/
gbtree.h
452 lines (408 loc) · 16 KB
/
gbtree.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
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
/*!
* Copyright 2014-2021 by Contributors
* \file gbtree.cc
* \brief gradient boosted tree implementation.
* \author Tianqi Chen
*/
#ifndef XGBOOST_GBM_GBTREE_H_
#define XGBOOST_GBM_GBTREE_H_
#include <dmlc/omp.h>
#include <algorithm>
#include <vector>
#include <map>
#include <memory>
#include <utility>
#include <string>
#include <unordered_map>
#include "xgboost/base.h"
#include "xgboost/data.h"
#include "xgboost/logging.h"
#include "xgboost/gbm.h"
#include "xgboost/predictor.h"
#include "xgboost/tree_updater.h"
#include "xgboost/parameter.h"
#include "xgboost/json.h"
#include "xgboost/host_device_vector.h"
#include "gbtree_model.h"
#include "../common/common.h"
#include "../common/timer.h"
namespace xgboost {
enum class TreeMethod : int {
kAuto = 0, kApprox = 1, kExact = 2, kHist = 3,
kGPUHist = 5
};
// boosting process types
enum class TreeProcessType : int {
kDefault = 0,
kUpdate = 1
};
enum class PredictorType : int {
kAuto = 0,
kCPUPredictor,
kGPUPredictor,
kOneAPIPredictor
};
} // namespace xgboost
DECLARE_FIELD_ENUM_CLASS(xgboost::TreeMethod);
DECLARE_FIELD_ENUM_CLASS(xgboost::TreeProcessType);
DECLARE_FIELD_ENUM_CLASS(xgboost::PredictorType);
namespace xgboost {
namespace gbm {
/*! \brief training parameters */
struct GBTreeTrainParam : public XGBoostParameter<GBTreeTrainParam> {
/*!
* \brief number of parallel trees constructed each iteration
* use this option to support boosted random forest
*/
int num_parallel_tree;
/*! \brief tree updater sequence */
std::string updater_seq;
/*! \brief type of boosting process to run */
TreeProcessType process_type;
// predictor type
PredictorType predictor;
// tree construction method
TreeMethod tree_method;
// declare parameters
DMLC_DECLARE_PARAMETER(GBTreeTrainParam) {
DMLC_DECLARE_FIELD(num_parallel_tree)
.set_default(1)
.set_lower_bound(1)
.describe("Number of parallel trees constructed during each iteration."\
" This option is used to support boosted random forest.");
DMLC_DECLARE_FIELD(updater_seq)
.set_default("grow_colmaker,prune")
.describe("Tree updater sequence.");
DMLC_DECLARE_FIELD(process_type)
.set_default(TreeProcessType::kDefault)
.add_enum("default", TreeProcessType::kDefault)
.add_enum("update", TreeProcessType::kUpdate)
.describe("Whether to run the normal boosting process that creates new trees,"\
" or to update the trees in an existing model.");
DMLC_DECLARE_ALIAS(updater_seq, updater);
DMLC_DECLARE_FIELD(predictor)
.set_default(PredictorType::kAuto)
.add_enum("auto", PredictorType::kAuto)
.add_enum("cpu_predictor", PredictorType::kCPUPredictor)
.add_enum("gpu_predictor", PredictorType::kGPUPredictor)
.add_enum("oneapi_predictor", PredictorType::kOneAPIPredictor)
.describe("Predictor algorithm type");
DMLC_DECLARE_FIELD(tree_method)
.set_default(TreeMethod::kAuto)
.add_enum("auto", TreeMethod::kAuto)
.add_enum("approx", TreeMethod::kApprox)
.add_enum("exact", TreeMethod::kExact)
.add_enum("hist", TreeMethod::kHist)
.add_enum("gpu_hist", TreeMethod::kGPUHist)
.describe("Choice of tree construction method.");
}
};
/*! \brief training parameters */
struct DartTrainParam : public XGBoostParameter<DartTrainParam> {
/*! \brief type of sampling algorithm */
int sample_type;
/*! \brief type of normalization algorithm */
int normalize_type;
/*! \brief fraction of trees to drop during the dropout */
float rate_drop;
/*! \brief whether at least one tree should always be dropped during the dropout */
bool one_drop;
/*! \brief probability of skipping the dropout during an iteration */
float skip_drop;
/*! \brief learning step size for a time */
float learning_rate;
// declare parameters
DMLC_DECLARE_PARAMETER(DartTrainParam) {
DMLC_DECLARE_FIELD(sample_type)
.set_default(0)
.add_enum("uniform", 0)
.add_enum("weighted", 1)
.describe("Different types of sampling algorithm.");
DMLC_DECLARE_FIELD(normalize_type)
.set_default(0)
.add_enum("tree", 0)
.add_enum("forest", 1)
.describe("Different types of normalization algorithm.");
DMLC_DECLARE_FIELD(rate_drop)
.set_range(0.0f, 1.0f)
.set_default(0.0f)
.describe("Fraction of trees to drop during the dropout.");
DMLC_DECLARE_FIELD(one_drop)
.set_default(false)
.describe("Whether at least one tree should always be dropped during the dropout.");
DMLC_DECLARE_FIELD(skip_drop)
.set_range(0.0f, 1.0f)
.set_default(0.0f)
.describe("Probability of skipping the dropout during a boosting iteration.");
DMLC_DECLARE_FIELD(learning_rate)
.set_lower_bound(0.0f)
.set_default(0.3f)
.describe("Learning rate(step size) of update.");
DMLC_DECLARE_ALIAS(learning_rate, eta);
}
};
namespace detail {
// From here on, layer becomes concrete trees.
inline std::pair<uint32_t, uint32_t> LayerToTree(gbm::GBTreeModel const &model,
GBTreeTrainParam const &tparam,
size_t layer_begin,
size_t layer_end) {
bst_group_t groups = model.learner_model_param->num_output_group;
uint32_t tree_begin = layer_begin * groups * tparam.num_parallel_tree;
uint32_t tree_end = layer_end * groups * tparam.num_parallel_tree;
if (tree_end == 0) {
tree_end = static_cast<uint32_t>(model.trees.size());
}
if (model.trees.size() != 0) {
CHECK_LE(tree_begin, tree_end);
}
return {tree_begin, tree_end};
}
// Call fn for each pair of input output tree. Return true if index is out of bound.
template <typename Func>
inline bool SliceTrees(int32_t layer_begin, int32_t layer_end, int32_t step,
GBTreeModel const &model, GBTreeTrainParam const &tparam,
uint32_t layer_trees, Func fn) {
uint32_t tree_begin, tree_end;
std::tie(tree_begin, tree_end) = detail::LayerToTree(model, tparam, layer_begin, layer_end);
if (tree_end > model.trees.size()) {
return true;
}
layer_end = layer_end == 0 ? model.trees.size() / layer_trees : layer_end;
uint32_t n_layers = (layer_end - layer_begin) / step;
int32_t in_it = tree_begin;
int32_t out_it = 0;
for (uint32_t l = 0; l < n_layers; ++l) {
for (uint32_t i = 0; i < layer_trees; ++i) {
CHECK_LT(in_it, tree_end);
fn(in_it, out_it);
out_it++;
in_it++;
}
in_it += (step - 1) * layer_trees;
}
return false;
}
} // namespace detail
// gradient boosted trees
class GBTree : public GradientBooster {
public:
explicit GBTree(LearnerModelParam const* booster_config) :
model_(booster_config) {}
void Configure(const Args& cfg) override;
// Revise `tree_method` and `updater` parameters after seeing the training
// data matrix, only useful when tree_method is auto.
void PerformTreeMethodHeuristic(DMatrix* fmat);
/*! \brief Map `tree_method` parameter to `updater` parameter */
void ConfigureUpdaters();
void ConfigureWithKnownData(Args const& cfg, DMatrix* fmat);
/*! \brief Carry out one iteration of boosting */
void DoBoost(DMatrix* p_fmat,
HostDeviceVector<GradientPair>* in_gpair,
PredictionCacheEntry* predt) override;
bool UseGPU() const override {
return
tparam_.predictor == PredictorType::kGPUPredictor ||
tparam_.tree_method == TreeMethod::kGPUHist;
}
GBTreeTrainParam const& GetTrainParam() const {
return tparam_;
}
void Load(dmlc::Stream* fi) override {
model_.Load(fi);
this->cfg_.clear();
}
void Save(dmlc::Stream* fo) const override {
model_.Save(fo);
}
void LoadConfig(Json const& in) override;
void SaveConfig(Json* p_out) const override;
void SaveModel(Json* p_out) const override;
void LoadModel(Json const& in) override;
bool AllowLazyCheckPoint() const override {
return model_.learner_model_param->num_output_group == 1;
}
// Number of trees per layer.
auto LayerTrees() const {
auto n_trees = model_.learner_model_param->num_output_group * tparam_.num_parallel_tree;
return n_trees;
}
// slice the trees, out must be already allocated
void Slice(int32_t layer_begin, int32_t layer_end, int32_t step,
GradientBooster *out, bool* out_of_bound) const override;
int32_t BoostedRounds() const override {
CHECK_NE(tparam_.num_parallel_tree, 0);
CHECK_NE(model_.learner_model_param->num_output_group, 0);
return model_.trees.size() / this->LayerTrees();
}
void PredictBatch(DMatrix *p_fmat, PredictionCacheEntry *out_preds,
bool training, unsigned layer_begin, unsigned layer_end) override;
void InplacePredict(dmlc::any const &x, std::shared_ptr<DMatrix> p_m,
float missing, PredictionCacheEntry *out_preds,
uint32_t layer_begin, unsigned layer_end) const override {
CHECK(configured_);
uint32_t tree_begin, tree_end;
std::tie(tree_begin, tree_end) =
detail::LayerToTree(model_, tparam_, layer_begin, layer_end);
std::vector<Predictor const *> predictors{
cpu_predictor_.get(),
#if defined(XGBOOST_USE_CUDA)
gpu_predictor_.get()
#endif // defined(XGBOOST_USE_CUDA)
};
StringView msg{"Unsupported data type for inplace predict."};
if (tparam_.predictor == PredictorType::kAuto) {
// Try both predictor implementations
for (auto const &p : predictors) {
if (p && p->InplacePredict(x, p_m, model_, missing, out_preds,
tree_begin, tree_end)) {
return;
}
}
LOG(FATAL) << msg;
} else {
bool success = this->GetPredictor()->InplacePredict(
x, p_m, model_, missing, out_preds, tree_begin, tree_end);
CHECK(success) << msg << std::endl
<< "Current Predictor: "
<< (tparam_.predictor == PredictorType::kCPUPredictor
? "cpu_predictor"
: "gpu_predictor");
}
}
void FeatureScore(std::string const &importance_type,
std::vector<bst_feature_t> *features,
std::vector<float> *scores) const override {
// Because feature with no importance doesn't appear in the return value so
// we need to set up another pair of vectors to store the values during
// computation.
std::vector<size_t> split_counts(this->model_.learner_model_param->num_feature, 0);
std::vector<float> gain_map(this->model_.learner_model_param->num_feature, 0);
auto add_score = [&](auto fn) {
for (auto const &p_tree : model_.trees) {
p_tree->WalkTree([&](bst_node_t nidx) {
auto const &node = (*p_tree)[nidx];
if (!node.IsLeaf()) {
split_counts[node.SplitIndex()]++;
fn(p_tree, nidx, node.SplitIndex());
}
return true;
});
}
};
if (importance_type == "weight") {
add_score([&](auto const &p_tree, bst_node_t, bst_feature_t split) {
gain_map[split] = split_counts[split];
});
}
if (importance_type == "gain" || importance_type == "total_gain") {
add_score([&](auto const &p_tree, bst_node_t nidx, bst_feature_t split) {
gain_map[split] += p_tree->Stat(nidx).loss_chg;
});
}
if (importance_type == "cover" || importance_type == "total_cover") {
add_score([&](auto const &p_tree, bst_node_t nidx, bst_feature_t split) {
gain_map[split] += p_tree->Stat(nidx).sum_hess;
});
}
if (importance_type == "gain" || importance_type == "cover") {
for (size_t i = 0; i < gain_map.size(); ++i) {
gain_map[i] /= std::max(1.0f, static_cast<float>(split_counts[i]));
}
}
features->clear();
scores->clear();
for (size_t i = 0; i < split_counts.size(); ++i) {
if (split_counts[i] != 0) {
features->push_back(i);
scores->push_back(gain_map[i]);
}
}
}
void PredictInstance(const SparsePage::Inst& inst,
std::vector<bst_float>* out_preds,
uint32_t layer_begin, uint32_t layer_end) override {
CHECK(configured_);
uint32_t tree_begin, tree_end;
std::tie(tree_begin, tree_end) = detail::LayerToTree(model_, tparam_, layer_begin, layer_end);
cpu_predictor_->PredictInstance(inst, out_preds, model_,
tree_end);
}
void PredictLeaf(DMatrix* p_fmat,
HostDeviceVector<bst_float>* out_preds,
uint32_t layer_begin, uint32_t layer_end) override {
uint32_t tree_begin, tree_end;
std::tie(tree_begin, tree_end) = detail::LayerToTree(model_, tparam_, layer_begin, layer_end);
CHECK_EQ(tree_begin, 0) << "Predict leaf supports only iteration end: (0, "
"n_iteration), use model slicing instead.";
this->GetPredictor()->PredictLeaf(p_fmat, out_preds, model_, tree_end);
}
void PredictContribution(DMatrix* p_fmat,
HostDeviceVector<bst_float>* out_contribs,
uint32_t layer_begin, uint32_t layer_end, bool approximate,
int, unsigned) override {
CHECK(configured_);
uint32_t tree_begin, tree_end;
std::tie(tree_begin, tree_end) = detail::LayerToTree(model_, tparam_, layer_begin, layer_end);
CHECK_EQ(tree_begin, 0)
<< "Predict contribution supports only iteration end: (0, "
"n_iteration), using model slicing instead.";
this->GetPredictor()->PredictContribution(
p_fmat, out_contribs, model_, tree_end, nullptr, approximate);
}
void PredictInteractionContributions(
DMatrix *p_fmat, HostDeviceVector<bst_float> *out_contribs,
uint32_t layer_begin, uint32_t layer_end, bool approximate) override {
CHECK(configured_);
uint32_t tree_begin, tree_end;
std::tie(tree_begin, tree_end) = detail::LayerToTree(model_, tparam_, layer_begin, layer_end);
CHECK_EQ(tree_begin, 0)
<< "Predict interaction contribution supports only iteration end: (0, "
"n_iteration), using model slicing instead.";
this->GetPredictor()->PredictInteractionContributions(
p_fmat, out_contribs, model_, tree_end, nullptr, approximate);
}
std::vector<std::string> DumpModel(const FeatureMap& fmap,
bool with_stats,
std::string format) const override {
return model_.DumpModel(fmap, with_stats, format);
}
protected:
// initialize updater before using them
void InitUpdater(Args const& cfg);
// do group specific group
void BoostNewTrees(HostDeviceVector<GradientPair>* gpair,
DMatrix *p_fmat,
int bst_group,
std::vector<std::unique_ptr<RegTree> >* ret);
std::unique_ptr<Predictor> const& GetPredictor(HostDeviceVector<float> const* out_pred = nullptr,
DMatrix* f_dmat = nullptr) const;
// commit new trees all at once
virtual void CommitModel(std::vector<std::vector<std::unique_ptr<RegTree>>>&& new_trees,
DMatrix* m,
PredictionCacheEntry* predts);
// --- data structure ---
GBTreeModel model_;
// training parameter
GBTreeTrainParam tparam_;
// ----training fields----
bool showed_updater_warning_ {false};
bool specified_updater_ {false};
bool configured_ {false};
// configurations for tree
Args cfg_;
// the updaters that can be applied to each of tree
std::vector<std::unique_ptr<TreeUpdater>> updaters_;
// Predictors
std::unique_ptr<Predictor> cpu_predictor_;
#if defined(XGBOOST_USE_CUDA)
std::unique_ptr<Predictor> gpu_predictor_;
#endif // defined(XGBOOST_USE_CUDA)
#if defined(XGBOOST_USE_ONEAPI)
std::unique_ptr<Predictor> oneapi_predictor_;
#endif // defined(XGBOOST_USE_ONEAPI)
common::Monitor monitor_;
};
} // namespace gbm
} // namespace xgboost
#endif // XGBOOST_GBM_GBTREE_H_