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Feature interaction for GPU Hist. (#4534)

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* GPU hist Interaction Constraints.
* Duplicate related parameters.
* Add tests for CPU interaction constraint.
* Add better error reporting.
* Thorough tests.
This commit is contained in:
Jiaming Yuan
2019-06-19 18:11:02 +08:00
committed by GitHub
parent 570374effe
commit ae05948e32
14 changed files with 1201 additions and 76 deletions
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50
src/tree/updater_gpu_hist.cu
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@@ -24,6 +24,7 @@
#include "../common/span.h"
#include "param.h"
#include "updater_gpu_common.cuh"
#include "constraints.cuh"
namespace xgboost {
namespace tree {
@@ -318,9 +319,8 @@ __device__ void EvaluateFeature(
template <int BLOCK_THREADS, typename GradientSumT>
__global__ void EvaluateSplitKernel(
common::Span<const GradientSumT>
node_histogram, // histogram for gradients
common::Span<const int> feature_set, // Selected features
common::Span<const GradientSumT> node_histogram, // histogram for gradients
common::Span<const int> feature_set, // Selected features
DeviceNodeStats node,
ELLPackMatrix matrix,
GPUTrainingParam gpu_param,
@@ -354,6 +354,7 @@ __global__ void EvaluateSplitKernel(
// One block for each feature. Features are sampled, so fidx != blockIdx.x
int fidx = feature_set[blockIdx.x];
int constraint = d_monotonic_constraints[fidx];
EvaluateFeature<BLOCK_THREADS, SumReduceT, BlockScanT, MaxReduceT>(
fidx, node_histogram, matrix, &best_split, node, gpu_param, &temp_storage,
@@ -714,6 +715,7 @@ struct DeviceShard {
common::Monitor monitor;
std::vector<ValueConstraint> node_value_constraints;
common::ColumnSampler column_sampler;
FeatureInteractionConstraint interaction_constraints;
using ExpandQueue =
std::priority_queue<ExpandEntry, std::vector<ExpandEntry>,
@@ -721,7 +723,8 @@ struct DeviceShard {
std::unique_ptr<ExpandQueue> qexpand;
DeviceShard(int _device_id, int shard_idx, bst_uint row_begin,
bst_uint row_end, TrainParam _param, uint32_t column_sampler_seed)
bst_uint row_end, TrainParam _param, uint32_t column_sampler_seed,
uint32_t n_features)
: device_id(_device_id),
shard_idx(shard_idx),
row_begin_idx(row_begin),
@@ -730,7 +733,8 @@ struct DeviceShard {
n_bins(0),
param(std::move(_param)),
prediction_cache_initialised(false),
column_sampler(column_sampler_seed) {
column_sampler(column_sampler_seed),
interaction_constraints(param, n_features) {
monitor.Init(std::string("DeviceShard") + std::to_string(device_id));
}
@@ -778,6 +782,8 @@ struct DeviceShard {
this->column_sampler.Init(num_columns, param.colsample_bynode,
param.colsample_bylevel, param.colsample_bytree);
dh::safe_cuda(cudaSetDevice(device_id));
this->interaction_constraints.Reset();
thrust::fill(
thrust::device_pointer_cast(position.Current()),
thrust::device_pointer_cast(position.Current() + position.Size()), 0);
@@ -806,7 +812,7 @@ struct DeviceShard {
std::vector<int> nidxs, const RegTree& tree,
size_t num_columns) {
dh::safe_cuda(cudaSetDevice(device_id));
auto result = pinned_memory.GetSpan<DeviceSplitCandidate>(nidxs.size());
auto result_all = pinned_memory.GetSpan<DeviceSplitCandidate>(nidxs.size());
// Work out cub temporary memory requirement
GPUTrainingParam gpu_param(param);
@@ -840,11 +846,26 @@ struct DeviceShard {
auto nidx = nidxs[i];
auto p_feature_set = column_sampler.GetFeatureSet(tree.GetDepth(nidx));
p_feature_set->Shard(GPUSet(device_id, 1));
auto d_feature_set = p_feature_set->DeviceSpan(device_id);
auto d_sampled_features = p_feature_set->DeviceSpan(device_id);
common::Span<int32_t> d_feature_set =
interaction_constraints.Query(d_sampled_features, nidx);
auto d_split_candidates =
d_split_candidates_all.subspan(i * num_columns, d_feature_set.size());
DeviceNodeStats node(node_sum_gradients[nidx], nidx, param);
auto d_result = d_result_all.subspan(i, 1);
if (d_feature_set.size() == 0) {
// Acting as a device side constructor for DeviceSplitCandidate.
// DeviceSplitCandidate::IsValid is false so that ApplySplit can reject this
// candidate.
auto worst_candidate = DeviceSplitCandidate();
dh::safe_cuda(cudaMemcpyAsync(d_result.data(), &worst_candidate,
sizeof(DeviceSplitCandidate),
cudaMemcpyHostToDevice));
continue;
}
// One block for each feature
int constexpr kBlockThreads = 256;
EvaluateSplitKernel<kBlockThreads, GradientSumT>
@@ -854,7 +875,6 @@ struct DeviceShard {
monotone_constraints);
// Reduce over features to find best feature
auto d_result = d_result_all.subspan(i, 1);
auto d_cub_memory =
d_cub_memory_all.subspan(i * cub_memory_size, cub_memory_size);
size_t cub_bytes = d_cub_memory.size() * sizeof(DeviceSplitCandidate);
@@ -864,11 +884,10 @@ struct DeviceShard {
DeviceSplitCandidate(), streams[i]);
}
dh::safe_cuda(cudaMemcpy(result.data(), d_result_all.data(),
dh::safe_cuda(cudaMemcpy(result_all.data(), d_result_all.data(),
sizeof(DeviceSplitCandidate) * d_result_all.size(),
cudaMemcpyDeviceToHost));
return std::vector<DeviceSplitCandidate>(result.begin(), result.end());
return std::vector<DeviceSplitCandidate>(result_all.begin(), result_all.end());
}
void BuildHist(int nidx) {
@@ -1137,6 +1156,10 @@ struct DeviceShard {
candidate.split.left_sum;
node_sum_gradients[tree[candidate.nid].RightChild()] =
candidate.split.right_sum;
interaction_constraints.Split(candidate.nid, tree[candidate.nid].SplitIndex(),
tree[candidate.nid].LeftChild(),
tree[candidate.nid].RightChild());
}
void InitRoot(RegTree* p_tree, HostDeviceVector<GradientPair>* gpair_all,
@@ -1202,7 +1225,7 @@ struct DeviceShard {
int right_child_nidx = tree[candidate.nid].RightChild();
// Only create child entries if needed
if (ExpandEntry::ChildIsValid(param, tree.GetDepth(left_child_nidx),
num_leaves)) {
num_leaves)) {
monitor.StartCuda("UpdatePosition");
this->UpdatePosition(candidate.nid, (*p_tree)[candidate.nid]);
monitor.StopCuda("UpdatePosition");
@@ -1487,7 +1510,8 @@ class GPUHistMakerSpecialised {
shard = std::unique_ptr<DeviceShard<GradientSumT>>(
new DeviceShard<GradientSumT>(dist_.Devices().DeviceId(idx), idx,
start, start + size, param_,
column_sampling_seed));
column_sampling_seed,
info_->num_col_));
});
monitor_.StartCuda("Quantiles");