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data.cc
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/**
* Copyright 2015-2024, XGBoost Contributors
* \file data.cc
*/
#include "xgboost/data.h"
#include <dmlc/registry.h> // for DMLC_REGISTRY_ENABLE, DMLC_REGISTRY_LINK_TAG
#include <algorithm> // for copy, max, none_of, min
#include <atomic> // for atomic
#include <cmath> // for abs
#include <cstdint> // for uint64_t, int32_t, uint8_t, uint32_t
#include <cstring> // for size_t, strcmp, memcpy
#include <iostream> // for operator<<, basic_ostream, basic_ostream::op...
#include <map> // for map, operator!=
#include <numeric> // for accumulate, partial_sum
#include <tuple> // for get, apply
#include <type_traits> // for remove_pointer_t, remove_reference
#include "../collective/allgather.h" // for AllgatherStrings
#include "../collective/allreduce.h" // for Allreduce
#include "../collective/communicator-inl.h" // for GetRank, IsFederated
#include "../common/algorithm.h" // for StableSort
#include "../common/api_entry.h" // for XGBAPIThreadLocalEntry
#include "../common/error_msg.h" // for GroupSize, GroupWeight, InfInData
#include "../common/group_data.h" // for ParallelGroupBuilder
#include "../common/io.h" // for PeekableInStream
#include "../common/linalg_op.h" // for ElementWiseTransformHost
#include "../common/math.h" // for CheckNAN
#include "../common/numeric.h" // for Iota, RunLengthEncode
#include "../common/threading_utils.h" // for ParallelFor
#include "../common/version.h" // for Version
#include "../data/adapter.h" // for COOTuple, FileAdapter, IsValidFunctor
#include "../data/extmem_quantile_dmatrix.h" // for ExtMemQuantileDMatrix
#include "../data/iterative_dmatrix.h" // for IterativeDMatrix
#include "./sparse_page_dmatrix.h" // for SparsePageDMatrix
#include "array_interface.h" // for ArrayInterfaceHandler, ArrayInterface, Dispa...
#include "dmlc/base.h" // for BeginPtr
#include "dmlc/common.h" // for OMPException
#include "dmlc/data.h" // for Parser
#include "dmlc/endian.h" // for ByteSwap, DMLC_IO_NO_ENDIAN_SWAP
#include "dmlc/io.h" // for Stream
#include "dmlc/thread_local.h" // for ThreadLocalStore
#include "ellpack_page.h" // for EllpackPage
#include "file_iterator.h" // for ValidateFileFormat, FileIterator, Next, Reset
#include "gradient_index.h" // for GHistIndexMatrix
#include "simple_dmatrix.h" // for SimpleDMatrix
#include "sparse_page_writer.h" // for SparsePageFormatReg
#include "validation.h" // for LabelsCheck, WeightsCheck, ValidateQueryGroup
#include "xgboost/base.h" // for bst_group_t, bst_idx_t, bst_float, bst_ulong
#include "xgboost/context.h" // for Context
#include "xgboost/host_device_vector.h" // for HostDeviceVector
#include "xgboost/learner.h" // for HostDeviceVector
#include "xgboost/linalg.h" // for Tensor, Stack, TensorView, Vector, ArrayInte...
#include "xgboost/logging.h" // for Error, LogCheck_EQ, CHECK, CHECK_EQ, LOG
#include "xgboost/span.h" // for Span, operator!=, SpanIterator
#include "xgboost/string_view.h" // for operator==, operator<<, StringView
namespace dmlc {
DMLC_REGISTRY_ENABLE(::xgboost::data::SparsePageFormatReg<::xgboost::SparsePage>);
DMLC_REGISTRY_ENABLE(::xgboost::data::SparsePageFormatReg<::xgboost::CSCPage>);
DMLC_REGISTRY_ENABLE(::xgboost::data::SparsePageFormatReg<::xgboost::SortedCSCPage>);
DMLC_REGISTRY_ENABLE(::xgboost::data::SparsePageFormatReg<::xgboost::EllpackPage>);
DMLC_REGISTRY_ENABLE(::xgboost::data::SparsePageFormatReg<::xgboost::GHistIndexMatrix>);
} // namespace dmlc
namespace {
template <typename T>
void SaveScalarField(dmlc::Stream *strm, const std::string &name,
xgboost::DataType type, const T &field) {
strm->Write(name);
strm->Write(static_cast<uint8_t>(type));
strm->Write(true); // is_scalar=True
strm->Write(field);
}
template <typename T>
void SaveVectorField(dmlc::Stream *strm, const std::string &name,
xgboost::DataType type, std::pair<uint64_t, uint64_t> shape,
const std::vector<T>& field) {
strm->Write(name);
strm->Write(static_cast<uint8_t>(type));
strm->Write(false); // is_scalar=False
strm->Write(shape.first);
strm->Write(shape.second);
strm->Write(field);
}
template <typename T>
void SaveVectorField(dmlc::Stream* strm, const std::string& name,
xgboost::DataType type, std::pair<uint64_t, uint64_t> shape,
const xgboost::HostDeviceVector<T>& field) {
SaveVectorField(strm, name, type, shape, field.ConstHostVector());
}
template <typename T, int32_t D>
void SaveTensorField(dmlc::Stream* strm, const std::string& name, xgboost::DataType type,
const xgboost::linalg::Tensor<T, D>& field) {
strm->Write(name);
strm->Write(static_cast<uint8_t>(type));
strm->Write(false); // is_scalar=False
for (size_t i = 0; i < D; ++i) {
strm->Write(field.Shape(i));
}
strm->Write(field.Data()->HostVector());
}
template <typename T>
void LoadScalarField(dmlc::Stream* strm, const std::string& expected_name,
xgboost::DataType expected_type, T* field) {
const std::string invalid{"MetaInfo: Invalid format for " + expected_name};
std::string name;
xgboost::DataType type;
bool is_scalar;
CHECK(strm->Read(&name)) << invalid;
CHECK_EQ(name, expected_name)
<< invalid << " Expected field: " << expected_name << ", got: " << name;
uint8_t type_val;
CHECK(strm->Read(&type_val)) << invalid;
type = static_cast<xgboost::DataType>(type_val);
CHECK(type == expected_type)
<< invalid << "Expected field of type: " << static_cast<int>(expected_type) << ", "
<< "got field type: " << static_cast<int>(type);
CHECK(strm->Read(&is_scalar)) << invalid;
CHECK(is_scalar)
<< invalid << "Expected field " << expected_name << " to be a scalar; got a vector";
CHECK(strm->Read(field)) << invalid;
}
template <typename T>
void LoadVectorField(dmlc::Stream* strm, const std::string& expected_name,
xgboost::DataType expected_type, std::vector<T>* field) {
const std::string invalid{"MetaInfo: Invalid format for " + expected_name};
std::string name;
xgboost::DataType type;
bool is_scalar;
CHECK(strm->Read(&name)) << invalid;
CHECK_EQ(name, expected_name)
<< invalid << " Expected field: " << expected_name << ", got: " << name;
uint8_t type_val;
CHECK(strm->Read(&type_val)) << invalid;
type = static_cast<xgboost::DataType>(type_val);
CHECK(type == expected_type)
<< invalid << "Expected field of type: " << static_cast<int>(expected_type) << ", "
<< "got field type: " << static_cast<int>(type);
CHECK(strm->Read(&is_scalar)) << invalid;
CHECK(!is_scalar)
<< invalid << "Expected field " << expected_name << " to be a vector; got a scalar";
std::pair<uint64_t, uint64_t> shape;
CHECK(strm->Read(&shape.first));
CHECK(strm->Read(&shape.second));
// TODO(hcho3): this restriction may be lifted, once we add a field with more than 1 column.
CHECK_EQ(shape.second, 1) << invalid << "Number of columns is expected to be 1.";
CHECK(strm->Read(field)) << invalid;
}
template <typename T>
void LoadVectorField(dmlc::Stream* strm, const std::string& expected_name,
xgboost::DataType expected_type,
xgboost::HostDeviceVector<T>* field) {
LoadVectorField(strm, expected_name, expected_type, &field->HostVector());
}
template <typename T, int32_t D>
void LoadTensorField(dmlc::Stream* strm, std::string const& expected_name,
xgboost::DataType expected_type, xgboost::linalg::Tensor<T, D>* p_out) {
const std::string invalid{"MetaInfo: Invalid format for " + expected_name};
std::string name;
xgboost::DataType type;
bool is_scalar;
CHECK(strm->Read(&name)) << invalid;
CHECK_EQ(name, expected_name) << invalid << " Expected field: " << expected_name
<< ", got: " << name;
uint8_t type_val;
CHECK(strm->Read(&type_val)) << invalid;
type = static_cast<xgboost::DataType>(type_val);
CHECK(type == expected_type) << invalid
<< "Expected field of type: " << static_cast<int>(expected_type)
<< ", "
<< "got field type: " << static_cast<int>(type);
CHECK(strm->Read(&is_scalar)) << invalid;
CHECK(!is_scalar) << invalid << "Expected field " << expected_name
<< " to be a tensor; got a scalar";
size_t shape[D];
for (size_t i = 0; i < D; ++i) {
CHECK(strm->Read(&(shape[i])));
}
p_out->Reshape(shape);
auto& field = p_out->Data()->HostVector();
CHECK(strm->Read(&field)) << invalid;
}
} // anonymous namespace
namespace xgboost {
uint64_t constexpr MetaInfo::kNumField;
// implementation of inline functions
void MetaInfo::Clear() {
num_row_ = num_col_ = num_nonzero_ = 0;
labels = decltype(labels){};
group_ptr_.clear();
weights_.HostVector().clear();
base_margin_ = decltype(base_margin_){};
}
/*
* Binary serialization format for MetaInfo:
*
* | name | type | is_scalar | num_row | num_col | value |
* |--------------------+----------+-----------+-------------+-------------+------------------------|
* | num_row | kUInt64 | True | NA | NA | ${num_row_} |
* | num_col | kUInt64 | True | NA | NA | ${num_col_} |
* | num_nonzero | kUInt64 | True | NA | NA | ${num_nonzero_} |
* | labels | kFloat32 | False | ${size} | 1 | ${labels_} |
* | group_ptr | kUInt32 | False | ${size} | 1 | ${group_ptr_} |
* | weights | kFloat32 | False | ${size} | 1 | ${weights_} |
* | base_margin | kFloat32 | False | ${Shape(0)} | ${Shape(1)} | ${base_margin_} |
* | labels_lower_bound | kFloat32 | False | ${size} | 1 | ${labels_lower_bound_} |
* | labels_upper_bound | kFloat32 | False | ${size} | 1 | ${labels_upper_bound_} |
* | feature_names | kStr | False | ${size} | 1 | ${feature_names} |
* | feature_types | kStr | False | ${size} | 1 | ${feature_types} |
* | feature_weights | kFloat32 | False | ${size} | 1 | ${feature_weights} |
*
* Note that the scalar fields (is_scalar=True) will have num_row and num_col missing.
* Also notice the difference between the saved name and the name used in `SetInfo':
* the former uses the plural form.
*/
void MetaInfo::SaveBinary(dmlc::Stream *fo) const {
Version::Save(fo);
fo->Write(kNumField);
int field_cnt = 0; // make sure we are actually writing kNumField fields
SaveScalarField(fo, u8"num_row", DataType::kUInt64, num_row_); ++field_cnt;
SaveScalarField(fo, u8"num_col", DataType::kUInt64, num_col_); ++field_cnt;
SaveScalarField(fo, u8"num_nonzero", DataType::kUInt64, num_nonzero_); ++field_cnt;
SaveTensorField(fo, u8"labels", DataType::kFloat32, labels); ++field_cnt;
SaveVectorField(fo, u8"group_ptr", DataType::kUInt32,
{group_ptr_.size(), 1}, group_ptr_); ++field_cnt;
SaveVectorField(fo, u8"weights", DataType::kFloat32,
{weights_.Size(), 1}, weights_); ++field_cnt;
SaveTensorField(fo, u8"base_margin", DataType::kFloat32, base_margin_); ++field_cnt;
SaveVectorField(fo, u8"labels_lower_bound", DataType::kFloat32,
{labels_lower_bound_.Size(), 1}, labels_lower_bound_); ++field_cnt;
SaveVectorField(fo, u8"labels_upper_bound", DataType::kFloat32,
{labels_upper_bound_.Size(), 1}, labels_upper_bound_); ++field_cnt;
SaveVectorField(fo, u8"feature_names", DataType::kStr,
{feature_names.size(), 1}, feature_names); ++field_cnt;
SaveVectorField(fo, u8"feature_types", DataType::kStr,
{feature_type_names.size(), 1}, feature_type_names); ++field_cnt;
SaveVectorField(fo, u8"feature_weights", DataType::kFloat32, {feature_weights.Size(), 1},
feature_weights);
++field_cnt;
CHECK_EQ(field_cnt, kNumField) << "Wrong number of fields";
}
/**
* @brief Load feature type info from names, returns whether there's categorical features.
*/
[[nodiscard]] bool LoadFeatureType(std::vector<std::string> const& type_names,
std::vector<FeatureType>* types) {
types->clear();
bool has_cat{false};
for (auto const& elem : type_names) {
if (elem == "int") {
types->emplace_back(FeatureType::kNumerical);
} else if (elem == "float") {
types->emplace_back(FeatureType::kNumerical);
} else if (elem == "i") {
types->emplace_back(FeatureType::kNumerical);
} else if (elem == "q") {
types->emplace_back(FeatureType::kNumerical);
} else if (elem == "c") {
types->emplace_back(FeatureType::kCategorical);
has_cat = true;
} else {
LOG(FATAL) << "All feature_types must be one of {int, float, i, q, c}.";
}
}
return has_cat;
}
const std::vector<size_t>& MetaInfo::LabelAbsSort(Context const* ctx) const {
if (label_order_cache_.size() == labels.Size()) {
return label_order_cache_;
}
label_order_cache_.resize(labels.Size());
common::Iota(ctx, label_order_cache_.begin(), label_order_cache_.end(), 0);
const auto& l = labels.Data()->HostVector();
common::StableSort(ctx, label_order_cache_.begin(), label_order_cache_.end(),
[&l](size_t i1, size_t i2) { return std::abs(l[i1]) < std::abs(l[i2]); });
return label_order_cache_;
}
void MetaInfo::LoadBinary(dmlc::Stream *fi) {
auto version = Version::Load(fi);
auto major = std::get<0>(version);
// MetaInfo is saved in `SparsePageSource'. So the version in MetaInfo represents the
// version of DMatrix.
std::stringstream msg;
msg << "Binary DMatrix generated by XGBoost: " << Version::String(version)
<< " is no longer supported. "
<< "Please process and save your data in current version: "
<< Version::String(Version::Self()) << " again.";
CHECK_GE(major, 1) << msg.str();
if (major == 1) {
auto minor = std::get<1>(version);
CHECK_GE(minor, 6) << msg.str();
}
const uint64_t expected_num_field = kNumField;
uint64_t num_field { 0 };
CHECK(fi->Read(&num_field)) << "MetaInfo: invalid format";
size_t expected = 0;
if (major == 1 && std::get<1>(version) < 2) {
// feature names and types are added in 1.2
expected = expected_num_field - 2;
} else {
expected = expected_num_field;
}
CHECK_GE(num_field, expected)
<< "MetaInfo: insufficient number of fields (expected at least "
<< expected << " fields, but the binary file only contains " << num_field
<< "fields.)";
if (num_field > expected_num_field) {
LOG(WARNING) << "MetaInfo: the given binary file contains extra fields "
"which will be ignored.";
}
LoadScalarField(fi, u8"num_row", DataType::kUInt64, &num_row_);
LoadScalarField(fi, u8"num_col", DataType::kUInt64, &num_col_);
LoadScalarField(fi, u8"num_nonzero", DataType::kUInt64, &num_nonzero_);
LoadTensorField(fi, u8"labels", DataType::kFloat32, &labels);
LoadVectorField(fi, u8"group_ptr", DataType::kUInt32, &group_ptr_);
LoadVectorField(fi, u8"weights", DataType::kFloat32, &weights_);
LoadTensorField(fi, u8"base_margin", DataType::kFloat32, &base_margin_);
LoadVectorField(fi, u8"labels_lower_bound", DataType::kFloat32, &labels_lower_bound_);
LoadVectorField(fi, u8"labels_upper_bound", DataType::kFloat32, &labels_upper_bound_);
LoadVectorField(fi, u8"feature_names", DataType::kStr, &feature_names);
LoadVectorField(fi, u8"feature_types", DataType::kStr, &feature_type_names);
LoadVectorField(fi, u8"feature_weights", DataType::kFloat32, &feature_weights);
this->has_categorical_ = LoadFeatureType(feature_type_names, &feature_types.HostVector());
}
namespace {
template <typename T>
std::vector<T> Gather(const std::vector<T>& in, common::Span<bst_idx_t const> ridxs,
size_t stride = 1) {
if (in.empty()) {
return {};
}
auto size = ridxs.size();
std::vector<T> out(size * stride);
for (auto i = 0ull; i < size; i++) {
auto ridx = ridxs[i];
for (size_t j = 0; j < stride; ++j) {
out[i * stride + j] = in[ridx * stride + j];
}
}
return out;
}
} // namespace
namespace cuda_impl {
void SliceMetaInfo(Context const* ctx, MetaInfo const& info, common::Span<bst_idx_t const> ridx,
MetaInfo* p_out);
#if !defined(XGBOOST_USE_CUDA)
void SliceMetaInfo(Context const*, MetaInfo const&, common::Span<bst_idx_t const>, MetaInfo*) {
common::AssertGPUSupport();
}
#endif
} // namespace cuda_impl
MetaInfo MetaInfo::Slice(Context const* ctx, common::Span<bst_idx_t const> ridxs,
bst_idx_t nnz) const {
/**
* Shape
*/
MetaInfo out;
out.num_row_ = ridxs.size();
out.num_col_ = this->num_col_;
out.num_nonzero_ = nnz;
/**
* Feature Info
*/
out.feature_weights.SetDevice(ctx->Device());
out.feature_weights.Resize(this->feature_weights.Size());
out.feature_weights.Copy(this->feature_weights);
out.feature_names = this->feature_names;
out.feature_types.SetDevice(ctx->Device());
out.feature_types.Resize(this->feature_types.Size());
out.feature_types.Copy(this->feature_types);
out.feature_type_names = this->feature_type_names;
/**
* Sample Info
*/
if (ctx->IsCUDA()) {
cuda_impl::SliceMetaInfo(ctx, *this, ridxs, &out);
return out;
}
// Groups is maintained by a higher level Python function. We should aim at deprecating
// the slice function.
if (this->labels.Size() != this->num_row_) {
auto t_labels = this->labels.View(this->labels.Data()->Device());
out.labels.Reshape(ridxs.size(), labels.Shape(1));
out.labels.Data()->HostVector() =
Gather(this->labels.Data()->HostVector(), ridxs, t_labels.Stride(0));
} else {
out.labels.ModifyInplace([&](auto* data, common::Span<size_t, 2> shape) {
data->HostVector() = Gather(this->labels.Data()->HostVector(), ridxs);
shape[0] = data->Size();
shape[1] = 1;
});
}
out.labels_upper_bound_.HostVector() = Gather(this->labels_upper_bound_.HostVector(), ridxs);
out.labels_lower_bound_.HostVector() = Gather(this->labels_lower_bound_.HostVector(), ridxs);
// weights
if (this->weights_.Size() + 1 == this->group_ptr_.size()) {
auto& h_weights = out.weights_.HostVector();
// Assuming all groups are available.
out.weights_.HostVector() = h_weights;
} else {
out.weights_.HostVector() = Gather(this->weights_.HostVector(), ridxs);
}
if (this->base_margin_.Size() != this->num_row_) {
CHECK_EQ(this->base_margin_.Size() % this->num_row_, 0)
<< "Incorrect size of base margin vector.";
auto t_margin = this->base_margin_.View(this->base_margin_.Data()->Device());
out.base_margin_.Reshape(ridxs.size(), t_margin.Shape(1));
out.base_margin_.Data()->HostVector() =
Gather(this->base_margin_.Data()->HostVector(), ridxs, t_margin.Stride(0));
} else {
out.base_margin_.ModifyInplace([&](auto* data, common::Span<size_t, 2> shape) {
data->HostVector() = Gather(this->base_margin_.Data()->HostVector(), ridxs);
shape[0] = data->Size();
shape[1] = 1;
});
}
return out;
}
MetaInfo MetaInfo::Copy() const {
MetaInfo out;
out.Extend(*this, /*accumulate_rows=*/true, /*check_column=*/false);
return out;
}
namespace {
template <int32_t D, typename T>
void CopyTensorInfoImpl(Context const& ctx, Json arr_interface, linalg::Tensor<T, D>* p_out) {
ArrayInterface<D> array{arr_interface};
if (array.n == 0) {
p_out->Reshape(array.shape);
return;
}
CHECK_EQ(array.valid.Capacity(), 0)
<< "Meta info like label or weight can not have missing value.";
if (array.is_contiguous && array.type == ToDType<T>::kType) {
// Handle contigious
p_out->ModifyInplace([&](HostDeviceVector<T>* data, common::Span<size_t, D> shape) {
// set shape
std::copy(array.shape, array.shape + D, shape.data());
// set data
data->Resize(array.n);
std::memcpy(data->HostPointer(), array.data, array.n * sizeof(T));
});
return;
}
p_out->Reshape(array.shape);
auto t_out = p_out->View(DeviceOrd::CPU());
CHECK(t_out.CContiguous());
auto const shape = t_out.Shape();
DispatchDType(array, DeviceOrd::CPU(), [&](auto&& in) {
linalg::ElementWiseTransformHost(t_out, ctx.Threads(), [&](auto i, auto) {
return std::apply(in, linalg::UnravelIndex<D>(i, shape));
});
});
}
} // namespace
void MetaInfo::SetInfo(Context const& ctx, StringView key, StringView interface_str) {
Json j_interface = Json::Load(interface_str);
bool is_cuda{false};
if (IsA<Array>(j_interface)) {
auto const& array = get<Array const>(j_interface);
CHECK_GE(array.size(), 0) << "Invalid " << key
<< ", must have at least 1 column even if it's empty.";
auto const& first = get<Object const>(array.front());
auto ptr = ArrayInterfaceHandler::GetPtrFromArrayData<void*>(first);
is_cuda = first.find("stream") != first.cend() || ArrayInterfaceHandler::IsCudaPtr(ptr);
} else {
auto const& first = get<Object const>(j_interface);
auto ptr = ArrayInterfaceHandler::GetPtrFromArrayData<void*>(first);
is_cuda = first.find("stream") != first.cend() || ArrayInterfaceHandler::IsCudaPtr(ptr);
}
if (is_cuda) {
this->SetInfoFromCUDA(ctx, key, j_interface);
} else {
this->SetInfoFromHost(ctx, key, j_interface);
}
}
void MetaInfo::SetInfoFromHost(Context const& ctx, StringView key, Json arr) {
// multi-dim float info
if (key == "base_margin") {
CopyTensorInfoImpl(ctx, arr, &this->base_margin_);
// FIXME(jiamingy): Remove the deprecated API and let all language bindings aware of
// input shape. This issue is CPU only since CUDA uses array interface from day 1.
//
// Python binding always understand the shape, so this condition should not occur for
// it.
if (this->num_row_ != 0 && this->base_margin_.Shape(0) != this->num_row_) {
// API functions that don't use array interface don't understand shape.
CHECK(this->base_margin_.Size() % this->num_row_ == 0) << "Incorrect size for base margin.";
size_t n_groups = this->base_margin_.Size() / this->num_row_;
this->base_margin_.Reshape(this->num_row_, n_groups);
}
return;
} else if (key == "label") {
CopyTensorInfoImpl(ctx, arr, &this->labels);
if (this->num_row_ != 0 && this->labels.Shape(0) != this->num_row_) {
CHECK_EQ(this->labels.Size() % this->num_row_, 0) << "Incorrect size for labels.";
size_t n_targets = this->labels.Size() / this->num_row_;
this->labels.Reshape(this->num_row_, n_targets);
}
auto const& h_labels = labels.Data()->ConstHostVector();
auto valid = std::none_of(h_labels.cbegin(), h_labels.cend(), data::LabelsCheck{});
CHECK(valid) << "Label contains NaN, infinity or a value too large.";
return;
}
// uint info
if (key == "group") {
linalg::Vector<bst_group_t> t;
CopyTensorInfoImpl(ctx, arr, &t);
auto const& h_groups = t.Data()->HostVector();
group_ptr_.clear();
group_ptr_.resize(h_groups.size() + 1, 0);
group_ptr_[0] = 0;
std::partial_sum(h_groups.cbegin(), h_groups.cend(), group_ptr_.begin() + 1);
data::ValidateQueryGroup(group_ptr_);
return;
} else if (key == "qid") {
linalg::Tensor<bst_group_t, 1> t;
CopyTensorInfoImpl(ctx, arr, &t);
bool non_dec = true;
auto const& query_ids = t.Data()->HostVector();
for (size_t i = 1; i < query_ids.size(); ++i) {
if (query_ids[i] < query_ids[i - 1]) {
non_dec = false;
break;
}
}
CHECK(non_dec) << "`qid` must be sorted in non-decreasing order along with data.";
common::RunLengthEncode(query_ids.cbegin(), query_ids.cend(), &group_ptr_);
data::ValidateQueryGroup(group_ptr_);
return;
}
// float info
linalg::Tensor<float, 1> t;
CopyTensorInfoImpl<1>(ctx, arr, &t);
if (key == "weight") {
this->weights_ = std::move(*t.Data());
auto const& h_weights = this->weights_.ConstHostVector();
auto valid = std::none_of(h_weights.cbegin(), h_weights.cend(),
[](float w) { return w < 0 || std::isinf(w) || std::isnan(w); });
CHECK(valid) << "Weights must be positive values.";
} else if (key == "label_lower_bound") {
this->labels_lower_bound_ = std::move(*t.Data());
} else if (key == "label_upper_bound") {
this->labels_upper_bound_ = std::move(*t.Data());
} else if (key == "feature_weights") {
this->feature_weights = std::move(*t.Data());
auto const& h_feature_weights = feature_weights.ConstHostVector();
bool valid =
std::none_of(h_feature_weights.cbegin(), h_feature_weights.cend(), data::WeightsCheck{});
CHECK(valid) << "Feature weight must be greater than 0.";
} else {
LOG(FATAL) << "Unknown key for MetaInfo: " << key;
}
}
void MetaInfo::GetInfo(char const* key, bst_ulong* out_len, DataType dtype,
const void** out_dptr) const {
if (dtype == DataType::kFloat32) {
const std::vector<bst_float>* vec = nullptr;
if (!std::strcmp(key, "label")) {
vec = &this->labels.Data()->HostVector();
} else if (!std::strcmp(key, "weight")) {
vec = &this->weights_.HostVector();
} else if (!std::strcmp(key, "base_margin")) {
vec = &this->base_margin_.Data()->HostVector();
} else if (!std::strcmp(key, "label_lower_bound")) {
vec = &this->labels_lower_bound_.HostVector();
} else if (!std::strcmp(key, "label_upper_bound")) {
vec = &this->labels_upper_bound_.HostVector();
} else if (!std::strcmp(key, "feature_weights")) {
vec = &this->feature_weights.HostVector();
} else {
LOG(FATAL) << "Unknown float field name: " << key;
}
*out_len = static_cast<xgboost::bst_ulong>(vec->size()); // NOLINT
*reinterpret_cast<float const**>(out_dptr) = dmlc::BeginPtr(*vec);
} else if (dtype == DataType::kUInt32) {
const std::vector<unsigned> *vec = nullptr;
if (!std::strcmp(key, "group_ptr")) {
vec = &this->group_ptr_;
} else {
LOG(FATAL) << "Unknown uint32 field name: " << key;
}
*out_len = static_cast<xgboost::bst_ulong>(vec->size());
*reinterpret_cast<unsigned const**>(out_dptr) = dmlc::BeginPtr(*vec);
} else {
LOG(FATAL) << "Unknown data type for getting meta info.";
}
}
void MetaInfo::SetFeatureInfo(const char* key, const char **info, const bst_ulong size) {
bool is_col_split = this->IsColumnSplit();
if (size != 0 && this->num_col_ != 0 && !is_col_split) {
CHECK_EQ(size, this->num_col_) << "Length of " << key << " must be equal to number of columns.";
CHECK(info);
}
// Gather column info when data is split by columns
auto gather_columns = [is_col_split, key, n_columns = this->num_col_](auto const& inputs) {
if (is_col_split) {
std::remove_const_t<std::remove_reference_t<decltype(inputs)>> result;
auto rc = collective::AllgatherStrings(inputs, &result);
collective::SafeColl(rc);
CHECK_EQ(result.size(), n_columns)
<< "Length of " << key << " must be equal to number of columns.";
return result;
}
return inputs;
};
if (StringView{key} == "feature_type") { // NOLINT
this->feature_type_names.clear();
std::copy(info, info + size, std::back_inserter(feature_type_names));
feature_type_names = gather_columns(feature_type_names);
auto& h_feature_types = feature_types.HostVector();
this->has_categorical_ = LoadFeatureType(feature_type_names, &h_feature_types);
} else if (StringView{key} == "feature_name") { // NOLINT
feature_names.clear();
if (is_col_split) {
auto const rank = collective::GetRank();
std::transform(info, info + size, std::back_inserter(feature_names),
[rank](char const* elem) { return std::to_string(rank) + "." + elem; });
} else {
std::copy(info, info + size, std::back_inserter(feature_names));
}
feature_names = gather_columns(feature_names);
} else {
LOG(FATAL) << "Unknown feature info name: " << key;
}
}
void MetaInfo::GetFeatureInfo(const char* field, std::vector<std::string>* out_str_vecs) const {
auto& str_vecs = *out_str_vecs;
if (!std::strcmp(field, "feature_type")) {
str_vecs.resize(feature_type_names.size());
std::copy(feature_type_names.cbegin(), feature_type_names.cend(), str_vecs.begin());
} else if (!strcmp(field, "feature_name")) {
str_vecs.resize(feature_names.size());
std::copy(feature_names.begin(), feature_names.end(), str_vecs.begin());
} else {
LOG(FATAL) << "Unknown feature info: " << field;
}
}
void MetaInfo::Extend(MetaInfo const& that, bool accumulate_rows, bool check_column) {
/**
* shape
*/
if (accumulate_rows) {
this->num_row_ += that.num_row_;
}
if (this->num_col_ != 0) {
if (check_column) {
CHECK_EQ(this->num_col_, that.num_col_)
<< "Number of columns must be consistent across batches.";
} else {
this->num_col_ = std::max(this->num_col_, that.num_col_);
}
}
this->num_col_ = that.num_col_;
/**
* info with n_samples
*/
linalg::Stack(&this->labels, that.labels);
this->weights_.SetDevice(that.weights_.Device());
this->weights_.Extend(that.weights_);
this->labels_lower_bound_.SetDevice(that.labels_lower_bound_.Device());
this->labels_lower_bound_.Extend(that.labels_lower_bound_);
this->labels_upper_bound_.SetDevice(that.labels_upper_bound_.Device());
this->labels_upper_bound_.Extend(that.labels_upper_bound_);
linalg::Stack(&this->base_margin_, that.base_margin_);
/**
* group
*/
if (this->group_ptr_.size() == 0) {
this->group_ptr_ = that.group_ptr_;
} else {
CHECK_NE(that.group_ptr_.size(), 0);
auto group_ptr = that.group_ptr_;
for (size_t i = 1; i < group_ptr.size(); ++i) {
group_ptr[i] += this->group_ptr_.back();
}
this->group_ptr_.insert(this->group_ptr_.end(), group_ptr.begin() + 1,
group_ptr.end());
}
/**
* info with n_features
*/
if (!that.feature_names.empty()) {
this->feature_names = that.feature_names;
}
if (!that.feature_type_names.empty()) {
this->feature_type_names = that.feature_type_names;
auto& h_feature_types = feature_types.HostVector();
this->has_categorical_ = LoadFeatureType(this->feature_type_names, &h_feature_types);
} else if (!that.feature_types.Empty()) {
// FIXME(jiamingy): https://github.com/dmlc/xgboost/pull/9171/files#r1440188612
this->feature_types.Resize(that.feature_types.Size());
this->feature_types.Copy(that.feature_types);
auto const& ft = this->feature_types.ConstHostVector();
this->has_categorical_ = std::any_of(ft.cbegin(), ft.cend(), common::IsCatOp{});
}
if (!that.feature_weights.Empty()) {
this->feature_weights.Resize(that.feature_weights.Size());
this->feature_weights.SetDevice(that.feature_weights.Device());
this->feature_weights.Copy(that.feature_weights);
}
}
void MetaInfo::SynchronizeNumberOfColumns(Context const* ctx) {
auto op = IsColumnSplit() ? collective::Op::kSum : collective::Op::kMax;
auto rc = collective::Allreduce(ctx, linalg::MakeVec(&num_col_, 1), op);
collective::SafeColl(rc);
}
namespace {
template <typename T>
void CheckDevice(DeviceOrd device, HostDeviceVector<T> const& v) {
bool valid = v.Device().IsCPU() || device.IsCPU() || v.Device() == device;
if (!valid) {
LOG(FATAL) << "Invalid device ordinal. Data is associated with a different device ordinal than "
"the booster. The device ordinal of the data is: "
<< v.Device() << "; the device ordinal of the Booster is: " << device;
}
}
template <typename T, std::int32_t D>
void CheckDevice(DeviceOrd device, linalg::Tensor<T, D> const& v) {
CheckDevice(device, *v.Data());
}
} // anonymous namespace
void MetaInfo::Validate(DeviceOrd device) const {
if (group_ptr_.size() != 0 && weights_.Size() != 0) {
CHECK_EQ(group_ptr_.size(), weights_.Size() + 1) << error::GroupWeight();
return;
}
if (group_ptr_.size() != 0) {
CHECK_EQ(group_ptr_.back(), num_row_)
<< error::GroupSize() << "the actual number of rows given by data.";
}
if (weights_.Size() != 0) {
CHECK_EQ(weights_.Size(), num_row_)
<< "Size of weights must equal to number of rows.";
CheckDevice(device, weights_);
return;
}
if (labels.Size() != 0) {
CHECK_EQ(labels.Shape(0), num_row_) << "Size of labels must equal to number of rows.";
CheckDevice(device, labels);
return;
}
if (labels_lower_bound_.Size() != 0) {
CHECK_EQ(labels_lower_bound_.Size(), num_row_)
<< "Size of label_lower_bound must equal to number of rows.";
CheckDevice(device, labels_lower_bound_);
return;
}
if (feature_weights.Size() != 0) {
CHECK_EQ(feature_weights.Size(), num_col_)
<< "Size of feature_weights must equal to number of columns.";
CheckDevice(device, feature_weights);
}
if (labels_upper_bound_.Size() != 0) {
CHECK_EQ(labels_upper_bound_.Size(), num_row_)
<< "Size of label_upper_bound must equal to number of rows.";
CheckDevice(device, labels_upper_bound_);
return;
}
CHECK_LE(num_nonzero_, num_col_ * num_row_);
if (base_margin_.Size() != 0) {
CHECK_EQ(base_margin_.Size() % num_row_, 0)
<< "Size of base margin must be a multiple of number of rows.";
CheckDevice(device, base_margin_);
}
}
#if !defined(XGBOOST_USE_CUDA)
void MetaInfo::SetInfoFromCUDA(Context const&, StringView, Json) { common::AssertGPUSupport(); }
#endif // !defined(XGBOOST_USE_CUDA)
bool MetaInfo::IsVerticalFederated() const {
return collective::IsFederated() && IsColumnSplit();
}
bool MetaInfo::ShouldHaveLabels() const {
return !IsVerticalFederated() || collective::GetRank() == 0;
}
using DMatrixThreadLocal =
dmlc::ThreadLocalStore<std::map<DMatrix const *, XGBAPIThreadLocalEntry>>;
XGBAPIThreadLocalEntry& DMatrix::GetThreadLocal() const {
return (*DMatrixThreadLocal::Get())[this];
}
DMatrix::~DMatrix() {
auto local_map = DMatrixThreadLocal::Get();
if (local_map->find(this) != local_map->cend()) {
local_map->erase(this);
}
}
namespace {
DMatrix* TryLoadBinary(std::string fname, bool silent) {
std::int32_t magic;
std::unique_ptr<dmlc::Stream> fi(dmlc::Stream::Create(fname.c_str(), "r", true));
if (fi != nullptr) {
common::PeekableInStream is(fi.get());
if (is.PeekRead(&magic, sizeof(magic)) == sizeof(magic)) {
if (!DMLC_IO_NO_ENDIAN_SWAP) {
dmlc::ByteSwap(&magic, sizeof(magic), 1);
}
if (magic == data::SimpleDMatrix::kMagic) {
DMatrix* dmat = new data::SimpleDMatrix(&is);
if (!silent) {
LOG(CONSOLE) << dmat->Info().num_row_ << 'x' << dmat->Info().num_col_ << " matrix with "
<< dmat->Info().num_nonzero_ << " entries loaded from " << fname;
}
return dmat;
}
}
}
return nullptr;
}
} // namespace
DMatrix* DMatrix::Load(const std::string& uri, bool silent, DataSplitMode data_split_mode) {
std::string fname, cache_file;
auto dlm_pos = uri.find('#');
if (dlm_pos != std::string::npos) {
cache_file = uri.substr(dlm_pos + 1, uri.length());
fname = uri.substr(0, dlm_pos);
CHECK_EQ(cache_file.find('#'), std::string::npos)
<< "Only one `#` is allowed in file path for cache file specification.";
} else {
fname = uri;
}
// legacy handling of binary data loading
DMatrix* loaded = TryLoadBinary(fname, silent);
if (loaded) {
return loaded;
}
int partid = 0, npart = 1;
DMatrix* dmat{};
if (cache_file.empty()) {
fname = data::ValidateFileFormat(fname);
std::unique_ptr<dmlc::Parser<std::uint32_t>> parser(
dmlc::Parser<std::uint32_t>::Create(fname.c_str(), partid, npart, "auto"));
data::FileAdapter adapter(parser.get());
dmat = DMatrix::Create(&adapter, std::numeric_limits<float>::quiet_NaN(), Context{}.Threads(),
cache_file, data_split_mode);
} else {
CHECK(data_split_mode != DataSplitMode::kCol)
<< "Column-wise data split is not supported for external memory.";
data::FileIterator iter{fname, static_cast<uint32_t>(partid), static_cast<uint32_t>(npart)};
dmat = new data::SparsePageDMatrix{&iter,
iter.Proxy(),
data::fileiter::Reset,
data::fileiter::Next,
std::numeric_limits<float>::quiet_NaN(),
1,
cache_file};
}
return dmat;
}
template <typename DataIterHandle, typename DMatrixHandle, typename DataIterResetCallback,
typename XGDMatrixCallbackNext>
DMatrix* DMatrix::Create(DataIterHandle iter, DMatrixHandle proxy, std::shared_ptr<DMatrix> ref,
DataIterResetCallback* reset, XGDMatrixCallbackNext* next, float missing,
int nthread, bst_bin_t max_bin) {
return new data::IterativeDMatrix(iter, proxy, ref, reset, next, missing, nthread, max_bin);
}
template <typename DataIterHandle, typename DMatrixHandle, typename DataIterResetCallback,
typename XGDMatrixCallbackNext>
DMatrix* DMatrix::Create(DataIterHandle iter, DMatrixHandle proxy, DataIterResetCallback* reset,
XGDMatrixCallbackNext* next, float missing, int32_t n_threads,
std::string cache, bool on_host) {
return new data::SparsePageDMatrix{iter, proxy, reset, next, missing, n_threads, cache, on_host};
}
template <typename DataIterHandle, typename DMatrixHandle, typename DataIterResetCallback,
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, bool on_host) {
return new data::ExtMemQuantileDMatrix{
iter, proxy, ref, reset, next, missing, nthread, std::move(cache), max_bin, on_host};
}
template DMatrix* DMatrix::Create<DataIterHandle, DMatrixHandle, DataIterResetCallback,
XGDMatrixCallbackNext>(DataIterHandle iter, DMatrixHandle proxy,
std::shared_ptr<DMatrix> ref,
DataIterResetCallback* reset,
XGDMatrixCallbackNext* next, float missing,
int nthread, int max_bin);
template DMatrix* DMatrix::Create<DataIterHandle, DMatrixHandle, DataIterResetCallback,
XGDMatrixCallbackNext>(DataIterHandle iter, DMatrixHandle proxy,
DataIterResetCallback* reset,
XGDMatrixCallbackNext* next, float missing,
int32_t n_threads, std::string, bool);
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, bool);
template <typename AdapterT>
DMatrix* DMatrix::Create(AdapterT* adapter, float missing, int nthread, const std::string&,
DataSplitMode data_split_mode) {
return new data::SimpleDMatrix(adapter, missing, nthread, data_split_mode);
}
// Instantiate the factory function for various adapters
#define INSTANTIATION_CREATE(_AdapterT) \
template DMatrix* DMatrix::Create<data::_AdapterT>( \
data::_AdapterT * adapter, float missing, std::int32_t nthread, \
const std::string& cache_prefix, DataSplitMode data_split_mode);
INSTANTIATION_CREATE(DenseAdapter)
INSTANTIATION_CREATE(ArrayAdapter)
INSTANTIATION_CREATE(CSRAdapter)
INSTANTIATION_CREATE(CSCAdapter)
INSTANTIATION_CREATE(DataTableAdapter)
INSTANTIATION_CREATE(FileAdapter)
INSTANTIATION_CREATE(CSRArrayAdapter)
INSTANTIATION_CREATE(CSCArrayAdapter)
INSTANTIATION_CREATE(ColumnarAdapter)
#undef INSTANTIATION_CREATE
template DMatrix* DMatrix::Create(
data::IteratorAdapter<DataIterHandle, XGBCallbackDataIterNext, XGBoostBatchCSR>* adapter,
float missing, int nthread, const std::string& cache_prefix, DataSplitMode data_split_mode);
SparsePage SparsePage::GetTranspose(int num_columns, int32_t n_threads) const {