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more correct way to build node stats in distributed fast hist (#4140)

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* add back train method but mark as deprecated

* add back train method but mark as deprecated

* add back train method but mark as deprecated

* fix scalastyle error

* fix scalastyle error

* fix scalastyle error

* fix scalastyle error

* more changes

* temp

* update

* udpate rabit

* change the histogram

* update kfactor

* sync per node stats

* temp

* update

* final

* code clean

* update rabit

* more cleanup

* fix errors

* fix failed tests

* enforce c++11

* broadcast subsampled feature correctly

* init col

* temp

* col sampling

* fix histmastrix init

* fix col sampling

* remove cout

* fix out of bound access

* fix core dump

remove core dump file

* update

* add fid

* update

* revert some changes

* temp

* temp

* pass all tests

* bring back some tests

* recover some changes

* fix lint issue

* enable monotone and interaction constraints

* don't specify default for monotone and interactions

* recover column init part

* more recovery

* fix core dumps

* code clean

* revert some changes

* fix test compilation issue

* fix lint issue

* resolve compilation issue

* fix issues of lint caused by rebase

* fix stylistic changes and change variable names

* modularize depth width

* address the comments

* fix failed tests

* wrap perf timers with class

* temp

* pass all lossguide

* pass tests

* add comments

* more changes

* use separate flow for single and tests

* add test for lossguide hist

* remove duplications

* syncing stats for only once

* recover more changes

* recover more changes

* fix root-stats

* simplify code

* remove outdated comments
This commit is contained in:
Nan Zhu 2019-02-18 13:45:30 -08:00 committed by GitHub
parent a985a99cf0
commit 1dac5e2410
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 40 additions and 35 deletions
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54
src/tree/updater_quantile_hist.cc
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@ -117,8 +117,8 @@ void QuantileHistMaker::Builder::BuildLocalHistograms(
RegTree::Node &node = (*p_tree)[nid];
if (rabit::IsDistributed()) {
if (node.IsRoot() || node.IsLeftChild()) {
// in distributed setting, we always calcuate from left child or root node
hist_.AddHistRow(nid);
// in distributed setting, we always calculate from left child or root node
BuildHist(gpair_h, row_set_collection_[nid], gmat, gmatb, hist_[nid], false);
if (!node.IsRoot()) {
nodes_for_subtraction_trick_[(*p_tree)[node.Parent()].RightChild()] = nid;
@ -144,7 +144,6 @@ void QuantileHistMaker::Builder::BuildLocalHistograms(
(*sync_count)++;
(*starting_index) = std::min((*starting_index), nid);
} else if (node.IsRoot()) {
// root node
hist_.AddHistRow(nid);
BuildHist(gpair_h, row_set_collection_[nid], gmat, gmatb, hist_[nid], false);
(*sync_count)++;
@ -211,8 +210,6 @@ void QuantileHistMaker::Builder::EvaluateSplits(
}
}
void QuantileHistMaker::Builder::ExpandWithDepthWidth(
const GHistIndexMatrix &gmat,
const GHistIndexBlockMatrix &gmatb,
@ -234,7 +231,7 @@ void QuantileHistMaker::Builder::ExpandWithDepthWidth(
SyncHistograms(starting_index, sync_count, p_tree);
BuildNodeStats(gmat, p_fmat, p_tree, gpair_h);
EvaluateSplits(gmat, column_matrix, p_fmat, p_tree, &num_leaves, depth, &timestamp,
&temp_qexpand_depth);
&temp_qexpand_depth);
// clean up
qexpand_depth_wise_.clear();
nodes_for_subtraction_trick_.clear();
@ -290,11 +287,12 @@ void QuantileHistMaker::Builder::ExpandWithLossGuide(
this->ApplySplit(nid, gmat, column_matrix, hist_, *p_fmat, p_tree);
perf_monitor.UpdatePerfTimer(TreeGrowingPerfMonitor::timer_name::APPLY_SPLIT);
perf_monitor.TickStart();
const int cleft = (*p_tree)[nid].LeftChild();
const int cright = (*p_tree)[nid].RightChild();
hist_.AddHistRow(cleft);
hist_.AddHistRow(cright);
perf_monitor.TickStart();
if (rabit::IsDistributed()) {
// in distributed mode, we need to keep consistent across workers
BuildHist(gpair_h, row_set_collection_[cleft], gmat, gmatb, hist_[cleft], true);
@ -316,7 +314,7 @@ void QuantileHistMaker::Builder::ExpandWithLossGuide(
bst_uint featureid = snode_[nid].best.SplitIndex();
spliteval_->AddSplit(nid, cleft, cright, featureid,
snode_[cleft].weight, snode_[cright].weight);
perf_monitor.UpdatePerfTimer(TreeGrowingPerfMonitor::timer_name::APPLY_SPLIT);
perf_monitor.UpdatePerfTimer(TreeGrowingPerfMonitor::timer_name::INIT_NEW_NODE);
perf_monitor.TickStart();
this->EvaluateSplit(cleft, gmat, hist_, *p_fmat, *p_tree);
@ -754,23 +752,9 @@ void QuantileHistMaker::Builder::InitNewNode(int nid,
{
auto& stats = snode_[nid].stats;
GHistRow hist = hist_[nid];
if (rabit::IsDistributed()) {
// in distributed mode, the node's stats should be calculated from histogram, otherwise,
// we will have wrong results in EnumerateSplit()
// here we take the last feature in cut
auto begin = hist.data();
for (size_t i = gmat.cut.row_ptr[0]; i < gmat.cut.row_ptr[1]; i++) {
stats.Add(begin[i].sum_grad, begin[i].sum_hess);
}
} else {
if (data_layout_ == kDenseDataZeroBased || data_layout_ == kDenseDataOneBased ||
rabit::IsDistributed()) {
/* specialized code for dense data
For dense data (with no missing value),
the sum of gradient histogram is equal to snode[nid]
GHistRow hist = hist_[nid];*/
if (tree[nid].IsRoot()) {
if (data_layout_ == kDenseDataZeroBased || data_layout_ == kDenseDataOneBased) {
const std::vector<uint32_t>& row_ptr = gmat.cut.row_ptr;
const uint32_t ibegin = row_ptr[fid_least_bins_];
const uint32_t iend = row_ptr[fid_least_bins_ + 1];
auto begin = hist.data();
@ -784,16 +768,24 @@ void QuantileHistMaker::Builder::InitNewNode(int nid,
stats.Add(gpair[*it]);
}
}
histred_.Allreduce(&snode_[nid].stats, 1);
} else {
int parent_id = tree[nid].Parent();
if (tree[nid].IsLeftChild()) {
snode_[nid].stats = snode_[parent_id].best.left_sum;
} else {
snode_[nid].stats = snode_[parent_id].best.right_sum;
}
}
}
// calculating the weights
{
bst_uint parentid = tree[nid].Parent();
snode_[nid].weight = static_cast<float>(
spliteval_->ComputeWeight(parentid, snode_[nid].stats));
snode_[nid].root_gain = static_cast<float>(
spliteval_->ComputeScore(parentid, snode_[nid].stats, snode_[nid].weight));
}
// calculating the weights
{
bst_uint parentid = tree[nid].Parent();
snode_[nid].weight = static_cast<float>(
spliteval_->ComputeWeight(parentid, snode_[nid].stats));
snode_[nid].root_gain = static_cast<float>(
spliteval_->ComputeScore(parentid, snode_[nid].stats, snode_[nid].weight));
}
}

1
src/tree/updater_quantile_hist.h
View File

@ -348,7 +348,6 @@ class QuantileHistMaker: public TreeUpdater {
DataLayout data_layout_;
TreeGrowingPerfMonitor perf_monitor;
rabit::Reducer<GradStats, GradStats::Reduce> histred_;
};

20
tests/python/test_monotone_constraints.py
View File

@ -63,7 +63,7 @@ class TestMonotoneConstraints(unittest.TestCase):
# first check monotonicity for the 'exact' tree method
params_for_constrained_exact_method = {
'tree_method': 'exact', 'silent': 1,
'tree_method': 'exact', 'verbosity': 1,
'monotone_constraints': '(1, -1)'
}
constrained_exact_method = xgb.train(
@ -71,11 +71,25 @@ class TestMonotoneConstraints(unittest.TestCase):
)
assert is_correctly_constrained(constrained_exact_method)
def test_monotone_constraints_for_hist_tree_method(self):
def test_monotone_constraints_for_depthwise_hist_tree_method(self):
# next check monotonicity for the 'hist' tree method
params_for_constrained_hist_method = {
'tree_method': 'hist', 'silent': 1,
'tree_method': 'hist', 'verbosity': 1,
'monotone_constraints': '(1, -1)'
}
constrained_hist_method = xgb.train(
params_for_constrained_hist_method, training_dset
)
assert is_correctly_constrained(constrained_hist_method)
def test_monotone_constraints_for_lossguide_hist_tree_method(self):
# next check monotonicity for the 'hist' tree method
params_for_constrained_hist_method = {
'tree_method': 'hist', 'verbosity': 1,
'grow_policy': 'lossguide',
'monotone_constraints': '(1, -1)'
}
constrained_hist_method = xgb.train(