Remove column major specialization.

src/c_api/c_api.cc

@@ -463,7 +463,8 @@ XGB_DLL int XGBoosterPredictFromDense(BoosterHandle handle, float *values,
   CHECK_EQ(cache_id, 0) << "Cache ID is not supported yet";
   auto *learner = static_cast<xgboost::Learner *>(handle);
 
-  auto x = xgboost::data::DenseAdapter(values, n_rows, n_cols);
+  auto x =
+      std::make_shared<xgboost::data::DenseAdapter>(values, n_rows, n_cols);
   HostDeviceVector<float>* p_predt { nullptr };
   std::string type { c_type };
   learner->InplacePredict(x, type, missing, &p_predt);
@@ -494,7 +495,8 @@ XGB_DLL int XGBoosterPredictFromCSR(BoosterHandle handle,
   CHECK_EQ(cache_id, 0) << "Cache ID is not supported yet";
   auto *learner = static_cast<xgboost::Learner *>(handle);
 
-  auto x = data::CSRAdapter(indptr, indices, data, nindptr - 1, nelem, num_col);
+  auto x = std::make_shared<data::CSRAdapter>(indptr, indices, data,
+                                              nindptr - 1, nelem, num_col);
   HostDeviceVector<float>* p_predt { nullptr };
   std::string type { c_type };
   learner->InplacePredict(x, type, missing, &p_predt);

src/c_api/c_api.cu

@@ -69,7 +69,7 @@ XGB_DLL int XGBoosterPredictFromArrayInterfaceColumns(BoosterHandle handle,
   auto *learner = static_cast<Learner*>(handle);
 
   std::string json_str{c_json_strs};
-  auto x = data::CudfAdapter(json_str);
+  auto x = std::make_shared<data::CudfAdapter>(json_str);
   HostDeviceVector<float>* p_predt { nullptr };
   std::string type { c_type };
   learner->InplacePredict(x, type, missing, &p_predt);
@@ -97,7 +97,7 @@ XGB_DLL int XGBoosterPredictFromArrayInterface(BoosterHandle handle,
   auto *learner = static_cast<Learner*>(handle);
 
   std::string json_str{c_json_strs};
-  auto x = data::CupyAdapter(json_str);
+  auto x = std::make_shared<data::CupyAdapter>(json_str);
   HostDeviceVector<float>* p_predt { nullptr };
   std::string type { c_type };
   learner->InplacePredict(x, type, missing, &p_predt);

src/data/adapter.h

@@ -169,6 +169,7 @@ class CSRAdapter : public detail::SingleBatchDataIter<CSRAdapterBatch> {
   const CSRAdapterBatch& Value() const override { return batch_; }
   size_t NumRows() const { return num_rows_; }
   size_t NumColumns() const { return num_columns_; }
+  ~CSRAdapter() noexcept(false) override = default;
 
  private:
   CSRAdapterBatch batch_;
@@ -222,6 +223,7 @@ class DenseAdapter : public detail::SingleBatchDataIter<DenseAdapterBatch> {
 
   size_t NumRows() const { return num_rows_; }
   size_t NumColumns() const { return num_columns_; }
+  ~DenseAdapter() noexcept(false) override = default;
 
  private:
   DenseAdapterBatch batch_;

src/data/device_adapter.cuh

@@ -34,36 +34,27 @@ struct IsValidFunctor : public thrust::unary_function<Entry, bool> {
 };
 
 class CudfAdapterBatch : public detail::NoMetaInfo {
+  friend class CudfAdapter;
+
  public:
   CudfAdapterBatch() = default;
-  CudfAdapterBatch(common::Span<ArrayInterface> columns,
-                   common::Span<size_t> column_ptr, size_t num_elements)
+  CudfAdapterBatch(common::Span<ArrayInterface> columns, size_t num_rows)
       : columns_(columns),
-        column_ptr_(column_ptr),
-        num_elements_(num_elements) {}
-  size_t Size() const { return num_elements_; }
+        num_rows_(num_rows) {}
+  size_t Size() const { return num_rows_ * columns_.size(); }
   __device__ COOTuple GetElement(size_t idx) const {
-    size_t column_idx =
-        thrust::upper_bound(thrust::seq,column_ptr_.begin(), column_ptr_.end(), idx) - column_ptr_.begin() - 1;
-    auto& column = columns_[column_idx];
-    size_t row_idx = idx - column_ptr_[column_idx];
+    size_t column_idx = idx % columns_.size();
+    size_t row_idx = idx / columns_.size();
+    auto const& column = columns_[column_idx];
     float value = column.valid.Data() == nullptr || column.valid.Check(row_idx)
                       ? column.GetElement(row_idx)
                       : std::numeric_limits<float>::quiet_NaN();
     return {row_idx, column_idx, value};
   }
-  __device__ float GetValue(size_t ridx, bst_feature_t fidx) const {
-    auto const& column = columns_[fidx];
-    float value = column.valid.Data() == nullptr || column.valid.Check(ridx)
-                      ? column.GetElement(ridx)
-                      : std::numeric_limits<float>::quiet_NaN();
-    return value;
-  }
 
  private:
   common::Span<ArrayInterface> columns_;
-  common::Span<size_t> column_ptr_;
-  size_t num_elements_;
+  size_t num_rows_;
 };
 
 /*!
@@ -127,7 +118,6 @@ class CudfAdapter : public detail::SingleBatchDataIter<CudfAdapterBatch> {
     CHECK_EQ(typestr.size(), 3) << ArrayInterfaceErrors::TypestrFormat();
     CHECK_NE(typestr.front(), '>') << ArrayInterfaceErrors::BigEndian();
     std::vector<ArrayInterface> columns;
-    std::vector<size_t> column_ptr({0});
     auto first_column = ArrayInterface(get<Object const>(json_columns[0]));
     device_idx_ = dh::CudaGetPointerDevice(first_column.data);
     CHECK_NE(device_idx_, -1);
@@ -137,31 +127,27 @@ class CudfAdapter : public detail::SingleBatchDataIter<CudfAdapterBatch> {
       auto column = ArrayInterface(get<Object const>(json_col));
       columns.push_back(column);
       CHECK_EQ(column.num_cols, 1);
-      column_ptr.emplace_back(column_ptr.back() + column.num_rows);
       num_rows_ = std::max(num_rows_, size_t(column.num_rows));
       CHECK_EQ(device_idx_, dh::CudaGetPointerDevice(column.data))
           << "All columns should use the same device.";
       CHECK_EQ(num_rows_, column.num_rows)
           << "All columns should have same number of rows.";
     }
     columns_ = columns;
-    column_ptr_ = column_ptr;
-    batch_ = CudfAdapterBatch(dh::ToSpan(columns_), dh::ToSpan(column_ptr_),
-                             column_ptr.back());
+    batch_ = CudfAdapterBatch(dh::ToSpan(columns_), num_rows_);
+  }
+  const CudfAdapterBatch& Value() const override {
+    CHECK_EQ(batch_.columns_.data(), columns_.data().get());
+    return batch_;
   }
-  const CudfAdapterBatch& Value() const override { return batch_; }
 
   size_t NumRows() const { return num_rows_; }
   size_t NumColumns() const { return columns_.size(); }
   size_t DeviceIdx() const { return device_idx_; }
 
-  // Cudf is column major
-  bool IsRowMajor() { return false; }
-
  private:
   CudfAdapterBatch batch_;
   dh::device_vector<ArrayInterface> columns_;
-  dh::device_vector<size_t> column_ptr_;  // Exclusive scan of column sizes
   size_t num_rows_{0};
   int device_idx_;
 };
@@ -201,8 +187,6 @@ class CupyAdapter : public detail::SingleBatchDataIter<CupyAdapterBatch> {
   size_t NumColumns() const { return array_interface_.num_cols; }
   size_t DeviceIdx() const { return device_idx_; }
 
-  bool IsRowMajor() { return true; }
-
  private:
   ArrayInterface array_interface_;
   CupyAdapterBatch batch_;

src/data/ellpack_page.cu

@@ -154,8 +154,8 @@ struct WriteCompressedEllpackFunctor {
 // Here the data is already correctly ordered and simply needs to be compacted
 // to remove missing data
 template <typename AdapterBatchT>
-void CopyDataRowMajor(const AdapterBatchT& batch, EllpackPageImpl* dst,
-                      int device_idx, float missing) {
+void CopyDataToEllpack(const AdapterBatchT& batch, EllpackPageImpl* dst,
+                       int device_idx, float missing) {
   // Some witchcraft happens here
   // The goal is to copy valid elements out of the input to an ellpack matrix
   // with a given row stride, using no extra working memory Standard stream
@@ -209,51 +209,6 @@ void CopyDataRowMajor(const AdapterBatchT& batch, EllpackPageImpl* dst,
                          });
 }
 
-template <typename AdapterT, typename AdapterBatchT>
-void CopyDataColumnMajor(AdapterT* adapter, const AdapterBatchT& batch,
-                         EllpackPageImpl* dst, float missing) {
-  // Step 1: Get the sizes of the input columns
-  dh::caching_device_vector<size_t> column_sizes(adapter->NumColumns(), 0);
-  auto d_column_sizes = column_sizes.data().get();
-  // Populate column sizes
-  dh::LaunchN(adapter->DeviceIdx(), batch.Size(), [=] __device__(size_t idx) {
-      const auto& e = batch.GetElement(idx);
-      atomicAdd(reinterpret_cast<unsigned long long*>(  // NOLINT
-          &d_column_sizes[e.column_idx]),
-                static_cast<unsigned long long>(1));  // NOLINT
-    });
-
-  thrust::host_vector<size_t> host_column_sizes = column_sizes;
-
-  // Step 2: Iterate over columns, place elements in correct row, increment
-  // temporary row pointers
-  dh::caching_device_vector<size_t> temp_row_ptr(adapter->NumRows(), 0);
-  auto d_temp_row_ptr = temp_row_ptr.data().get();
-  auto row_stride = dst->row_stride;
-  size_t begin = 0;
-  auto device_accessor = dst->GetDeviceAccessor(adapter->DeviceIdx());
-  common::CompressedBufferWriter writer(device_accessor.NumSymbols());
-  auto d_compressed_buffer = dst->gidx_buffer.DevicePointer();
-  data::IsValidFunctor is_valid(missing);
-  for (auto size : host_column_sizes) {
-    size_t end = begin + size;
-    dh::LaunchN(adapter->DeviceIdx(), end - begin, [=] __device__(size_t idx) {
-        auto writer_non_const =
-            writer;  // For some reason this variable gets captured as const
-        const auto& e = batch.GetElement(idx + begin);
-        if (!is_valid(e)) return;
-        size_t output_position =
-            e.row_idx * row_stride + d_temp_row_ptr[e.row_idx];
-        auto bin_idx = device_accessor.SearchBin(e.value, e.column_idx);
-        writer_non_const.AtomicWriteSymbol(d_compressed_buffer, bin_idx,
-                                           output_position);
-        d_temp_row_ptr[e.row_idx] += 1;
-      });
-
-    begin = end;
-  }
-}
-
 void WriteNullValues(EllpackPageImpl* dst, int device_idx,
                      common::Span<size_t> row_counts) {
   // Write the null values
@@ -284,12 +239,7 @@ EllpackPageImpl::EllpackPageImpl(AdapterT* adapter, float missing, bool is_dense
 
   *this = EllpackPageImpl(adapter->DeviceIdx(), cuts, is_dense, row_stride,
                           adapter->NumRows());
-  if (adapter->IsRowMajor()) {
-    CopyDataRowMajor(batch, this, adapter->DeviceIdx(), missing);
-  } else {
-    CopyDataColumnMajor(adapter, batch, this, missing);
-  }
-
+  CopyDataToEllpack(batch, this, adapter->DeviceIdx(), missing);
   WriteNullValues(this, adapter->DeviceIdx(), row_counts_span);
 }
 

src/data/simple_dmatrix.cu

@@ -35,51 +35,12 @@ void CountRowOffsets(const AdapterBatchT& batch, common::Span<bst_row_t> offset,
       thrust::device_pointer_cast(offset.data()));
 }
 
-template <typename AdapterT>
-void CopyDataColumnMajor(AdapterT* adapter, common::Span<Entry> data,
-                         int device_idx, float missing,
-                         common::Span<size_t> row_ptr) {
-  // Step 1: Get the sizes of the input columns
-  dh::device_vector<size_t> column_sizes(adapter->NumColumns());
-  auto d_column_sizes = column_sizes.data().get();
-  auto& batch = adapter->Value();
-  // Populate column sizes
-  dh::LaunchN(device_idx, batch.Size(), [=] __device__(size_t idx) {
-    const auto& e = batch.GetElement(idx);
-    atomicAdd(reinterpret_cast<unsigned long long*>(  // NOLINT
-                  &d_column_sizes[e.column_idx]),
-              static_cast<unsigned long long>(1));  // NOLINT
-  });
-
-  thrust::host_vector<size_t> host_column_sizes = column_sizes;
-
-  // Step 2: Iterate over columns, place elements in correct row, increment
-  // temporary row pointers
-  dh::device_vector<size_t> temp_row_ptr(
-      thrust::device_pointer_cast(row_ptr.data()),
-      thrust::device_pointer_cast(row_ptr.data() + row_ptr.size()));
-  auto d_temp_row_ptr = temp_row_ptr.data().get();
-  size_t begin = 0;
-  IsValidFunctor is_valid(missing);
-  for (auto size : host_column_sizes) {
-    size_t end = begin + size;
-    dh::LaunchN(device_idx, end - begin, [=] __device__(size_t idx) {
-      const auto& e = batch.GetElement(idx + begin);
-      if (!is_valid(e)) return;
-      data[d_temp_row_ptr[e.row_idx]] = Entry(e.column_idx, e.value);
-      d_temp_row_ptr[e.row_idx] += 1;
-    });
-
-    begin = end;
-  }
-}
-
 // Here the data is already correctly ordered and simply needs to be compacted
 // to remove missing data
 template <typename AdapterT>
-void CopyDataRowMajor(AdapterT* adapter, common::Span<Entry> data,
-                      int device_idx, float missing,
-                      common::Span<size_t> row_ptr) {
+void CopyDataToDMatrix(AdapterT* adapter, common::Span<Entry> data,
+                       int device_idx, float missing,
+                       common::Span<size_t> row_ptr) {
   auto& batch = adapter->Value();
   auto transform_f = [=] __device__(size_t idx) {
     const auto& e = batch.GetElement(idx);
@@ -116,13 +77,8 @@ SimpleDMatrix::SimpleDMatrix(AdapterT* adapter, float missing, int nthread) {
   CountRowOffsets(batch, s_offset, adapter->DeviceIdx(), missing);
   info_.num_nonzero_ = sparse_page_.offset.HostVector().back();
   sparse_page_.data.Resize(info_.num_nonzero_);
-  if (adapter->IsRowMajor()) {
-    CopyDataRowMajor(adapter, sparse_page_.data.DeviceSpan(),
-                        adapter->DeviceIdx(), missing, s_offset);
-  } else {
-    CopyDataColumnMajor(adapter, sparse_page_.data.DeviceSpan(),
-                        adapter->DeviceIdx(), missing, s_offset);
-  }
+  CopyDataToDMatrix(adapter, sparse_page_.data.DeviceSpan(),
+                    adapter->DeviceIdx(), missing, s_offset);
 
   info_.num_col_ = adapter->NumColumns();
   info_.num_row_ = adapter->NumRows();

src/predictor/cpu_predictor.cc

@@ -271,30 +271,30 @@ class CPUPredictor : public Predictor {
                                 PredictionCacheEntry *out_preds,
                                 uint32_t tree_begin, uint32_t tree_end) const {
     auto threads = omp_get_max_threads();
-    auto m = dmlc::get<Adapter>(x);
-    CHECK_EQ(m.NumColumns(), model.learner_model_param->num_feature)
+    auto m = dmlc::get<std::shared_ptr<Adapter>>(x);
+    CHECK_EQ(m->NumColumns(), model.learner_model_param->num_feature)
         << "Number of columns in data must equal to trained model.";
     MetaInfo info;
-    info.num_col_ = m.NumColumns();
-    info.num_row_ = m.NumRows();
+    info.num_col_ = m->NumColumns();
+    info.num_row_ = m->NumRows();
     this->InitOutPredictions(info, &(out_preds->predictions), model);
     std::vector<Entry> workspace(info.num_col_ * 8 * threads);
     auto &predictions = out_preds->predictions.HostVector();
     std::vector<RegTree::FVec> thread_temp;
     InitThreadTemp(threads, model.learner_model_param->num_feature, &thread_temp);
     size_t constexpr kUnroll = 8;
     PredictBatchKernel(AdapterView<Adapter, kUnroll>(
-                           &m, missing, common::Span<Entry>{workspace}),
+                           m.get(), missing, common::Span<Entry>{workspace}),
                        &predictions, model, tree_begin, tree_end, &thread_temp);
   }
 
   void InplacePredict(dmlc::any const &x, const gbm::GBTreeModel &model,
                       float missing, PredictionCacheEntry *out_preds,
                       uint32_t tree_begin, unsigned tree_end) const override {
-    if (x.type() == typeid(data::DenseAdapter)) {
+    if (x.type() == typeid(std::shared_ptr<data::DenseAdapter>)) {
       this->DispatchedInplacePredict<data::DenseAdapter>(
           x, model, missing, out_preds, tree_begin, tree_end);
-    } else if (x.type() == typeid(data::CSRAdapter)) {
+    } else if (x.type() == typeid(std::shared_ptr<data::CSRAdapter>)) {
       this->DispatchedInplacePredict<data::CSRAdapter>(
           x, model, missing, out_preds, tree_begin, tree_end);
     } else {