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Support categorical data for dask functional interface and DQM. (#7043)

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* Support categorical data for dask functional interface and DQM.

* Implement categorical data support for GPU GK-merge.
* Add support for dask functional interface.
* Add support for DQM.

* Get newer cupy.
This commit is contained in:
Jiaming Yuan
2021-06-18 13:06:52 +08:00
committed by GitHub
parent 7dd29ffd47
commit 86715e4cd4
16 changed files with 364 additions and 167 deletions
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58
src/common/quantile.cuh
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@@ -16,6 +16,19 @@ class HistogramCuts;
using WQSketch = WQuantileSketch<bst_float, bst_float>;
using SketchEntry = WQSketch::Entry;
namespace detail {
struct IsCatOp {
XGBOOST_DEVICE bool operator()(FeatureType ft) {
return ft == FeatureType::kCategorical;
}
};
struct SketchUnique {
XGBOOST_DEVICE bool operator()(SketchEntry const& a, SketchEntry const& b) const {
return a.value - b.value == 0;
}
};
} // namespace detail
/*!
* \brief A container that holds the device sketches. Sketching is performed per-column,
* but fused into single operation for performance.
@@ -43,6 +56,8 @@ class SketchContainer {
HostDeviceVector<OffsetT> columns_ptr_;
HostDeviceVector<OffsetT> columns_ptr_b_;
bool has_categorical_{false};
dh::device_vector<SketchEntry>& Current() {
if (current_buffer_) {
return entries_a_;
@@ -102,14 +117,21 @@ class SketchContainer {
this->feature_types_.SetDevice(device);
this->feature_types_.ConstDeviceSpan();
this->feature_types_.ConstHostSpan();
auto d_feature_types = feature_types_.ConstDeviceSpan();
has_categorical_ =
!d_feature_types.empty() &&
thrust::any_of(dh::tbegin(d_feature_types), dh::tend(d_feature_types),
detail::IsCatOp{});
timer_.Init(__func__);
}
/* \brief Return GPU ID for this container. */
int32_t DeviceIdx() const { return device_; }
/* \brief Whether the predictor matrix contains categorical features. */
bool HasCategorical() const { return has_categorical_; }
/* \brief Accumulate weights of duplicated entries in input. */
size_t ScanInput(Span<SketchEntry> entries, Span<OffsetT> d_columns_ptr_in);
/* \brief Removes all the duplicated elements in quantile structure. */
size_t Unique();
/* Fix rounding error and re-establish invariance. The error is mostly generated by the
* addition inside `RMinNext` and subtraction in `RMaxPrev`. */
void FixError();
@@ -154,15 +176,35 @@ class SketchContainer {
SketchContainer(const SketchContainer&) = delete;
SketchContainer& operator=(const SketchContainer&) = delete;
};
namespace detail {
struct SketchUnique {
XGBOOST_DEVICE bool operator()(SketchEntry const& a, SketchEntry const& b) const {
return a.value - b.value == 0;
/* \brief Removes all the duplicated elements in quantile structure. */
template <typename KeyComp = thrust::equal_to<size_t>>
size_t Unique(KeyComp key_comp = thrust::equal_to<size_t>{}) {
timer_.Start(__func__);
dh::safe_cuda(cudaSetDevice(device_));
this->columns_ptr_.SetDevice(device_);
Span<OffsetT> d_column_scan = this->columns_ptr_.DeviceSpan();
CHECK_EQ(d_column_scan.size(), num_columns_ + 1);
Span<SketchEntry> entries = dh::ToSpan(this->Current());
HostDeviceVector<OffsetT> scan_out(d_column_scan.size());
scan_out.SetDevice(device_);
auto d_scan_out = scan_out.DeviceSpan();
dh::XGBCachingDeviceAllocator<char> alloc;
d_column_scan = this->columns_ptr_.DeviceSpan();
size_t n_uniques = dh::SegmentedUnique(
thrust::cuda::par(alloc), d_column_scan.data(),
d_column_scan.data() + d_column_scan.size(), entries.data(),
entries.data() + entries.size(), scan_out.DevicePointer(),
entries.data(), detail::SketchUnique{}, key_comp);
this->columns_ptr_.Copy(scan_out);
CHECK(!this->columns_ptr_.HostCanRead());
this->Current().resize(n_uniques);
timer_.Stop(__func__);
return n_uniques;
}
};
} // namespace detail
} // namespace common
} // namespace xgboost