-
Notifications
You must be signed in to change notification settings - Fork 3.8k
/
dense_bin.hpp
649 lines (596 loc) · 24.7 KB
/
dense_bin.hpp
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
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
/*!
* Copyright (c) 2016 Microsoft Corporation. All rights reserved.
* Licensed under the MIT License. See LICENSE file in the project root for
* license information.
*/
#ifndef LIGHTGBM_IO_DENSE_BIN_HPP_
#define LIGHTGBM_IO_DENSE_BIN_HPP_
#include <LightGBM/bin.h>
#include <LightGBM/cuda/vector_cudahost.h>
#include <cstdint>
#include <cstring>
#include <vector>
namespace LightGBM {
template <typename VAL_T, bool IS_4BIT>
class DenseBin;
template <typename VAL_T, bool IS_4BIT>
class DenseBinIterator : public BinIterator {
public:
explicit DenseBinIterator(const DenseBin<VAL_T, IS_4BIT>* bin_data,
uint32_t min_bin, uint32_t max_bin,
uint32_t most_freq_bin)
: bin_data_(bin_data),
min_bin_(static_cast<VAL_T>(min_bin)),
max_bin_(static_cast<VAL_T>(max_bin)),
most_freq_bin_(static_cast<VAL_T>(most_freq_bin)) {
if (most_freq_bin_ == 0) {
offset_ = 1;
} else {
offset_ = 0;
}
}
inline uint32_t RawGet(data_size_t idx) override;
inline uint32_t Get(data_size_t idx) override;
inline void Reset(data_size_t) override {}
private:
const DenseBin<VAL_T, IS_4BIT>* bin_data_;
VAL_T min_bin_;
VAL_T max_bin_;
VAL_T most_freq_bin_;
uint8_t offset_;
};
/*!
* \brief Used to store bins for dense feature
* Use template to reduce memory cost
*/
template <typename VAL_T, bool IS_4BIT>
class DenseBin : public Bin {
public:
friend DenseBinIterator<VAL_T, IS_4BIT>;
explicit DenseBin(data_size_t num_data)
: num_data_(num_data) {
if (IS_4BIT) {
CHECK_EQ(sizeof(VAL_T), 1);
data_.resize((num_data_ + 1) / 2, static_cast<uint8_t>(0));
buf_.resize((num_data_ + 1) / 2, static_cast<uint8_t>(0));
} else {
data_.resize(num_data_, static_cast<VAL_T>(0));
}
}
~DenseBin() {}
void Push(int, data_size_t idx, uint32_t value) override {
if (IS_4BIT) {
const int i1 = idx >> 1;
const int i2 = (idx & 1) << 2;
const uint8_t val = static_cast<uint8_t>(value) << i2;
if (i2 == 0) {
data_[i1] = val;
} else {
buf_[i1] = val;
}
} else {
data_[idx] = static_cast<VAL_T>(value);
}
}
void ReSize(data_size_t num_data) override {
if (num_data_ != num_data) {
num_data_ = num_data;
if (IS_4BIT) {
data_.resize((num_data_ + 1) / 2, static_cast<VAL_T>(0));
} else {
data_.resize(num_data_);
}
}
}
BinIterator* GetIterator(uint32_t min_bin, uint32_t max_bin,
uint32_t most_freq_bin) const override;
template <bool USE_INDICES, bool USE_PREFETCH, bool USE_HESSIAN>
void ConstructHistogramInner(const data_size_t* data_indices,
data_size_t start, data_size_t end,
const score_t* ordered_gradients,
const score_t* ordered_hessians,
hist_t* out) const {
data_size_t i = start;
hist_t* grad = out;
hist_t* hess = out + 1;
hist_cnt_t* cnt = reinterpret_cast<hist_cnt_t*>(hess);
if (USE_PREFETCH) {
const data_size_t pf_offset = 64 / sizeof(VAL_T);
const data_size_t pf_end = end - pf_offset;
for (; i < pf_end; ++i) {
const auto idx = USE_INDICES ? data_indices[i] : i;
const auto pf_idx =
USE_INDICES ? data_indices[i + pf_offset] : i + pf_offset;
if (IS_4BIT) {
PREFETCH_T0(data_.data() + (pf_idx >> 1));
} else {
PREFETCH_T0(data_.data() + pf_idx);
}
const auto ti = static_cast<uint32_t>(data(idx)) << 1;
if (USE_HESSIAN) {
grad[ti] += ordered_gradients[i];
hess[ti] += ordered_hessians[i];
} else {
grad[ti] += ordered_gradients[i];
++cnt[ti];
}
}
}
for (; i < end; ++i) {
const auto idx = USE_INDICES ? data_indices[i] : i;
const auto ti = static_cast<uint32_t>(data(idx)) << 1;
if (USE_HESSIAN) {
grad[ti] += ordered_gradients[i];
hess[ti] += ordered_hessians[i];
} else {
grad[ti] += ordered_gradients[i];
++cnt[ti];
}
}
}
void ConstructHistogram(const data_size_t* data_indices, data_size_t start,
data_size_t end, const score_t* ordered_gradients,
const score_t* ordered_hessians,
hist_t* out) const override {
ConstructHistogramInner<true, true, true>(
data_indices, start, end, ordered_gradients, ordered_hessians, out);
}
void ConstructHistogram(data_size_t start, data_size_t end,
const score_t* ordered_gradients,
const score_t* ordered_hessians,
hist_t* out) const override {
ConstructHistogramInner<false, false, true>(
nullptr, start, end, ordered_gradients, ordered_hessians, out);
}
void ConstructHistogram(const data_size_t* data_indices, data_size_t start,
data_size_t end, const score_t* ordered_gradients,
hist_t* out) const override {
ConstructHistogramInner<true, true, false>(data_indices, start, end,
ordered_gradients, nullptr, out);
}
void ConstructHistogram(data_size_t start, data_size_t end,
const score_t* ordered_gradients,
hist_t* out) const override {
ConstructHistogramInner<false, false, false>(
nullptr, start, end, ordered_gradients, nullptr, out);
}
template <bool USE_INDICES, bool USE_PREFETCH, bool USE_HESSIAN, typename PACKED_HIST_T, int HIST_BITS>
void ConstructHistogramIntInner(const data_size_t* data_indices,
data_size_t start, data_size_t end,
const score_t* ordered_gradients,
hist_t* out) const {
data_size_t i = start;
PACKED_HIST_T* out_ptr = reinterpret_cast<PACKED_HIST_T*>(out);
const int16_t* gradients_ptr = reinterpret_cast<const int16_t*>(ordered_gradients);
const VAL_T* data_ptr_base = data_.data();
if (USE_PREFETCH) {
const data_size_t pf_offset = 64 / sizeof(VAL_T);
const data_size_t pf_end = end - pf_offset;
for (; i < pf_end; ++i) {
const auto idx = USE_INDICES ? data_indices[i] : i;
const auto pf_idx =
USE_INDICES ? data_indices[i + pf_offset] : i + pf_offset;
if (IS_4BIT) {
PREFETCH_T0(data_ptr_base + (pf_idx >> 1));
} else {
PREFETCH_T0(data_ptr_base + pf_idx);
}
const auto ti = static_cast<uint32_t>(data(idx));
const int16_t gradient_16 = gradients_ptr[i];
if (USE_HESSIAN) {
const PACKED_HIST_T gradient_packed = HIST_BITS == 8 ? gradient_16 :
(static_cast<PACKED_HIST_T>(static_cast<int8_t>(gradient_16 >> 8)) << HIST_BITS) | (gradient_16 & 0xff);
out_ptr[ti] += gradient_packed;
} else {
const PACKED_HIST_T gradient_packed = HIST_BITS == 8 ? gradient_16 :
(static_cast<PACKED_HIST_T>(static_cast<int8_t>(gradient_16 >> 8)) << HIST_BITS) | (1);
out_ptr[ti] += gradient_packed;
}
}
}
for (; i < end; ++i) {
const auto idx = USE_INDICES ? data_indices[i] : i;
const auto ti = static_cast<uint32_t>(data(idx));
const int16_t gradient_16 = gradients_ptr[i];
if (USE_HESSIAN) {
const PACKED_HIST_T gradient_packed = HIST_BITS == 8 ? gradient_16 :
(static_cast<PACKED_HIST_T>(static_cast<int8_t>(gradient_16 >> 8)) << HIST_BITS) | (gradient_16 & 0xff);
out_ptr[ti] += gradient_packed;
} else {
const PACKED_HIST_T gradient_packed = HIST_BITS == 8 ? gradient_16 :
(static_cast<PACKED_HIST_T>(static_cast<int8_t>(gradient_16 >> 8)) << HIST_BITS) | (1);
out_ptr[ti] += gradient_packed;
}
}
}
void ConstructHistogramInt8(const data_size_t* data_indices, data_size_t start,
data_size_t end, const score_t* ordered_gradients,
const score_t* /*ordered_hessians*/,
hist_t* out) const override {
ConstructHistogramIntInner<true, true, true, int16_t, 8>(
data_indices, start, end, ordered_gradients, out);
}
void ConstructHistogramInt8(data_size_t start, data_size_t end,
const score_t* ordered_gradients,
const score_t* /*ordered_hessians*/,
hist_t* out) const override {
ConstructHistogramIntInner<false, false, true, int16_t, 8>(
nullptr, start, end, ordered_gradients, out);
}
void ConstructHistogramInt8(const data_size_t* data_indices, data_size_t start,
data_size_t end, const score_t* ordered_gradients,
hist_t* out) const override {
ConstructHistogramIntInner<true, true, false, int16_t, 8>(
data_indices, start, end, ordered_gradients, out);
}
void ConstructHistogramInt8(data_size_t start, data_size_t end,
const score_t* ordered_gradients,
hist_t* out) const override {
ConstructHistogramIntInner<false, false, false, int16_t, 8>(
nullptr, start, end, ordered_gradients, out);
}
void ConstructHistogramInt16(const data_size_t* data_indices, data_size_t start,
data_size_t end, const score_t* ordered_gradients,
const score_t* /*ordered_hessians*/,
hist_t* out) const override {
ConstructHistogramIntInner<true, true, true, int32_t, 16>(
data_indices, start, end, ordered_gradients, out);
}
void ConstructHistogramInt16(data_size_t start, data_size_t end,
const score_t* ordered_gradients,
const score_t* /*ordered_hessians*/,
hist_t* out) const override {
ConstructHistogramIntInner<false, false, true, int32_t, 16>(
nullptr, start, end, ordered_gradients, out);
}
void ConstructHistogramInt16(const data_size_t* data_indices, data_size_t start,
data_size_t end, const score_t* ordered_gradients,
hist_t* out) const override {
ConstructHistogramIntInner<true, true, false, int32_t, 16>(
data_indices, start, end, ordered_gradients, out);
}
void ConstructHistogramInt16(data_size_t start, data_size_t end,
const score_t* ordered_gradients,
hist_t* out) const override {
ConstructHistogramIntInner<false, false, false, int32_t, 16>(
nullptr, start, end, ordered_gradients, out);
}
void ConstructHistogramInt32(const data_size_t* data_indices, data_size_t start,
data_size_t end, const score_t* ordered_gradients,
const score_t* /*ordered_hessians*/,
hist_t* out) const override {
ConstructHistogramIntInner<true, true, true, int64_t, 32>(
data_indices, start, end, ordered_gradients, out);
}
void ConstructHistogramInt32(data_size_t start, data_size_t end,
const score_t* ordered_gradients,
const score_t* /*ordered_hessians*/,
hist_t* out) const override {
ConstructHistogramIntInner<false, false, true, int64_t, 32>(
nullptr, start, end, ordered_gradients, out);
}
void ConstructHistogramInt32(const data_size_t* data_indices, data_size_t start,
data_size_t end, const score_t* ordered_gradients,
hist_t* out) const override {
ConstructHistogramIntInner<true, true, false, int64_t, 32>(
data_indices, start, end, ordered_gradients, out);
}
void ConstructHistogramInt32(data_size_t start, data_size_t end,
const score_t* ordered_gradients,
hist_t* out) const override {
ConstructHistogramIntInner<false, false, false, int64_t, 32>(
nullptr, start, end, ordered_gradients, out);
}
template <bool MISS_IS_ZERO, bool MISS_IS_NA, bool MFB_IS_ZERO,
bool MFB_IS_NA, bool USE_MIN_BIN>
data_size_t SplitInner(uint32_t min_bin, uint32_t max_bin,
uint32_t default_bin, uint32_t most_freq_bin,
bool default_left, uint32_t threshold,
const data_size_t* data_indices, data_size_t cnt,
data_size_t* lte_indices,
data_size_t* gt_indices) const {
auto th = static_cast<VAL_T>(threshold + min_bin);
auto t_zero_bin = static_cast<VAL_T>(min_bin + default_bin);
if (most_freq_bin == 0) {
--th;
--t_zero_bin;
}
const auto minb = static_cast<VAL_T>(min_bin);
const auto maxb = static_cast<VAL_T>(max_bin);
data_size_t lte_count = 0;
data_size_t gt_count = 0;
data_size_t* default_indices = gt_indices;
data_size_t* default_count = >_count;
data_size_t* missing_default_indices = gt_indices;
data_size_t* missing_default_count = >_count;
if (most_freq_bin <= threshold) {
default_indices = lte_indices;
default_count = <e_count;
}
if (MISS_IS_ZERO || MISS_IS_NA) {
if (default_left) {
missing_default_indices = lte_indices;
missing_default_count = <e_count;
}
}
if (min_bin < max_bin) {
for (data_size_t i = 0; i < cnt; ++i) {
const data_size_t idx = data_indices[i];
const auto bin = data(idx);
if ((MISS_IS_ZERO && !MFB_IS_ZERO && bin == t_zero_bin) ||
(MISS_IS_NA && !MFB_IS_NA && bin == maxb)) {
missing_default_indices[(*missing_default_count)++] = idx;
} else if ((USE_MIN_BIN && (bin < minb || bin > maxb)) ||
(!USE_MIN_BIN && bin == 0)) {
if ((MISS_IS_NA && MFB_IS_NA) || (MISS_IS_ZERO && MFB_IS_ZERO)) {
missing_default_indices[(*missing_default_count)++] = idx;
} else {
default_indices[(*default_count)++] = idx;
}
} else if (bin > th) {
gt_indices[gt_count++] = idx;
} else {
lte_indices[lte_count++] = idx;
}
}
} else {
data_size_t* max_bin_indices = gt_indices;
data_size_t* max_bin_count = >_count;
if (maxb <= th) {
max_bin_indices = lte_indices;
max_bin_count = <e_count;
}
for (data_size_t i = 0; i < cnt; ++i) {
const data_size_t idx = data_indices[i];
const auto bin = data(idx);
if (MISS_IS_ZERO && !MFB_IS_ZERO && bin == t_zero_bin) {
missing_default_indices[(*missing_default_count)++] = idx;
} else if (bin != maxb) {
if ((MISS_IS_NA && MFB_IS_NA) || (MISS_IS_ZERO && MFB_IS_ZERO)) {
missing_default_indices[(*missing_default_count)++] = idx;
} else {
default_indices[(*default_count)++] = idx;
}
} else {
if (MISS_IS_NA && !MFB_IS_NA) {
missing_default_indices[(*missing_default_count)++] = idx;
} else {
max_bin_indices[(*max_bin_count)++] = idx;
}
}
}
}
return lte_count;
}
data_size_t Split(uint32_t min_bin, uint32_t max_bin, uint32_t default_bin,
uint32_t most_freq_bin, MissingType missing_type,
bool default_left, uint32_t threshold,
const data_size_t* data_indices, data_size_t cnt,
data_size_t* lte_indices,
data_size_t* gt_indices) const override {
#define ARGUMENTS \
min_bin, max_bin, default_bin, most_freq_bin, default_left, threshold, \
data_indices, cnt, lte_indices, gt_indices
if (missing_type == MissingType::None) {
return SplitInner<false, false, false, false, true>(ARGUMENTS);
} else if (missing_type == MissingType::Zero) {
if (default_bin == most_freq_bin) {
return SplitInner<true, false, true, false, true>(ARGUMENTS);
} else {
return SplitInner<true, false, false, false, true>(ARGUMENTS);
}
} else {
if (max_bin == most_freq_bin + min_bin && most_freq_bin > 0) {
return SplitInner<false, true, false, true, true>(ARGUMENTS);
} else {
return SplitInner<false, true, false, false, true>(ARGUMENTS);
}
}
#undef ARGUMENTS
}
data_size_t Split(uint32_t max_bin, uint32_t default_bin,
uint32_t most_freq_bin, MissingType missing_type,
bool default_left, uint32_t threshold,
const data_size_t* data_indices, data_size_t cnt,
data_size_t* lte_indices,
data_size_t* gt_indices) const override {
#define ARGUMENTS \
1, max_bin, default_bin, most_freq_bin, default_left, threshold, \
data_indices, cnt, lte_indices, gt_indices
if (missing_type == MissingType::None) {
return SplitInner<false, false, false, false, false>(ARGUMENTS);
} else if (missing_type == MissingType::Zero) {
if (default_bin == most_freq_bin) {
return SplitInner<true, false, true, false, false>(ARGUMENTS);
} else {
return SplitInner<true, false, false, false, false>(ARGUMENTS);
}
} else {
if (max_bin == most_freq_bin + 1 && most_freq_bin > 0) {
return SplitInner<false, true, false, true, false>(ARGUMENTS);
} else {
return SplitInner<false, true, false, false, false>(ARGUMENTS);
}
}
#undef ARGUMENTS
}
template <bool USE_MIN_BIN>
data_size_t SplitCategoricalInner(uint32_t min_bin, uint32_t max_bin,
uint32_t most_freq_bin,
const uint32_t* threshold,
int num_threshold,
const data_size_t* data_indices,
data_size_t cnt, data_size_t* lte_indices,
data_size_t* gt_indices) const {
data_size_t lte_count = 0;
data_size_t gt_count = 0;
data_size_t* default_indices = gt_indices;
data_size_t* default_count = >_count;
int8_t offset = most_freq_bin == 0 ? 1 : 0;
if (most_freq_bin > 0 &&
Common::FindInBitset(threshold, num_threshold, most_freq_bin)) {
default_indices = lte_indices;
default_count = <e_count;
}
for (data_size_t i = 0; i < cnt; ++i) {
const data_size_t idx = data_indices[i];
const uint32_t bin = data(idx);
if (USE_MIN_BIN && (bin < min_bin || bin > max_bin)) {
default_indices[(*default_count)++] = idx;
} else if (!USE_MIN_BIN && bin == 0) {
default_indices[(*default_count)++] = idx;
} else if (Common::FindInBitset(threshold, num_threshold,
bin - min_bin + offset)) {
lte_indices[lte_count++] = idx;
} else {
gt_indices[gt_count++] = idx;
}
}
return lte_count;
}
data_size_t SplitCategorical(uint32_t min_bin, uint32_t max_bin,
uint32_t most_freq_bin,
const uint32_t* threshold, int num_threshold,
const data_size_t* data_indices, data_size_t cnt,
data_size_t* lte_indices,
data_size_t* gt_indices) const override {
return SplitCategoricalInner<true>(min_bin, max_bin, most_freq_bin,
threshold, num_threshold, data_indices,
cnt, lte_indices, gt_indices);
}
data_size_t SplitCategorical(uint32_t max_bin, uint32_t most_freq_bin,
const uint32_t* threshold, int num_threshold,
const data_size_t* data_indices, data_size_t cnt,
data_size_t* lte_indices,
data_size_t* gt_indices) const override {
return SplitCategoricalInner<false>(1, max_bin, most_freq_bin, threshold,
num_threshold, data_indices, cnt,
lte_indices, gt_indices);
}
data_size_t num_data() const override { return num_data_; }
void* get_data() override { return data_.data(); }
void FinishLoad() override {
if (IS_4BIT) {
if (buf_.empty()) {
return;
}
int len = (num_data_ + 1) / 2;
for (int i = 0; i < len; ++i) {
data_[i] |= buf_[i];
}
buf_.clear();
}
}
void LoadFromMemory(
const void* memory,
const std::vector<data_size_t>& local_used_indices) override {
const VAL_T* mem_data = reinterpret_cast<const VAL_T*>(memory);
if (!local_used_indices.empty()) {
if (IS_4BIT) {
const data_size_t rest = num_data_ & 1;
for (int i = 0; i < num_data_ - rest; i += 2) {
// get old bins
data_size_t idx = local_used_indices[i];
const auto bin1 = static_cast<uint8_t>(
(mem_data[idx >> 1] >> ((idx & 1) << 2)) & 0xf);
idx = local_used_indices[i + 1];
const auto bin2 = static_cast<uint8_t>(
(mem_data[idx >> 1] >> ((idx & 1) << 2)) & 0xf);
// add
const int i1 = i >> 1;
data_[i1] = (bin1 | (bin2 << 4));
}
if (rest) {
data_size_t idx = local_used_indices[num_data_ - 1];
data_[num_data_ >> 1] =
(mem_data[idx >> 1] >> ((idx & 1) << 2)) & 0xf;
}
} else {
for (int i = 0; i < num_data_; ++i) {
data_[i] = mem_data[local_used_indices[i]];
}
}
} else {
for (size_t i = 0; i < data_.size(); ++i) {
data_[i] = mem_data[i];
}
}
}
inline VAL_T data(data_size_t idx) const {
if (IS_4BIT) {
return (data_[idx >> 1] >> ((idx & 1) << 2)) & 0xf;
} else {
return data_[idx];
}
}
void CopySubrow(const Bin* full_bin, const data_size_t* used_indices,
data_size_t num_used_indices) override {
auto other_bin = dynamic_cast<const DenseBin<VAL_T, IS_4BIT>*>(full_bin);
if (IS_4BIT) {
const data_size_t rest = num_used_indices & 1;
for (int i = 0; i < num_used_indices - rest; i += 2) {
data_size_t idx = used_indices[i];
const auto bin1 = static_cast<uint8_t>(
(other_bin->data_[idx >> 1] >> ((idx & 1) << 2)) & 0xf);
idx = used_indices[i + 1];
const auto bin2 = static_cast<uint8_t>(
(other_bin->data_[idx >> 1] >> ((idx & 1) << 2)) & 0xf);
const int i1 = i >> 1;
data_[i1] = (bin1 | (bin2 << 4));
}
if (rest) {
data_size_t idx = used_indices[num_used_indices - 1];
data_[num_used_indices >> 1] =
(other_bin->data_[idx >> 1] >> ((idx & 1) << 2)) & 0xf;
}
} else {
for (int i = 0; i < num_used_indices; ++i) {
data_[i] = other_bin->data_[used_indices[i]];
}
}
}
void SaveBinaryToFile(BinaryWriter* writer) const override {
writer->AlignedWrite(data_.data(), sizeof(VAL_T) * data_.size());
}
size_t SizesInByte() const override {
return VirtualFileWriter::AlignedSize(sizeof(VAL_T) * data_.size());
}
DenseBin<VAL_T, IS_4BIT>* Clone() override;
const void* GetColWiseData(uint8_t* bit_type, bool* is_sparse, std::vector<BinIterator*>* bin_iterator, const int num_threads) const override;
const void* GetColWiseData(uint8_t* bit_type, bool* is_sparse, BinIterator** bin_iterator) const override;
private:
data_size_t num_data_;
#ifdef USE_CUDA
std::vector<VAL_T, CHAllocator<VAL_T>> data_;
#else
std::vector<VAL_T, Common::AlignmentAllocator<VAL_T, kAlignedSize>> data_;
#endif
std::vector<uint8_t> buf_;
DenseBin(const DenseBin<VAL_T, IS_4BIT>& other)
: num_data_(other.num_data_), data_(other.data_) {}
};
template <typename VAL_T, bool IS_4BIT>
DenseBin<VAL_T, IS_4BIT>* DenseBin<VAL_T, IS_4BIT>::Clone() {
return new DenseBin<VAL_T, IS_4BIT>(*this);
}
template <typename VAL_T, bool IS_4BIT>
uint32_t DenseBinIterator<VAL_T, IS_4BIT>::Get(data_size_t idx) {
auto ret = bin_data_->data(idx);
if (ret >= min_bin_ && ret <= max_bin_) {
return ret - min_bin_ + offset_;
} else {
return most_freq_bin_;
}
}
template <typename VAL_T, bool IS_4BIT>
inline uint32_t DenseBinIterator<VAL_T, IS_4BIT>::RawGet(data_size_t idx) {
return bin_data_->data(idx);
}
template <typename VAL_T, bool IS_4BIT>
BinIterator* DenseBin<VAL_T, IS_4BIT>::GetIterator(
uint32_t min_bin, uint32_t max_bin, uint32_t most_freq_bin) const {
return new DenseBinIterator<VAL_T, IS_4BIT>(this, min_bin, max_bin,
most_freq_bin);
}
} // namespace LightGBM
#endif // LightGBM_IO_DENSE_BIN_HPP_