-
-
Notifications
You must be signed in to change notification settings - Fork 8.7k
/
iterative_dmatrix.cc
250 lines (225 loc) · 7.91 KB
/
iterative_dmatrix.cc
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
/*!
* Copyright 2022 XGBoost contributors
*/
#include "iterative_dmatrix.h"
#include <rabit/rabit.h>
#include "../common/column_matrix.h"
#include "../common/hist_util.h"
#include "gradient_index.h"
#include "proxy_dmatrix.h"
#include "simple_batch_iterator.h"
namespace xgboost {
namespace data {
IterativeDMatrix::IterativeDMatrix(DataIterHandle iter_handle, DMatrixHandle proxy,
std::shared_ptr<DMatrix> ref, DataIterResetCallback* reset,
XGDMatrixCallbackNext* next, float missing, int nthread,
bst_bin_t max_bin)
: proxy_{proxy}, reset_{reset}, next_{next} {
// fetch the first batch
auto iter =
DataIterProxy<DataIterResetCallback, XGDMatrixCallbackNext>{iter_handle, reset_, next_};
iter.Reset();
bool valid = iter.Next();
CHECK(valid) << "Iterative DMatrix must have at least 1 batch.";
auto d = MakeProxy(proxy_)->DeviceIdx();
StringView msg{"All batch should be on the same device."};
if (batch_param_.gpu_id != Context::kCpuId) {
CHECK_EQ(d, batch_param_.gpu_id) << msg;
}
batch_param_ = BatchParam{d, max_bin};
batch_param_.sparse_thresh = 0.2; // default from TrainParam
ctx_.UpdateAllowUnknown(
Args{{"nthread", std::to_string(nthread)}, {"gpu_id", std::to_string(d)}});
if (ctx_.IsCPU()) {
this->InitFromCPU(iter_handle, missing, ref);
} else {
this->InitFromCUDA(iter_handle, missing, ref);
}
}
void GetCutsFromRef(std::shared_ptr<DMatrix> ref_, bst_feature_t n_features, BatchParam p,
common::HistogramCuts* p_cuts) {
CHECK(ref_);
CHECK(p_cuts);
auto csr = [&]() {
for (auto const& page : ref_->GetBatches<GHistIndexMatrix>(p)) {
*p_cuts = page.cut;
break;
}
};
auto ellpack = [&]() {
for (auto const& page : ref_->GetBatches<EllpackPage>(p)) {
GetCutsFromEllpack(page, p_cuts);
break;
}
};
if (ref_->PageExists<GHistIndexMatrix>()) {
csr();
} else if (ref_->PageExists<EllpackPage>()) {
ellpack();
} else {
if (p.gpu_id == Context::kCpuId) {
csr();
} else {
ellpack();
}
}
CHECK_EQ(ref_->Info().num_col_, n_features)
<< "Invalid ref DMatrix, different number of features.";
}
void IterativeDMatrix::InitFromCPU(DataIterHandle iter_handle, float missing,
std::shared_ptr<DMatrix> ref) {
DMatrixProxy* proxy = MakeProxy(proxy_);
CHECK(proxy);
// The external iterator
auto iter =
DataIterProxy<DataIterResetCallback, XGDMatrixCallbackNext>{iter_handle, reset_, next_};
common::HistogramCuts cuts;
auto num_rows = [&]() {
return HostAdapterDispatch(proxy, [](auto const& value) { return value.Size(); });
};
auto num_cols = [&]() {
return HostAdapterDispatch(proxy, [](auto const& value) { return value.NumCols(); });
};
std::vector<size_t> column_sizes;
auto const is_valid = data::IsValidFunctor{missing};
auto nnz_cnt = [&]() {
return HostAdapterDispatch(proxy, [&](auto const& value) {
size_t n_threads = ctx_.Threads();
size_t n_features = column_sizes.size();
linalg::Tensor<size_t, 2> column_sizes_tloc({n_threads, n_features}, Context::kCpuId);
auto view = column_sizes_tloc.HostView();
common::ParallelFor(value.Size(), n_threads, common::Sched::Static(256), [&](auto i) {
auto const& line = value.GetLine(i);
for (size_t j = 0; j < line.Size(); ++j) {
data::COOTuple const& elem = line.GetElement(j);
if (is_valid(elem)) {
view(omp_get_thread_num(), elem.column_idx)++;
}
}
});
auto ptr = column_sizes_tloc.Data()->HostPointer();
auto result = std::accumulate(ptr, ptr + column_sizes_tloc.Size(), static_cast<size_t>(0));
for (size_t tidx = 0; tidx < n_threads; ++tidx) {
for (size_t fidx = 0; fidx < n_features; ++fidx) {
column_sizes[fidx] += view(tidx, fidx);
}
}
return result;
});
};
size_t n_features = 0;
size_t n_batches = 0;
size_t accumulated_rows{0};
size_t nnz{0};
/**
* CPU impl needs an additional loop for accumulating the column size.
*/
std::unique_ptr<common::HostSketchContainer> p_sketch;
std::vector<size_t> batch_nnz;
do {
// We use do while here as the first batch is fetched in ctor
if (n_features == 0) {
n_features = num_cols();
rabit::Allreduce<rabit::op::Max>(&n_features, 1);
column_sizes.resize(n_features);
info_.num_col_ = n_features;
} else {
CHECK_EQ(n_features, num_cols()) << "Inconsistent number of columns.";
}
size_t batch_size = num_rows();
batch_nnz.push_back(nnz_cnt());
nnz += batch_nnz.back();
accumulated_rows += batch_size;
n_batches++;
} while (iter.Next());
iter.Reset();
// From here on Info() has the correct data shape
Info().num_row_ = accumulated_rows;
Info().num_nonzero_ = nnz;
rabit::Allreduce<rabit::op::Max>(&info_.num_col_, 1);
CHECK(std::none_of(column_sizes.cbegin(), column_sizes.cend(), [&](auto f) {
return f > accumulated_rows;
})) << "Something went wrong during iteration.";
/**
* Generate quantiles
*/
accumulated_rows = 0;
if (ref) {
GetCutsFromRef(ref, Info().num_col_, batch_param_, &cuts);
} else {
size_t i = 0;
while (iter.Next()) {
if (!p_sketch) {
p_sketch.reset(new common::HostSketchContainer{batch_param_.max_bin,
proxy->Info().feature_types.ConstHostSpan(),
column_sizes, false, ctx_.Threads()});
}
HostAdapterDispatch(proxy, [&](auto const& batch) {
proxy->Info().num_nonzero_ = batch_nnz[i];
// We don't need base row idx here as Info is from proxy and the number of rows in
// it is consistent with data batch.
p_sketch->PushAdapterBatch(batch, 0, proxy->Info(), missing);
});
accumulated_rows += num_rows();
++i;
}
iter.Reset();
CHECK_EQ(accumulated_rows, Info().num_row_);
CHECK(p_sketch);
p_sketch->MakeCuts(&cuts);
}
/**
* Generate gradient index.
*/
this->ghist_ = std::make_unique<GHistIndexMatrix>(Info(), std::move(cuts), batch_param_.max_bin);
size_t rbegin = 0;
size_t prev_sum = 0;
size_t i = 0;
while (iter.Next()) {
HostAdapterDispatch(proxy, [&](auto const& batch) {
proxy->Info().num_nonzero_ = batch_nnz[i];
this->ghist_->PushAdapterBatch(&ctx_, rbegin, prev_sum, batch, missing,
proxy->Info().feature_types.ConstHostSpan(),
batch_param_.sparse_thresh, Info().num_row_);
});
if (n_batches != 1) {
this->info_.Extend(std::move(proxy->Info()), false, true);
}
size_t batch_size = num_rows();
prev_sum = this->ghist_->row_ptr[rbegin + batch_size];
rbegin += batch_size;
++i;
}
iter.Reset();
CHECK_EQ(rbegin, Info().num_row_);
/**
* Generate column matrix
*/
accumulated_rows = 0;
while (iter.Next()) {
HostAdapterDispatch(proxy, [&](auto const& batch) {
this->ghist_->PushAdapterBatchColumns(&ctx_, batch, missing, accumulated_rows);
});
accumulated_rows += num_rows();
}
iter.Reset();
CHECK_EQ(accumulated_rows, Info().num_row_);
if (n_batches == 1) {
this->info_ = std::move(proxy->Info());
this->info_.num_nonzero_ = nnz;
this->info_.num_col_ = n_features; // proxy might be empty.
CHECK_EQ(proxy->Info().labels.Size(), 0);
}
}
BatchSet<GHistIndexMatrix> IterativeDMatrix::GetGradientIndex(BatchParam const& param) {
CheckParam(param);
if (!ghist_) {
CHECK(ellpack_);
ghist_ = std::make_shared<GHistIndexMatrix>(&ctx_, Info(), *ellpack_, param);
}
auto begin_iter =
BatchIterator<GHistIndexMatrix>(new SimpleBatchIteratorImpl<GHistIndexMatrix>(ghist_));
return BatchSet<GHistIndexMatrix>(begin_iter);
}
} // namespace data
} // namespace xgboost