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Support multiple batches in gpu_hist (#5014)

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* Initial external memory training support for GPU Hist tree method.
This commit is contained in:
Rong Ou
2019-11-15 22:50:20 -08:00
committed by Jiaming Yuan
parent 97abcc7ee2
commit 0afcc55d98
15 changed files with 559 additions and 134 deletions
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6
src/tree/gpu_hist/row_partitioner.cuh
View File

@@ -33,6 +33,7 @@ class RowPartitioner {
public:
using RowIndexT = bst_uint;
struct Segment;
static constexpr bst_node_t kIgnoredTreePosition = -1;
private:
int device_idx;
@@ -124,6 +125,7 @@ class RowPartitioner {
idx += segment.begin;
RowIndexT ridx = d_ridx[idx];
bst_node_t new_position = op(ridx); // new node id
if (new_position == kIgnoredTreePosition) return;
KERNEL_CHECK(new_position == left_nidx || new_position == right_nidx);
AtomicIncrement(d_left_count, new_position == left_nidx);
d_position[idx] = new_position;
@@ -163,7 +165,9 @@ class RowPartitioner {
dh::LaunchN(device_idx, position.Size(), [=] __device__(size_t idx) {
auto position = d_position[idx];
RowIndexT ridx = d_ridx[idx];
d_position[idx] = op(ridx, position);
bst_node_t new_position = op(ridx, position);
if (new_position == kIgnoredTreePosition) return;
d_position[idx] = new_position;
});
}

171
src/tree/updater_gpu_hist.cu
View File

@@ -409,13 +409,16 @@ __global__ void SharedMemHistKernel(xgboost::EllpackMatrix matrix,
extern __shared__ char smem[];
GradientSumT* smem_arr = reinterpret_cast<GradientSumT*>(smem); // NOLINT
if (use_shared_memory_histograms) {
dh::BlockFill(smem_arr, matrix.BinCount(), GradientSumT());
dh::BlockFill(smem_arr, matrix.info.n_bins, GradientSumT());
__syncthreads();
}
for (auto idx : dh::GridStrideRange(static_cast<size_t>(0), n_elements)) {
int ridx = d_ridx[idx / matrix.info.row_stride ];
int gidx =
matrix.gidx_iter[ridx * matrix.info.row_stride + idx % matrix.info.row_stride];
int ridx = d_ridx[idx / matrix.info.row_stride];
if (!matrix.IsInRange(ridx)) {
continue;
}
int gidx = matrix.gidx_iter[(ridx - matrix.base_rowid) * matrix.info.row_stride
+ idx % matrix.info.row_stride];
if (gidx != matrix.info.n_bins) {
// If we are not using shared memory, accumulate the values directly into
// global memory
@@ -428,8 +431,7 @@ __global__ void SharedMemHistKernel(xgboost::EllpackMatrix matrix,
if (use_shared_memory_histograms) {
// Write shared memory back to global memory
__syncthreads();
for (auto i :
dh::BlockStrideRange(static_cast<size_t>(0), matrix.BinCount())) {
for (auto i : dh::BlockStrideRange(static_cast<size_t>(0), matrix.info.n_bins)) {
dh::AtomicAddGpair(d_node_hist + i, smem_arr[i]);
}
}
@@ -440,6 +442,7 @@ template <typename GradientSumT>
struct GPUHistMakerDevice {
int device_id;
EllpackPageImpl* page;
BatchParam batch_param;
dh::BulkAllocator ba;
@@ -481,14 +484,16 @@ struct GPUHistMakerDevice {
bst_uint _n_rows,
TrainParam _param,
uint32_t column_sampler_seed,
uint32_t n_features)
uint32_t n_features,
BatchParam _batch_param)
: device_id(_device_id),
page(_page),
n_rows(_n_rows),
param(std::move(_param)),
prediction_cache_initialised(false),
column_sampler(column_sampler_seed),
interaction_constraints(param, n_features) {
interaction_constraints(param, n_features),
batch_param(_batch_param) {
monitor.Init(std::string("GPUHistMakerDevice") + std::to_string(device_id));
}
@@ -626,6 +631,14 @@ struct GPUHistMakerDevice {
return std::vector<DeviceSplitCandidate>(result_all.begin(), result_all.end());
}
// Build gradient histograms for a given node across all the batches in the DMatrix.
void BuildHistBatches(int nidx, DMatrix* p_fmat) {
for (auto& batch : p_fmat->GetBatches<EllpackPage>(batch_param)) {
page = batch.Impl();
BuildHist(nidx);
}
}
void BuildHist(int nidx) {
hist.AllocateHistogram(nidx);
auto d_node_hist = hist.GetNodeHistogram(nidx);
@@ -636,7 +649,7 @@ struct GPUHistMakerDevice {
const size_t smem_size =
use_shared_memory_histograms
? sizeof(GradientSumT) * page->matrix.BinCount()
? sizeof(GradientSumT) * page->matrix.info.n_bins
: 0;
uint32_t items_per_thread = 8;
uint32_t block_threads = 256;
@@ -673,7 +686,10 @@ struct GPUHistMakerDevice {
row_partitioner->UpdatePosition(
nidx, split_node.LeftChild(), split_node.RightChild(),
[=] __device__(bst_uint ridx) {
[=] __device__(size_t ridx) {
if (!d_matrix.IsInRange(ridx)) {
return RowPartitioner::kIgnoredTreePosition;
}
// given a row index, returns the node id it belongs to
bst_float cut_value =
d_matrix.GetElement(ridx, split_node.SplitIndex());
@@ -693,35 +709,42 @@ struct GPUHistMakerDevice {
}
// After tree update is finished, update the position of all training
// instances to their final leaf This information is used later to update the
// instances to their final leaf. This information is used later to update the
// prediction cache
void FinalisePosition(RegTree* p_tree) {
void FinalisePosition(RegTree* p_tree, DMatrix* p_fmat) {
const auto d_nodes =
temp_memory.GetSpan<RegTree::Node>(p_tree->GetNodes().size());
dh::safe_cuda(cudaMemcpy(d_nodes.data(), p_tree->GetNodes().data(),
d_nodes.size() * sizeof(RegTree::Node),
cudaMemcpyHostToDevice));
auto d_matrix = page->matrix;
row_partitioner->FinalisePosition(
[=] __device__(bst_uint ridx, int position) {
auto node = d_nodes[position];
while (!node.IsLeaf()) {
bst_float element = d_matrix.GetElement(ridx, node.SplitIndex());
// Missing value
if (isnan(element)) {
position = node.DefaultChild();
} else {
if (element <= node.SplitCond()) {
position = node.LeftChild();
} else {
position = node.RightChild();
}
for (auto& batch : p_fmat->GetBatches<EllpackPage>(batch_param)) {
page = batch.Impl();
auto d_matrix = page->matrix;
row_partitioner->FinalisePosition(
[=] __device__(size_t row_id, int position) {
if (!d_matrix.IsInRange(row_id)) {
return RowPartitioner::kIgnoredTreePosition;
}
node = d_nodes[position];
}
return position;
});
auto node = d_nodes[position];
while (!node.IsLeaf()) {
bst_float element = d_matrix.GetElement(row_id, node.SplitIndex());
// Missing value
if (isnan(element)) {
position = node.DefaultChild();
} else {
if (element <= node.SplitCond()) {
position = node.LeftChild();
} else {
position = node.RightChild();
}
}
node = d_nodes[position];
}
return position;
});
}
}
void UpdatePredictionCache(bst_float* out_preds_d) {
@@ -764,7 +787,7 @@ struct GPUHistMakerDevice {
reducer->AllReduceSum(
reinterpret_cast<typename GradientSumT::ValueT*>(d_node_hist),
reinterpret_cast<typename GradientSumT::ValueT*>(d_node_hist),
page->matrix.BinCount() * (sizeof(GradientSumT) / sizeof(typename GradientSumT::ValueT)));
page->matrix.info.n_bins * (sizeof(GradientSumT) / sizeof(typename GradientSumT::ValueT)));
reducer->Synchronize();
monitor.StopCuda("AllReduce");
@@ -773,12 +796,10 @@ struct GPUHistMakerDevice {
/**
* \brief Build GPU local histograms for the left and right child of some parent node
*/
void BuildHistLeftRight(const ExpandEntry &candidate, int nidx_left,
int nidx_right, dh::AllReducer* reducer) {
void BuildHistLeftRight(const ExpandEntry &candidate, int nidx_left, int nidx_right) {
auto build_hist_nidx = nidx_left;
auto subtraction_trick_nidx = nidx_right;
// Decide whether to build the left histogram or right histogram
// Use sum of Hessian as a heuristic to select node with fewest training instances
bool fewer_right = candidate.split.right_sum.GetHess() < candidate.split.left_sum.GetHess();
@@ -787,22 +808,50 @@ struct GPUHistMakerDevice {
}
this->BuildHist(build_hist_nidx);
this->AllReduceHist(build_hist_nidx, reducer);
// Check whether we can use the subtraction trick to calculate the other
bool do_subtraction_trick = this->CanDoSubtractionTrick(
candidate.nid, build_hist_nidx, subtraction_trick_nidx);
if (!do_subtraction_trick) {
// Calculate other histogram manually
this->BuildHist(subtraction_trick_nidx);
}
}
/**
* \brief AllReduce GPU histograms for the left and right child of some parent node.
*/
void ReduceHistLeftRight(const ExpandEntry& candidate,
int nidx_left,
int nidx_right,
dh::AllReducer* reducer) {
auto build_hist_nidx = nidx_left;
auto subtraction_trick_nidx = nidx_right;
// Decide whether to build the left histogram or right histogram
// Use sum of Hessian as a heuristic to select node with fewest training instances
bool fewer_right = candidate.split.right_sum.GetHess() < candidate.split.left_sum.GetHess();
if (fewer_right) {
std::swap(build_hist_nidx, subtraction_trick_nidx);
}
this->AllReduceHist(build_hist_nidx, reducer);
// Check whether we can use the subtraction trick to calculate the other
bool do_subtraction_trick = this->CanDoSubtractionTrick(
candidate.nid, build_hist_nidx, subtraction_trick_nidx);
if (do_subtraction_trick) {
// Calculate other histogram using subtraction trick
this->SubtractionTrick(candidate.nid, build_hist_nidx,
subtraction_trick_nidx);
} else {
// Calculate other histogram manually
this->BuildHist(subtraction_trick_nidx);
this->AllReduceHist(subtraction_trick_nidx, reducer);
}
}
void ApplySplit(const ExpandEntry& candidate, RegTree* p_tree) {
RegTree& tree = *p_tree;
@@ -839,7 +888,7 @@ struct GPUHistMakerDevice {
tree[candidate.nid].RightChild());
}
void InitRoot(RegTree* p_tree, HostDeviceVector<GradientPair>* gpair_all,
void InitRoot(RegTree* p_tree, HostDeviceVector<GradientPair>* gpair_all, DMatrix* p_fmat,
dh::AllReducer* reducer, int64_t num_columns) {
constexpr int kRootNIdx = 0;
@@ -855,7 +904,7 @@ struct GPUHistMakerDevice {
node_sum_gradients_d.data(), sizeof(GradientPair),
cudaMemcpyDeviceToHost));
this->BuildHist(kRootNIdx);
this->BuildHistBatches(kRootNIdx, p_fmat);
this->AllReduceHist(kRootNIdx, reducer);
// Remember root stats
@@ -882,7 +931,7 @@ struct GPUHistMakerDevice {
monitor.StopCuda("Reset");
monitor.StartCuda("InitRoot");
this->InitRoot(p_tree, gpair_all, reducer, p_fmat->Info().num_col_);
this->InitRoot(p_tree, gpair_all, p_fmat, reducer, p_fmat->Info().num_col_);
monitor.StopCuda("InitRoot");
auto timestamp = qexpand->size();
@@ -901,15 +950,21 @@ struct GPUHistMakerDevice {
int left_child_nidx = tree[candidate.nid].LeftChild();
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)) {
monitor.StartCuda("UpdatePosition");
this->UpdatePosition(candidate.nid, (*p_tree)[candidate.nid]);
monitor.StopCuda("UpdatePosition");
if (ExpandEntry::ChildIsValid(param, tree.GetDepth(left_child_nidx), num_leaves)) {
for (auto& batch : p_fmat->GetBatches<EllpackPage>(batch_param)) {
page = batch.Impl();
monitor.StartCuda("BuildHist");
this->BuildHistLeftRight(candidate, left_child_nidx, right_child_nidx, reducer);
monitor.StopCuda("BuildHist");
monitor.StartCuda("UpdatePosition");
this->UpdatePosition(candidate.nid, (*p_tree)[candidate.nid]);
monitor.StopCuda("UpdatePosition");
monitor.StartCuda("BuildHist");
this->BuildHistLeftRight(candidate, left_child_nidx, right_child_nidx);
monitor.StopCuda("BuildHist");
}
monitor.StartCuda("ReduceHist");
this->ReduceHistLeftRight(candidate, left_child_nidx, right_child_nidx, reducer);
monitor.StopCuda("ReduceHist");
monitor.StartCuda("EvaluateSplits");
auto splits = this->EvaluateSplits({left_child_nidx, right_child_nidx},
@@ -926,7 +981,7 @@ struct GPUHistMakerDevice {
}
monitor.StartCuda("FinalisePosition");
this->FinalisePosition(p_tree);
this->FinalisePosition(p_tree, p_fmat);
monitor.StopCuda("FinalisePosition");
}
};
@@ -1016,21 +1071,21 @@ class GPUHistMakerSpecialised {
uint32_t column_sampling_seed = common::GlobalRandom()();
rabit::Broadcast(&column_sampling_seed, sizeof(column_sampling_seed), 0);
// TODO(rongou): support multiple Ellpack pages.
EllpackPageImpl* page{};
for (auto& batch : dmat->GetBatches<EllpackPage>({device_,
param_.max_bin,
hist_maker_param_.gpu_batch_nrows})) {
page = batch.Impl();
}
BatchParam batch_param{
device_,
param_.max_bin,
hist_maker_param_.gpu_batch_nrows,
generic_param_->gpu_page_size
};
auto page = (*dmat->GetBatches<EllpackPage>(batch_param).begin()).Impl();
dh::safe_cuda(cudaSetDevice(device_));
maker.reset(new GPUHistMakerDevice<GradientSumT>(device_,
page,
info_->num_row_,
param_,
column_sampling_seed,
info_->num_col_));
info_->num_col_,
batch_param));
monitor_.StartCuda("InitHistogram");
dh::safe_cuda(cudaSetDevice(device_));