/
column_matrix.h
434 lines (382 loc) · 15.5 KB
/
column_matrix.h
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
/*!
* Copyright 2017-2022 by Contributors
* \file column_matrix.h
* \brief Utility for fast column-wise access
* \author Philip Cho
*/
#ifndef XGBOOST_COMMON_COLUMN_MATRIX_H_
#define XGBOOST_COMMON_COLUMN_MATRIX_H_
#include <dmlc/endian.h>
#include <algorithm>
#include <limits>
#include <memory>
#include <utility> // std::move
#include <vector>
#include "../data/gradient_index.h"
#include "hist_util.h"
namespace xgboost {
namespace common {
class ColumnMatrix;
/*! \brief column type */
enum ColumnType : uint8_t { kDenseColumn, kSparseColumn };
/*! \brief a column storage, to be used with ApplySplit. Note that each
bin id is stored as index[i] + index_base.
Different types of column index for each column allow
to reduce the memory usage. */
template <typename BinIdxType>
class Column {
public:
static constexpr bst_bin_t kMissingId = -1;
Column(common::Span<const BinIdxType> index, bst_bin_t least_bin_idx)
: index_(index), index_base_(least_bin_idx) {}
virtual ~Column() = default;
bst_bin_t GetGlobalBinIdx(size_t idx) const {
return index_base_ + static_cast<bst_bin_t>(index_[idx]);
}
/* returns number of elements in column */
size_t Size() const { return index_.size(); }
private:
/* bin indexes in range [0, max_bins - 1] */
common::Span<const BinIdxType> index_;
/* bin index offset for specific feature */
bst_bin_t const index_base_;
};
template <typename BinIdxT>
class SparseColumnIter : public Column<BinIdxT> {
private:
using Base = Column<BinIdxT>;
/* indexes of rows */
common::Span<const size_t> row_ind_;
size_t idx_;
size_t const* RowIndices() const { return row_ind_.data(); }
public:
SparseColumnIter(common::Span<const BinIdxT> index, bst_bin_t least_bin_idx,
common::Span<const size_t> row_ind, bst_row_t first_row_idx)
: Base{index, least_bin_idx}, row_ind_(row_ind) {
// first_row_id is the first row in the leaf partition
const size_t* row_data = RowIndices();
const size_t column_size = this->Size();
// search first nonzero row with index >= rid_span.front()
// note that the input row partition is always sorted.
const size_t* p = std::lower_bound(row_data, row_data + column_size, first_row_idx);
// column_size if all missing
idx_ = p - row_data;
}
SparseColumnIter(SparseColumnIter const&) = delete;
SparseColumnIter(SparseColumnIter&&) = default;
size_t GetRowIdx(size_t idx) const { return RowIndices()[idx]; }
bst_bin_t operator[](size_t rid) {
const size_t column_size = this->Size();
if (!((idx_) < column_size)) {
return this->kMissingId;
}
// find next non-missing row
while ((idx_) < column_size && GetRowIdx(idx_) < rid) {
++(idx_);
}
if (((idx_) < column_size) && GetRowIdx(idx_) == rid) {
// non-missing row found
return this->GetGlobalBinIdx(idx_);
} else {
// at the end of column
return this->kMissingId;
}
}
};
template <typename BinIdxT, bool any_missing>
class DenseColumnIter : public Column<BinIdxT> {
private:
using Base = Column<BinIdxT>;
/* flags for missing values in dense columns */
std::vector<bool> const& missing_flags_;
size_t feature_offset_;
public:
explicit DenseColumnIter(common::Span<const BinIdxT> index, bst_bin_t index_base,
std::vector<bool> const& missing_flags, size_t feature_offset)
: Base{index, index_base}, missing_flags_{missing_flags}, feature_offset_{feature_offset} {}
DenseColumnIter(DenseColumnIter const&) = delete;
DenseColumnIter(DenseColumnIter&&) = default;
bool IsMissing(size_t ridx) const { return missing_flags_[feature_offset_ + ridx]; }
bst_bin_t operator[](size_t ridx) const {
if (any_missing) {
return IsMissing(ridx) ? this->kMissingId : this->GetGlobalBinIdx(ridx);
} else {
return this->GetGlobalBinIdx(ridx);
}
}
};
/*! \brief a collection of columns, with support for construction from
GHistIndexMatrix. */
class ColumnMatrix {
public:
// get number of features
bst_feature_t GetNumFeature() const { return static_cast<bst_feature_t>(type_.size()); }
// construct column matrix from GHistIndexMatrix
inline void Init(SparsePage const& page, const GHistIndexMatrix& gmat, double sparse_threshold,
int32_t n_threads) {
auto const nfeature = static_cast<bst_feature_t>(gmat.cut.Ptrs().size() - 1);
const size_t nrow = gmat.row_ptr.size() - 1;
// identify type of each column
feature_counts_.resize(nfeature);
type_.resize(nfeature);
std::fill(feature_counts_.begin(), feature_counts_.end(), 0);
uint32_t max_val = std::numeric_limits<uint32_t>::max();
for (bst_feature_t fid = 0; fid < nfeature; ++fid) {
CHECK_LE(gmat.cut.Ptrs()[fid + 1] - gmat.cut.Ptrs()[fid], max_val);
}
bool all_dense = gmat.IsDense();
gmat.GetFeatureCounts(&feature_counts_[0]);
// classify features
for (bst_feature_t fid = 0; fid < nfeature; ++fid) {
if (static_cast<double>(feature_counts_[fid]) < sparse_threshold * nrow) {
type_[fid] = kSparseColumn;
all_dense = false;
} else {
type_[fid] = kDenseColumn;
}
}
// want to compute storage boundary for each feature
// using variants of prefix sum scan
feature_offsets_.resize(nfeature + 1);
size_t accum_index_ = 0;
feature_offsets_[0] = accum_index_;
for (bst_feature_t fid = 1; fid < nfeature + 1; ++fid) {
if (type_[fid - 1] == kDenseColumn) {
accum_index_ += static_cast<size_t>(nrow);
} else {
accum_index_ += feature_counts_[fid - 1];
}
feature_offsets_[fid] = accum_index_;
}
SetTypeSize(gmat.max_num_bins);
index_.resize(feature_offsets_[nfeature] * bins_type_size_, 0);
if (!all_dense) {
row_ind_.resize(feature_offsets_[nfeature]);
}
// store least bin id for each feature
index_base_ = const_cast<uint32_t*>(gmat.cut.Ptrs().data());
const bool noMissingValues = NoMissingValues(gmat.row_ptr[nrow], nrow, nfeature);
any_missing_ = !noMissingValues;
missing_flags_.clear();
if (noMissingValues) {
missing_flags_.resize(feature_offsets_[nfeature], false);
} else {
missing_flags_.resize(feature_offsets_[nfeature], true);
}
// pre-fill index_ for dense columns
if (all_dense) {
BinTypeSize gmat_bin_size = gmat.index.GetBinTypeSize();
if (gmat_bin_size == kUint8BinsTypeSize) {
SetIndexAllDense(page, gmat.index.data<uint8_t>(), gmat, nrow, nfeature, noMissingValues,
n_threads);
} else if (gmat_bin_size == kUint16BinsTypeSize) {
SetIndexAllDense(page, gmat.index.data<uint16_t>(), gmat, nrow, nfeature, noMissingValues,
n_threads);
} else {
CHECK_EQ(gmat_bin_size, kUint32BinsTypeSize);
SetIndexAllDense(page, gmat.index.data<uint32_t>(), gmat, nrow, nfeature, noMissingValues,
n_threads);
}
/* For sparse DMatrix gmat.index.getBinTypeSize() returns always kUint32BinsTypeSize
but for ColumnMatrix we still have a chance to reduce the memory consumption */
} else {
if (bins_type_size_ == kUint8BinsTypeSize) {
SetIndex<uint8_t>(page, gmat.index.data<uint32_t>(), gmat, nfeature);
} else if (bins_type_size_ == kUint16BinsTypeSize) {
SetIndex<uint16_t>(page, gmat.index.data<uint32_t>(), gmat, nfeature);
} else {
CHECK_EQ(bins_type_size_, kUint32BinsTypeSize);
SetIndex<uint32_t>(page, gmat.index.data<uint32_t>(), gmat, nfeature);
}
}
}
/* Set the number of bytes based on numeric limit of maximum number of bins provided by user */
void SetTypeSize(size_t max_num_bins) {
if ((max_num_bins - 1) <= static_cast<int>(std::numeric_limits<uint8_t>::max())) {
bins_type_size_ = kUint8BinsTypeSize;
} else if ((max_num_bins - 1) <= static_cast<int>(std::numeric_limits<uint16_t>::max())) {
bins_type_size_ = kUint16BinsTypeSize;
} else {
bins_type_size_ = kUint32BinsTypeSize;
}
}
template <typename BinIdxType>
auto SparseColumn(bst_feature_t fidx, bst_row_t first_row_idx) const {
const size_t feature_offset = feature_offsets_[fidx]; // to get right place for certain feature
const size_t column_size = feature_offsets_[fidx + 1] - feature_offset;
common::Span<const BinIdxType> bin_index = {
reinterpret_cast<const BinIdxType*>(&index_[feature_offset * bins_type_size_]),
column_size};
return SparseColumnIter<BinIdxType>(bin_index, index_base_[fidx],
{&row_ind_[feature_offset], column_size}, first_row_idx);
}
template <typename BinIdxType, bool any_missing>
auto DenseColumn(bst_feature_t fidx) const {
const size_t feature_offset = feature_offsets_[fidx]; // to get right place for certain feature
const size_t column_size = feature_offsets_[fidx + 1] - feature_offset;
common::Span<const BinIdxType> bin_index = {
reinterpret_cast<const BinIdxType*>(&index_[feature_offset * bins_type_size_]),
column_size};
return std::move(DenseColumnIter<BinIdxType, any_missing>{
bin_index, static_cast<bst_bin_t>(index_base_[fidx]), missing_flags_, feature_offset});
}
template <typename T>
inline void SetIndexAllDense(SparsePage const& page, T const* index, const GHistIndexMatrix& gmat,
const size_t nrow, const size_t nfeature, const bool noMissingValues,
int32_t n_threads) {
T* local_index = reinterpret_cast<T*>(&index_[0]);
/* missing values make sense only for column with type kDenseColumn,
and if no missing values were observed it could be handled much faster. */
if (noMissingValues) {
ParallelFor(nrow, n_threads, [&](auto rid) {
const size_t ibegin = rid * nfeature;
const size_t iend = (rid + 1) * nfeature;
size_t j = 0;
for (size_t i = ibegin; i < iend; ++i, ++j) {
const size_t idx = feature_offsets_[j];
local_index[idx + rid] = index[i];
}
});
} else {
/* to handle rows in all batches, sum of all batch sizes equal to gmat.row_ptr.size() - 1 */
auto get_bin_idx = [&](auto bin_id, auto rid, bst_feature_t fid) {
// T* begin = &local_index[feature_offsets_[fid]];
const size_t idx = feature_offsets_[fid];
/* rbegin allows to store indexes from specific SparsePage batch */
local_index[idx + rid] = bin_id;
missing_flags_[idx + rid] = false;
};
this->SetIndexSparse(page, index, gmat, nfeature, get_bin_idx);
}
}
// FIXME(jiamingy): In the future we might want to simply use binary search to simplify
// this and remove the dependency on SparsePage. This way we can have quantilized
// matrix for host similar to `DeviceQuantileDMatrix`.
template <typename T, typename BinFn>
void SetIndexSparse(SparsePage const& batch, T* index, const GHistIndexMatrix& gmat,
const size_t nfeature, BinFn&& assign_bin) {
std::vector<size_t> num_nonzeros(nfeature, 0ul);
const xgboost::Entry* data_ptr = batch.data.HostVector().data();
const std::vector<bst_row_t>& offset_vec = batch.offset.HostVector();
auto rbegin = 0;
const size_t batch_size = gmat.Size();
CHECK_LT(batch_size, offset_vec.size());
for (size_t rid = 0; rid < batch_size; ++rid) {
const size_t ibegin = gmat.row_ptr[rbegin + rid];
const size_t iend = gmat.row_ptr[rbegin + rid + 1];
const size_t size = offset_vec[rid + 1] - offset_vec[rid];
SparsePage::Inst inst = {data_ptr + offset_vec[rid], size};
CHECK_EQ(ibegin + inst.size(), iend);
size_t j = 0;
for (size_t i = ibegin; i < iend; ++i, ++j) {
const uint32_t bin_id = index[i];
auto fid = inst[j].index;
assign_bin(bin_id, rid, fid);
}
}
}
template <typename T>
inline void SetIndex(SparsePage const& page, uint32_t const* index, const GHistIndexMatrix& gmat,
const size_t nfeature) {
T* local_index = reinterpret_cast<T*>(&index_[0]);
std::vector<size_t> num_nonzeros;
num_nonzeros.resize(nfeature);
std::fill(num_nonzeros.begin(), num_nonzeros.end(), 0);
auto get_bin_idx = [&](auto bin_id, auto rid, bst_feature_t fid) {
if (type_[fid] == kDenseColumn) {
T* begin = &local_index[feature_offsets_[fid]];
begin[rid] = bin_id - index_base_[fid];
missing_flags_[feature_offsets_[fid] + rid] = false;
} else {
T* begin = &local_index[feature_offsets_[fid]];
begin[num_nonzeros[fid]] = bin_id - index_base_[fid];
row_ind_[feature_offsets_[fid] + num_nonzeros[fid]] = rid;
++num_nonzeros[fid];
}
};
this->SetIndexSparse(page, index, gmat, nfeature, get_bin_idx);
}
BinTypeSize GetTypeSize() const { return bins_type_size_; }
auto GetColumnType(bst_feature_t fidx) const { return type_[fidx]; }
// This is just an utility function
bool NoMissingValues(const size_t n_elements, const size_t n_row, const size_t n_features) {
return n_elements == n_features * n_row;
}
// And this returns part of state
bool AnyMissing() const { return any_missing_; }
// IO procedures for external memory.
bool Read(dmlc::SeekStream* fi, uint32_t const* index_base) {
fi->Read(&index_);
fi->Read(&feature_counts_);
#if !DMLC_LITTLE_ENDIAN
// s390x
std::vector<std::underlying_type<ColumnType>::type> int_types;
fi->Read(&int_types);
type_.resize(int_types.size());
std::transform(
int_types.begin(), int_types.end(), type_.begin(),
[](std::underlying_type<ColumnType>::type i) { return static_cast<ColumnType>(i); });
#else
fi->Read(&type_);
#endif // !DMLC_LITTLE_ENDIAN
fi->Read(&row_ind_);
fi->Read(&feature_offsets_);
index_base_ = index_base;
#if !DMLC_LITTLE_ENDIAN
std::underlying_type<BinTypeSize>::type v;
fi->Read(&v);
bins_type_size_ = static_cast<BinTypeSize>(v);
#else
fi->Read(&bins_type_size_);
#endif
fi->Read(&any_missing_);
return true;
}
size_t Write(dmlc::Stream* fo) const {
size_t bytes{0};
auto write_vec = [&](auto const& vec) {
fo->Write(vec);
bytes += vec.size() * sizeof(typename std::remove_reference_t<decltype(vec)>::value_type) +
sizeof(uint64_t);
};
write_vec(index_);
write_vec(feature_counts_);
#if !DMLC_LITTLE_ENDIAN
// s390x
std::vector<std::underlying_type<ColumnType>::type> int_types(type_.size());
std::transform(type_.begin(), type_.end(), int_types.begin(), [](ColumnType t) {
return static_cast<std::underlying_type<ColumnType>::type>(t);
});
write_vec(int_types);
#else
write_vec(type_);
#endif // !DMLC_LITTLE_ENDIAN
write_vec(row_ind_);
write_vec(feature_offsets_);
#if !DMLC_LITTLE_ENDIAN
auto v = static_cast<std::underlying_type<BinTypeSize>::type>(bins_type_size_);
fo->Write(v);
#else
fo->Write(bins_type_size_);
#endif // DMLC_LITTLE_ENDIAN
bytes += sizeof(bins_type_size_);
fo->Write(any_missing_);
bytes += sizeof(any_missing_);
return bytes;
}
private:
std::vector<uint8_t> index_;
std::vector<size_t> feature_counts_;
std::vector<ColumnType> type_;
std::vector<size_t> row_ind_;
/* indicate where each column's index and row_ind is stored. */
std::vector<size_t> feature_offsets_;
// index_base_[fid]: least bin id for feature fid
uint32_t const* index_base_;
std::vector<bool> missing_flags_;
BinTypeSize bins_type_size_;
bool any_missing_;
};
} // namespace common
} // namespace xgboost
#endif // XGBOOST_COMMON_COLUMN_MATRIX_H_