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/*!
* Copyright 2015-2021 by Contributors
* \file data.cc
*/
#include <dmlc/registry.h>
#include <array>
#include <cstring>
#include "dmlc/io.h"
#include "xgboost/data.h"
#include "xgboost/c_api.h"
#include "xgboost/host_device_vector.h"
#include "xgboost/logging.h"
#include "xgboost/version_config.h"
#include "xgboost/learner.h"
#include "xgboost/string_view.h"
#include "sparse_page_writer.h"
#include "simple_dmatrix.h"
#include "../common/io.h"
#include "../common/linalg_op.h"
#include "../common/math.h"
#include "../common/version.h"
#include "../common/group_data.h"
#include "../common/threading_utils.h"
#include "../data/adapter.h"
#include "../data/iterative_device_dmatrix.h"
#include "file_iterator.h"
#include "validation.h"
#include "./sparse_page_source.h"
#include "./sparse_page_dmatrix.h"
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";
std::array<size_t, D> shape;
for (size_t i = 0; i < D; ++i) {
CHECK(strm->Read(&(shape[i])));
}
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_.HostVector().clear();
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;
SaveVectorField(fo, u8"labels", DataType::kFloat32,
{labels_.Size(), 1}, 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";
}
void LoadFeatureType(std::vector<std::string>const& type_names, std::vector<FeatureType>* types) {
types->clear();
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);
} else {
LOG(FATAL) << "All feature_types must be one of {int, float, i, q, c}.";
}
}
}
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_EQ(major, 1) << msg.str();
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_);
LoadVectorField(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);
LoadFeatureType(feature_type_names, &feature_types.HostVector());
}
template <typename T>
std::vector<T> Gather(const std::vector<T> &in, common::Span<int 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;
}
MetaInfo MetaInfo::Slice(common::Span<int32_t const> ridxs) const {
MetaInfo out;
out.num_row_ = ridxs.size();
out.num_col_ = this->num_col_;
// Groups is maintained by a higher level Python function. We should aim at deprecating
// the slice function.
out.labels_.HostVector() = Gather(this->labels_.HostVector(), ridxs);
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 margin = this->base_margin_.View(this->base_margin_.Data()->DeviceIdx());
out.base_margin_.Reshape(ridxs.size(), margin.Shape()[1]);
size_t stride = margin.Stride(0);
out.base_margin_.Data()->HostVector() =
Gather(this->base_margin_.Data()->HostVector(), ridxs, stride);
} else {
out.base_margin_.Data()->HostVector() = Gather(this->base_margin_.Data()->HostVector(), ridxs);
}
out.feature_weights.Resize(this->feature_weights.Size());
out.feature_weights.Copy(this->feature_weights);
out.feature_names = this->feature_names;
out.feature_types.Resize(this->feature_types.Size());
out.feature_types.Copy(this->feature_types);
out.feature_type_names = this->feature_type_names;
return out;
}
// try to load group information from file, if exists
inline bool MetaTryLoadGroup(const std::string& fname,
std::vector<unsigned>* group) {
std::unique_ptr<dmlc::Stream> fi(dmlc::Stream::Create(fname.c_str(), "r", true));
if (fi == nullptr) return false;
dmlc::istream is(fi.get());
group->clear();
group->push_back(0);
unsigned nline = 0;
while (is >> nline) {
group->push_back(group->back() + nline);
}
return true;
}
// try to load weight information from file, if exists
inline bool MetaTryLoadFloatInfo(const std::string& fname,
std::vector<bst_float>* data) {
std::unique_ptr<dmlc::Stream> fi(dmlc::Stream::Create(fname.c_str(), "r", true));
if (fi == nullptr) return false;
dmlc::istream is(fi.get());
data->clear();
bst_float value;
while (is >> value) {
data->push_back(value);
}
return true;
}
namespace {
template <int32_t D, typename T>
void CopyTensorInfoImpl(Json arr_interface, linalg::Tensor<T, D>* p_out) {
ArrayInterface<D> array{arr_interface};
if (array.n == 0) {
return;
}
CHECK(array.valid.Size() == 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 = p_out->View(GenericParameter::kCpuId);
CHECK(t.CContiguous());
// FIXME(jiamingy): Remove the use of this default thread.
linalg::ElementWiseKernelHost(t, common::OmpGetNumThreads(0), [&](auto i, auto) {
return linalg::detail::Apply(TypedIndex<T, D>{array}, linalg::UnravelIndex<D>(i, t.Shape()));
});
}
} // namespace
void MetaInfo::SetInfo(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 = ArrayInterfaceHandler::IsCudaPtr(ptr);
} else {
auto const& first = get<Object const>(j_interface);
auto ptr = ArrayInterfaceHandler::GetPtrFromArrayData<void*>(first);
is_cuda = ArrayInterfaceHandler::IsCudaPtr(ptr);
}
if (is_cuda) {
this->SetInfoFromCUDA(key, j_interface);
} else {
this->SetInfoFromHost(key, j_interface);
}
}
void MetaInfo::SetInfoFromHost(StringView key, Json arr) {
// multi-dim float info
if (key == "base_margin") {
CopyTensorInfoImpl(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;
}
// uint info
if (key == "group") {
linalg::Tensor<bst_group_t, 1> t;
CopyTensorInfoImpl(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(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.";
group_ptr_.clear();
group_ptr_.push_back(0);
for (size_t i = 1; i < query_ids.size(); ++i) {
if (query_ids[i] != query_ids[i - 1]) {
group_ptr_.push_back(i);
}
}
if (group_ptr_.back() != query_ids.size()) {
group_ptr_.push_back(query_ids.size());
}
data::ValidateQueryGroup(group_ptr_);
return;
}
// float info
linalg::Tensor<float, 1> t;
CopyTensorInfoImpl<1>(arr, &t);
if (key == "label") {
this->labels_ = std::move(*t.Data());
auto const& h_labels = labels_.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.";
} else 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::SetInfo(const char* key, const void* dptr, DataType dtype, size_t num) {
auto proc = [&](auto cast_d_ptr) {
using T = std::remove_pointer_t<decltype(cast_d_ptr)>;
auto t =
linalg::TensorView<T, 1>(common::Span<T>{cast_d_ptr, num}, {num}, GenericParameter::kCpuId);
CHECK(t.CContiguous());
Json interface { linalg::ArrayInterface(t) };
assert(ArrayInterface<1>{interface}.is_contiguous);
return interface;
};
// Legacy code using XGBoost dtype, which is a small subset of array interface types.
switch (dtype) {
case xgboost::DataType::kFloat32: {
auto cast_ptr = reinterpret_cast<const float*>(dptr);
this->SetInfoFromHost(key, proc(cast_ptr));
break;
}
case xgboost::DataType::kDouble: {
auto cast_ptr = reinterpret_cast<const double*>(dptr);
this->SetInfoFromHost(key, proc(cast_ptr));
break;
}
case xgboost::DataType::kUInt32: {
auto cast_ptr = reinterpret_cast<const uint32_t*>(dptr);
this->SetInfoFromHost(key, proc(cast_ptr));
break;
}
case xgboost::DataType::kUInt64: {
auto cast_ptr = reinterpret_cast<const uint64_t*>(dptr);
this->SetInfoFromHost(key, proc(cast_ptr));
break;
}
default:
LOG(FATAL) << "Unknown data type" << static_cast<uint8_t>(dtype);
}
}
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_.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) {
if (size != 0) {
CHECK_EQ(size, this->num_col_)
<< "Length of " << key << " must be equal to number of columns.";
}
if (!std::strcmp(key, "feature_type")) {
feature_type_names.clear();
auto& h_feature_types = feature_types.HostVector();
for (size_t i = 0; i < size; ++i) {
auto elem = info[i];
feature_type_names.emplace_back(elem);
}
LoadFeatureType(feature_type_names, &h_feature_types);
} else if (!std::strcmp(key, "feature_name")) {
feature_names.clear();
for (size_t i = 0; i < size; ++i) {
feature_names.emplace_back(info[i]);
}
} 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) {
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_;
this->labels_.SetDevice(that.labels_.DeviceIdx());
this->labels_.Extend(that.labels_);
this->weights_.SetDevice(that.weights_.DeviceIdx());
this->weights_.Extend(that.weights_);
this->labels_lower_bound_.SetDevice(that.labels_lower_bound_.DeviceIdx());
this->labels_lower_bound_.Extend(that.labels_lower_bound_);
this->labels_upper_bound_.SetDevice(that.labels_upper_bound_.DeviceIdx());
this->labels_upper_bound_.Extend(that.labels_upper_bound_);
linalg::Stack(&this->base_margin_, that.base_margin_);
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());
}
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();
LoadFeatureType(this->feature_type_names, &h_feature_types);
}
if (!that.feature_weights.Empty()) {
this->feature_weights.Resize(that.feature_weights.Size());
this->feature_weights.SetDevice(that.feature_weights.DeviceIdx());
this->feature_weights.Copy(that.feature_weights);
}
}
void MetaInfo::Validate(int32_t device) const {
if (group_ptr_.size() != 0 && weights_.Size() != 0) {
CHECK_EQ(group_ptr_.size(), weights_.Size() + 1)
<< "Size of weights must equal to number of groups when ranking "
"group is used.";
return;
}
if (group_ptr_.size() != 0) {
CHECK_EQ(group_ptr_.back(), num_row_)
<< "Invalid group structure. Number of rows obtained from groups "
"doesn't equal to actual number of rows given by data.";
}
auto check_device = [device](HostDeviceVector<float> const &v) {
CHECK(v.DeviceIdx() == GenericParameter::kCpuId ||
device == GenericParameter::kCpuId ||
v.DeviceIdx() == device)
<< "Data is resided on a different device than `gpu_id`. "
<< "Device that data is on: " << v.DeviceIdx() << ", "
<< "`gpu_id` for XGBoost: " << device;
};
if (weights_.Size() != 0) {
CHECK_EQ(weights_.Size(), num_row_)
<< "Size of weights must equal to number of rows.";
check_device(weights_);
return;
}
if (labels_.Size() != 0) {
CHECK_EQ(labels_.Size(), num_row_)
<< "Size of labels must equal to number of rows.";
check_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.";
check_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.";
check_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.";
check_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.";
check_device(*base_margin_.Data());
}
}
#if !defined(XGBOOST_USE_CUDA)
void MetaInfo::SetInfoFromCUDA(StringView key, Json arr) { common::AssertGPUSupport(); }
#endif // !defined(XGBOOST_USE_CUDA)
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);
}
}
DMatrix *TryLoadBinary(std::string fname, bool silent) {
int 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;
}
DMatrix* DMatrix::Load(const std::string& uri,
bool silent,
bool load_row_split,
const std::string& file_format) {
std::string fname, cache_file;
size_t 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.";
if (load_row_split) {
std::ostringstream os;
std::vector<std::string> cache_shards = common::Split(cache_file, ':');
for (size_t i = 0; i < cache_shards.size(); ++i) {
size_t pos = cache_shards[i].rfind('.');
if (pos == std::string::npos) {
os << cache_shards[i]
<< ".r" << rabit::GetRank()
<< "-" << rabit::GetWorldSize();
} else {
os << cache_shards[i].substr(0, pos)
<< ".r" << rabit::GetRank()
<< "-" << rabit::GetWorldSize()
<< cache_shards[i].substr(pos, cache_shards[i].length());
}
if (i + 1 != cache_shards.size()) {
os << ':';
}
}
cache_file = os.str();
}
} else {
fname = uri;
}
int partid = 0, npart = 1;
if (load_row_split) {
partid = rabit::GetRank();
npart = rabit::GetWorldSize();
} else {
// test option to load in part
npart = dmlc::GetEnv("XGBOOST_TEST_NPART", 1);
}
if (npart != 1) {
LOG(CONSOLE) << "Load part of data " << partid
<< " of " << npart << " parts";
}
// legacy handling of binary data loading
if (file_format == "auto" && npart == 1) {
DMatrix *loaded = TryLoadBinary(fname, silent);
if (loaded) {
return loaded;
}
}
DMatrix* dmat {nullptr};
try {
if (cache_file.empty()) {
std::unique_ptr<dmlc::Parser<uint32_t>> parser(
dmlc::Parser<uint32_t>::Create(fname.c_str(), partid, npart,
file_format.c_str()));
data::FileAdapter adapter(parser.get());
dmat = DMatrix::Create(&adapter, std::numeric_limits<float>::quiet_NaN(),
1, cache_file);
} else {
data::FileIterator iter{fname, uint32_t(partid), uint32_t(npart),
file_format};
dmat = new data::SparsePageDMatrix{
&iter,
iter.Proxy(),
data::fileiter::Reset,
data::fileiter::Next,
std::numeric_limits<float>::quiet_NaN(),
1,
cache_file};
}
} catch (dmlc::Error &e) {
std::vector<std::string> splited = common::Split(fname, '#');
std::vector<std::string> args = common::Split(splited.front(), '?');
std::string format {file_format};
if (args.size() == 1 && file_format == "auto") {
auto extension = common::Split(args.front(), '.').back();
if (extension == "csv" || extension == "libsvm") {
format = extension;
}
if (format == extension) {
LOG(WARNING)
<< "No format parameter is provided in input uri, but found file extension: "
<< format << " . "
<< "Consider providing a uri parameter: filename?format=" << format;
} else {
LOG(WARNING)
<< "No format parameter is provided in input uri. "
<< "Choosing default parser in dmlc-core. "
<< "Consider providing a uri parameter like: filename?format=csv";
}
}
LOG(FATAL) << "Encountered parser error:\n" << e.what();
}
/* sync up number of features after matrix loaded.
* partitioned data will fail the train/val validation check
* since partitioned data not knowing the real number of features. */
rabit::Allreduce<rabit::op::Max>(&dmat->Info().num_col_, 1);
// backward compatiblity code.
if (!load_row_split) {
MetaInfo& info = dmat->Info();
if (MetaTryLoadGroup(fname + ".group", &info.group_ptr_) && !silent) {
LOG(CONSOLE) << info.group_ptr_.size() - 1
<< " groups are loaded from " << fname << ".group";
}
if (MetaTryLoadFloatInfo(fname + ".base_margin", &info.base_margin_.Data()->HostVector()) &&
!silent) {
LOG(CONSOLE) << info.base_margin_.Size() << " base_margin are loaded from " << fname
<< ".base_margin";
}
if (MetaTryLoadFloatInfo
(fname + ".weight", &info.weights_.HostVector()) && !silent) {
LOG(CONSOLE) << info.weights_.Size()
<< " weights are loaded from " << fname << ".weight";
}
}
return dmat;
}
template <typename DataIterHandle, typename DMatrixHandle,
typename DataIterResetCallback, typename XGDMatrixCallbackNext>
DMatrix *DMatrix::Create(DataIterHandle iter, DMatrixHandle proxy,
DataIterResetCallback *reset,
XGDMatrixCallbackNext *next, float missing,
int nthread,
int max_bin) {
return new data::IterativeDeviceDMatrix(iter, proxy, 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) {
return new data::SparsePageDMatrix(iter, proxy, reset, next, missing, n_threads,
cache);
}
template DMatrix *DMatrix::Create<DataIterHandle, DMatrixHandle,
DataIterResetCallback, XGDMatrixCallbackNext>(
DataIterHandle iter, DMatrixHandle proxy, 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);
template <typename AdapterT>
DMatrix* DMatrix::Create(AdapterT* adapter, float missing, int nthread,
const std::string& cache_prefix) {
return new data::SimpleDMatrix(adapter, missing, nthread);
}
template DMatrix* DMatrix::Create<data::DenseAdapter>(
data::DenseAdapter* adapter, float missing, int nthread,
const std::string& cache_prefix);
template DMatrix* DMatrix::Create<data::ArrayAdapter>(
data::ArrayAdapter* adapter, float missing, int nthread,
const std::string& cache_prefix);
template DMatrix* DMatrix::Create<data::CSRAdapter>(
data::CSRAdapter* adapter, float missing, int nthread,
const std::string& cache_prefix);
template DMatrix* DMatrix::Create<data::CSCAdapter>(
data::CSCAdapter* adapter, float missing, int nthread,
const std::string& cache_prefix);
template DMatrix* DMatrix::Create<data::DataTableAdapter>(
data::DataTableAdapter* adapter, float missing, int nthread,
const std::string& cache_prefix);
template DMatrix* DMatrix::Create<data::FileAdapter>(
data::FileAdapter* adapter, float missing, int nthread,
const std::string& cache_prefix);
template DMatrix* DMatrix::Create<data::CSRArrayAdapter>(
data::CSRArrayAdapter* adapter, float missing, int nthread,
const std::string& cache_prefix);
template DMatrix *
DMatrix::Create(data::IteratorAdapter<DataIterHandle, XGBCallbackDataIterNext,
XGBoostBatchCSR> *adapter,
float missing, int nthread, const std::string &cache_prefix);
SparsePage SparsePage::GetTranspose(int num_columns) const {
SparsePage transpose;
common::ParallelGroupBuilder<Entry, bst_row_t> builder(&transpose.offset.HostVector(),
&transpose.data.HostVector());
const int nthread = omp_get_max_threads();
builder.InitBudget(num_columns, nthread);
long batch_size = static_cast<long>(this->Size()); // NOLINT(*)
auto page = this->GetView();
common::ParallelFor(batch_size, [&](long i) { // NOLINT(*)
int tid = omp_get_thread_num();
auto inst = page[i];
for (const auto& entry : inst) {
builder.AddBudget(entry.index, tid);
}
});
builder.InitStorage();
common::ParallelFor(batch_size, [&](long i) { // NOLINT(*)