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
|
/**
* Copyright 2020-2024, XGBoost contributors
*/
#include <gtest/gtest.h>
#include "../../../src/data/device_adapter.cuh"
#include "../../../src/data/ellpack_page.cuh"
#include "../../../src/data/ellpack_page.h"
#include "../../../src/data/iterative_dmatrix.h"
#include "../../../src/tree/param.h" // TrainParam
#include "../helpers.h"
#include "test_iterative_dmatrix.h"
namespace xgboost::data {
void TestEquivalent(float sparsity) {
auto ctx = MakeCUDACtx(0);
CudaArrayIterForTest iter{sparsity};
IterativeDMatrix m{&iter, iter.Proxy(), nullptr, Reset, Next,
std::numeric_limits<float>::quiet_NaN(), 0, 256,
std::numeric_limits<std::int64_t>::max()};
std::size_t offset = 0;
auto first = (*m.GetEllpackBatches(&ctx, {}).begin()).Impl();
std::unique_ptr<EllpackPageImpl> page_concatenated{new EllpackPageImpl{
&ctx, first->CutsShared(), first->is_dense, first->info.row_stride, 1000 * 100}};
for (auto& batch : m.GetBatches<EllpackPage>(&ctx, {})) {
auto page = batch.Impl();
size_t num_elements = page_concatenated->Copy(&ctx, page, offset);
offset += num_elements;
}
std::vector<common::CompressedByteT> h_iter_buffer;
auto from_iter = page_concatenated->GetHostAccessor(&ctx, &h_iter_buffer);
ASSERT_EQ(m.Info().num_col_, CudaArrayIterForTest::Cols());
ASSERT_EQ(m.Info().num_row_, CudaArrayIterForTest::Rows());
std::string interface_str = iter.AsArray();
auto adapter = CupyAdapter(interface_str);
std::unique_ptr<DMatrix> dm{
DMatrix::Create(&adapter, std::numeric_limits<float>::quiet_NaN(), 0)};
auto bp = BatchParam{256, tree::TrainParam::DftSparseThreshold()};
for (auto& ellpack : dm->GetBatches<EllpackPage>(&ctx, bp)) {
std::vector<common::CompressedByteT> h_data_buffer;
auto from_data = ellpack.Impl()->GetHostAccessor(&ctx, &h_data_buffer);
ASSERT_EQ(from_iter.gidx_fvalue_map.size(), from_data.gidx_fvalue_map.size());
for (size_t i = 0; i < from_iter.gidx_fvalue_map.size(); ++i) {
EXPECT_NEAR(from_iter.gidx_fvalue_map[i], from_data.gidx_fvalue_map[i], kRtEps);
}
ASSERT_EQ(from_iter.min_fvalue.size(), from_data.min_fvalue.size());
for (size_t i = 0; i < from_iter.min_fvalue.size(); ++i) {
ASSERT_NEAR(from_iter.min_fvalue[i], from_data.min_fvalue[i], kRtEps);
}
ASSERT_EQ(from_iter.NumFeatures(), from_data.NumFeatures());
for (size_t i = 0; i < from_iter.NumFeatures() + 1; ++i) {
ASSERT_EQ(from_iter.feature_segments[i], from_data.feature_segments[i]);
}
std::vector<common::CompressedByteT> buffer_from_iter, buffer_from_data;
auto data_iter = page_concatenated->GetHostAccessor(&ctx, &buffer_from_iter);
auto data_buf = ellpack.Impl()->GetHostAccessor(&ctx, &buffer_from_data);
ASSERT_NE(buffer_from_data.size(), 0);
ASSERT_NE(buffer_from_iter.size(), 0);
CHECK_EQ(ellpack.Impl()->NumSymbols(), page_concatenated->NumSymbols());
CHECK_EQ(from_data.n_rows * from_data.row_stride, from_data.n_rows * from_iter.row_stride);
for (size_t i = 0; i < from_data.n_rows * from_data.row_stride; ++i) {
CHECK_EQ(data_buf.gidx_iter[i], data_iter.gidx_iter[i]);
}
}
}
TEST(IterativeDeviceDMatrix, Basic) {
TestEquivalent(0.0);
TestEquivalent(0.5);
}
TEST(IterativeDeviceDMatrix, RowMajor) {
CudaArrayIterForTest iter(0.0f);
IterativeDMatrix m{&iter, iter.Proxy(), nullptr,
Reset, Next, std::numeric_limits<float>::quiet_NaN(),
0, 256, std::numeric_limits<std::int64_t>::max()};
size_t n_batches = 0;
std::string interface_str = iter.AsArray();
Context ctx{MakeCUDACtx(0)};
for (auto& ellpack : m.GetBatches<EllpackPage>(&ctx, {})) {
n_batches ++;
auto impl = ellpack.Impl();
std::vector<common::CompressedByteT> h_gidx;
auto h_accessor = impl->GetHostAccessor(&ctx, &h_gidx);
auto cols = CudaArrayIterForTest::Cols();
auto rows = CudaArrayIterForTest::Rows();
auto j_interface =
Json::Load({interface_str.c_str(), interface_str.size()});
ArrayInterface<2> loaded {get<Object const>(j_interface)};
std::vector<float> h_data(cols * rows);
common::Span<float const> s_data{static_cast<float const*>(loaded.data), cols * rows};
dh::CopyDeviceSpanToVector(&h_data, s_data);
auto cut_ptr = h_accessor.feature_segments;
for (auto i = 0ull; i < rows * cols; i++) {
int column_idx = i % cols;
EXPECT_EQ(impl->Cuts().SearchBin(h_data[i], column_idx),
h_accessor.gidx_iter[i] + cut_ptr[column_idx]);
}
EXPECT_EQ(m.Info().num_col_, cols);
EXPECT_EQ(m.Info().num_row_, rows);
EXPECT_EQ(m.Info().num_nonzero_, rows * cols);
}
// All batches are concatenated.
ASSERT_EQ(n_batches, 1);
}
TEST(IterativeDeviceDMatrix, RowMajorMissing) {
const float kMissing = std::numeric_limits<float>::quiet_NaN();
bst_idx_t rows = 4;
size_t cols = 3;
CudaArrayIterForTest iter{0.0f, rows, cols, 2};
std::string interface_str = iter.AsArray();
auto j_interface = Json::Load({interface_str.c_str(), interface_str.size()});
ArrayInterface<2> loaded{get<Object const>(j_interface)};
std::vector<float> h_data(cols * rows);
common::Span<float const> s_data{static_cast<float const*>(loaded.data), cols * rows};
dh::CopyDeviceSpanToVector(&h_data, s_data);
h_data[1] = kMissing;
h_data[5] = kMissing;
h_data[6] = kMissing;
h_data[9] = kMissing; // idx = (2, 0)
h_data[10] = kMissing; // idx = (2, 1)
auto ptr =
thrust::device_ptr<float>(reinterpret_cast<float*>(get<Integer>(j_interface["data"][0])));
thrust::copy(h_data.cbegin(), h_data.cend(), ptr);
IterativeDMatrix m{&iter, iter.Proxy(), nullptr,
Reset, Next, std::numeric_limits<float>::quiet_NaN(),
0, 256, std::numeric_limits<std::int64_t>::max()};
auto ctx = MakeCUDACtx(0);
auto& ellpack =
*m.GetBatches<EllpackPage>(&ctx, BatchParam{256, tree::TrainParam::DftSparseThreshold()})
.begin();
auto impl = ellpack.Impl();
std::vector<common::CompressedByteT> h_gidx;
auto h_acc = impl->GetHostAccessor(&ctx, &h_gidx);
// null values get placed after valid values in a row
ASSERT_FALSE(h_acc.IsDenseCompressed());
ASSERT_EQ(h_acc.row_stride, cols - 1);
ASSERT_EQ(h_acc.gidx_iter[7], impl->GetDeviceAccessor(&ctx).NullValue());
for (std::size_t i = 0; i < 7; ++i) {
ASSERT_NE(h_acc.gidx_iter[i], impl->GetDeviceAccessor(&ctx).NullValue());
}
EXPECT_EQ(m.Info().num_col_, cols);
EXPECT_EQ(m.Info().num_row_, rows);
EXPECT_EQ(m.Info().num_nonzero_, rows * cols - 5);
}
TEST(IterativeDeviceDMatrix, IsDense) {
int num_bins = 16;
auto test = [num_bins](float sparsity) {
CudaArrayIterForTest iter(sparsity);
IterativeDMatrix m(&iter, iter.Proxy(), nullptr, Reset, Next,
std::numeric_limits<float>::quiet_NaN(), 0, num_bins,
std::numeric_limits<std::int64_t>::max());
if (sparsity == 0.0) {
ASSERT_TRUE(m.IsDense());
} else {
ASSERT_FALSE(m.IsDense());
}
};
test(0.0);
test(0.1);
test(1.0);
}
TEST(IterativeDeviceDMatrix, Ref) {
Context ctx{MakeCUDACtx(0)};
TestRefDMatrix<EllpackPage, CudaArrayIterForTest>(
&ctx, [](EllpackPage const& page) { return page.Impl()->Cuts(); });
}
} // namespace xgboost::data
|
