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Dmatrix refactor stage 1 (#3301)

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* Use sparse page as singular CSR matrix representation

* Simplify dmatrix methods

* Reduce statefullness of batch iterators

* BREAKING CHANGE: Remove prob_buffer_row parameter. Users are instead recommended to sample their dataset as a preprocessing step before using XGBoost.
This commit is contained in:
Rory Mitchell
2018-06-07 10:25:58 +12:00
committed by GitHub
parent 286dccb8e8
commit a96039141a
47 changed files with 650 additions and 1036 deletions
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22
src/predictor/gpu_predictor.cu
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@@ -52,7 +52,7 @@ struct DeviceMatrix {
DMatrix* p_mat; // Pointer to the original matrix on the host
dh::BulkAllocator<dh::MemoryType::kDevice> ba;
dh::DVec<size_t> row_ptr;
dh::DVec<SparseBatch::Entry> data;
dh::DVec<Entry> data;
thrust::device_vector<float> predictions;
DeviceMatrix(DMatrix* dmat, int device_idx, bool silent) : p_mat(dmat) {
@@ -66,17 +66,17 @@ struct DeviceMatrix {
while (iter->Next()) {
auto batch = iter->Value();
// Copy row ptr
thrust::copy(batch.ind_ptr, batch.ind_ptr + batch.size + 1,
thrust::copy(batch.offset.data(), batch.offset.data() + batch.Size() + 1,
row_ptr.tbegin() + batch.base_rowid);
if (batch.base_rowid > 0) {
auto begin_itr = row_ptr.tbegin() + batch.base_rowid;
auto end_itr = begin_itr + batch.size + 1;
auto end_itr = begin_itr + batch.Size() + 1;
IncrementOffset(begin_itr, end_itr, batch.base_rowid);
}
// Copy data
thrust::copy(batch.data_ptr, batch.data_ptr + batch.ind_ptr[batch.size],
thrust::copy(batch.data.begin(), batch.data.end(),
data.tbegin() + data_offset);
data_offset += batch.ind_ptr[batch.size];
data_offset += batch.data.size();
}
}
};
@@ -139,12 +139,12 @@ struct DevicePredictionNode {
struct ElementLoader {
bool use_shared;
size_t* d_row_ptr;
SparseBatch::Entry* d_data;
Entry* d_data;
int num_features;
float* smem;
__device__ ElementLoader(bool use_shared, size_t* row_ptr,
SparseBatch::Entry* entry, int num_features,
Entry* entry, int num_features,
float* smem, int num_rows)
: use_shared(use_shared),
d_row_ptr(row_ptr),
@@ -161,7 +161,7 @@ struct ElementLoader {
bst_uint elem_begin = d_row_ptr[global_idx];
bst_uint elem_end = d_row_ptr[global_idx + 1];
for (bst_uint elem_idx = elem_begin; elem_idx < elem_end; elem_idx++) {
SparseBatch::Entry elem = d_data[elem_idx];
Entry elem = d_data[elem_idx];
smem[threadIdx.x * num_features + elem.index] = elem.fvalue;
}
}
@@ -175,7 +175,7 @@ struct ElementLoader {
// Binary search
auto begin_ptr = d_data + d_row_ptr[ridx];
auto end_ptr = d_data + d_row_ptr[ridx + 1];
SparseBatch::Entry* previous_middle = nullptr;
Entry* previous_middle = nullptr;
while (end_ptr != begin_ptr) {
auto middle = begin_ptr + (end_ptr - begin_ptr) / 2;
if (middle == previous_middle) {
@@ -221,7 +221,7 @@ template <int BLOCK_THREADS>
__global__ void PredictKernel(const DevicePredictionNode* d_nodes,
float* d_out_predictions, size_t* d_tree_segments,
int* d_tree_group, size_t* d_row_ptr,
SparseBatch::Entry* d_data, size_t tree_begin,
Entry* d_data, size_t tree_begin,
size_t tree_end, size_t num_features,
size_t num_rows, bool use_shared, int num_group) {
extern __shared__ float smem[];
@@ -422,7 +422,7 @@ class GPUPredictor : public xgboost::Predictor {
}
}
void PredictInstance(const SparseBatch::Inst& inst,
void PredictInstance(const SparsePage::Inst& inst,
std::vector<bst_float>* out_preds,
const gbm::GBTreeModel& model, unsigned ntree_limit,
unsigned root_index) override {