This PR changes base_margin into a 3-dim array, with one of them being reserved for multi-target classification. Also, a breaking change is made for binary serialization due to extra dimension along with a fix for saving the feature weights. Lastly, it unifies the prediction initialization between CPU and GPU. After this PR, the meta info setter in Python will be based on array interface.
162 lines
4.6 KiB
Plaintext
162 lines
4.6 KiB
Plaintext
/*! Copyright 2019-2021 by XGBoost Contributors */
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#include <gtest/gtest.h>
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#include <xgboost/data.h>
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#include <xgboost/json.h>
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#include <xgboost/generic_parameters.h>
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#include <thrust/device_vector.h>
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#include "test_array_interface.h"
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#include "../../../src/common/device_helpers.cuh"
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#include "test_metainfo.h"
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namespace xgboost {
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template <typename T>
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std::string PrepareData(std::string typestr, thrust::device_vector<T>* out, const size_t kRows=16) {
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out->resize(kRows);
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auto& d_data = *out;
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for (size_t i = 0; i < d_data.size(); ++i) {
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d_data[i] = i * 2.0;
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}
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Json column { Object() };
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std::vector<Json> j_shape {Json(Integer(static_cast<Integer::Int>(kRows)))};
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column["shape"] = Array(j_shape);
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column["strides"] = Array(std::vector<Json>{Json(Integer(static_cast<Integer::Int>(sizeof(T))))});
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column["version"] = 3;
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column["typestr"] = String(typestr);
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auto p_d_data = d_data.data().get();
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std::vector<Json> j_data {
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Json(Integer(reinterpret_cast<Integer::Int>(p_d_data))),
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Json(Boolean(false))};
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column["data"] = j_data;
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column["stream"] = nullptr;
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Json array(std::vector<Json>{column});
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std::string str;
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Json::Dump(array, &str);
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return str;
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}
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TEST(MetaInfo, FromInterface) {
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cudaSetDevice(0);
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thrust::device_vector<float> d_data;
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std::string str = PrepareData<float>("<f4", &d_data);
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MetaInfo info;
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info.SetInfo("label", str.c_str());
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auto const& h_label = info.labels_.HostVector();
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ASSERT_EQ(h_label.size(), d_data.size());
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for (size_t i = 0; i < d_data.size(); ++i) {
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ASSERT_EQ(h_label[i], d_data[i]);
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}
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info.SetInfo("weight", str.c_str());
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auto const& h_weight = info.weights_.HostVector();
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for (size_t i = 0; i < d_data.size(); ++i) {
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ASSERT_EQ(h_weight[i], d_data[i]);
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}
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info.SetInfo("base_margin", str.c_str());
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auto const h_base_margin = info.base_margin_.View(GenericParameter::kCpuId);
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ASSERT_EQ(h_base_margin.Size(), d_data.size());
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for (size_t i = 0; i < d_data.size(); ++i) {
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ASSERT_EQ(h_base_margin(i), d_data[i]);
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}
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thrust::device_vector<int> d_group_data;
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std::string group_str = PrepareData<int>("<i4", &d_group_data, 4);
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d_group_data[0] = 4;
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d_group_data[1] = 3;
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d_group_data[2] = 2;
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d_group_data[3] = 1;
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info.SetInfo("group", group_str.c_str());
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std::vector<bst_group_t> expected_group_ptr = {0, 4, 7, 9, 10};
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EXPECT_EQ(info.group_ptr_, expected_group_ptr);
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}
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TEST(MetaInfo, GPUStridedData) {
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TestMetaInfoStridedData(0);
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}
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TEST(MetaInfo, Group) {
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cudaSetDevice(0);
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MetaInfo info;
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thrust::device_vector<uint32_t> d_uint;
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std::string uint_str = PrepareData<uint32_t>("<u4", &d_uint);
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info.SetInfo("group", uint_str.c_str());
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auto& h_group = info.group_ptr_;
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ASSERT_EQ(h_group.size(), d_uint.size() + 1);
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for (size_t i = 1; i < h_group.size(); ++i) {
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ASSERT_EQ(h_group[i], d_uint[i - 1] + h_group[i - 1]) << "i: " << i;
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}
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thrust::device_vector<int64_t> d_int64;
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std::string int_str = PrepareData<int64_t>("<i8", &d_int64);
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info = MetaInfo();
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info.SetInfo("group", int_str.c_str());
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h_group = info.group_ptr_;
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ASSERT_EQ(h_group.size(), d_uint.size() + 1);
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for (size_t i = 1; i < h_group.size(); ++i) {
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ASSERT_EQ(h_group[i], d_uint[i - 1] + h_group[i - 1]) << "i: " << i;
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}
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// Incorrect type
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thrust::device_vector<float> d_float;
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std::string float_str = PrepareData<float>("<f4", &d_float);
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info = MetaInfo();
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EXPECT_ANY_THROW(info.SetInfo("group", float_str.c_str()));
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}
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TEST(MetaInfo, GPUQid) {
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xgboost::MetaInfo info;
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info.num_row_ = 100;
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thrust::device_vector<uint32_t> qid(info.num_row_, 0);
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for (size_t i = 0; i < qid.size(); ++i) {
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qid[i] = i;
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}
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auto column = Generate2dArrayInterface(info.num_row_, 1, "<u4", &qid);
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Json array{std::vector<Json>{column}};
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std::string array_str;
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Json::Dump(array, &array_str);
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info.SetInfo("qid", array_str.c_str());
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ASSERT_EQ(info.group_ptr_.size(), info.num_row_ + 1);
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ASSERT_EQ(info.group_ptr_.front(), 0);
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ASSERT_EQ(info.group_ptr_.back(), info.num_row_);
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for (size_t i = 0; i < info.num_row_ + 1; ++i) {
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ASSERT_EQ(info.group_ptr_[i], i);
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}
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}
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TEST(MetaInfo, DeviceExtend) {
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dh::safe_cuda(cudaSetDevice(0));
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size_t const kRows = 100;
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MetaInfo lhs, rhs;
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thrust::device_vector<float> d_data;
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std::string str = PrepareData<float>("<f4", &d_data, kRows);
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lhs.SetInfo("label", str.c_str());
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rhs.SetInfo("label", str.c_str());
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ASSERT_FALSE(rhs.labels_.HostCanRead());
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lhs.num_row_ = kRows;
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rhs.num_row_ = kRows;
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lhs.Extend(rhs, true, true);
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ASSERT_EQ(lhs.num_row_, kRows * 2);
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ASSERT_FALSE(lhs.labels_.HostCanRead());
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ASSERT_FALSE(lhs.labels_.HostCanRead());
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ASSERT_FALSE(rhs.labels_.HostCanRead());
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}
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} // namespace xgboost
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