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Various bug fixes (#2825)

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* Fatal error if GPU algorithm selected without GPU support compiled

* Resolve type conversion warnings

* Fix gpu unit test failure

* Fix compressed iterator edge case

* Fix python unit test failures due to flake8 update on pip
This commit is contained in:
Rory Mitchell 2017-10-25 14:45:01 +13:00 committed by GitHub
parent c71b62d48d
commit 13e7a2cff0
21 changed files with 163 additions and 180 deletions
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8
include/xgboost/base.h
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@ -171,19 +171,19 @@ class bst_gpair_internal {
template<>
inline XGBOOST_DEVICE float bst_gpair_internal<int64_t>::GetGrad() const {
return grad_ * 1e-5;
return grad_ * 1e-5f;
}
template<>
inline XGBOOST_DEVICE float bst_gpair_internal<int64_t>::GetHess() const {
return hess_ * 1e-5;
return hess_ * 1e-5f;
}
template<>
inline XGBOOST_DEVICE void bst_gpair_internal<int64_t>::SetGrad(float g) {
grad_ = std::round(g * 1e5);
grad_ = static_cast<int64_t>(std::round(g * 1e5));
}
template<>
inline XGBOOST_DEVICE void bst_gpair_internal<int64_t>::SetHess(float h) {
hess_ = std::round(h * 1e5);
hess_ = static_cast<int64_t>(std::round(h * 1e5));
}
} // namespace detail

2
include/xgboost/data.h
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@ -328,7 +328,7 @@ class DMatrix {
// implementation of inline functions
inline bst_uint RowSet::operator[](size_t i) const {
return rows_.size() == 0 ? i : rows_[i];
return rows_.size() == 0 ? static_cast<bst_uint>(i) : rows_[i];
}
inline size_t RowSet::size() const {

8
include/xgboost/tree_model.h
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@ -651,7 +651,7 @@ inline void ExtendPath(PathElement *unique_path, unsigned unique_depth,
unique_path[unique_depth].feature_index = feature_index;
unique_path[unique_depth].zero_fraction = zero_fraction;
unique_path[unique_depth].one_fraction = one_fraction;
unique_path[unique_depth].pweight = (unique_depth == 0 ? 1 : 0);
unique_path[unique_depth].pweight = static_cast<bst_float>(unique_depth == 0 ? 1 : 0);
for (int i = unique_depth-1; i >= 0; i--) {
unique_path[i+1].pweight += one_fraction*unique_path[i].pweight*(i+1)
/ static_cast<bst_float>(unique_depth+1);
@ -679,7 +679,7 @@ inline void UnwindPath(PathElement *unique_path, unsigned unique_depth, unsigned
}
}
for (int i = path_index; i < unique_depth; ++i) {
for (auto i = path_index; i < unique_depth; ++i) {
unique_path[i].feature_index = unique_path[i+1].feature_index;
unique_path[i].zero_fraction = unique_path[i+1].zero_fraction;
unique_path[i].one_fraction = unique_path[i+1].one_fraction;
@ -725,7 +725,7 @@ inline void RegTree::TreeShap(const RegTree::FVec& feat, bst_float *phi,
// leaf node
if (node.is_leaf()) {
for (int i = 1; i <= unique_depth; ++i) {
for (unsigned i = 1; i <= unique_depth; ++i) {
const bst_float w = UnwoundPathSum(unique_path, unique_depth, i);
const PathElement &el = unique_path[i];
phi[el.feature_index] += w*(el.one_fraction-el.zero_fraction)*node.leaf_value();
@ -775,7 +775,7 @@ inline void RegTree::CalculateContributions(const RegTree::FVec& feat, unsigned
// find the expected value of the tree's predictions
bst_float base_value = 0.0;
bst_float total_cover = 0;
for (unsigned i = 0; i < (*this).param.num_nodes; ++i) {
for (int i = 0; i < (*this).param.num_nodes; ++i) {
const auto node = (*this)[i];
if (node.is_leaf()) {
const auto cover = this->stat(i).sum_hess;

31
src/common/compressed_iterator.h
View File

@ -6,7 +6,7 @@
#include <xgboost/base.h>
#include <cmath>
#include <cstddef>
#include "dmlc/logging.h"
#include <algorithm>
namespace xgboost {
namespace common {
@ -28,8 +28,9 @@ static const int padding = 4; // Assign padding so we can read slightly off
// the beginning of the array
// The number of bits required to represent a given unsigned range
static int SymbolBits(int num_symbols) {
return std::ceil(std::log2(num_symbols));
static size_t SymbolBits(size_t num_symbols) {
auto bits = std::ceil(std::log2(num_symbols));
return std::max(static_cast<size_t>(bits), size_t(1));
}
} // namespace detail
@ -72,9 +73,9 @@ class CompressedBufferWriter {
static size_t CalculateBufferSize(size_t num_elements, size_t num_symbols) {
const int bits_per_byte = 8;
size_t compressed_size = std::ceil(
size_t compressed_size = static_cast<size_t>(std::ceil(
static_cast<double>(detail::SymbolBits(num_symbols) * num_elements) /
bits_per_byte);
bits_per_byte));
return compressed_size + detail::padding;
}
@ -98,8 +99,8 @@ class CompressedBufferWriter {
template <typename iter_t>
void Write(compressed_byte_t *buffer, iter_t input_begin, iter_t input_end) {
uint64_t tmp = 0;
int stored_bits = 0;
const int max_stored_bits = 64 - symbol_bits_;
size_t stored_bits = 0;
const size_t max_stored_bits = 64 - symbol_bits_;
size_t buffer_position = detail::padding;
const size_t num_symbols = input_end - input_begin;
for (size_t i = 0; i < num_symbols; i++) {
@ -108,7 +109,8 @@ class CompressedBufferWriter {
// Eject only full bytes
size_t tmp_bytes = stored_bits / 8;
for (size_t j = 0; j < tmp_bytes; j++) {
buffer[buffer_position] = tmp >> (stored_bits - (j + 1) * 8);
buffer[buffer_position] = static_cast<compressed_byte_t>(
tmp >> (stored_bits - (j + 1) * 8));
buffer_position++;
}
stored_bits -= tmp_bytes * 8;
@ -121,13 +123,16 @@ class CompressedBufferWriter {
}
// Eject all bytes
size_t tmp_bytes = std::ceil(static_cast<float>(stored_bits) / 8);
for (size_t j = 0; j < tmp_bytes; j++) {
int shift_bits = stored_bits - (j + 1) * 8;
int tmp_bytes =
static_cast<int>(std::ceil(static_cast<float>(stored_bits) / 8));
for (int j = 0; j < tmp_bytes; j++) {
int shift_bits = static_cast<int>(stored_bits) - (j + 1) * 8;
if (shift_bits >= 0) {
buffer[buffer_position] = tmp >> shift_bits;
buffer[buffer_position] =
static_cast<compressed_byte_t>(tmp >> shift_bits);
} else {
buffer[buffer_position] = tmp << std::abs(shift_bits);
buffer[buffer_position] =
static_cast<compressed_byte_t>(tmp << std::abs(shift_bits));
}
buffer_position++;
}

69
src/common/device_helpers.cuh
View File

@ -125,7 +125,7 @@ inline size_t available_memory(int device_idx) {
* \param device_idx Zero-based index of the device.
*/
inline int max_shared_memory(int device_idx) {
inline size_t max_shared_memory(int device_idx) {
cudaDeviceProp prop;
dh::safe_cuda(cudaGetDeviceProperties(&prop, device_idx));
return prop.sharedMemPerBlock;
@ -241,8 +241,7 @@ inline void launch_n(int device_idx, size_t n, L lambda) {
}
safe_cuda(cudaSetDevice(device_idx));
// TODO: Template on n so GRID_SIZE always fits into int.
const int GRID_SIZE = div_round_up(n, ITEMS_PER_THREAD * BLOCK_THREADS);
const int GRID_SIZE = static_cast<int>(div_round_up(n, ITEMS_PER_THREAD * BLOCK_THREADS));
launch_n_kernel<<<GRID_SIZE, BLOCK_THREADS>>>(static_cast<size_t>(0), n,
lambda);
}
@ -428,74 +427,66 @@ class bulk_allocator {
const int align = 256;
template <typename SizeT>
size_t align_round_up(SizeT n) {
size_t align_round_up(size_t n) const {
n = (n + align - 1) / align;
return n * align;
}
template <typename T, typename SizeT>
size_t get_size_bytes(dvec<T> *first_vec, SizeT first_size) {
return align_round_up<SizeT>(first_size * sizeof(T));
template <typename T>
size_t get_size_bytes(dvec<T> *first_vec, size_t first_size) {
return align_round_up(first_size * sizeof(T));
}
template <typename T, typename SizeT, typename... Args>
size_t get_size_bytes(dvec<T> *first_vec, SizeT first_size, Args... args) {
return get_size_bytes<T, SizeT>(first_vec, first_size) +
get_size_bytes(args...);
template <typename T, typename... Args>
size_t get_size_bytes(dvec<T> *first_vec, size_t first_size, Args... args) {
return get_size_bytes<T>(first_vec, first_size) + get_size_bytes(args...);
}
template <typename T, typename SizeT>
template <typename T>
void allocate_dvec(int device_idx, char *ptr, dvec<T> *first_vec,
SizeT first_size) {
size_t first_size) {
first_vec->external_allocate(device_idx, static_cast<void *>(ptr),
first_size);
}
template <typename T, typename SizeT, typename... Args>
template <typename T, typename... Args>
void allocate_dvec(int device_idx, char *ptr, dvec<T> *first_vec,
SizeT first_size, Args... args) {
first_vec->external_allocate(device_idx, static_cast<void *>(ptr),
first_size);
size_t first_size, Args... args) {
allocate_dvec<T>(device_idx, ptr, first_vec, first_size);
ptr += align_round_up(first_size * sizeof(T));
allocate_dvec(device_idx, ptr, args...);
}
// template <memory_type MemoryT>
char *allocate_device(int device_idx, size_t bytes, memory_type t) {
char *ptr;
if (t == memory_type::DEVICE) {
safe_cuda(cudaSetDevice(device_idx));
safe_cuda(cudaMalloc(&ptr, bytes));
} else {
safe_cuda(cudaMallocManaged(&ptr, bytes));
}
return ptr;
}
template <typename T, typename SizeT>
size_t get_size_bytes(dvec2<T> *first_vec, SizeT first_size) {
template <typename T>
size_t get_size_bytes(dvec2<T> *first_vec, size_t first_size) {
return 2 * align_round_up(first_size * sizeof(T));
}
template <typename T, typename SizeT, typename... Args>
size_t get_size_bytes(dvec2<T> *first_vec, SizeT first_size, Args... args) {
return get_size_bytes<T, SizeT>(first_vec, first_size) +
template <typename T, typename... Args>
size_t get_size_bytes(dvec2<T> *first_vec, size_t first_size, Args... args) {
return get_size_bytes<T>(first_vec, first_size) +
get_size_bytes(args...);
}
template <typename T, typename SizeT>
template <typename T>
void allocate_dvec(int device_idx, char *ptr, dvec2<T> *first_vec,
SizeT first_size) {
size_t first_size) {
first_vec->external_allocate(
device_idx, static_cast<void *>(ptr),
static_cast<void *>(ptr + align_round_up(first_size * sizeof(T))),
first_size);
}
template <typename T, typename SizeT, typename... Args>
template <typename T, typename... Args>
void allocate_dvec(int device_idx, char *ptr, dvec2<T> *first_vec,
SizeT first_size, Args... args) {
allocate_dvec<T, SizeT>(device_idx, ptr, first_vec, first_size);
size_t first_size, Args... args) {
allocate_dvec<T>(device_idx, ptr, first_vec, first_size);
ptr += (align_round_up(first_size * sizeof(T)) * 2);
allocate_dvec(device_idx, ptr, args...);
}
@ -544,13 +535,12 @@ struct CubMemory {
// Thrust
typedef char value_type;
CubMemory() : d_temp_storage(NULL), temp_storage_bytes(0) {}
CubMemory() : d_temp_storage(nullptr), temp_storage_bytes(0) {}
~CubMemory() { Free(); }
template <typename T>
T* Pointer()
{
T *Pointer() {
return static_cast<T *>(d_temp_storage);
}
@ -611,7 +601,7 @@ void print(const dvec<T> &v, size_t max_items = 10) {
template <typename coordinate_t, typename segments_t, typename offset_t>
void FindMergePartitions(int device_idx, coordinate_t *d_tile_coordinates,
int num_tiles, int tile_size, segments_t segments,
size_t num_tiles, int tile_size, segments_t segments,
offset_t num_rows, offset_t num_elements) {
dh::launch_n(device_idx, num_tiles + 1, [=] __device__(int idx) {
offset_t diagonal = idx * tile_size;
@ -692,7 +682,8 @@ void SparseTransformLbs(int device_idx, dh::CubMemory *temp_memory,
const int BLOCK_THREADS = 256;
const int ITEMS_PER_THREAD = 1;
const int TILE_SIZE = BLOCK_THREADS * ITEMS_PER_THREAD;
int num_tiles = dh::div_round_up(count + num_segments, BLOCK_THREADS);
auto num_tiles = dh::div_round_up(count + num_segments, BLOCK_THREADS);
CHECK(num_tiles < std::numeric_limits<unsigned int>::max());
temp_memory->LazyAllocate(sizeof(coordinate_t) * (num_tiles + 1));
coordinate_t *tmp_tile_coordinates =
@ -702,7 +693,7 @@ void SparseTransformLbs(int device_idx, dh::CubMemory *temp_memory,
BLOCK_THREADS, segments, num_segments, count);
LbsKernel<TILE_SIZE, ITEMS_PER_THREAD, BLOCK_THREADS, offset_t>
<<<num_tiles, BLOCK_THREADS>>>(tmp_tile_coordinates, segments + 1, f,
<<<uint32_t(num_tiles), BLOCK_THREADS>>>(tmp_tile_coordinates, segments + 1, f,
num_segments);
}

20
src/common/hist_util.cc
View File

@ -26,7 +26,7 @@ void HistCutMatrix::Init(DMatrix* p_fmat, uint32_t max_num_bins) {
const int nthread = omp_get_max_threads();
unsigned nstep = (info.num_col + nthread - 1) / nthread;
unsigned nstep = static_cast<unsigned>((info.num_col + nthread - 1) / nthread);
unsigned ncol = static_cast<unsigned>(info.num_col);
sketchs.resize(info.num_col);
for (auto& s : sketchs) {
@ -79,7 +79,7 @@ void HistCutMatrix::Init(DMatrix* p_fmat, uint32_t max_num_bins) {
if (a.size > 1 && a.size <= 16) {
/* specialized code categorial / ordinal data -- use midpoints */
for (size_t i = 1; i < a.size; ++i) {
bst_float cpt = (a.data[i].value + a.data[i - 1].value) / 2.0;
bst_float cpt = (a.data[i].value + a.data[i - 1].value) / 2.0f;
if (i == 1 || cpt > cut.back()) {
cut.push_back(cpt);
}
@ -99,7 +99,7 @@ void HistCutMatrix::Init(DMatrix* p_fmat, uint32_t max_num_bins) {
bst_float last = cpt + fabs(cpt);
cut.push_back(last);
}
row_ptr.push_back(cut.size());
row_ptr.push_back(static_cast<bst_uint>(cut.size()));
}
}
@ -148,7 +148,7 @@ void GHistIndexMatrix::Init(DMatrix* p_fmat) {
}
#pragma omp parallel for num_threads(nthread) schedule(static)
for (bst_omp_uint idx = 0; idx < nbins; ++idx) {
for (bst_omp_uint idx = 0; idx < bst_omp_uint(nbins); ++idx) {
for (int tid = 0; tid < nthread; ++tid) {
hit_count[idx] += hit_count_tloc_[tid * nbins + idx];
}
@ -226,7 +226,7 @@ FindGroups_(const std::vector<unsigned>& feature_list,
bool need_new_group = true;
// randomly choose some of existing groups as candidates
std::vector<unsigned> search_groups;
std::vector<size_t> search_groups;
for (size_t gid = 0; gid < groups.size(); ++gid) {
if (group_nnz[gid] + cur_fid_nnz <= nrow + max_conflict_cnt) {
search_groups.push_back(gid);
@ -434,7 +434,7 @@ void GHistBuilder::BuildHist(const std::vector<bst_gpair>& gpair,
}
}
}
for (bst_omp_uint i = nrows - rest; i < nrows; ++i) {
for (size_t i = nrows - rest; i < nrows; ++i) {
const size_t rid = row_indices.begin[i];
const size_t ibegin = gmat.row_ptr[rid];
const size_t iend = gmat.row_ptr[rid + 1];
@ -448,7 +448,7 @@ void GHistBuilder::BuildHist(const std::vector<bst_gpair>& gpair,
/* reduction */
const uint32_t nbins = nbins_;
#pragma omp parallel for num_threads(nthread) schedule(static)
for (bst_omp_uint bin_id = 0; bin_id < nbins; ++bin_id) {
for (bst_omp_uint bin_id = 0; bin_id < bst_omp_uint(nbins); ++bin_id) {
for (bst_omp_uint tid = 0; tid < nthread; ++tid) {
hist.begin[bin_id].Add(data_[tid * nbins_ + bin_id]);
}
@ -462,7 +462,7 @@ void GHistBuilder::BuildBlockHist(const std::vector<bst_gpair>& gpair,
GHistRow hist) {
const int K = 8; // loop unrolling factor
const bst_omp_uint nthread = static_cast<bst_omp_uint>(this->nthread_);
const uint32_t nblock = gmatb.GetNumBlock();
const size_t nblock = gmatb.GetNumBlock();
const size_t nrows = row_indices.end - row_indices.begin;
const size_t rest = nrows % K;
@ -492,7 +492,7 @@ void GHistBuilder::BuildBlockHist(const std::vector<bst_gpair>& gpair,
}
}
}
for (bst_omp_uint i = nrows - rest; i < nrows; ++i) {
for (size_t i = nrows - rest; i < nrows; ++i) {
const size_t rid = row_indices.begin[i];
const size_t ibegin = gmat.row_ptr[rid];
const size_t iend = gmat.row_ptr[rid + 1];
@ -511,7 +511,7 @@ void GHistBuilder::SubtractionTrick(GHistRow self, GHistRow sibling, GHistRow pa
const int K = 8; // loop unrolling factor
const uint32_t rest = nbins % K;
#pragma omp parallel for num_threads(nthread) schedule(static)
for (bst_omp_uint bin_id = 0; bin_id < nbins - rest; bin_id += K) {
for (bst_omp_uint bin_id = 0; bin_id < static_cast<bst_omp_uint>(nbins - rest); bin_id += K) {
GHistEntry pb[K];
GHistEntry sb[K];
for (int k = 0; k < K; ++k) {

10
src/common/hist_util.h
View File

@ -118,11 +118,11 @@ struct GHistIndexMatrix {
return GHistIndexRow(&index[0] + row_ptr[i], row_ptr[i + 1] - row_ptr[i]);
}
inline void GetFeatureCounts(size_t* counts) const {
const unsigned nfeature = cut->row_ptr.size() - 1;
auto nfeature = cut->row_ptr.size() - 1;
for (unsigned fid = 0; fid < nfeature; ++fid) {
const unsigned ibegin = cut->row_ptr[fid];
const unsigned iend = cut->row_ptr[fid + 1];
for (unsigned i = ibegin; i < iend; ++i) {
auto ibegin = cut->row_ptr[fid];
auto iend = cut->row_ptr[fid + 1];
for (auto i = ibegin; i < iend; ++i) {
counts[fid] += hit_count[i];
}
}
@ -235,7 +235,7 @@ class HistCollection {
std::vector<GHistEntry> data_;
/*! \brief row_ptr_[nid] locates bin for historgram of node nid */
std::vector<uint32_t> row_ptr_;
std::vector<size_t> row_ptr_;
};
/*!

4
src/common/quantile.h
View File

@ -680,12 +680,12 @@ class QuantileSketchTemplate {
nlevel = 1;
while (true) {
limit_size = static_cast<size_t>(ceil(nlevel / eps)) + 1;
size_t n = (1UL << nlevel);
size_t n = (1ULL << nlevel);
if (n * limit_size >= maxn) break;
++nlevel;
}
// check invariant
size_t n = (1UL << nlevel);
size_t n = (1ULL << nlevel);
CHECK(n * limit_size >= maxn) << "invalid init parameter";
CHECK(nlevel <= limit_size * eps) << "invalid init parameter";
// lazy reserve the space, if there is only one value, no need to allocate space

2
src/common/row_set.h
View File

@ -88,7 +88,7 @@ class RowSetCollection {
unsigned left_node_id,
unsigned right_node_id) {
const Elem e = elem_of_each_node_[node_id];
const unsigned nthread = row_split_tloc.size();
const bst_omp_uint nthread = static_cast<bst_omp_uint>(row_split_tloc.size());
CHECK(e.begin != nullptr);
size_t* all_begin = dmlc::BeginPtr(row_indices_);
size_t* begin = all_begin + (e.begin - all_begin);

4
src/data/sparse_page_source.cc
View File

@ -120,7 +120,7 @@ void SparsePageSource::Create(dmlc::Parser<uint32_t>* src,
double tstart = dmlc::GetTime();
// print every 4 sec.
const double kStep = 4.0;
size_t tick_expected = kStep;
size_t tick_expected = static_cast<double>(kStep);
while (src->Next()) {
const dmlc::RowBlock<uint32_t>& batch = src->Value();
@ -149,7 +149,7 @@ void SparsePageSource::Create(dmlc::Parser<uint32_t>* src,
LOG(CONSOLE) << "Writing row.page to " << cache_info << " in "
<< ((bytes_write >> 20UL) / tdiff) << " MB/s, "
<< (bytes_write >> 20UL) << " written";
tick_expected += kStep;
tick_expected += static_cast<size_t>(kStep);
}
}
}

9
src/learner.cc
View File

@ -146,6 +146,12 @@ class LearnerImpl : public Learner {
name_gbm_ = "gbtree";
}
static void AssertGPUSupport() {
#ifndef XGBOOST_USE_CUDA
LOG(FATAL) << "XGBoost version not compiled with GPU support.";
#endif
}
void ConfigureUpdaters() {
if (tparam.tree_method == 0 || tparam.tree_method == 1 ||
tparam.tree_method == 2) {
@ -166,6 +172,7 @@ class LearnerImpl : public Learner {
<< "grow_fast_histmaker.";
cfg_["updater"] = "grow_fast_histmaker";
} else if (tparam.tree_method == 4) {
this->AssertGPUSupport();
if (cfg_.count("updater") == 0) {
cfg_["updater"] = "grow_gpu,prune";
}
@ -173,6 +180,7 @@ class LearnerImpl : public Learner {
cfg_["predictor"] = "gpu_predictor";
}
} else if (tparam.tree_method == 5) {
this->AssertGPUSupport();
if (cfg_.count("updater") == 0) {
cfg_["updater"] = "grow_gpu_hist";
}
@ -180,6 +188,7 @@ class LearnerImpl : public Learner {
cfg_["predictor"] = "gpu_predictor";
}
} else if (tparam.tree_method == 6) {
this->AssertGPUSupport();
if (cfg_.count("updater") == 0) {
cfg_["updater"] = "grow_gpu_hist_experimental,prune";
}

42
src/predictor/gpu_predictor.cu
View File

@ -216,11 +216,11 @@ __device__ float GetLeafWeight(bst_uint ridx, const DevicePredictionNode* tree,
template <int BLOCK_THREADS>
__global__ void PredictKernel(const DevicePredictionNode* d_nodes,
float* d_out_predictions, int* d_tree_segments,
float* d_out_predictions, size_t* d_tree_segments,
int* d_tree_group, size_t* d_row_ptr,
SparseBatch::Entry* d_data, int tree_begin,
int tree_end, int num_features, bst_uint num_rows,
bool use_shared, int num_group) {
SparseBatch::Entry* d_data, size_t tree_begin,
size_t tree_end, size_t num_features,
size_t num_rows, bool use_shared, int num_group) {
extern __shared__ float smem[];
bst_uint global_idx = blockDim.x * blockIdx.x + threadIdx.x;
ElementLoader loader(use_shared, d_row_ptr, d_data, num_features, smem,
@ -249,8 +249,8 @@ __global__ void PredictKernel(const DevicePredictionNode* d_nodes,
class GPUPredictor : public xgboost::Predictor {
private:
void DevicePredictInternal(DMatrix* dmat, std::vector<bst_float>* out_preds,
const gbm::GBTreeModel& model, int tree_begin,
int tree_end) {
const gbm::GBTreeModel& model, size_t tree_begin,
size_t tree_end) {
if (tree_end - tree_begin == 0) {
return;
}
@ -267,17 +267,17 @@ class GPUPredictor : public xgboost::Predictor {
dh::safe_cuda(cudaSetDevice(param.gpu_id));
CHECK_EQ(model.param.size_leaf_vector, 0);
// Copy decision trees to device
thrust::host_vector<int> h_tree_segments;
thrust::host_vector<size_t> h_tree_segments;
h_tree_segments.reserve((tree_end - tree_end) + 1);
int sum = 0;
size_t sum = 0;
h_tree_segments.push_back(sum);
for (int tree_idx = tree_begin; tree_idx < tree_end; tree_idx++) {
for (auto tree_idx = tree_begin; tree_idx < tree_end; tree_idx++) {
sum += model.trees[tree_idx]->GetNodes().size();
h_tree_segments.push_back(sum);
}
thrust::host_vector<DevicePredictionNode> h_nodes(h_tree_segments.back());
for (int tree_idx = tree_begin; tree_idx < tree_end; tree_idx++) {
for (auto tree_idx = tree_begin; tree_idx < tree_end; tree_idx++) {
auto& src_nodes = model.trees[tree_idx]->GetNodes();
std::copy(src_nodes.begin(), src_nodes.end(),
h_nodes.begin() + h_tree_segments[tree_idx - tree_begin]);
@ -299,11 +299,11 @@ class GPUPredictor : public xgboost::Predictor {
}
const int BLOCK_THREADS = 128;
const int GRID_SIZE =
dh::div_round_up(device_matrix->row_ptr.size() - 1, BLOCK_THREADS);
const int GRID_SIZE = static_cast<int>(
dh::div_round_up(device_matrix->row_ptr.size() - 1, BLOCK_THREADS));
int shared_memory_bytes =
sizeof(float) * device_matrix->p_mat->info().num_col * BLOCK_THREADS;
int shared_memory_bytes = static_cast<int>(
sizeof(float) * device_matrix->p_mat->info().num_col * BLOCK_THREADS);
bool use_shared = true;
if (shared_memory_bytes > dh::max_shared_memory(param.gpu_id)) {
shared_memory_bytes = 0;
@ -347,8 +347,7 @@ class GPUPredictor : public xgboost::Predictor {
const gbm::GBTreeModel& model,
std::vector<std::unique_ptr<TreeUpdater>>* updaters,
int num_new_trees) override {
// dh::Timer t;
int old_ntree = model.trees.size() - num_new_trees;
auto old_ntree = model.trees.size() - num_new_trees;
// update cache entry
for (auto& kv : cache_) {
PredictionCacheEntry& e = kv.second;
@ -356,7 +355,7 @@ class GPUPredictor : public xgboost::Predictor {
if (e.predictions.size() == 0) {
cpu_predictor->PredictBatch(dmat, &(e.predictions), model, 0,
model.trees.size());
static_cast<bst_uint>(model.trees.size()));
} else if (model.param.num_output_group == 1 && updaters->size() > 0 &&
num_new_trees == 1 &&
updaters->back()->UpdatePredictionCache(e.data.get(),
@ -383,11 +382,10 @@ class GPUPredictor : public xgboost::Predictor {
void PredictContribution(DMatrix* p_fmat,
std::vector<bst_float>* out_contribs,
const gbm::GBTreeModel& model,
unsigned ntree_limit,
const gbm::GBTreeModel& model, unsigned ntree_limit,
bool approximate) override {
cpu_predictor->PredictContribution(p_fmat, out_contribs, model,
ntree_limit, approximate);
cpu_predictor->PredictContribution(p_fmat, out_contribs, model, ntree_limit,
approximate);
}
void Init(const std::vector<std::pair<std::string, std::string>>& cfg,
@ -403,7 +401,7 @@ class GPUPredictor : public xgboost::Predictor {
std::unordered_map<DMatrix*, std::unique_ptr<DeviceMatrix>>
device_matrix_cache_;
thrust::device_vector<DevicePredictionNode> nodes;
thrust::device_vector<int> tree_segments;
thrust::device_vector<size_t> tree_segments;
thrust::device_vector<int> tree_group;
};
XGBOOST_REGISTER_PREDICTOR(GPUPredictor, "gpu_predictor")

8
src/tree/param.h
View File

@ -241,7 +241,7 @@ XGBOOST_DEVICE inline T CalcGainGivenWeight(const TrainingParams &p, T sum_grad,
template <typename TrainingParams, typename T>
XGBOOST_DEVICE inline T CalcGain(const TrainingParams &p, T sum_grad, T sum_hess) {
if (sum_hess < p.min_child_weight)
return 0.0;
return T(0.0);
if (p.max_delta_step == 0.0f) {
if (p.reg_alpha == 0.0f) {
return Sqr(sum_grad) / (sum_hess + p.reg_lambda);
@ -251,11 +251,11 @@ XGBOOST_DEVICE inline T CalcGain(const TrainingParams &p, T sum_grad, T sum_hess
}
} else {
T w = CalcWeight(p, sum_grad, sum_hess);
T ret = sum_grad * w + 0.5 * (sum_hess + p.reg_lambda) * Sqr(w);
T ret = sum_grad * w + T(0.5) * (sum_hess + p.reg_lambda) * Sqr(w);
if (p.reg_alpha == 0.0f) {
return -2.0 * ret;
return T(-2.0) * ret;
} else {
return -2.0 * (ret + p.reg_alpha * std::abs(w));
return T(-2.0) * (ret + p.reg_alpha * std::abs(w));
}
}
}

16
src/tree/updater_gpu.cu
View File

@ -630,7 +630,8 @@ class GPUMaker : public TreeUpdater {
throw std::runtime_error("exact::GPUBuilder - must have 1 column block");
}
std::vector<float> fval;
std::vector<int> fId, offset;
std::vector<int> fId;
std::vector<size_t> offset;
convertToCsc(dmat, &fval, &fId, &offset);
allocateAllData(static_cast<int>(offset.size()));
transferAndSortData(fval, fId, offset);
@ -638,10 +639,12 @@ class GPUMaker : public TreeUpdater {
}
void convertToCsc(DMatrix* dmat, std::vector<float>* fval,
std::vector<int>* fId, std::vector<int>* offset) {
std::vector<int>* fId, std::vector<size_t>* offset) {
MetaInfo info = dmat->info();
nRows = info.num_row;
nCols = info.num_col;
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);
nCols = static_cast<int>(info.num_col);
offset->reserve(nCols + 1);
offset->push_back(0);
fval->reserve(nCols * nRows);
@ -667,12 +670,13 @@ class GPUMaker : public TreeUpdater {
offset->push_back(fval->size());
}
}
nVals = fval->size();
CHECK(fval->size() < std::numeric_limits<int>::max());
nVals = static_cast<int>(fval->size());
}
void transferAndSortData(const std::vector<float>& fval,
const std::vector<int>& fId,
const std::vector<int>& offset) {
const std::vector<size_t>& offset) {
vals.current_dvec() = fval;
instIds.current_dvec() = fId;
colOffsets = offset;

21
src/tree/updater_gpu_hist.cu
View File

@ -104,7 +104,7 @@ struct DeviceHist {
template <int BLOCK_THREADS>
__global__ void find_split_kernel(
const gpair_sum_t* d_level_hist, int* d_feature_segments, int depth,
int n_features, int n_bins, DeviceNodeStats* d_nodes,
uint64_t n_features, int n_bins, DeviceNodeStats* d_nodes,
int nodes_offset_device, float* d_fidx_min_map, float* d_gidx_fvalue_map,
GPUTrainingParam gpu_param, bool* d_left_child_smallest_temp,
bool colsample, int* d_feature_flags) {
@ -293,7 +293,8 @@ class GPUHistMaker : public TreeUpdater {
dh::Timer time1;
// set member num_rows and n_devices for rest of GPUHistBuilder members
info = &fmat.info();
num_rows = info->num_row;
CHECK(info->num_row < std::numeric_limits<bst_uint>::max());
num_rows = static_cast<bst_uint>(info->num_row);
n_devices = dh::n_devices(param.n_gpus, num_rows);
if (!initialised) {
@ -396,15 +397,15 @@ class GPUHistMaker : public TreeUpdater {
fflush(stdout);
}
int n_bins = hmat_.row_ptr.back();
int n_features = hmat_.row_ptr.size() - 1;
int n_bins = static_cast<int >(hmat_.row_ptr.back());
int n_features = static_cast<int >(hmat_.row_ptr.size() - 1);
// deliniate data onto multiple gpus
device_row_segments.push_back(0);
device_element_segments.push_back(0);
bst_uint offset = 0;
bst_uint shard_size =
std::ceil(static_cast<double>(num_rows) / n_devices);
bst_uint shard_size = static_cast<bst_uint>(
std::ceil(static_cast<double>(num_rows) / n_devices));
for (int d_idx = 0; d_idx < n_devices; d_idx++) {
int device_idx = dList[d_idx];
offset += shard_size;
@ -425,7 +426,7 @@ class GPUHistMaker : public TreeUpdater {
// Construct feature map
std::vector<int> h_gidx_feature_map(n_bins);
for (int fidx = 0; fidx < n_features; fidx++) {
for (int i = hmat_.row_ptr[fidx]; i < hmat_.row_ptr[fidx + 1]; i++) {
for (auto i = hmat_.row_ptr[fidx]; i < hmat_.row_ptr[fidx + 1]; i++) {
h_gidx_feature_map[i] = fidx;
}
}
@ -456,7 +457,7 @@ class GPUHistMaker : public TreeUpdater {
gidx_feature_map.resize(n_devices);
gidx_fvalue_map.resize(n_devices);
int find_split_n_devices = std::pow(2, std::floor(std::log2(n_devices)));
int find_split_n_devices = static_cast<int >(std::pow(2, std::floor(std::log2(n_devices))));
find_split_n_devices =
std::min(n_nodes_level(param.max_depth), find_split_n_devices);
int max_num_nodes_device =
@ -707,7 +708,7 @@ class GPUHistMaker : public TreeUpdater {
int nodes_offset_device = 0;
find_split_kernel<BLOCK_THREADS><<<GRID_SIZE, BLOCK_THREADS>>>(
hist_vec[d_idx].GetLevelPtr(depth), feature_segments[d_idx].data(),
depth, (info->num_col), (hmat_.row_ptr.back()), nodes[d_idx].data(),
depth, info->num_col, hmat_.row_ptr.back(), nodes[d_idx].data(),
nodes_offset_device, fidx_min_map[d_idx].data(),
gidx_fvalue_map[d_idx].data(), GPUTrainingParam(param),
left_child_smallest[d_idx].data(), colsample,
@ -769,7 +770,7 @@ class GPUHistMaker : public TreeUpdater {
DeviceNodeStats* d_nodes = nodes[d_idx].data();
auto d_gidx_fvalue_map = gidx_fvalue_map[d_idx].data();
auto d_gidx = device_matrix[d_idx].gidx;
int n_columns = info->num_col;
auto n_columns = info->num_col;
size_t begin = device_row_segments[d_idx];
size_t end = device_row_segments[d_idx + 1];

70
src/tree/updater_gpu_hist_experimental.cu
View File

@ -113,13 +113,11 @@ __device__ void EvaluateFeature(int fidx, const bst_gpair_integer* hist,
}
template <int BLOCK_THREADS>
__global__ void evaluate_split_kernel(const bst_gpair_integer* d_hist, int nidx,
int n_features, DeviceNodeStats nodes,
const int* d_feature_segments,
const float* d_fidx_min_map,
const float* d_gidx_fvalue_map,
GPUTrainingParam gpu_param,
DeviceSplitCandidate* d_split) {
__global__ void evaluate_split_kernel(
const bst_gpair_integer* d_hist, int nidx, uint64_t n_features,
DeviceNodeStats nodes, const int* d_feature_segments,
const float* d_fidx_min_map, const float* d_gidx_fvalue_map,
GPUTrainingParam gpu_param, DeviceSplitCandidate* d_split) {
typedef cub::KeyValuePair<int, float> ArgMaxT;
typedef cub::BlockScan<bst_gpair_integer, BLOCK_THREADS,
cub::BLOCK_SCAN_WARP_SCANS>
@ -190,24 +188,6 @@ __device__ int BinarySearchRow(bst_uint begin, bst_uint end, gidx_iter_t data,
return -1;
}
template <int BLOCK_THREADS>
__global__ void RadixSortSmall(bst_uint* d_ridx, int* d_position, bst_uint n) {
typedef cub::BlockRadixSort<int, BLOCK_THREADS, 1, bst_uint> BlockRadixSort;
__shared__ typename BlockRadixSort::TempStorage temp_storage;
bool thread_active = threadIdx.x < n;
int thread_key[1];
bst_uint thread_value[1];
thread_key[0] = thread_active ? d_position[threadIdx.x] : INT_MAX;
thread_value[0] = thread_active ? d_ridx[threadIdx.x] : UINT_MAX;
BlockRadixSort(temp_storage).Sort(thread_key, thread_value);
if (thread_active) {
d_position[threadIdx.x] = thread_key[0];
d_ridx[threadIdx.x] = thread_value[0];
}
}
struct DeviceHistogram {
dh::bulk_allocator<dh::memory_type::DEVICE> ba;
dh::dvec<bst_gpair_integer> data;
@ -269,7 +249,7 @@ struct DeviceShard {
null_gidx_value(n_bins) {
// Convert to ELLPACK matrix representation
int max_elements_row = 0;
for (int i = row_begin; i < row_end; i++) {
for (auto i = row_begin; i < row_end; i++) {
max_elements_row =
(std::max)(max_elements_row,
static_cast<int>(gmat.row_ptr[i + 1] - gmat.row_ptr[i]));
@ -277,9 +257,9 @@ struct DeviceShard {
row_stride = max_elements_row;
std::vector<int> ellpack_matrix(row_stride * n_rows, null_gidx_value);
for (int i = row_begin; i < row_end; i++) {
for (auto i = row_begin; i < row_end; i++) {
int row_count = 0;
for (int j = gmat.row_ptr[i]; j < gmat.row_ptr[i + 1]; j++) {
for (auto j = gmat.row_ptr[i]; j < gmat.row_ptr[i + 1]; j++) {
ellpack_matrix[i * row_stride + row_count] = gmat.index[j];
row_count++;
}
@ -394,13 +374,8 @@ struct DeviceShard {
int right_nidx) {
auto n = segment.second - segment.first;
int min_bits = 0;
int max_bits = std::ceil(std::log2((std::max)(left_nidx, right_nidx) + 1));
// const int SINGLE_TILE_SIZE = 1024;
// if (n < SINGLE_TILE_SIZE) {
// RadixSortSmall<SINGLE_TILE_SIZE>
// <<<1, SINGLE_TILE_SIZE>>>(ridx.current() + segment.first,
// position.current() + segment.first, n);
//} else {
int max_bits = static_cast<int>(
std::ceil(std::log2((std::max)(left_nidx, right_nidx) + 1)));
size_t temp_storage_bytes = 0;
cub::DeviceRadixSort::SortPairs(
@ -509,7 +484,7 @@ class GPUHistMakerExperimental : public TreeUpdater {
nidx_set.size());
auto d_split = shard.temp_memory.Pointer<DeviceSplitCandidate>();
auto& streams = shard.GetStreams(nidx_set.size());
auto& streams = shard.GetStreams(static_cast<int>(nidx_set.size()));
// Use streams to process nodes concurrently
for (auto i = 0; i < nidx_set.size(); i++) {
@ -518,7 +493,7 @@ class GPUHistMakerExperimental : public TreeUpdater {
const int BLOCK_THREADS = 256;
evaluate_split_kernel<BLOCK_THREADS>
<<<columns, BLOCK_THREADS, 0, streams[i]>>>(
<<<uint32_t(columns), BLOCK_THREADS, 0, streams[i]>>>(
shard.hist.node_map[nidx], nidx, info->num_col, node,
shard.feature_segments.data(), shard.min_fvalue.data(),
shard.gidx_fvalue_map.data(), GPUTrainingParam(param),
@ -573,10 +548,11 @@ class GPUHistMakerExperimental : public TreeUpdater {
__host__ __device__ int operator()(int x) const { return x == val; }
};
__device__ void CountLeft(bst_uint* d_count, int val, int left_nidx) {
__device__ void CountLeft(int64_t* d_count, int val, int left_nidx) {
unsigned ballot = __ballot(val == left_nidx);
if (threadIdx.x % 32 == 0) {
atomicAdd(d_count, __popc(ballot));
atomicAdd(reinterpret_cast<unsigned long long*>(d_count), // NOLINT
static_cast<unsigned long long>(__popc(ballot))); // NOLINT
}
}
@ -601,9 +577,9 @@ class GPUHistMakerExperimental : public TreeUpdater {
for (auto& shard : shards) {
monitor.Start("update position kernel");
shard.temp_memory.LazyAllocate(sizeof(bst_uint));
auto d_left_count = shard.temp_memory.Pointer<bst_uint>();
dh::safe_cuda(cudaMemset(d_left_count, 0, sizeof(bst_uint)));
shard.temp_memory.LazyAllocate(sizeof(int64_t));
auto d_left_count = shard.temp_memory.Pointer<int64_t>();
dh::safe_cuda(cudaMemset(d_left_count, 0, sizeof(int64_t)));
dh::safe_cuda(cudaSetDevice(shard.device_idx));
auto segment = shard.ridx_segments[nidx];
CHECK_GT(segment.second - segment.first, 0);
@ -639,8 +615,8 @@ class GPUHistMakerExperimental : public TreeUpdater {
d_position[idx] = position;
});
bst_uint left_count;
dh::safe_cuda(cudaMemcpy(&left_count, d_left_count, sizeof(bst_uint),
int64_t left_count;
dh::safe_cuda(cudaMemcpy(&left_count, d_left_count, sizeof(int64_t),
cudaMemcpyDeviceToHost));
monitor.Stop("update position kernel");
@ -722,7 +698,7 @@ class GPUHistMakerExperimental : public TreeUpdater {
this->InitRoot(gpair, p_tree);
monitor.Stop("InitRoot");
unsigned timestamp = qexpand_->size();
auto timestamp = qexpand_->size();
auto num_leaves = 1;
while (!qexpand_->empty()) {
@ -764,9 +740,9 @@ class GPUHistMakerExperimental : public TreeUpdater {
int nid;
int depth;
DeviceSplitCandidate split;
unsigned timestamp;
uint64_t timestamp;
ExpandEntry(int nid, int depth, const DeviceSplitCandidate& split,
unsigned timestamp)
uint64_t timestamp)
: nid(nid), depth(depth), split(split), timestamp(timestamp) {}
bool IsValid(const TrainParam& param, int num_leaves) const {
if (split.loss_chg <= rt_eps) return false;

2
tests/cpp/common/test_compressed_iterator.cc
View File

@ -7,7 +7,7 @@ namespace common {
TEST(CompressedIterator, Test) {
ASSERT_TRUE(detail::SymbolBits(256) == 8);
ASSERT_TRUE(detail::SymbolBits(150) == 8);
std::vector<int> test_cases = {3, 426, 21, 64, 256, 100000, INT32_MAX};
std::vector<int> test_cases = {1, 3, 426, 21, 64, 256, 100000, INT32_MAX};
int num_elements = 1000;
int repetitions = 1000;
srand(9);

2
tests/cpp/common/test_device_helpers.cu
View File

@ -12,7 +12,7 @@ void CreateTestData(xgboost::bst_uint num_rows, int max_row_size,
thrust::host_vector<xgboost::bst_uint> *rows) {
row_ptr->resize(num_rows + 1);
int sum = 0;
for (int i = 0; i <= num_rows; i++) {
for (xgboost::bst_uint i = 0; i <= num_rows; i++) {
(*row_ptr)[i] = sum;
sum += rand() % max_row_size; // NOLINT

3
tests/cpp/predictor/test_gpu_predictor.cu
View File

@ -16,7 +16,7 @@ TEST(gpu_predictor, Test) {
std::unique_ptr<Predictor>(Predictor::Create("cpu_predictor"));
std::vector<std::unique_ptr<RegTree>> trees;
trees.push_back(std::unique_ptr<RegTree>());
trees.push_back(std::unique_ptr<RegTree>(new RegTree()));
trees.back()->InitModel();
(*trees.back())[0].set_leaf(1.5f);
(*trees.back()).stat(0).sum_hess = 1.0f;
@ -39,7 +39,6 @@ TEST(gpu_predictor, Test) {
ASSERT_LT(std::abs(gpu_out_predictions[i] - cpu_out_predictions[i]),
abs_tolerance);
}
// Test predict instance
auto batch = dmat->RowIterator()->Value();
for (int i = 0; i < batch.size; i++) {

4
tests/cpp/tree/test_gpu_hist_experimental.cu
View File

@ -16,7 +16,7 @@ TEST(gpu_hist_experimental, TestSparseShard) {
int rows = 100;
int columns = 80;
int max_bins = 4;
auto dmat = CreateDMatrix(rows, columns, 0.9);
auto dmat = CreateDMatrix(rows, columns, 0.9f);
common::HistCutMatrix hmat;
common::GHistIndexMatrix gmat;
hmat.Init(dmat.get(), max_bins);
@ -33,7 +33,7 @@ TEST(gpu_hist_experimental, TestSparseShard) {
for (int i = 0; i < rows; i++) {
int row_offset = 0;
for (int j = gmat.row_ptr[i]; j < gmat.row_ptr[i + 1]; j++) {
for (auto j = gmat.row_ptr[i]; j < gmat.row_ptr[i + 1]; j++) {
ASSERT_EQ(gidx[i * shard.row_stride + row_offset], gmat.index[j]);
row_offset++;
}

2
tests/travis/run_test.sh
View File

@ -61,7 +61,7 @@ if [ ${TASK} == "python_lightweight_test" ]; then
conda install numpy scipy nose
python -m pip install graphviz
python -m nose tests/python || exit -1
python -m pip install flake8
python -m pip install flake8==3.4.1
flake8 --ignore E501 python-package || exit -1
flake8 --ignore E501 tests/python || exit -1
exit 0