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[EM] Add GPU version of the external memory QDM. (#10689)

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This commit is contained in:
Jiaming Yuan 2024-08-10 10:49:43 +08:00 committed by GitHub
parent 18b28d9315
commit d414fdf2e7
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GPG Key ID: B5690EEEBB952194
25 changed files with 536 additions and 261 deletions
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2
include/xgboost/data.h
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@ -641,7 +641,7 @@ class DMatrix {
typename XGDMatrixCallbackNext>
static DMatrix* Create(DataIterHandle iter, DMatrixHandle proxy, std::shared_ptr<DMatrix> ref,
DataIterResetCallback* reset, XGDMatrixCallbackNext* next, float missing,
std::int32_t nthread, bst_bin_t max_bin, std::string cache);
std::int32_t nthread, bst_bin_t max_bin, std::string cache, bool on_host);
virtual DMatrix *Slice(common::Span<int32_t const> ridxs) = 0;

7
src/common/device_helpers.cuh
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@ -116,6 +116,13 @@ inline int32_t CurrentDevice() {
return device;
}
// Helper function to get a device from a potentially CPU context.
inline auto GetDevice(xgboost::Context const *ctx) {
auto d = (ctx->IsCUDA()) ? ctx->Device() : xgboost::DeviceOrd::CUDA(dh::CurrentDevice());
CHECK(!d.IsCPU());
return d;
}
inline size_t TotalMemory(int device_idx) {
size_t device_free = 0;
size_t device_total = 0;

6
src/data/data.cc
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@ -914,9 +914,9 @@ template <typename DataIterHandle, typename DMatrixHandle, typename DataIterRese
typename XGDMatrixCallbackNext>
DMatrix* DMatrix::Create(DataIterHandle iter, DMatrixHandle proxy, std::shared_ptr<DMatrix> ref,
DataIterResetCallback* reset, XGDMatrixCallbackNext* next, float missing,
std::int32_t nthread, bst_bin_t max_bin, std::string cache) {
std::int32_t nthread, bst_bin_t max_bin, std::string cache, bool on_host) {
return new data::ExtMemQuantileDMatrix{
iter, proxy, ref, reset, next, missing, nthread, std::move(cache), max_bin};
iter, proxy, ref, reset, next, missing, nthread, std::move(cache), max_bin, on_host};
}
template DMatrix* DMatrix::Create<DataIterHandle, DMatrixHandle, DataIterResetCallback,
@ -935,7 +935,7 @@ template DMatrix* DMatrix::Create<DataIterHandle, DMatrixHandle, DataIterResetCa
template DMatrix*
DMatrix::Create<DataIterHandle, DMatrixHandle, DataIterResetCallback, XGDMatrixCallbackNext>(
DataIterHandle, DMatrixHandle, std::shared_ptr<DMatrix>, DataIterResetCallback*,
XGDMatrixCallbackNext*, float, std::int32_t, bst_bin_t, std::string);
XGDMatrixCallbackNext*, float, std::int32_t, bst_bin_t, std::string, bool);
template <typename AdapterT>
DMatrix* DMatrix::Create(AdapterT* adapter, float missing, int nthread, const std::string&,

12
src/data/ellpack_page.cc
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@ -47,6 +47,18 @@ bst_idx_t EllpackPage::Size() const {
"EllpackPage is required";
return impl_->Cuts();
}
[[nodiscard]] bst_idx_t EllpackPage::BaseRowId() const {
LOG(FATAL) << "Internal Error: XGBoost is not compiled with CUDA but "
"EllpackPage is required";
return 0;
}
[[nodiscard]] bool EllpackPage::IsDense() const {
LOG(FATAL) << "Internal Error: XGBoost is not compiled with CUDA but "
"EllpackPage is required";
return false;
}
} // namespace xgboost
#endif // XGBOOST_USE_CUDA

5
src/data/ellpack_page.cu
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@ -39,6 +39,9 @@ void EllpackPage::SetBaseRowId(std::size_t row_id) { impl_->SetBaseRowId(row_id)
return impl_->Cuts();
}
[[nodiscard]] bst_idx_t EllpackPage::BaseRowId() const { return this->Impl()->base_rowid; }
[[nodiscard]] bool EllpackPage::IsDense() const { return this->Impl()->IsDense(); }
// Bin each input data entry, store the bin indices in compressed form.
__global__ void CompressBinEllpackKernel(
common::CompressedBufferWriter wr,
@ -397,7 +400,7 @@ struct CopyPage {
size_t EllpackPageImpl::Copy(Context const* ctx, EllpackPageImpl const* page, bst_idx_t offset) {
monitor_.Start(__func__);
bst_idx_t num_elements = page->n_rows * page->row_stride;
CHECK_EQ(row_stride, page->row_stride);
CHECK_EQ(this->row_stride, page->row_stride);
CHECK_EQ(NumSymbols(), page->NumSymbols());
CHECK_GE(n_rows * row_stride, offset + num_elements);
if (page == this) {

1
src/data/ellpack_page.cuh
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@ -203,6 +203,7 @@ class EllpackPageImpl {
[[nodiscard]] std::shared_ptr<common::HistogramCuts const> CutsShared() const { return cuts_; }
void SetCuts(std::shared_ptr<common::HistogramCuts const> cuts) { cuts_ = cuts; }
[[nodiscard]] bool IsDense() const { return is_dense; }
/** @return Estimation of memory cost of this page. */
static size_t MemCostBytes(size_t num_rows, size_t row_stride, const common::HistogramCuts&cuts) ;

2
src/data/ellpack_page.h
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@ -42,6 +42,7 @@ class EllpackPage {
/*! \return Number of instances in the page. */
[[nodiscard]] bst_idx_t Size() const;
[[nodiscard]] bool IsDense() const;
/*! \brief Set the base row id for this page. */
void SetBaseRowId(std::size_t row_id);
@ -50,6 +51,7 @@ class EllpackPage {
EllpackPageImpl* Impl() { return impl_.get(); }
[[nodiscard]] common::HistogramCuts const& Cuts() const;
[[nodiscard]] bst_idx_t BaseRowId() const;
private:
std::unique_ptr<EllpackPageImpl> impl_;

48
src/data/ellpack_page_source.cu
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@ -15,6 +15,7 @@
#include "ellpack_page.cuh" // for EllpackPageImpl
#include "ellpack_page.h" // for EllpackPage
#include "ellpack_page_source.h"
#include "proxy_dmatrix.cuh" // for Dispatch
#include "xgboost/base.h" // for bst_idx_t
namespace xgboost::data {
@ -182,4 +183,51 @@ template void
EllpackPageSourceImpl<EllpackCacheStreamPolicy<EllpackPage, EllpackFormatPolicy>>::Fetch();
template void
EllpackPageSourceImpl<EllpackMmapStreamPolicy<EllpackPage, EllpackFormatPolicy>>::Fetch();
/**
* ExtEllpackPageSourceImpl
*/
template <typename F>
void ExtEllpackPageSourceImpl<F>::Fetch() {
dh::safe_cuda(cudaSetDevice(this->Device().ordinal));
if (!this->ReadCache()) {
auto iter = this->source_->Iter();
CHECK_EQ(this->count_, iter);
++(*this->source_);
CHECK_GE(this->source_->Iter(), 1);
cuda_impl::Dispatch(proxy_, [this](auto const& value) {
proxy_->Info().feature_types.SetDevice(dh::GetDevice(this->ctx_));
auto d_feature_types = proxy_->Info().feature_types.ConstDeviceSpan();
auto n_samples = value.NumRows();
dh::device_vector<size_t> row_counts(n_samples + 1, 0);
common::Span<size_t> row_counts_span(row_counts.data().get(), row_counts.size());
cuda_impl::Dispatch(proxy_, [=](auto const& value) {
return GetRowCounts(value, row_counts_span, dh::GetDevice(this->ctx_), this->missing_);
});
this->page_.reset(new EllpackPage{});
*this->page_->Impl() = EllpackPageImpl{this->ctx_,
value,
this->missing_,
this->info_->IsDense(),
row_counts_span,
d_feature_types,
this->ext_info_.row_stride,
n_samples,
this->GetCuts()};
this->info_->Extend(proxy_->Info(), false, true);
});
this->page_->SetBaseRowId(this->ext_info_.base_rows.at(iter));
this->WriteCache();
}
}
// Instantiation
template void
ExtEllpackPageSourceImpl<DefaultFormatStreamPolicy<EllpackPage, EllpackFormatPolicy>>::Fetch();
template void
ExtEllpackPageSourceImpl<EllpackCacheStreamPolicy<EllpackPage, EllpackFormatPolicy>>::Fetch();
template void
ExtEllpackPageSourceImpl<EllpackMmapStreamPolicy<EllpackPage, EllpackFormatPolicy>>::Fetch();
} // namespace xgboost::data

51
src/data/ellpack_page_source.h
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@ -8,6 +8,7 @@
#include <cstdint> // for int32_t
#include <memory> // for shared_ptr
#include <utility> // for move
#include <vector> // for vector
#include "../common/cuda_rt_utils.h" // for SupportsPageableMem
#include "../common/hist_util.h" // for HistogramCuts
@ -169,6 +170,51 @@ using EllpackPageHostSource =
using EllpackPageSource =
EllpackPageSourceImpl<EllpackMmapStreamPolicy<EllpackPage, EllpackFormatPolicy>>;
template <typename FormatCreatePolicy>
class ExtEllpackPageSourceImpl : public ExtQantileSourceMixin<EllpackPage, FormatCreatePolicy> {
using Super = ExtQantileSourceMixin<EllpackPage, FormatCreatePolicy>;
Context const* ctx_;
BatchParam p_;
DMatrixProxy* proxy_;
MetaInfo* info_;
ExternalDataInfo ext_info_;
std::vector<bst_idx_t> base_rows_;
public:
ExtEllpackPageSourceImpl(
Context const* ctx, float missing, MetaInfo* info, ExternalDataInfo ext_info,
std::shared_ptr<Cache> cache, BatchParam param, std::shared_ptr<common::HistogramCuts> cuts,
std::shared_ptr<DataIterProxy<DataIterResetCallback, XGDMatrixCallbackNext>> source,
DMatrixProxy* proxy, std::vector<bst_idx_t> base_rows)
: Super{missing,
ctx->Threads(),
static_cast<bst_feature_t>(info->num_col_),
ext_info.n_batches,
source,
cache},
ctx_{ctx},
p_{std::move(param)},
proxy_{proxy},
info_{info},
ext_info_{std::move(ext_info)},
base_rows_{std::move(base_rows)} {
this->SetCuts(std::move(cuts), ctx->Device());
this->Fetch();
}
void Fetch() final;
};
// Cache to host
using ExtEllpackPageHostSource =
ExtEllpackPageSourceImpl<EllpackCacheStreamPolicy<EllpackPage, EllpackFormatPolicy>>;
// Cache to disk
using ExtEllpackPageSource =
ExtEllpackPageSourceImpl<EllpackMmapStreamPolicy<EllpackPage, EllpackFormatPolicy>>;
#if !defined(XGBOOST_USE_CUDA)
template <typename F>
inline void EllpackPageSourceImpl<F>::Fetch() {
@ -177,6 +223,11 @@ inline void EllpackPageSourceImpl<F>::Fetch() {
(void)(is_dense_);
common::AssertGPUSupport();
}
template <typename F>
inline void ExtEllpackPageSourceImpl<F>::Fetch() {
common::AssertGPUSupport();
}
#endif // !defined(XGBOOST_USE_CUDA)
} // namespace xgboost::data

22
src/data/extmem_quantile_dmatrix.cc
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@ -24,8 +24,8 @@ ExtMemQuantileDMatrix::ExtMemQuantileDMatrix(DataIterHandle iter_handle, DMatrix
DataIterResetCallback *reset,
XGDMatrixCallbackNext *next, float missing,
std::int32_t n_threads, std::string cache,
bst_bin_t max_bin)
: cache_prefix_{std::move(cache)} {
bst_bin_t max_bin, bool on_host)
: cache_prefix_{std::move(cache)}, on_host_{on_host} {
auto iter = std::make_shared<DataIterProxy<DataIterResetCallback, XGDMatrixCallbackNext>>(
iter_handle, reset, next);
iter->Reset();
@ -72,13 +72,7 @@ void ExtMemQuantileDMatrix::InitFromCPU(
common::HistogramCuts cuts;
ExternalDataInfo ext_info;
cpu_impl::GetDataShape(ctx, proxy, *iter, missing, &ext_info);
// From here on Info() has the correct data shape
this->Info().num_row_ = ext_info.accumulated_rows;
this->Info().num_col_ = ext_info.n_features;
this->Info().num_nonzero_ = ext_info.nnz;
this->Info().SynchronizeNumberOfColumns(ctx);
ext_info.Validate();
ext_info.SetInfo(ctx, &this->info_);
/**
* Generate quantiles
@ -110,7 +104,7 @@ void ExtMemQuantileDMatrix::InitFromCPU(
CHECK_EQ(n_total_samples, ext_info.accumulated_rows);
}
BatchSet<GHistIndexMatrix> ExtMemQuantileDMatrix::GetGradientIndexImpl() {
[[nodiscard]] BatchSet<GHistIndexMatrix> ExtMemQuantileDMatrix::GetGradientIndexImpl() {
return BatchSet{BatchIterator<GHistIndexMatrix>{this->ghist_index_source_}};
}
@ -148,5 +142,13 @@ BatchSet<EllpackPage> ExtMemQuantileDMatrix::GetEllpackBatches(Context const *,
this->ellpack_page_source_);
return batch_set;
}
BatchSet<EllpackPage> ExtMemQuantileDMatrix::GetEllpackPageImpl() {
common::AssertGPUSupport();
auto batch_set =
std::visit([this](auto &&ptr) { return BatchSet{BatchIterator<EllpackPage>{ptr}}; },
this->ellpack_page_source_);
return batch_set;
}
#endif
} // namespace xgboost::data

72
src/data/extmem_quantile_dmatrix.cu
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@ -4,21 +4,81 @@
#include <memory> // for shared_ptr
#include <variant> // for visit
#include "batch_utils.h" // for CheckParam, RegenGHist
#include "ellpack_page.cuh" // for EllpackPage
#include "extmem_quantile_dmatrix.h"
#include "proxy_dmatrix.h" // for DataIterProxy
#include "xgboost/context.h" // for Context
#include "xgboost/data.h" // for BatchParam
namespace xgboost::data {
void ExtMemQuantileDMatrix::InitFromCUDA(
Context const *, std::shared_ptr<DataIterProxy<DataIterResetCallback, XGDMatrixCallbackNext>>,
DMatrixHandle, BatchParam const &, float, std::shared_ptr<DMatrix>) {
LOG(FATAL) << "Not implemented.";
Context const *ctx,
std::shared_ptr<DataIterProxy<DataIterResetCallback, XGDMatrixCallbackNext>> iter,
DMatrixHandle proxy_handle, BatchParam const &p, float missing, std::shared_ptr<DMatrix> ref) {
// A handle passed to external iterator.
auto proxy = MakeProxy(proxy_handle);
CHECK(proxy);
/**
* Generate quantiles
*/
auto cuts = std::make_shared<common::HistogramCuts>();
ExternalDataInfo ext_info;
cuda_impl::MakeSketches(ctx, iter.get(), proxy, ref, p, missing, cuts, this->Info(), &ext_info);
ext_info.SetInfo(ctx, &this->info_);
/**
* Generate gradient index
*/
auto id = MakeCache(this, ".ellpack.page", false, cache_prefix_, &cache_info_);
if (on_host_ && std::get_if<EllpackHostPtr>(&ellpack_page_source_) == nullptr) {
ellpack_page_source_.emplace<EllpackHostPtr>(nullptr);
}
std::visit(
[&](auto &&ptr) {
using SourceT = typename std::remove_reference_t<decltype(ptr)>::element_type;
ptr = std::make_shared<SourceT>(ctx, missing, &this->Info(), ext_info, cache_info_.at(id),
p, cuts, iter, proxy, ext_info.base_rows);
},
ellpack_page_source_);
/**
* Force initialize the cache and do some sanity checks along the way
*/
bst_idx_t batch_cnt = 0, k = 0;
bst_idx_t n_total_samples = 0;
for (auto const &page : this->GetEllpackPageImpl()) {
n_total_samples += page.Size();
CHECK_EQ(page.Impl()->base_rowid, ext_info.base_rows[k]);
CHECK_EQ(page.Impl()->row_stride, ext_info.row_stride);
++k, ++batch_cnt;
}
CHECK_EQ(batch_cnt, ext_info.n_batches);
CHECK_EQ(n_total_samples, ext_info.accumulated_rows);
}
BatchSet<EllpackPage> ExtMemQuantileDMatrix::GetEllpackBatches(Context const *,
const BatchParam &) {
LOG(FATAL) << "Not implemented.";
[[nodiscard]] BatchSet<EllpackPage> ExtMemQuantileDMatrix::GetEllpackPageImpl() {
auto batch_set =
std::visit([this](auto &&ptr) { return BatchSet{BatchIterator<EllpackPage>{ptr}}; },
this->ellpack_page_source_);
return batch_set;
}
BatchSet<EllpackPage> ExtMemQuantileDMatrix::GetEllpackBatches(Context const *,
const BatchParam &param) {
if (param.Initialized()) {
detail::CheckParam(this->batch_, param);
CHECK(!detail::RegenGHist(param, batch_)) << error::InconsistentMaxBin();
}
std::visit(
[this](auto &&ptr) {
CHECK(ptr);
ptr->Reset();
},
this->ellpack_page_source_);
return this->GetEllpackPageImpl();
}
} // namespace xgboost::data

10
src/data/extmem_quantile_dmatrix.h
View File

@ -30,7 +30,7 @@ class ExtMemQuantileDMatrix : public QuantileDMatrix {
ExtMemQuantileDMatrix(DataIterHandle iter_handle, DMatrixHandle proxy,
std::shared_ptr<DMatrix> ref, DataIterResetCallback *reset,
XGDMatrixCallbackNext *next, float missing, std::int32_t n_threads,
std::string cache, bst_bin_t max_bin);
std::string cache, bst_bin_t max_bin, bool on_host);
~ExtMemQuantileDMatrix() override;
[[nodiscard]] bool SingleColBlock() const override { return false; }
@ -45,9 +45,10 @@ class ExtMemQuantileDMatrix : public QuantileDMatrix {
std::shared_ptr<DataIterProxy<DataIterResetCallback, XGDMatrixCallbackNext>> iter,
DMatrixHandle proxy_handle, BatchParam const &p, float missing, std::shared_ptr<DMatrix> ref);
BatchSet<GHistIndexMatrix> GetGradientIndexImpl();
[[nodiscard]] BatchSet<GHistIndexMatrix> GetGradientIndexImpl();
BatchSet<GHistIndexMatrix> GetGradientIndex(Context const *ctx, BatchParam const &param) override;
[[nodiscard]] BatchSet<EllpackPage> GetEllpackPageImpl();
BatchSet<EllpackPage> GetEllpackBatches(Context const *ctx, const BatchParam &param) override;
[[nodiscard]] bool EllpackExists() const override {
@ -60,10 +61,11 @@ class ExtMemQuantileDMatrix : public QuantileDMatrix {
std::map<std::string, std::shared_ptr<Cache>> cache_info_;
std::string cache_prefix_;
bool on_host_;
BatchParam batch_;
using EllpackDiskPtr = std::shared_ptr<EllpackPageSource>;
using EllpackHostPtr = std::shared_ptr<EllpackPageHostSource>;
using EllpackDiskPtr = std::shared_ptr<ExtEllpackPageSource>;
using EllpackHostPtr = std::shared_ptr<ExtEllpackPageHostSource>;
std::variant<EllpackDiskPtr, EllpackHostPtr> ellpack_page_source_;
std::shared_ptr<ExtGradientIndexPageSource> ghist_index_source_;
};

1
src/data/gradient_index.h
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@ -242,6 +242,7 @@ class GHistIndexMatrix {
[[nodiscard]] bool IsDense() const { return isDense_; }
void SetDense(bool is_dense) { isDense_ = is_dense; }
[[nodiscard]] bst_idx_t BaseRowId() const { return base_rowid; }
/**
* @brief Get the local row index.
*/

42
src/data/gradient_index_page_source.h
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@ -39,45 +39,6 @@ class GHistIndexFormatPolicy {
void SetCuts(common::HistogramCuts cuts) { std::swap(cuts_, cuts); }
};
template <typename S,
typename FormatCreatePolicy = DefaultFormatStreamPolicy<S, DefaultFormatPolicy>>
class ExtQantileSourceMixin : public SparsePageSourceImpl<S, FormatCreatePolicy> {
protected:
std::shared_ptr<DataIterProxy<DataIterResetCallback, XGDMatrixCallbackNext>> source_;
using Super = SparsePageSourceImpl<S, FormatCreatePolicy>;
public:
ExtQantileSourceMixin(float missing, std::int32_t nthreads, bst_feature_t n_features,
bst_idx_t n_batches, std::shared_ptr<Cache> cache)
: Super::SparsePageSourceImpl{missing, nthreads, n_features, n_batches, cache} {}
// This function always operate on the source first, then the downstream. The downstream
// can assume the source to be ready.
[[nodiscard]] ExtQantileSourceMixin& operator++() final {
TryLockGuard guard{this->single_threaded_};
// Increment self.
++this->count_;
// Set at end.
this->at_end_ = this->count_ == this->n_batches_;
if (this->at_end_) {
this->EndIter();
CHECK(this->cache_info_->written);
source_ = nullptr; // release the source
}
this->Fetch();
return *this;
}
void Reset() final {
if (this->source_) {
this->source_->Reset();
}
Super::Reset();
}
};
class GradientIndexPageSource
: public PageSourceIncMixIn<
GHistIndexMatrix, DefaultFormatStreamPolicy<GHistIndexMatrix, GHistIndexFormatPolicy>> {
@ -125,14 +86,13 @@ class ExtGradientIndexPageSource
std::shared_ptr<DataIterProxy<DataIterResetCallback, XGDMatrixCallbackNext>> source,
DMatrixProxy* proxy, std::vector<bst_idx_t> base_rows)
: ExtQantileSourceMixin{missing, ctx->Threads(), static_cast<bst_feature_t>(info->num_col_),
n_batches, cache},
n_batches, source, cache},
p_{std::move(param)},
ctx_{ctx},
proxy_{proxy},
info_{info},
feature_types_{info_->feature_types.ConstHostSpan()},
base_rows_{std::move(base_rows)} {
this->source_ = source;
this->SetCuts(std::move(cuts));
this->Fetch();
}

8
src/data/iterative_dmatrix.cc
View File

@ -63,13 +63,7 @@ void IterativeDMatrix::InitFromCPU(Context const* ctx, BatchParam const& p,
common::HistogramCuts cuts;
ExternalDataInfo ext_info;
cpu_impl::GetDataShape(ctx, proxy, iter, missing, &ext_info);
// From here on Info() has the correct data shape
this->Info().num_row_ = ext_info.accumulated_rows;
this->Info().num_col_ = ext_info.n_features;
this->Info().num_nonzero_ = ext_info.nnz;
this->Info().SynchronizeNumberOfColumns(ctx);
ext_info.Validate();
ext_info.SetInfo(ctx, &this->info_);
/**
* Generate quantiles

117
src/data/iterative_dmatrix.cu
View File

@ -1,20 +1,15 @@
/**
* Copyright 2020-2024, XGBoost contributors
*/
#include <algorithm> // for max
#include <memory> // for shared_ptr
#include <utility> // for move
#include <vector> // for vector
#include "../collective/allreduce.h"
#include "../common/cuda_rt_utils.h" // for AllVisibleGPUs
#include "../common/hist_util.cuh"
#include "batch_utils.h" // for RegenGHist, CheckParam
#include "device_adapter.cuh"
#include "ellpack_page.cuh"
#include "iterative_dmatrix.h"
#include "proxy_dmatrix.cuh"
#include "proxy_dmatrix.h"
#include "proxy_dmatrix.h" // for BatchSamples, BatchColumns
#include "simple_batch_iterator.h"
namespace xgboost::data {
@ -31,103 +26,32 @@ void IterativeDMatrix::InitFromCUDA(Context const* ctx, BatchParam const& p,
dh::XGBCachingDeviceAllocator<char> alloc;
auto num_rows = [&]() {
return cuda_impl::Dispatch(proxy, [](auto const& value) { return value.NumRows(); });
};
auto num_cols = [&]() {
return cuda_impl::Dispatch(proxy, [](auto const& value) { return value.NumCols(); });
};
size_t row_stride = 0;
size_t nnz = 0;
// Sketch for all batches.
std::vector<common::SketchContainer> sketch_containers;
size_t batches = 0;
size_t accumulated_rows = 0;
bst_feature_t cols = 0;
int32_t current_device;
dh::safe_cuda(cudaGetDevice(&current_device));
std::int32_t current_device{dh::CurrentDevice()};
auto get_ctx = [&]() {
Context d_ctx = (ctx->IsCUDA()) ? *ctx : Context{}.MakeCUDA(current_device);
CHECK(!d_ctx.IsCPU());
return d_ctx;
};
auto get_device = [&]() {
auto d = (ctx->IsCUDA()) ? ctx->Device() : DeviceOrd::CUDA(current_device);
CHECK(!d.IsCPU());
return d;
};
fmat_ctx_ = get_ctx();
/**
* Generate quantiles
*/
auto cuts = std::make_shared<common::HistogramCuts>();
do {
// We use do while here as the first batch is fetched in ctor
CHECK_LT(ctx->Ordinal(), common::AllVisibleGPUs());
dh::safe_cuda(cudaSetDevice(get_device().ordinal));
if (cols == 0) {
cols = num_cols();
auto rc = collective::Allreduce(ctx, linalg::MakeVec(&cols, 1), collective::Op::kMax);
SafeColl(rc);
this->info_.num_col_ = cols;
} else {
CHECK_EQ(cols, num_cols()) << "Inconsistent number of columns.";
}
if (!ref) {
sketch_containers.emplace_back(proxy->Info().feature_types, p.max_bin, cols, num_rows(),
get_device());
auto* p_sketch = &sketch_containers.back();
proxy->Info().weights_.SetDevice(get_device());
cuda_impl::Dispatch(proxy, [&](auto const& value) {
common::AdapterDeviceSketch(value, p.max_bin, proxy->Info(), missing, p_sketch);
});
}
auto batch_rows = num_rows();
accumulated_rows += batch_rows;
dh::device_vector<size_t> row_counts(batch_rows + 1, 0);
common::Span<size_t> row_counts_span(row_counts.data().get(), row_counts.size());
row_stride = std::max(row_stride, cuda_impl::Dispatch(proxy, [=](auto const& value) {
return GetRowCounts(value, row_counts_span, get_device(), missing);
}));
nnz += thrust::reduce(thrust::cuda::par(alloc), row_counts.begin(), row_counts.end());
batches++;
} while (iter.Next());
iter.Reset();
ExternalDataInfo ext_info;
cuda_impl::MakeSketches(ctx, &iter, proxy, ref, p, missing, cuts, this->Info(), &ext_info);
ext_info.SetInfo(ctx, &this->info_);
auto n_features = cols;
CHECK_GE(n_features, 1) << "Data must has at least 1 column.";
dh::safe_cuda(cudaSetDevice(get_device().ordinal));
if (!ref) {
HostDeviceVector<FeatureType> ft;
common::SketchContainer final_sketch(
sketch_containers.empty() ? ft : sketch_containers.front().FeatureTypes(), p.max_bin, cols,
accumulated_rows, get_device());
for (auto const& sketch : sketch_containers) {
final_sketch.Merge(sketch.ColumnsPtr(), sketch.Data());
final_sketch.FixError();
}
sketch_containers.clear();
sketch_containers.shrink_to_fit();
final_sketch.MakeCuts(ctx, cuts.get(), this->info_.IsColumnSplit());
} else {
GetCutsFromRef(ctx, ref, Info().num_col_, p, cuts.get());
}
this->info_.num_row_ = accumulated_rows;
this->info_.num_nonzero_ = nnz;
auto init_page = [this, &cuts, row_stride, accumulated_rows, get_device]() {
auto init_page = [this, &cuts, &ext_info]() {
if (!ellpack_) {
// Should be put inside the while loop to protect against empty batch. In
// that case device id is invalid.
ellpack_.reset(new EllpackPage);
*(ellpack_->Impl()) =
EllpackPageImpl(&fmat_ctx_, cuts, this->IsDense(), row_stride, accumulated_rows);
*(ellpack_->Impl()) = EllpackPageImpl(&fmat_ctx_, cuts, this->IsDense(), ext_info.row_stride,
ext_info.accumulated_rows);
}
};
@ -139,43 +63,42 @@ void IterativeDMatrix::InitFromCUDA(Context const* ctx, BatchParam const& p,
size_t n_batches_for_verification = 0;
while (iter.Next()) {
init_page();
dh::safe_cuda(cudaSetDevice(get_device().ordinal));
auto rows = num_rows();
dh::safe_cuda(cudaSetDevice(dh::GetDevice(ctx).ordinal));
auto rows = BatchSamples(proxy);
dh::device_vector<size_t> row_counts(rows + 1, 0);
common::Span<size_t> row_counts_span(row_counts.data().get(), row_counts.size());
cuda_impl::Dispatch(proxy, [=](auto const& value) {
return GetRowCounts(value, row_counts_span, get_device(), missing);
return GetRowCounts(value, row_counts_span, dh::GetDevice(ctx), missing);
});
auto is_dense = this->IsDense();
proxy->Info().feature_types.SetDevice(get_device());
proxy->Info().feature_types.SetDevice(dh::GetDevice(ctx));
auto d_feature_types = proxy->Info().feature_types.ConstDeviceSpan();
auto new_impl = cuda_impl::Dispatch(proxy, [&](auto const& value) {
return EllpackPageImpl(&fmat_ctx_, value, missing, is_dense, row_counts_span, d_feature_types,
row_stride, rows, cuts);
ext_info.row_stride, rows, cuts);
});
std::size_t num_elements = ellpack_->Impl()->Copy(&fmat_ctx_, &new_impl, offset);
offset += num_elements;
proxy->Info().num_row_ = num_rows();
proxy->Info().num_col_ = cols;
if (batches != 1) {
proxy->Info().num_row_ = BatchSamples(proxy);
proxy->Info().num_col_ = ext_info.n_features;
if (ext_info.n_batches != 1) {
this->info_.Extend(std::move(proxy->Info()), false, true);
}
n_batches_for_verification++;
}
CHECK_EQ(batches, n_batches_for_verification)
CHECK_EQ(ext_info.n_batches, n_batches_for_verification)
<< "Different number of batches returned between 2 iterations";
if (batches == 1) {
if (ext_info.n_batches == 1) {
this->info_ = std::move(proxy->Info());
this->info_.num_nonzero_ = nnz;
this->info_.num_nonzero_ = ext_info.nnz;
CHECK_EQ(proxy->Info().labels.Size(), 0);
}
iter.Reset();
// Synchronise worker columns
info_.SynchronizeNumberOfColumns(ctx);
}
BatchSet<EllpackPage> IterativeDMatrix::GetEllpackBatches(Context const* ctx,

13
src/data/proxy_dmatrix.h
View File

@ -142,13 +142,14 @@ inline DMatrixProxy* MakeProxy(DMatrixHandle proxy) {
* @brief Shape and basic information for data fetched from an external data iterator.
*/
struct ExternalDataInfo {
std::uint64_t n_features = 0; // The number of columns
bst_idx_t n_features = 0; // The number of columns
bst_idx_t n_batches = 0; // The number of batches
bst_idx_t accumulated_rows = 0; // The total number of rows
bst_idx_t nnz = 0; // The number of non-missing values
std::vector<bst_idx_t> column_sizes; // The nnz for each column
std::vector<bst_idx_t> batch_nnz; // nnz for each batch
std::vector<bst_idx_t> base_rows{0}; // base_rowid
bst_idx_t row_stride{0}; // Used by ellpack
void Validate() const {
CHECK(std::none_of(this->column_sizes.cbegin(), this->column_sizes.cend(), [&](auto f) {
@ -157,6 +158,16 @@ struct ExternalDataInfo {
CHECK_GE(this->n_features, 1) << "Data must has at least 1 column.";
}
void SetInfo(Context const* ctx, MetaInfo* p_info) {
// From here on Info() has the correct data shape
auto& info = *p_info;
info.num_row_ = this->accumulated_rows;
info.num_col_ = this->n_features;
info.num_nonzero_ = this->nnz;
info.SynchronizeNumberOfColumns(ctx);
this->Validate();
}
};
/**

87
src/data/quantile_dmatrix.cu
View File

@ -1,10 +1,93 @@
/**
* Copyright 2024, XGBoost Contributors
* Copyright 2020-2024, XGBoost Contributors
*/
#include "ellpack_page.cuh"
#include <algorithm> // for max
#include <numeric> // for partial_sum
#include <vector> // for vector
#include "../collective/allreduce.h" // for Allreduce
#include "../common/cuda_rt_utils.h" // for AllVisibleGPUs
#include "../common/device_vector.cuh" // for XGBCachingDeviceAllocator
#include "../common/hist_util.cuh" // for AdapterDeviceSketch
#include "../common/quantile.cuh" // for SketchContainer
#include "ellpack_page.cuh" // for EllpackPage
#include "proxy_dmatrix.cuh" // for Dispatch
#include "proxy_dmatrix.h" // for DataIterProxy
#include "quantile_dmatrix.h" // for GetCutsFromRef
namespace xgboost::data {
void GetCutsFromEllpack(EllpackPage const& page, common::HistogramCuts* cuts) {
*cuts = page.Impl()->Cuts();
}
namespace cuda_impl {
void MakeSketches(Context const* ctx,
DataIterProxy<DataIterResetCallback, XGDMatrixCallbackNext>* iter,
DMatrixProxy* proxy, std::shared_ptr<DMatrix> ref, BatchParam const& p,
float missing, std::shared_ptr<common::HistogramCuts> cuts, MetaInfo const& info,
ExternalDataInfo* p_ext_info) {
dh::XGBCachingDeviceAllocator<char> alloc;
std::vector<common::SketchContainer> sketch_containers;
auto& ext_info = *p_ext_info;
do {
// We use do while here as the first batch is fetched in ctor
CHECK_LT(ctx->Ordinal(), common::AllVisibleGPUs());
dh::safe_cuda(cudaSetDevice(dh::GetDevice(ctx).ordinal));
if (ext_info.n_features == 0) {
ext_info.n_features = data::BatchColumns(proxy);
auto rc = collective::Allreduce(ctx, linalg::MakeVec(&ext_info.n_features, 1),
collective::Op::kMax);
SafeColl(rc);
} else {
CHECK_EQ(ext_info.n_features, ::xgboost::data::BatchColumns(proxy))
<< "Inconsistent number of columns.";
}
if (!ref) {
sketch_containers.emplace_back(proxy->Info().feature_types, p.max_bin, ext_info.n_features,
data::BatchSamples(proxy), dh::GetDevice(ctx));
auto* p_sketch = &sketch_containers.back();
proxy->Info().weights_.SetDevice(dh::GetDevice(ctx));
cuda_impl::Dispatch(proxy, [&](auto const& value) {
common::AdapterDeviceSketch(value, p.max_bin, proxy->Info(), missing, p_sketch);
});
}
auto batch_rows = data::BatchSamples(proxy);
ext_info.accumulated_rows += batch_rows;
dh::device_vector<size_t> row_counts(batch_rows + 1, 0);
common::Span<size_t> row_counts_span(row_counts.data().get(), row_counts.size());
ext_info.row_stride =
std::max(ext_info.row_stride, cuda_impl::Dispatch(proxy, [=](auto const& value) {
return GetRowCounts(value, row_counts_span, dh::GetDevice(ctx), missing);
}));
ext_info.nnz += thrust::reduce(thrust::cuda::par(alloc), row_counts.begin(), row_counts.end());
ext_info.n_batches++;
ext_info.base_rows.push_back(batch_rows);
} while (iter->Next());
iter->Reset();
CHECK_GE(ext_info.n_features, 1) << "Data must has at least 1 column.";
std::partial_sum(ext_info.base_rows.cbegin(), ext_info.base_rows.cend(),
ext_info.base_rows.begin());
// Get reference
dh::safe_cuda(cudaSetDevice(dh::GetDevice(ctx).ordinal));
if (!ref) {
HostDeviceVector<FeatureType> ft;
common::SketchContainer final_sketch(
sketch_containers.empty() ? ft : sketch_containers.front().FeatureTypes(), p.max_bin,
ext_info.n_features, ext_info.accumulated_rows, dh::GetDevice(ctx));
for (auto const& sketch : sketch_containers) {
final_sketch.Merge(sketch.ColumnsPtr(), sketch.Data());
final_sketch.FixError();
}
sketch_containers.clear();
sketch_containers.shrink_to_fit();
final_sketch.MakeCuts(ctx, cuts.get(), info.IsColumnSplit());
} else {
GetCutsFromRef(ctx, ref, ext_info.n_features, p, cuts.get());
}
}
} // namespace cuda_impl
} // namespace xgboost::data

8
src/data/quantile_dmatrix.h
View File

@ -104,4 +104,12 @@ void MakeSketches(Context const *ctx,
common::HistogramCuts *cuts, BatchParam const &p, MetaInfo const &info,
ExternalDataInfo const &ext_info, std::vector<FeatureType> *p_h_ft);
} // namespace cpu_impl
namespace cuda_impl {
void MakeSketches(Context const *ctx,
DataIterProxy<DataIterResetCallback, XGDMatrixCallbackNext> *iter,
DMatrixProxy *proxy, std::shared_ptr<DMatrix> ref, BatchParam const &p,
float missing, std::shared_ptr<common::HistogramCuts> cuts, MetaInfo const &info,
ExternalDataInfo *p_ext_info);
} // namespace cuda_impl
} // namespace xgboost::data

23
src/data/sparse_page_dmatrix.cc
View File

@ -38,30 +38,23 @@ SparsePageDMatrix::SparsePageDMatrix(DataIterHandle iter_handle, DMatrixHandle p
auto iter = DataIterProxy<DataIterResetCallback, XGDMatrixCallbackNext>{
iter_, reset_, next_};
std::uint32_t n_batches = 0;
bst_feature_t n_features = 0;
bst_idx_t n_samples = 0;
bst_idx_t nnz = 0;
ExternalDataInfo ext_info;
// The proxy is iterated together with the sparse page source so we can obtain all
// information in 1 pass.
for (auto const &page : this->GetRowBatchesImpl(&ctx)) {
this->info_.Extend(std::move(proxy->Info()), false, false);
n_features = std::max(n_features, BatchColumns(proxy));
n_samples += BatchSamples(proxy);
nnz += page.data.Size();
n_batches++;
ext_info.n_features =
std::max(static_cast<bst_feature_t>(ext_info.n_features), BatchColumns(proxy));
ext_info.accumulated_rows += BatchSamples(proxy);
ext_info.nnz += page.data.Size();
ext_info.n_batches++;
}
iter.Reset();
this->n_batches_ = n_batches;
this->info_.num_row_ = n_samples;
this->info_.num_col_ = n_features;
this->info_.num_nonzero_ = nnz;
info_.SynchronizeNumberOfColumns(&ctx);
CHECK_NE(info_.num_col_, 0);
this->n_batches_ = ext_info.n_batches;
ext_info.SetInfo(&ctx, &this->info_);
fmat_ctx_ = ctx;
}

45
src/data/sparse_page_source.h
View File

@ -585,5 +585,50 @@ class SortedCSCPageSource : public PageSourceIncMixIn<SortedCSCPage> {
this->Fetch();
}
};
/**
* @brief operator++ implementation for QDM.
*/
template <typename S,
typename FormatCreatePolicy = DefaultFormatStreamPolicy<S, DefaultFormatPolicy>>
class ExtQantileSourceMixin : public SparsePageSourceImpl<S, FormatCreatePolicy> {
protected:
std::shared_ptr<DataIterProxy<DataIterResetCallback, XGDMatrixCallbackNext>> source_;
using Super = SparsePageSourceImpl<S, FormatCreatePolicy>;
public:
ExtQantileSourceMixin(
float missing, std::int32_t nthreads, bst_feature_t n_features, bst_idx_t n_batches,
std::shared_ptr<DataIterProxy<DataIterResetCallback, XGDMatrixCallbackNext>> source,
std::shared_ptr<Cache> cache)
: Super::SparsePageSourceImpl{missing, nthreads, n_features, n_batches, cache},
source_{std::move(source)} {}
// This function always operate on the source first, then the downstream. The downstream
// can assume the source to be ready.
[[nodiscard]] ExtQantileSourceMixin& operator++() final {
TryLockGuard guard{this->single_threaded_};
// Increment self.
++this->count_;
// Set at end.
this->at_end_ = this->count_ == this->n_batches_;
if (this->at_end_) {
this->EndIter();
CHECK(this->cache_info_->written);
source_ = nullptr; // release the source
}
this->Fetch();
return *this;
}
void Reset() final {
if (this->source_) {
this->source_->Reset();
}
Super::Reset();
}
};
} // namespace xgboost::data
#endif // XGBOOST_DATA_SPARSE_PAGE_SOURCE_H_

80
tests/cpp/data/test_extmem_quantile_dmatrix.cc
View File

@ -1,6 +1,8 @@
/**
* Copyright 2024, XGBoost Contributors
*/
#include "test_extmem_quantile_dmatrix.h" // for TestExtMemQdmBasic
#include <gtest/gtest.h>
#include <xgboost/data.h> // for BatchParam
@ -9,76 +11,30 @@
#include "../../../src/common/column_matrix.h" // for ColumnMatrix
#include "../../../src/data/gradient_index.h" // for GHistIndexMatrix
#include "../../../src/tree/param.h" // for TrainParam
#include "../helpers.h" // for RandomDataGenerator
namespace xgboost::data {
namespace {
class ExtMemQuantileDMatrixCpu : public ::testing::TestWithParam<float> {
public:
void Run(float sparsity) {
bst_idx_t n_samples = 256, n_features = 16, n_batches = 4;
bst_bin_t max_bin = 64;
bst_target_t n_targets = 3;
auto p_fmat = RandomDataGenerator{n_samples, n_features, sparsity}
.Bins(max_bin)
.Batches(n_batches)
.Targets(n_targets)
.GenerateExtMemQuantileDMatrix("temp", true);
ASSERT_FALSE(p_fmat->SingleColBlock());
BatchParam p{max_bin, tree::TrainParam::DftSparseThreshold()};
Context ctx;
// Loop over the batches and count the number of pages
bst_idx_t batch_cnt = 0;
bst_idx_t base_cnt = 0;
bst_idx_t row_cnt = 0;
for (auto const& page : p_fmat->GetBatches<GHistIndexMatrix>(&ctx, p)) {
ASSERT_EQ(page.base_rowid, base_cnt);
++batch_cnt;
base_cnt += n_samples / n_batches;
row_cnt += page.Size();
ASSERT_EQ((sparsity == 0.0f), page.IsDense());
}
ASSERT_EQ(n_batches, batch_cnt);
ASSERT_EQ(p_fmat->Info().num_row_, n_samples);
EXPECT_EQ(p_fmat->Info().num_row_, row_cnt);
ASSERT_EQ(p_fmat->Info().num_col_, n_features);
if (sparsity == 0.0f) {
ASSERT_EQ(p_fmat->Info().num_nonzero_, n_samples * n_features);
} else {
ASSERT_LT(p_fmat->Info().num_nonzero_, n_samples * n_features);
ASSERT_GT(p_fmat->Info().num_nonzero_, 0);
}
ASSERT_EQ(p_fmat->Info().labels.Shape(0), n_samples);
ASSERT_EQ(p_fmat->Info().labels.Shape(1), n_targets);
// Compare against the sparse page DMatrix
auto p_sparse = RandomDataGenerator{n_samples, n_features, sparsity}
.Bins(max_bin)
.Batches(n_batches)
.Targets(n_targets)
.GenerateSparsePageDMatrix("temp", true);
auto it = p_fmat->GetBatches<GHistIndexMatrix>(&ctx, p).begin();
for (auto const& page : p_sparse->GetBatches<GHistIndexMatrix>(&ctx, p)) {
auto orig = it.Page();
auto equal = [](Context const*, GHistIndexMatrix const& orig, GHistIndexMatrix const& sparse) {
// Check the CSR matrix
auto orig_cuts = it.Page()->Cuts();
auto sparse_cuts = page.Cuts();
auto orig_cuts = orig.Cuts();
auto sparse_cuts = sparse.Cuts();
ASSERT_EQ(orig_cuts.Values(), sparse_cuts.Values());
ASSERT_EQ(orig_cuts.MinValues(), sparse_cuts.MinValues());
ASSERT_EQ(orig_cuts.Ptrs(), sparse_cuts.Ptrs());
auto orig_ptr = orig->data.data();
auto sparse_ptr = page.data.data();
ASSERT_EQ(orig->data.size(), page.data.size());
auto orig_ptr = orig.data.data();
auto sparse_ptr = sparse.data.data();
ASSERT_EQ(orig.data.size(), sparse.data.size());
auto equal = std::equal(orig_ptr, orig_ptr + orig->data.size(), sparse_ptr);
auto equal = std::equal(orig_ptr, orig_ptr + orig.data.size(), sparse_ptr);
ASSERT_TRUE(equal);
// Check the column matrix
common::ColumnMatrix const& orig_columns = orig->Transpose();
common::ColumnMatrix const& sparse_columns = page.Transpose();
common::ColumnMatrix const& orig_columns = orig.Transpose();
common::ColumnMatrix const& sparse_columns = sparse.Transpose();
std::string str_orig, str_sparse;
common::AlignedMemWriteStream fo_orig{&str_orig}, fo_sparse{&str_sparse};
@ -86,18 +42,10 @@ class ExtMemQuantileDMatrixCpu : public ::testing::TestWithParam<float> {
auto n_bytes_sparse = sparse_columns.Write(&fo_sparse);
ASSERT_EQ(n_bytes_orig, n_bytes_sparse);
ASSERT_EQ(str_orig, str_sparse);
};
++it;
}
// Check meta info
auto h_y_sparse = p_sparse->Info().labels.HostView();
auto h_y = p_fmat->Info().labels.HostView();
for (std::size_t i = 0, m = h_y_sparse.Shape(0); i < m; ++i) {
for (std::size_t j = 0, n = h_y_sparse.Shape(1); j < n; ++j) {
ASSERT_EQ(h_y(i, j), h_y_sparse(i, j));
}
}
Context ctx;
TestExtMemQdmBasic<GHistIndexMatrix>(&ctx, false, sparsity, equal);
}
};
} // anonymous namespace

45
tests/cpp/data/test_extmem_quantile_dmatrix.cu Normal file
View File

@ -0,0 +1,45 @@
/**
* Copyright 2024, XGBoost Contributors
*/
#include <gtest/gtest.h>
#include <xgboost/data.h> // for BatchParam
#include <vector> // for vector
#include "../../../src/data/ellpack_page.cuh" // for EllpackPageImpl
#include "../helpers.h" // for RandomDataGenerator
#include "test_extmem_quantile_dmatrix.h" // for TestExtMemQdmBasic
namespace xgboost::data {
class ExtMemQuantileDMatrixGpu : public ::testing::TestWithParam<float> {
public:
void Run(float sparsity) {
auto equal = [](Context const* ctx, EllpackPage const& orig, EllpackPage const& sparse) {
auto const& orig_cuts = orig.Cuts();
auto const& sparse_cuts = sparse.Cuts();
ASSERT_EQ(orig_cuts.Values(), sparse_cuts.Values());
ASSERT_EQ(orig_cuts.MinValues(), sparse_cuts.MinValues());
ASSERT_EQ(orig_cuts.Ptrs(), sparse_cuts.Ptrs());
std::vector<common::CompressedByteT> h_orig, h_sparse;
auto orig_acc = orig.Impl()->GetHostAccessor(ctx, &h_orig, {});
auto sparse_acc = sparse.Impl()->GetHostAccessor(ctx, &h_sparse, {});
ASSERT_EQ(h_orig.size(), h_sparse.size());
auto equal = std::equal(h_orig.cbegin(), h_orig.cend(), h_sparse.cbegin());
ASSERT_TRUE(equal);
};
auto ctx = MakeCUDACtx(0);
TestExtMemQdmBasic<EllpackPage>(&ctx, true, sparsity, equal);
TestExtMemQdmBasic<EllpackPage>(&ctx, false, sparsity, equal);
}
};
TEST_P(ExtMemQuantileDMatrixGpu, Basic) { this->Run(this->GetParam()); }
INSTANTIATE_TEST_SUITE_P(ExtMemQuantileDMatrix, ExtMemQuantileDMatrixGpu, ::testing::ValuesIn([] {
std::vector<float> sparsities{0.0f, 0.2f, 0.4f, 0.8f};
return sparsities;
}()));
} // namespace xgboost::data

73
tests/cpp/data/test_extmem_quantile_dmatrix.h Normal file
View File

@ -0,0 +1,73 @@
/**
* Copyright 2024, XGBoost Contributors
*/
#include <xgboost/base.h>
#include <xgboost/context.h>
#include "../../../src/tree/param.h" // for TrainParam
#include "../helpers.h" // for RandomDataGenerator
namespace xgboost::data {
template <typename Page, typename Equal>
void TestExtMemQdmBasic(Context const* ctx, bool on_host, float sparsity, Equal&& check_equal) {
bst_idx_t n_samples = 256, n_features = 16, n_batches = 4;
bst_bin_t max_bin = 64;
bst_target_t n_targets = 3;
BatchParam p{max_bin, tree::TrainParam::DftSparseThreshold()};
auto p_fmat = RandomDataGenerator{n_samples, n_features, sparsity}
.Bins(max_bin)
.Batches(n_batches)
.Targets(n_targets)
.Device(ctx->Device())
.OnHost(on_host)
.GenerateExtMemQuantileDMatrix("temp", true);
ASSERT_FALSE(p_fmat->SingleColBlock());
// Loop over the batches and count the number of pages
bst_idx_t batch_cnt = 0, base_cnt = 0, row_cnt = 0;
for (auto const& page : p_fmat->GetBatches<Page>(ctx, p)) {
ASSERT_EQ(page.BaseRowId(), base_cnt);
++batch_cnt;
base_cnt += n_samples / n_batches;
row_cnt += page.Size();
ASSERT_EQ((sparsity == 0.0f), page.IsDense());
}
ASSERT_EQ(n_batches, batch_cnt);
ASSERT_EQ(p_fmat->Info().num_row_, n_samples);
EXPECT_EQ(p_fmat->Info().num_row_, row_cnt);
ASSERT_EQ(p_fmat->Info().num_col_, n_features);
if (sparsity == 0.0f) {
ASSERT_EQ(p_fmat->Info().num_nonzero_, n_samples * n_features);
} else {
ASSERT_LT(p_fmat->Info().num_nonzero_, n_samples * n_features);
ASSERT_GT(p_fmat->Info().num_nonzero_, 0);
}
ASSERT_EQ(p_fmat->Info().labels.Shape(0), n_samples);
ASSERT_EQ(p_fmat->Info().labels.Shape(1), n_targets);
// Compare against the sparse page DMatrix
auto p_sparse = RandomDataGenerator{n_samples, n_features, sparsity}
.Bins(max_bin)
.Batches(n_batches)
.Targets(n_targets)
.Device(ctx->Device())
.OnHost(on_host)
.GenerateSparsePageDMatrix("temp", true);
auto it = p_fmat->GetBatches<Page>(ctx, p).begin();
for (auto const& page : p_sparse->GetBatches<Page>(ctx, p)) {
auto orig = it.Page();
check_equal(ctx, *orig, page);
++it;
}
// Check meta info
auto h_y_sparse = p_sparse->Info().labels.HostView();
auto h_y = p_fmat->Info().labels.HostView();
for (std::size_t i = 0, m = h_y_sparse.Shape(0); i < m; ++i) {
for (std::size_t j = 0, n = h_y_sparse.Shape(1); j < n; ++j) {
ASSERT_EQ(h_y(i, j), h_y_sparse(i, j));
}
}
}
} // namespace xgboost::data

11
tests/cpp/helpers.cc
View File

@ -483,12 +483,15 @@ void RandomDataGenerator::GenerateCSR(
}
CHECK(iter);
std::shared_ptr<DMatrix> p_fmat{
DMatrix::Create(static_cast<DataIterHandle>(iter.get()), iter->Proxy(), nullptr, Reset, Next,
std::numeric_limits<float>::quiet_NaN(), 0, this->bins_, prefix)};
std::shared_ptr<DMatrix> p_fmat{DMatrix::Create(
static_cast<DataIterHandle>(iter.get()), iter->Proxy(), nullptr, Reset, Next,
std::numeric_limits<float>::quiet_NaN(), 0, this->bins_, prefix, this->on_host_)};
auto page_path = data::MakeId(prefix, p_fmat.get()) + ".gradient_index.page";
auto page_path = data::MakeId(prefix, p_fmat.get());
page_path += device_.IsCPU() ? ".gradient_index.page" : ".ellpack.page";
if (!this->on_host_) {
EXPECT_TRUE(FileExists(page_path)) << page_path;
}
if (with_label) {
RandomDataGenerator{static_cast<bst_idx_t>(p_fmat->Info().num_row_), this->n_targets_, 0.0f}