* p_hist = hist.data();

#pragma omp parallel for num_threads(nthread) schedule(guided)
for (bst_omp_uint bid = 0; bid < nblock; ++bid) {
Expand All @@ -1079,7 +1116,7 @@ void GHistBuilder::BuildBlockHist(const std::vector<GradientPair>& gpair,
for (int k = 0; k < kUnroll; ++k) {
for (size_t j = ibegin[k]; j < iend[k]; ++j) {
const uint32_t bin = gmat.index[j];
p_hist[bin].Add(stat[k]);
p_hist[bin].Add(stat[k].GetGrad(), stat[k].GetHess());
}
}
}
Expand All @@ -1090,13 +1127,27 @@ void GHistBuilder::BuildBlockHist(const std::vector<GradientPair>& gpair,
const GradientPair stat = gpair[rid];
for (size_t j = ibegin; j < iend; ++j) {
const uint32_t bin = gmat.index[j];
p_hist[bin].Add(stat);
p_hist[bin].Add(stat.GetGrad(), stat.GetHess());
}
}
}
}
template
void GHistBuilder<float>::BuildBlockHist(const std::vector<GradientPair>& gpair,
const RowSetCollection::Elem row_indices,
const GHistIndexBlockMatrix& gmatb,
GHistRow<float> hist);
template
void GHistBuilder<double>::BuildBlockHist(const std::vector<GradientPair>& gpair,
const RowSetCollection::Elem row_indices,
const GHistIndexBlockMatrix& gmatb,
GHistRow<double> hist);


void GHistBuilder::SubtractionTrick(GHistRow self, GHistRow sibling, GHistRow parent) {
template<typename GradientSumT>
void GHistBuilder<GradientSumT>::SubtractionTrick(GHistRowT self,
GHistRowT sibling,
GHistRowT parent) {
const size_t size = self.size();
CHECK_EQ(sibling.size(), size);
CHECK_EQ(parent.size(), size);
Expand All @@ -1111,6 +1162,14 @@ void GHistBuilder::SubtractionTrick(GHistRow self, GHistRow sibling, GHistRow pa
SubtractionHist(self, parent, sibling, ibegin, iend);
}
}
template
void GHistBuilder<float>::SubtractionTrick(GHistRow<float> self,
GHistRow<float> sibling,
GHistRow<float> parent);
template
void GHistBuilder<double>::SubtractionTrick(GHistRow<double> self,
GHistRow<double> sibling,
GHistRow<double> parent);

} // namespace common
} // namespace xgboost
Loading

0 comments on commit 0d19506

Please sign in to comment.