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/*!
* Copyright 2017-2024 by Contributors
* \file gradient_index.cc
*/
#include <vector>
#include <limits>
#include <algorithm>
#include "gradient_index.h"
#include <sycl/sycl.hpp>
namespace xgboost {
namespace sycl {
namespace common {
uint32_t SearchBin(const bst_float* cut_values, const uint32_t* cut_ptrs, Entry const& e) {
auto beg = cut_ptrs[e.index];
auto end = cut_ptrs[e.index + 1];
auto it = std::upper_bound(cut_values + beg, cut_values + end, e.fvalue);
uint32_t idx = it - cut_values;
if (idx == end) {
idx -= 1;
}
return idx;
}
template <typename BinIdxType>
void mergeSort(BinIdxType* begin, BinIdxType* end, BinIdxType* buf) {
const size_t total_len = end - begin;
for (size_t block_len = 1; block_len < total_len; block_len <<= 1) {
for (size_t cur_block = 0; cur_block + block_len < total_len; cur_block += 2 * block_len) {
size_t start = cur_block;
size_t mid = start + block_len;
size_t finish = mid + block_len < total_len ? mid + block_len : total_len;
size_t left_pos = start;
size_t right_pos = mid;
size_t pos = start;
while (left_pos < mid || right_pos < finish) {
if (left_pos < mid && (right_pos == finish || begin[left_pos] < begin[right_pos])) {
buf[pos++] = begin[left_pos++];
} else {
buf[pos++] = begin[right_pos++];
}
}
for (size_t i = start; i < finish; i++) begin[i] = buf[i];
}
}
}
template <typename BinIdxType, bool isDense>
void GHistIndexMatrix::SetIndexData(::sycl::queue* qu,
BinIdxType* index_data,
DMatrix *dmat,
size_t nbins,
size_t row_stride) {
if (nbins == 0) return;
const bst_float* cut_values = cut.cut_values_.ConstDevicePointer();
const uint32_t* cut_ptrs = cut.cut_ptrs_.ConstDevicePointer();
size_t* hit_count_ptr = hit_count.DevicePointer();
BinIdxType* sort_data = reinterpret_cast<BinIdxType*>(sort_buff.Data());
::sycl::event event;
for (auto &batch : dmat->GetBatches<SparsePage>()) {
for (auto &batch : dmat->GetBatches<SparsePage>()) {
const xgboost::Entry *data_ptr = batch.data.ConstDevicePointer();
const bst_idx_t *offset_vec = batch.offset.ConstDevicePointer();
size_t batch_size = batch.Size();
if (batch_size > 0) {
const auto base_rowid = batch.base_rowid;
event = qu->submit([&](::sycl::handler& cgh) {
cgh.depends_on(event);
cgh.parallel_for<>(::sycl::range<1>(batch_size), [=](::sycl::item<1> pid) {
const size_t i = pid.get_id(0);
const size_t ibegin = offset_vec[i];
const size_t iend = offset_vec[i + 1];
const size_t size = iend - ibegin;
const size_t start = (i + base_rowid) * row_stride;
for (bst_uint j = 0; j < size; ++j) {
uint32_t idx = SearchBin(cut_values, cut_ptrs, data_ptr[ibegin + j]);
index_data[start + j] = isDense ? idx - cut_ptrs[j] : idx;
AtomicRef<size_t> hit_count_ref(hit_count_ptr[idx]);
hit_count_ref.fetch_add(1);
}
if constexpr (!isDense) {
// Sparse case only
mergeSort<BinIdxType>(index_data + start, index_data + start + size, sort_data + start);
for (bst_uint j = size; j < row_stride; ++j) {
index_data[start + j] = nbins;
}
}
});
});
}
}
}
qu->wait();
}
void GHistIndexMatrix::ResizeIndex(size_t n_index, bool isDense) {
if ((max_num_bins - 1 <= static_cast<int>(std::numeric_limits<uint8_t>::max())) && isDense) {
index.SetBinTypeSize(BinTypeSize::kUint8BinsTypeSize);
index.Resize((sizeof(uint8_t)) * n_index);
} else if ((max_num_bins - 1 > static_cast<int>(std::numeric_limits<uint8_t>::max()) &&
max_num_bins - 1 <= static_cast<int>(std::numeric_limits<uint16_t>::max())) && isDense) {
index.SetBinTypeSize(BinTypeSize::kUint16BinsTypeSize);
index.Resize((sizeof(uint16_t)) * n_index);
} else {
index.SetBinTypeSize(BinTypeSize::kUint32BinsTypeSize);
index.Resize((sizeof(uint32_t)) * n_index);
}
}
void GHistIndexMatrix::Init(::sycl::queue* qu,
Context const * ctx,
DMatrix *dmat,
int max_bins) {
nfeatures = dmat->Info().num_col_;
cut = xgboost::common::SketchOnDMatrix(ctx, dmat, max_bins);
cut.SetDevice(ctx->Device());
max_num_bins = max_bins;
const uint32_t nbins = cut.Ptrs().back();
this->nbins = nbins;
hit_count.SetDevice(ctx->Device());
hit_count.Resize(nbins, 0);
this->p_fmat = dmat;
const bool isDense = dmat->IsDense();
this->isDense_ = isDense;
index.setQueue(qu);
row_stride = 0;
size_t n_rows = 0;
for (const auto& batch : dmat->GetBatches<SparsePage>()) {
const auto& row_offset = batch.offset.ConstHostVector();
batch.data.SetDevice(ctx->Device());
batch.offset.SetDevice(ctx->Device());
n_rows += batch.Size();
for (auto i = 1ull; i < row_offset.size(); i++) {
row_stride = std::max(row_stride, static_cast<size_t>(row_offset[i] - row_offset[i - 1]));
}
}
const size_t n_offsets = cut.cut_ptrs_.Size() - 1;
const size_t n_index = n_rows * row_stride;
ResizeIndex(n_index, isDense);
CHECK_GT(cut.cut_values_.Size(), 0U);
if (isDense) {
BinTypeSize curent_bin_size = index.GetBinTypeSize();
if (curent_bin_size == BinTypeSize::kUint8BinsTypeSize) {
SetIndexData<uint8_t, true>(qu, index.data<uint8_t>(), dmat, nbins, row_stride);
} else if (curent_bin_size == BinTypeSize::kUint16BinsTypeSize) {
SetIndexData<uint16_t, true>(qu, index.data<uint16_t>(), dmat, nbins, row_stride);
} else {
CHECK_EQ(curent_bin_size, BinTypeSize::kUint32BinsTypeSize);
SetIndexData<uint32_t, true>(qu, index.data<uint32_t>(), dmat, nbins, row_stride);
}
/* For sparse DMatrix we have to store index of feature for each bin
in index field to chose right offset. So offset is nullptr and index is not reduced */
} else {
sort_buff.Resize(qu, n_rows * row_stride * sizeof(uint32_t));
SetIndexData<uint32_t, false>(qu, index.data<uint32_t>(), dmat, nbins, row_stride);
}
}
} // namespace common
} // namespace sycl
} // namespace xgboost
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