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Monotone constraints for gpu_hist (#2904)

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This commit is contained in:
Rory Mitchell
2017-11-30 10:26:19 +13:00
committed by GitHub
parent 5867c1b96d
commit c51adb49b6
8 changed files with 171 additions and 59 deletions
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45
src/tree/updater_gpu_hist.cu
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@@ -56,7 +56,8 @@ __device__ void EvaluateFeature(int fidx, const gpair_sum_t* hist,
DeviceSplitCandidate* best_split,
const DeviceNodeStats& node,
const GPUTrainingParam& param,
temp_storage_t* temp_storage) {
temp_storage_t* temp_storage, int constraint,
const ValueConstraint& value_constraint) {
int gidx_begin = feature_segments[fidx];
int gidx_end = feature_segments[fidx + 1];
@@ -82,7 +83,7 @@ __device__ void EvaluateFeature(int fidx, const gpair_sum_t* hist,
float gain = null_gain;
if (thread_active) {
gain = loss_chg_missing(bin, missing, parent_sum, node.root_gain, param,
missing_left);
constraint, value_constraint, missing_left);
}
__syncthreads();
@@ -120,7 +121,8 @@ __global__ void evaluate_split_kernel(
const gpair_sum_t* 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) {
GPUTrainingParam gpu_param, DeviceSplitCandidate* d_split,
ValueConstraint value_constraint, int* d_monotonic_constraints) {
typedef cub::KeyValuePair<int, float> ArgMaxT;
typedef cub::BlockScan<gpair_sum_t, BLOCK_THREADS, cub::BLOCK_SCAN_WARP_SCANS>
BlockScanT;
@@ -145,9 +147,11 @@ __global__ void evaluate_split_kernel(
__syncthreads();
auto fidx = blockIdx.x;
auto constraint = d_monotonic_constraints[fidx];
EvaluateFeature<BLOCK_THREADS, SumReduceT, BlockScanT, MaxReduceT>(
fidx, d_hist, d_feature_segments, d_fidx_min_map[fidx], d_gidx_fvalue_map,
&best_split, nodes, gpu_param, &temp_storage);
&best_split, nodes, gpu_param, &temp_storage, constraint,
value_constraint);
__syncthreads();
@@ -230,6 +234,7 @@ struct DeviceShard {
dh::dvec<int> feature_segments;
dh::dvec<float> gidx_fvalue_map;
dh::dvec<float> min_fvalue;
dh::dvec<int> monotone_constraints;
std::vector<bst_gpair> node_sum_gradients;
common::CompressedIterator<uint32_t> gidx;
int row_stride;
@@ -287,10 +292,12 @@ struct DeviceShard {
ba.allocate(device_idx, param.silent, &gidx_buffer, compressed_size_bytes,
&gpair, n_rows, &ridx, n_rows, &position, n_rows,
&feature_segments, gmat.cut->row_ptr.size(), &gidx_fvalue_map,
gmat.cut->cut.size(), &min_fvalue, gmat.cut->min_val.size());
gmat.cut->cut.size(), &min_fvalue, gmat.cut->min_val.size(),
&monotone_constraints, param.monotone_constraints.size());
gidx_fvalue_map = gmat.cut->cut;
min_fvalue = gmat.cut->min_val;
feature_segments = gmat.cut->row_ptr;
monotone_constraints = param.monotone_constraints;
node_sum_gradients.resize(max_nodes);
ridx_segments.resize(max_nodes);
@@ -500,6 +507,7 @@ class GPUHistMaker : public TreeUpdater {
// rescale learning rate according to size of trees
float lr = param.learning_rate;
param.learning_rate = lr / trees.size();
ValueConstraint::Init(&param, dmat->info().num_col);
// build tree
try {
for (size_t i = 0; i < trees.size(); ++i) {
@@ -651,7 +659,8 @@ class GPUHistMaker : public TreeUpdater {
shard->hist.GetHistPtr(nidx), nidx, info->num_col, node,
shard->feature_segments.data(), shard->min_fvalue.data(),
shard->gidx_fvalue_map.data(), GPUTrainingParam(param),
d_split + i * columns);
d_split + i * columns, node_value_constraints_[nidx],
shard->monotone_constraints.data());
}
dh::safe_cuda(
@@ -707,6 +716,9 @@ class GPUHistMaker : public TreeUpdater {
shard->node_sum_gradients[root_nidx] = sum_gradient;
}
// Initialise root constraint
node_value_constraints_.resize(p_tree->GetNodes().size());
// Generate first split
auto splits = this->EvaluateSplits({root_nidx}, p_tree);
qexpand_->push(
@@ -752,14 +764,27 @@ class GPUHistMaker : public TreeUpdater {
candidate.split.dir == LeftDir);
tree.stat(candidate.nid).loss_chg = candidate.split.loss_chg;
// Set up child constraints
node_value_constraints_.resize(tree.GetNodes().size());
GradStats left_stats(param);
left_stats.Add(candidate.split.left_sum);
GradStats right_stats(param);
right_stats.Add(candidate.split.right_sum);
node_value_constraints_[candidate.nid].SetChild(
param, parent.split_index(), left_stats, right_stats,
&node_value_constraints_[parent.cleft()],
&node_value_constraints_[parent.cright()]);
// Configure left child
auto left_weight = CalcWeight(param, candidate.split.left_sum);
auto left_weight =
node_value_constraints_[parent.cleft()].CalcWeight(param, left_stats);
tree[parent.cleft()].set_leaf(left_weight * param.learning_rate, 0);
tree.stat(parent.cleft()).base_weight = left_weight;
tree.stat(parent.cleft()).sum_hess = candidate.split.left_sum.GetHess();
// Configure right child
auto right_weight = CalcWeight(param, candidate.split.right_sum);
auto right_weight =
node_value_constraints_[parent.cright()].CalcWeight(param, right_stats);
tree[parent.cright()].set_leaf(right_weight * param.learning_rate, 0);
tree.stat(parent.cright()).base_weight = right_weight;
tree.stat(parent.cright()).sum_hess = candidate.split.right_sum.GetHess();
@@ -889,10 +914,10 @@ class GPUHistMaker : public TreeUpdater {
std::unique_ptr<ExpandQueue> qexpand_;
common::Monitor monitor;
dh::AllReducer reducer;
std::vector<ValueConstraint> node_value_constraints_;
};
XGBOOST_REGISTER_TREE_UPDATER(GPUHistMaker,
"grow_gpu_hist")
XGBOOST_REGISTER_TREE_UPDATER(GPUHistMaker, "grow_gpu_hist")
.describe("Grow tree with GPU.")
.set_body([]() { return new GPUHistMaker(); });
} // namespace tree