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External data adapters (#5044)

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* Use external data adapters as lightweight intermediate layer between external data and DMatrix
This commit is contained in:
Rory Mitchell
2019-12-04 10:56:17 +13:00
committed by GitHub
parent f2277e7106
commit e3c34c79be
15 changed files with 1058 additions and 593 deletions
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375
src/c_api/c_api.cc
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@@ -18,9 +18,8 @@
#include "c_api_error.h"
#include "../data/simple_csr_source.h"
#include "../common/math.h"
#include "../common/io.h"
#include "../common/group_data.h"
#include "../data/adapter.h"
namespace xgboost {
@@ -218,37 +217,9 @@ XGB_DLL int XGDMatrixCreateFromCSREx(const size_t* indptr,
size_t nelem,
size_t num_col,
DMatrixHandle* out) {
std::unique_ptr<data::SimpleCSRSource> source(new data::SimpleCSRSource());
API_BEGIN();
data::SimpleCSRSource& mat = *source;
auto& offset_vec = mat.page_.offset.HostVector();
auto& data_vec = mat.page_.data.HostVector();
offset_vec.reserve(nindptr);
data_vec.reserve(nelem);
offset_vec.resize(1);
offset_vec[0] = 0;
size_t num_column = 0;
for (size_t i = 1; i < nindptr; ++i) {
for (size_t j = indptr[i - 1]; j < indptr[i]; ++j) {
if (!common::CheckNAN(data[j])) {
// automatically skip nan.
data_vec.emplace_back(Entry(indices[j], data[j]));
num_column = std::max(num_column, static_cast<size_t>(indices[j] + 1));
}
}
offset_vec.push_back(mat.page_.data.Size());
}
mat.info.num_col_ = num_column;
if (num_col > 0) {
CHECK_LE(mat.info.num_col_, num_col)
<< "num_col=" << num_col << " vs " << mat.info.num_col_;
mat.info.num_col_ = num_col;
}
mat.info.num_row_ = nindptr - 1;
mat.info.num_nonzero_ = mat.page_.data.Size();
*out = new std::shared_ptr<DMatrix>(DMatrix::Create(std::move(source)));
data::CSRAdapter adapter(indptr, indices, data, nindptr - 1, nelem, num_col);
*out = new std::shared_ptr<DMatrix>(DMatrix::Create(&adapter, std::nan(""), 1));
API_END();
}
@@ -259,361 +230,41 @@ XGB_DLL int XGDMatrixCreateFromCSCEx(const size_t* col_ptr,
size_t nelem,
size_t num_row,
DMatrixHandle* out) {
std::unique_ptr<data::SimpleCSRSource> source(new data::SimpleCSRSource());
API_BEGIN();
// FIXME: User should be able to control number of threads
const int nthread = omp_get_max_threads();
data::SimpleCSRSource& mat = *source;
auto& offset_vec = mat.page_.offset.HostVector();
auto& data_vec = mat.page_.data.HostVector();
common::ParallelGroupBuilder<
Entry, std::remove_reference<decltype(offset_vec)>::type::value_type>
builder(&offset_vec, &data_vec);
builder.InitBudget(0, nthread);
size_t ncol = nindptr - 1; // NOLINT(*)
#pragma omp parallel for schedule(static)
for (omp_ulong i = 0; i < static_cast<omp_ulong>(ncol); ++i) { // NOLINT(*)
int tid = omp_get_thread_num();
for (size_t j = col_ptr[i]; j < col_ptr[i+1]; ++j) {
if (!common::CheckNAN(data[j])) {
builder.AddBudget(indices[j], tid);
}
}
}
builder.InitStorage();
#pragma omp parallel for schedule(static)
for (omp_ulong i = 0; i < static_cast<omp_ulong>(ncol); ++i) { // NOLINT(*)
int tid = omp_get_thread_num();
for (size_t j = col_ptr[i]; j < col_ptr[i+1]; ++j) {
if (!common::CheckNAN(data[j])) {
builder.Push(indices[j],
Entry(static_cast<bst_uint>(i), data[j]),
tid);
}
}
}
mat.info.num_row_ = mat.page_.offset.Size() - 1;
if (num_row > 0) {
CHECK_LE(mat.info.num_row_, num_row);
// provision for empty rows at the bottom of matrix
auto& offset_vec = mat.page_.offset.HostVector();
for (uint64_t i = mat.info.num_row_; i < static_cast<uint64_t>(num_row); ++i) {
offset_vec.push_back(offset_vec.back());
}
mat.info.num_row_ = num_row;
CHECK_EQ(mat.info.num_row_, offset_vec.size() - 1); // sanity check
}
mat.info.num_col_ = ncol;
mat.info.num_nonzero_ = nelem;
*out = new std::shared_ptr<DMatrix>(DMatrix::Create(std::move(source)));
data::CSCAdapter adapter(col_ptr, indices, data, nindptr - 1, num_row);
*out = new std::shared_ptr<DMatrix>(DMatrix::Create(&adapter, std::nan(""), 1));
API_END();
}
XGB_DLL int XGDMatrixCreateFromMat(const bst_float* data,
xgboost::bst_ulong nrow,
xgboost::bst_ulong ncol,
bst_float missing,
xgboost::bst_ulong ncol, bst_float missing,
DMatrixHandle* out) {
std::unique_ptr<data::SimpleCSRSource> source(new data::SimpleCSRSource());
API_BEGIN();
data::SimpleCSRSource& mat = *source;
auto& offset_vec = mat.page_.offset.HostVector();
auto& data_vec = mat.page_.data.HostVector();
offset_vec.resize(1+nrow);
bool nan_missing = common::CheckNAN(missing);
mat.info.num_row_ = nrow;
mat.info.num_col_ = ncol;
const bst_float* data0 = data;
// count elements for sizing data
data = data0;
for (xgboost::bst_ulong i = 0; i < nrow; ++i, data += ncol) {
xgboost::bst_ulong nelem = 0;
for (xgboost::bst_ulong j = 0; j < ncol; ++j) {
if (common::CheckNAN(data[j])) {
CHECK(nan_missing)
<< "There are NAN in the matrix, however, you did not set missing=NAN";
} else {
if (nan_missing || data[j] != missing) {
++nelem;
}
}
}
offset_vec[i+1] = offset_vec[i] + nelem;
}
data_vec.resize(mat.page_.data.Size() + offset_vec.back());
data = data0;
for (xgboost::bst_ulong i = 0; i < nrow; ++i, data += ncol) {
xgboost::bst_ulong matj = 0;
for (xgboost::bst_ulong j = 0; j < ncol; ++j) {
if (common::CheckNAN(data[j])) {
} else {
if (nan_missing || data[j] != missing) {
data_vec[offset_vec[i] + matj] = Entry(j, data[j]);
++matj;
}
}
}
}
mat.info.num_nonzero_ = mat.page_.data.Size();
*out = new std::shared_ptr<DMatrix>(DMatrix::Create(std::move(source)));
data::DenseAdapter adapter(data, nrow, nrow * ncol, ncol);
*out = new std::shared_ptr<DMatrix>(DMatrix::Create(&adapter, missing, 1));
API_END();
}
template <typename T>
void PrefixSum(T *x, size_t N) {
std::vector<T> suma;
#pragma omp parallel
{
const int ithread = omp_get_thread_num();
const int nthreads = omp_get_num_threads();
#pragma omp single
{
suma.resize(nthreads+1);
suma[0] = 0;
}
T sum = 0;
T offset = 0;
#pragma omp for schedule(static)
for (omp_ulong i = 0; i < N; i++) {
sum += x[i];
x[i] = sum;
}
suma[ithread+1] = sum;
#pragma omp barrier
for (omp_ulong i = 0; i < static_cast<omp_ulong>(ithread+1); i++) {
offset += suma[i];
}
#pragma omp for schedule(static)
for (omp_ulong i = 0; i < N; i++) {
x[i] += offset;
}
}
}
XGB_DLL int XGDMatrixCreateFromMat_omp(const bst_float* data, // NOLINT
xgboost::bst_ulong nrow,
xgboost::bst_ulong ncol,
bst_float missing, DMatrixHandle* out,
int nthread) {
// avoid openmp unless enough data to be worth it to avoid overhead costs
if (nrow*ncol <= 10000*50) {
return(XGDMatrixCreateFromMat(data, nrow, ncol, missing, out));
}
API_BEGIN();
const int nthreadmax = std::max(omp_get_num_procs() / 2 - 1, 1);
// const int nthreadmax = omp_get_max_threads();
if (nthread <= 0) nthread=nthreadmax;
int nthread_orig = omp_get_max_threads();
omp_set_num_threads(nthread);
std::unique_ptr<data::SimpleCSRSource> source(new data::SimpleCSRSource());
data::SimpleCSRSource& mat = *source;
auto& offset_vec = mat.page_.offset.HostVector();
auto& data_vec = mat.page_.data.HostVector();
offset_vec.resize(1+nrow);
mat.info.num_row_ = nrow;
mat.info.num_col_ = ncol;
// Check for errors in missing elements
// Count elements per row (to avoid otherwise need to copy)
bool nan_missing = common::CheckNAN(missing);
std::vector<int> badnan;
badnan.resize(nthread, 0);
#pragma omp parallel num_threads(nthread)
{
int ithread = omp_get_thread_num();
// Count elements per row
#pragma omp for schedule(static)
for (omp_ulong i = 0; i < nrow; ++i) {
xgboost::bst_ulong nelem = 0;
for (xgboost::bst_ulong j = 0; j < ncol; ++j) {
if (common::CheckNAN(data[ncol*i + j]) && !nan_missing) {
badnan[ithread] = 1;
} else if (common::CheckNAN(data[ncol * i + j])) {
} else if (nan_missing || data[ncol * i + j] != missing) {
++nelem;
}
}
offset_vec[i+1] = nelem;
}
}
// Inform about any NaNs and resize data matrix
for (int i = 0; i < nthread; i++) {
CHECK(!badnan[i]) << "There are NAN in the matrix, however, you did not set missing=NAN";
}
// do cumulative sum (to avoid otherwise need to copy)
PrefixSum(&offset_vec[0], offset_vec.size());
data_vec.resize(mat.page_.data.Size() + offset_vec.back());
// Fill data matrix (now that know size, no need for slow push_back())
#pragma omp parallel num_threads(nthread)
{
#pragma omp for schedule(static)
for (omp_ulong i = 0; i < nrow; ++i) {
xgboost::bst_ulong matj = 0;
for (xgboost::bst_ulong j = 0; j < ncol; ++j) {
if (common::CheckNAN(data[ncol * i + j])) {
} else if (nan_missing || data[ncol * i + j] != missing) {
data_vec[offset_vec[i] + matj] =
Entry(j, data[ncol * i + j]);
++matj;
}
}
}
}
// restore omp state
omp_set_num_threads(nthread_orig);
mat.info.num_nonzero_ = mat.page_.data.Size();
*out = new std::shared_ptr<DMatrix>(DMatrix::Create(std::move(source)));
data::DenseAdapter adapter(data, nrow, nrow * ncol, ncol);
*out = new std::shared_ptr<DMatrix>(DMatrix::Create(&adapter, missing, nthread));
API_END();
}
enum class DTType : uint8_t {
kFloat32 = 0,
kFloat64 = 1,
kBool8 = 2,
kInt32 = 3,
kInt8 = 4,
kInt16 = 5,
kInt64 = 6,
kUnknown = 7
};
DTType DTGetType(std::string type_string) {
if (type_string == "float32") {
return DTType::kFloat32;
} else if (type_string == "float64") {
return DTType::kFloat64;
} else if (type_string == "bool8") {
return DTType::kBool8;
} else if (type_string == "int32") {
return DTType::kInt32;
} else if (type_string == "int8") {
return DTType::kInt8;
} else if (type_string == "int16") {
return DTType::kInt16;
} else if (type_string == "int64") {
return DTType::kInt64;
} else {
LOG(FATAL) << "Unknown data table type.";
return DTType::kUnknown;
}
}
float DTGetValue(void* column, DTType dt_type, size_t ridx) {
float missing = std::numeric_limits<float>::quiet_NaN();
switch (dt_type) {
case DTType::kFloat32: {
float val = reinterpret_cast<float*>(column)[ridx];
return std::isfinite(val) ? val : missing;
}
case DTType::kFloat64: {
double val = reinterpret_cast<double*>(column)[ridx];
return std::isfinite(val) ? static_cast<float>(val) : missing;
}
case DTType::kBool8: {
bool val = reinterpret_cast<bool*>(column)[ridx];
return static_cast<float>(val);
}
case DTType::kInt32: {
int32_t val = reinterpret_cast<int32_t*>(column)[ridx];
return val != (-2147483647 - 1) ? static_cast<float>(val) : missing;
}
case DTType::kInt8: {
int8_t val = reinterpret_cast<int8_t*>(column)[ridx];
return val != -128 ? static_cast<float>(val) : missing;
}
case DTType::kInt16: {
int16_t val = reinterpret_cast<int16_t*>(column)[ridx];
return val != -32768 ? static_cast<float>(val) : missing;
}
case DTType::kInt64: {
int64_t val = reinterpret_cast<int64_t*>(column)[ridx];
return val != -9223372036854775807 - 1 ? static_cast<float>(val)
: missing;
}
default: {
LOG(FATAL) << "Unknown data table type.";
return 0.0f;
}
}
}
XGB_DLL int XGDMatrixCreateFromDT(void** data, const char** feature_stypes,
xgboost::bst_ulong nrow,
xgboost::bst_ulong ncol, DMatrixHandle* out,
int nthread) {
// avoid openmp unless enough data to be worth it to avoid overhead costs
if (nrow * ncol <= 10000 * 50) {
nthread = 1;
}
API_BEGIN();
const int nthreadmax = std::max(omp_get_num_procs() / 2 - 1, 1);
if (nthread <= 0) nthread = nthreadmax;
int nthread_orig = omp_get_max_threads();
omp_set_num_threads(nthread);
std::unique_ptr<data::SimpleCSRSource> source(new data::SimpleCSRSource());
data::SimpleCSRSource& mat = *source;
mat.page_.offset.Resize(1 + nrow);
mat.info.num_row_ = nrow;
mat.info.num_col_ = ncol;
auto& page_offset = mat.page_.offset.HostVector();
#pragma omp parallel num_threads(nthread)
{
// Count elements per row, column by column
for (auto j = 0u; j < ncol; ++j) {
DTType dtype = DTGetType(feature_stypes[j]);
#pragma omp for schedule(static)
for (omp_ulong i = 0; i < nrow; ++i) {
float val = DTGetValue(data[j], dtype, i);
if (!std::isnan(val)) {
page_offset[i + 1]++;
}
}
}
}
// do cumulative sum (to avoid otherwise need to copy)
PrefixSum(&page_offset[0], page_offset.size());
mat.page_.data.Resize(mat.page_.data.Size() + page_offset.back());
auto& page_data = mat.page_.data.HostVector();
// Fill data matrix (now that know size, no need for slow push_back())
std::vector<size_t> position(nrow);
#pragma omp parallel num_threads(nthread)
{
for (xgboost::bst_ulong j = 0; j < ncol; ++j) {
DTType dtype = DTGetType(feature_stypes[j]);
#pragma omp for schedule(static)
for (omp_ulong i = 0; i < nrow; ++i) {
float val = DTGetValue(data[j], dtype, i);
if (!std::isnan(val)) {
page_data[page_offset[i] + position[i]] = Entry(j, val);
position[i]++;
}
}
}
}
// restore omp state
omp_set_num_threads(nthread_orig);
mat.info.num_nonzero_ = mat.page_.data.Size();
*out = new std::shared_ptr<DMatrix>(DMatrix::Create(std::move(source)));
data::DataTableAdapter adapter(data, feature_stypes, nrow, ncol);
*out = new std::shared_ptr<DMatrix>(
DMatrix::Create(&adapter, std::nan(""), nthread));
API_END();
}