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Use std::uint64_t for row index. (#10120)

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- Use std::uint64_t instead of size_t to avoid implementation-defined type.
- Rename to bst_idx_t, to account for other types of indexing.
- Small cleanup to the base header.
This commit is contained in:
Jiaming Yuan
2024-03-15 18:43:49 +08:00
committed by GitHub
parent 56b1868278
commit 53fc17578f
57 changed files with 228 additions and 238 deletions
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8
src/predictor/cpu_predictor.cc
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@@ -184,7 +184,7 @@ void FVecDrop(std::size_t const block_size, std::size_t const fvec_offset,
static std::size_t constexpr kUnroll = 8;
struct SparsePageView {
bst_row_t base_rowid;
bst_idx_t base_rowid;
HostSparsePageView view;
explicit SparsePageView(SparsePage const *p) : base_rowid{p->base_rowid} { view = p->GetView(); }
@@ -193,7 +193,7 @@ struct SparsePageView {
};
struct SingleInstanceView {
bst_row_t base_rowid{};
bst_idx_t base_rowid{};
SparsePage::Inst const &inst;
explicit SingleInstanceView(SparsePage::Inst const &instance) : inst{instance} {}
@@ -214,7 +214,7 @@ struct GHistIndexMatrixView {
std::vector<float> const& values_;
public:
size_t base_rowid;
bst_idx_t base_rowid;
public:
GHistIndexMatrixView(GHistIndexMatrix const &_page, uint64_t n_feat,
@@ -292,7 +292,7 @@ class AdapterView {
[[nodiscard]] size_t Size() const { return adapter_->NumRows(); }
bst_row_t const static base_rowid = 0; // NOLINT
bst_idx_t const static base_rowid = 0; // NOLINT
};
template <typename DataView, size_t block_of_rows_size>

26
src/predictor/gpu_predictor.cu
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@@ -67,12 +67,12 @@ struct TreeView {
struct SparsePageView {
common::Span<const Entry> d_data;
common::Span<const bst_row_t> d_row_ptr;
common::Span<const bst_idx_t> d_row_ptr;
bst_feature_t num_features;
SparsePageView() = default;
XGBOOST_DEVICE SparsePageView(common::Span<const Entry> data,
common::Span<const bst_row_t> row_ptr,
common::Span<const bst_idx_t> row_ptr,
bst_feature_t num_features)
: d_data{data}, d_row_ptr{row_ptr}, num_features(num_features) {}
[[nodiscard]] __device__ float GetElement(size_t ridx, size_t fidx) const {
@@ -113,7 +113,7 @@ struct SparsePageLoader {
float* smem;
__device__ SparsePageLoader(SparsePageView data, bool use_shared, bst_feature_t num_features,
bst_row_t num_rows, size_t entry_start, float)
bst_idx_t num_rows, size_t entry_start, float)
: use_shared(use_shared),
data(data) {
extern __shared__ float _smem[];
@@ -146,7 +146,7 @@ struct SparsePageLoader {
struct EllpackLoader {
EllpackDeviceAccessor const& matrix;
XGBOOST_DEVICE EllpackLoader(EllpackDeviceAccessor const& m, bool, bst_feature_t, bst_row_t,
XGBOOST_DEVICE EllpackLoader(EllpackDeviceAccessor const& m, bool, bst_feature_t, bst_idx_t,
size_t, float)
: matrix{m} {}
[[nodiscard]] __device__ __forceinline__ float GetElement(size_t ridx, size_t fidx) const {
@@ -177,7 +177,7 @@ struct DeviceAdapterLoader {
using BatchT = Batch;
XGBOOST_DEV_INLINE DeviceAdapterLoader(Batch const batch, bool use_shared,
bst_feature_t num_features, bst_row_t num_rows,
bst_feature_t num_features, bst_idx_t num_rows,
size_t entry_start, float missing)
: batch{batch}, columns{num_features}, use_shared{use_shared}, is_valid{missing} {
extern __shared__ float _smem[];
@@ -215,7 +215,7 @@ struct DeviceAdapterLoader {
};
template <bool has_missing, bool has_categorical, typename Loader>
__device__ bst_node_t GetLeafIndex(bst_row_t ridx, TreeView const &tree,
__device__ bst_node_t GetLeafIndex(bst_idx_t ridx, TreeView const &tree,
Loader *loader) {
bst_node_t nidx = 0;
RegTree::Node n = tree.d_tree[nidx];
@@ -230,7 +230,7 @@ __device__ bst_node_t GetLeafIndex(bst_row_t ridx, TreeView const &tree,
}
template <bool has_missing, typename Loader>
__device__ float GetLeafWeight(bst_row_t ridx, TreeView const &tree,
__device__ float GetLeafWeight(bst_idx_t ridx, TreeView const &tree,
Loader *loader) {
bst_node_t nidx = -1;
if (tree.HasCategoricalSplit()) {
@@ -255,7 +255,7 @@ PredictLeafKernel(Data data, common::Span<const RegTree::Node> d_nodes,
size_t tree_begin, size_t tree_end, size_t num_features,
size_t num_rows, size_t entry_start, bool use_shared,
float missing) {
bst_row_t ridx = blockDim.x * blockIdx.x + threadIdx.x;
bst_idx_t ridx = blockDim.x * blockIdx.x + threadIdx.x;
if (ridx >= num_rows) {
return;
}
@@ -664,7 +664,7 @@ __global__ void MaskBitVectorKernel(
}
}
__device__ bst_node_t GetLeafIndexByBitVector(bst_row_t ridx, TreeView const& tree,
__device__ bst_node_t GetLeafIndexByBitVector(bst_idx_t ridx, TreeView const& tree,
BitVector const& decision_bits,
BitVector const& missing_bits, std::size_t num_nodes,
std::size_t tree_offset) {
@@ -682,7 +682,7 @@ __device__ bst_node_t GetLeafIndexByBitVector(bst_row_t ridx, TreeView const& tr
return nidx;
}
__device__ float GetLeafWeightByBitVector(bst_row_t ridx, TreeView const& tree,
__device__ float GetLeafWeightByBitVector(bst_idx_t ridx, TreeView const& tree,
BitVector const& decision_bits,
BitVector const& missing_bits, std::size_t num_nodes,
std::size_t tree_offset) {
@@ -1171,7 +1171,7 @@ class GPUPredictor : public xgboost::Predictor {
auto max_shared_memory_bytes = ConfigureDevice(ctx_->Device());
const MetaInfo& info = p_fmat->Info();
bst_row_t num_rows = info.num_row_;
bst_idx_t num_rows = info.num_row_;
if (tree_end == 0 || tree_end > model.trees.size()) {
tree_end = static_cast<uint32_t>(model.trees.size());
}
@@ -1196,7 +1196,7 @@ class GPUPredictor : public xgboost::Predictor {
for (auto const& batch : p_fmat->GetBatches<SparsePage>()) {
batch.data.SetDevice(ctx_->Device());
batch.offset.SetDevice(ctx_->Device());
bst_row_t batch_offset = 0;
bst_idx_t batch_offset = 0;
SparsePageView data{batch.data.DeviceSpan(), batch.offset.DeviceSpan(),
model.learner_model_param->num_feature};
size_t num_rows = batch.Size();
@@ -1219,7 +1219,7 @@ class GPUPredictor : public xgboost::Predictor {
}
} else {
for (auto const& batch : p_fmat->GetBatches<EllpackPage>(ctx_, BatchParam{})) {
bst_row_t batch_offset = 0;
bst_idx_t batch_offset = 0;
EllpackDeviceAccessor data{batch.Impl()->GetDeviceAccessor(ctx_->Device())};
size_t num_rows = batch.Size();
auto grid =

4
src/predictor/predictor.cc
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@@ -9,7 +9,7 @@
#include <string> // for string, to_string
#include "../gbm/gbtree_model.h" // for GBTreeModel
#include "xgboost/base.h" // for bst_float, Args, bst_group_t, bst_row_t
#include "xgboost/base.h" // for bst_float, Args, bst_group_t, bst_idx_t
#include "xgboost/context.h" // for Context
#include "xgboost/data.h" // for MetaInfo
#include "xgboost/host_device_vector.h" // for HostDeviceVector
@@ -34,7 +34,7 @@ Predictor* Predictor::Create(std::string const& name, Context const* ctx) {
}
template <int32_t D>
void ValidateBaseMarginShape(linalg::Tensor<float, D> const& margin, bst_row_t n_samples,
void ValidateBaseMarginShape(linalg::Tensor<float, D> const& margin, bst_idx_t n_samples,
bst_group_t n_groups) {
// FIXME: Bindings other than Python doesn't have shape.
std::string expected{"Invalid shape of base_margin. Expected: (" + std::to_string(n_samples) +