Fix bugs in multithreaded ApplySplitSparseData() (#2161)
* Bugfix 1: Fix segfault in multithreaded ApplySplitSparseData() When there are more threads than rows in rowset, some threads end up with empty ranges, causing them to crash. (iend - 1 needs to be accessible as part of algorithm) Fix: run only those threads with nonempty ranges. * Add regression test for Bugfix 1 * Moving python_omp_test to existing python test group Turns out you don't need to set "OMP_NUM_THREADS" to enable multithreading. Just add nthread parameter. * Bugfix 2: Fix corner case of ApplySplitSparseData() for categorical feature When split value is less than all cut points, split_cond is set incorrectly. Fix: set split_cond = -1 to indicate this scenario * Bugfix 3: Initialize data layout indicator before using it data_layout_ is accessed before being set; this variable determines whether feature 0 is included in feat_set. Fix: re-order code in InitData() to initialize data_layout_ first * Adding regression test for Bugfix 2 Unfortunately, no regression test for Bugfix 3, as there is no way to deterministically assign value to an uninitialized variable.
This commit is contained in:
Philip Cho
Tianqi Chen
parent
ed5e75de2f
commit
2715baef64
@ -64,6 +64,7 @@ namespace xgboost {
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* used for feature index and row index.
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*/
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typedef uint32_t bst_uint;
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typedef int32_t bst_int;
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/*! \brief long integers */
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typedef uint64_t bst_ulong; // NOLINT(*)
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/*! \brief float type, used for storing statistics */
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@ -374,6 +374,24 @@ class FastHistMaker: public TreeUpdater {
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row_set_collection_.Init();
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}
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{
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/* determine layout of data */
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const auto nrow = info.num_row;
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const auto ncol = info.num_col;
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const auto nnz = info.num_nonzero;
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// number of discrete bins for feature 0
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const unsigned nbins_f0 = gmat.cut->row_ptr[1] - gmat.cut->row_ptr[0];
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if (nrow * ncol == nnz) {
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// dense data with zero-based indexing
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data_layout_ = kDenseDataZeroBased;
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} else if (nbins_f0 == 0 && nrow * (ncol - 1) == nnz) {
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// dense data with one-based indexing
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data_layout_ = kDenseDataOneBased;
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} else {
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// sparse data
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data_layout_ = kSparseData;
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}
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}
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{
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// store a pointer to the tree
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p_last_tree_ = &tree;
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@ -398,24 +416,6 @@ class FastHistMaker: public TreeUpdater {
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<< " is too small that no feature can be included";
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feat_index.resize(n);
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}
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{
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/* determine layout of data */
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const auto nrow = info.num_row;
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const auto ncol = info.num_col;
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const auto nnz = info.num_nonzero;
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// number of discrete bins for feature 0
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const unsigned nbins_f0 = gmat.cut->row_ptr[1] - gmat.cut->row_ptr[0];
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if (nrow * ncol == nnz) {
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// dense data with zero-based indexing
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data_layout_ = kDenseDataZeroBased;
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} else if (nbins_f0 == 0 && nrow * (ncol - 1) == nnz) {
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// dense data with one-based indexing
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data_layout_ = kDenseDataOneBased;
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} else {
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// sparse data
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data_layout_ = kSparseData;
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}
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}
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if (data_layout_ == kDenseDataZeroBased || data_layout_ == kDenseDataOneBased) {
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/* specialized code for dense data:
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choose the column that has a least positive number of discrete bins.
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@ -520,11 +520,11 @@ class FastHistMaker: public TreeUpdater {
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const bst_float split_pt = (*p_tree)[nid].split_cond();
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const bst_uint lower_bound = gmat.cut->row_ptr[fid];
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const bst_uint upper_bound = gmat.cut->row_ptr[fid + 1];
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// set the split condition correctly
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bst_uint split_cond = 0;
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// set the condition
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bst_int split_cond = -1;
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// convert floating-point split_pt into corresponding bin_id
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// split_cond = -1 indicates that split_pt is less than all known cut points
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for (unsigned i = gmat.cut->row_ptr[fid]; i < gmat.cut->row_ptr[fid + 1]; ++i) {
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if (split_pt == gmat.cut->cut[i]) split_cond = i;
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if (split_pt == gmat.cut->cut[i]) split_cond = static_cast<bst_int>(i);
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}
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const auto& rowset = row_set_collection_[nid];
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@ -547,7 +547,7 @@ class FastHistMaker: public TreeUpdater {
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const GHistIndexMatrix& gmat,
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std::vector<RowSetCollection::Split>* p_row_split_tloc,
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const Column<T>& column,
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bst_uint split_cond,
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bst_int split_cond,
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bool default_left) {
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std::vector<RowSetCollection::Split>& row_split_tloc = *p_row_split_tloc;
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const int K = 8; // loop unrolling factor
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@ -575,7 +575,7 @@ class FastHistMaker: public TreeUpdater {
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right.push_back(rid[k]);
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}
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} else {
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if (rbin[k] + column.index_base <= split_cond) {
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if (static_cast<bst_int>(rbin[k] + column.index_base) <= split_cond) {
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left.push_back(rid[k]);
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} else {
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right.push_back(rid[k]);
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@ -595,7 +595,7 @@ class FastHistMaker: public TreeUpdater {
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right.push_back(rid);
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}
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} else {
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if (rbin + column.index_base <= split_cond) {
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if (static_cast<bst_int>(rbin + column.index_base) <= split_cond) {
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left.push_back(rid);
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} else {
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right.push_back(rid);
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@ -609,7 +609,7 @@ class FastHistMaker: public TreeUpdater {
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std::vector<RowSetCollection::Split>* p_row_split_tloc,
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bst_uint lower_bound,
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bst_uint upper_bound,
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bst_uint split_cond,
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bst_int split_cond,
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bool default_left) {
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std::vector<RowSetCollection::Split>& row_split_tloc = *p_row_split_tloc;
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const int K = 8; // loop unrolling factor
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@ -634,7 +634,7 @@ class FastHistMaker: public TreeUpdater {
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}
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for (int k = 0; k < K; ++k) {
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if (p[k] != row[k].index + row[k].size && *p[k] < upper_bound) {
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if (*p[k] <= split_cond) {
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if (static_cast<bst_int>(*p[k]) <= split_cond) {
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left.push_back(rid[k]);
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} else {
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right.push_back(rid[k]);
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@ -655,7 +655,7 @@ class FastHistMaker: public TreeUpdater {
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auto& left = row_split_tloc[0].left;
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auto& right = row_split_tloc[0].right;
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if (p != row.index + row.size && *p < upper_bound) {
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if (*p <= split_cond) {
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if (static_cast<bst_int>(*p) <= split_cond) {
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left.push_back(rid);
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} else {
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right.push_back(rid);
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@ -677,7 +677,7 @@ class FastHistMaker: public TreeUpdater {
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const Column<T>& column,
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bst_uint lower_bound,
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bst_uint upper_bound,
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bst_uint split_cond,
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bst_int split_cond,
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bool default_left) {
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std::vector<RowSetCollection::Split>& row_split_tloc = *p_row_split_tloc;
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const bst_omp_uint nrows = rowset.end - rowset.begin;
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@ -687,6 +687,7 @@ class FastHistMaker: public TreeUpdater {
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const bst_uint tid = omp_get_thread_num();
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const bst_omp_uint ibegin = tid * nrows / nthread;
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const bst_omp_uint iend = (tid + 1) * nrows / nthread;
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if (ibegin < iend) { // ensure that [ibegin, iend) is nonempty range
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// search first nonzero row with index >= rowset[ibegin]
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const uint32_t* p = std::lower_bound(column.row_ind,
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column.row_ind + column.len,
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@ -706,7 +707,7 @@ class FastHistMaker: public TreeUpdater {
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}
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if (cursor < column.len && column.row_ind[cursor] == rid) {
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const T rbin = column.index[cursor];
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if (rbin + column.index_base <= split_cond) {
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if (static_cast<bst_int>(rbin + column.index_base) <= split_cond) {
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left.push_back(rid);
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} else {
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right.push_back(rid);
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@ -736,6 +737,7 @@ class FastHistMaker: public TreeUpdater {
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}
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}
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}
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}
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inline void InitNewNode(int nid,
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const GHistIndexMatrix& gmat,
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43
tests/python/test_openmp.py
Normal file
43
tests/python/test_openmp.py
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@ -0,0 +1,43 @@
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# -*- coding: utf-8 -*-
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from scipy.sparse import csr_matrix
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import xgboost as xgb
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import unittest
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class TestOMP(unittest.TestCase):
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def test_omp(self):
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# a contrived example where one node has an instance set of size 2.
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data = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
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indices = [2, 1, 1, 2, 0, 0, 2, 0, 1, 3]
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indptr = [0, 1, 2, 4, 5, 7, 9, 10]
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A = csr_matrix((data, indices, indptr), shape=(7, 4))
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y = [1, 1, 0, 0, 0, 1, 1]
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dtrain = xgb.DMatrix(A, label=y)
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# 1. use 3 threads to train a tree with an instance set of size 2
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param = {'booster': 'gbtree',
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'objective': 'binary:logistic',
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'grow_policy': 'lossguide',
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'tree_method': 'hist',
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'eval_metric': 'auc',
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'max_depth': 0,
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'max_leaves': 1024,
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'min_child_weight': 0,
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'nthread': 3}
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watchlist = [(dtrain, 'train')]
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num_round = 1
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res = {}
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xgb.train(param, dtrain, num_round, watchlist, evals_result=res)
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assert res['train']['auc'][-1] > 0.99
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# 2. vary number of threads and test whether you get the same result
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param['nthread'] = 1
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res2 = {}
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xgb.train(param, dtrain, num_round, watchlist, evals_result=res2)
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assert res['train']['auc'][-1] == res2['train']['auc'][-1]
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param['nthread'] = 2
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res3 = {}
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xgb.train(param, dtrain, num_round, watchlist, evals_result=res3)
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assert res['train']['auc'][-1] == res3['train']['auc'][-1]
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