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Sycl implementation for objective functions (#9846)

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---------

Co-authored-by: Dmitry Razdoburdin <>
This commit is contained in:
Dmitry Razdoburdin 2023-12-12 07:41:50 +01:00 committed by GitHub
parent ddab49a8be
commit 43897b8296
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19 changed files with 1129 additions and 423 deletions
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6
include/xgboost/objective.h
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@ -129,6 +129,12 @@ class ObjFunction : public Configurable {
* \param name Name of the objective. * \param name Name of the objective.
*/ */
static ObjFunction* Create(const std::string& name, Context const* ctx); static ObjFunction* Create(const std::string& name, Context const* ctx);
/*!
* \brief Return sycl specific implementation name if possible.
* \param name Name of the objective.
*/
static std::string GetSyclImplementationName(const std::string& name);
}; };
/*! /*!

2
plugin/CMakeLists.txt
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@ -1,6 +1,8 @@
if(PLUGIN_SYCL) if(PLUGIN_SYCL)
set(CMAKE_CXX_COMPILER "icpx") set(CMAKE_CXX_COMPILER "icpx")
add_library(plugin_sycl OBJECT add_library(plugin_sycl OBJECT
${xgboost_SOURCE_DIR}/plugin/sycl/objective/regression_obj.cc
${xgboost_SOURCE_DIR}/plugin/sycl/objective/multiclass_obj.cc
${xgboost_SOURCE_DIR}/plugin/sycl/device_manager.cc ${xgboost_SOURCE_DIR}/plugin/sycl/device_manager.cc
${xgboost_SOURCE_DIR}/plugin/sycl/predictor/predictor.cc) ${xgboost_SOURCE_DIR}/plugin/sycl/predictor/predictor.cc)
target_include_directories(plugin_sycl target_include_directories(plugin_sycl

210
plugin/sycl/objective/multiclass_obj.cc Normal file
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@ -0,0 +1,210 @@
/*!
* Copyright 2015-2023 by Contributors
* \file multiclass_obj.cc
* \brief Definition of multi-class classification objectives.
*/
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wtautological-constant-compare"
#pragma GCC diagnostic ignored "-W#pragma-messages"
#include <rabit/rabit.h>
#pragma GCC diagnostic pop
#include <vector>
#include <algorithm>
#include <limits>
#include <utility>
#include "xgboost/parameter.h"
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wtautological-constant-compare"
#include "xgboost/data.h"
#include "../../src/common/math.h"
#pragma GCC diagnostic pop
#include "xgboost/logging.h"
#include "xgboost/objective.h"
#include "xgboost/json.h"
#include "xgboost/span.h"
#include "../../../src/objective/multiclass_param.h"
#include "../device_manager.h"
#include <CL/sycl.hpp>
namespace xgboost {
namespace sycl {
namespace obj {
DMLC_REGISTRY_FILE_TAG(multiclass_obj_sycl);
class SoftmaxMultiClassObj : public ObjFunction {
public:
explicit SoftmaxMultiClassObj(bool output_prob)
: output_prob_(output_prob) {}
void Configure(Args const& args) override {
param_.UpdateAllowUnknown(args);
qu_ = device_manager.GetQueue(ctx_->Device());
}
void GetGradient(const HostDeviceVector<bst_float>& preds,
const MetaInfo& info,
int iter,
linalg::Matrix<GradientPair>* out_gpair) override {
if (preds.Size() == 0) return;
if (info.labels.Size() == 0) return;
CHECK(preds.Size() == (static_cast<size_t>(param_.num_class) * info.labels.Size()))
<< "SoftmaxMultiClassObj: label size and pred size does not match.\n"
<< "label.Size() * num_class: "
<< info.labels.Size() * static_cast<size_t>(param_.num_class) << "\n"
<< "num_class: " << param_.num_class << "\n"
<< "preds.Size(): " << preds.Size();
const int nclass = param_.num_class;
const auto ndata = static_cast<int64_t>(preds.Size() / nclass);
out_gpair->Reshape(info.num_row_, static_cast<std::uint64_t>(nclass));
const bool is_null_weight = info.weights_.Size() == 0;
if (!is_null_weight) {
CHECK_EQ(info.weights_.Size(), ndata)
<< "Number of weights should be equal to number of data points.";
}
::sycl::buffer<bst_float, 1> preds_buf(preds.HostPointer(), preds.Size());
::sycl::buffer<bst_float, 1> labels_buf(info.labels.Data()->HostPointer(), info.labels.Size());
::sycl::buffer<GradientPair, 1> out_gpair_buf(out_gpair->Data()->HostPointer(),
out_gpair->Size());
::sycl::buffer<bst_float, 1> weights_buf(is_null_weight ? NULL : info.weights_.HostPointer(),
is_null_weight ? 1 : info.weights_.Size());
int flag = 1;
{
::sycl::buffer<int, 1> flag_buf(&flag, 1);
qu_.submit([&](::sycl::handler& cgh) {
auto preds_acc = preds_buf.get_access<::sycl::access::mode::read>(cgh);
auto labels_acc = labels_buf.get_access<::sycl::access::mode::read>(cgh);
auto weights_acc = weights_buf.get_access<::sycl::access::mode::read>(cgh);
auto out_gpair_acc = out_gpair_buf.get_access<::sycl::access::mode::write>(cgh);
auto flag_buf_acc = flag_buf.get_access<::sycl::access::mode::write>(cgh);
cgh.parallel_for<>(::sycl::range<1>(ndata), [=](::sycl::id<1> pid) {
int idx = pid[0];
bst_float const * point = &preds_acc[idx * nclass];
// Part of Softmax function
bst_float wmax = std::numeric_limits<bst_float>::min();
for (int k = 0; k < nclass; k++) { wmax = ::sycl::max(point[k], wmax); }
float wsum = 0.0f;
for (int k = 0; k < nclass; k++) { wsum += ::sycl::exp(point[k] - wmax); }
auto label = labels_acc[idx];
if (label < 0 || label >= nclass) {
flag_buf_acc[0] = 0;
label = 0;
}
bst_float wt = is_null_weight ? 1.0f : weights_acc[idx];
for (int k = 0; k < nclass; ++k) {
bst_float p = expf(point[k] - wmax) / static_cast<float>(wsum);
const float eps = 1e-16f;
const bst_float h = ::sycl::max(2.0f * p * (1.0f - p) * wt, eps);
p = label == k ? p - 1.0f : p;
out_gpair_acc[idx * nclass + k] = GradientPair(p * wt, h);
}
});
}).wait();
}
// flag_buf is destroyed, content is copyed to the "flag"
if (flag == 0) {
LOG(FATAL) << "SYCL::SoftmaxMultiClassObj: label must be in [0, num_class).";
}
}
void PredTransform(HostDeviceVector<bst_float>* io_preds) const override {
this->Transform(io_preds, output_prob_);
}
void EvalTransform(HostDeviceVector<bst_float>* io_preds) override {
this->Transform(io_preds, true);
}
const char* DefaultEvalMetric() const override {
return "mlogloss";
}
inline void Transform(HostDeviceVector<bst_float> *io_preds, bool prob) const {
if (io_preds->Size() == 0) return;
const int nclass = param_.num_class;
const auto ndata = static_cast<int64_t>(io_preds->Size() / nclass);
max_preds_.Resize(ndata);
{
::sycl::buffer<bst_float, 1> io_preds_buf(io_preds->HostPointer(), io_preds->Size());
if (prob) {
qu_.submit([&](::sycl::handler& cgh) {
auto io_preds_acc = io_preds_buf.get_access<::sycl::access::mode::read_write>(cgh);
cgh.parallel_for<>(::sycl::range<1>(ndata), [=](::sycl::id<1> pid) {
int idx = pid[0];
auto it = io_preds_acc.begin() + idx * nclass;
common::Softmax(it, it + nclass);
});
}).wait();
} else {
::sycl::buffer<bst_float, 1> max_preds_buf(max_preds_.HostPointer(), max_preds_.Size());
qu_.submit([&](::sycl::handler& cgh) {
auto io_preds_acc = io_preds_buf.get_access<::sycl::access::mode::read>(cgh);
auto max_preds_acc = max_preds_buf.get_access<::sycl::access::mode::read_write>(cgh);
cgh.parallel_for<>(::sycl::range<1>(ndata), [=](::sycl::id<1> pid) {
int idx = pid[0];
auto it = io_preds_acc.begin() + idx * nclass;
max_preds_acc[idx] = common::FindMaxIndex(it, it + nclass) - it;
});
}).wait();
}
}
if (!prob) {
io_preds->Resize(max_preds_.Size());
io_preds->Copy(max_preds_);
}
}
struct ObjInfo Task() const override {return {ObjInfo::kClassification}; }
void SaveConfig(Json* p_out) const override {
auto& out = *p_out;
if (this->output_prob_) {
out["name"] = String("multi:softprob");
} else {
out["name"] = String("multi:softmax");
}
out["softmax_multiclass_param"] = ToJson(param_);
}
void LoadConfig(Json const& in) override {
FromJson(in["softmax_multiclass_param"], &param_);
}
private:
// output probability
bool output_prob_;
// parameter
xgboost::obj::SoftmaxMultiClassParam param_;
// Cache for max_preds
mutable HostDeviceVector<bst_float> max_preds_;
sycl::DeviceManager device_manager;
mutable ::sycl::queue qu_;
};
XGBOOST_REGISTER_OBJECTIVE(SoftmaxMultiClass, "multi:softmax_sycl")
.describe("Softmax for multi-class classification, output class index.")
.set_body([]() { return new SoftmaxMultiClassObj(false); });
XGBOOST_REGISTER_OBJECTIVE(SoftprobMultiClass, "multi:softprob_sycl")
.describe("Softmax for multi-class classification, output probability distribution.")
.set_body([]() { return new SoftmaxMultiClassObj(true); });
} // namespace obj
} // namespace sycl
} // namespace xgboost

197
plugin/sycl/objective/regression_obj.cc Normal file
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@ -0,0 +1,197 @@
/*!
* Copyright 2015-2023 by Contributors
* \file regression_obj.cc
* \brief Definition of regression objectives.
*/
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wtautological-constant-compare"
#pragma GCC diagnostic ignored "-W#pragma-messages"
#include <xgboost/logging.h>
#include <xgboost/objective.h>
#pragma GCC diagnostic pop
#include <rabit/rabit.h>
#include <cmath>
#include <memory>
#include <vector>
#include "xgboost/host_device_vector.h"
#include "xgboost/json.h"
#include "xgboost/parameter.h"
#include "xgboost/span.h"
#include "../../src/common/transform.h"
#include "../../src/common/common.h"
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wtautological-constant-compare"
#include "../../../src/objective/regression_loss.h"
#pragma GCC diagnostic pop
#include "../../../src/objective/regression_param.h"
#include "../device_manager.h"
#include <CL/sycl.hpp>
namespace xgboost {
namespace sycl {
namespace obj {
DMLC_REGISTRY_FILE_TAG(regression_obj_sycl);
template<typename Loss>
class RegLossObj : public ObjFunction {
protected:
HostDeviceVector<int> label_correct_;
public:
RegLossObj() = default;
void Configure(const std::vector<std::pair<std::string, std::string> >& args) override {
param_.UpdateAllowUnknown(args);
qu_ = device_manager.GetQueue(ctx_->Device());
}
void GetGradient(const HostDeviceVector<bst_float>& preds,
const MetaInfo &info,
int iter,
linalg::Matrix<GradientPair>* out_gpair) override {
if (info.labels.Size() == 0) return;
CHECK_EQ(preds.Size(), info.labels.Size())
<< " " << "labels are not correctly provided"
<< "preds.size=" << preds.Size() << ", label.size=" << info.labels.Size() << ", "
<< "Loss: " << Loss::Name();
size_t const ndata = preds.Size();
auto const n_targets = this->Targets(info);
out_gpair->Reshape(info.num_row_, n_targets);
// TODO(razdoburdin): add label_correct check
label_correct_.Resize(1);
label_correct_.Fill(1);
bool is_null_weight = info.weights_.Size() == 0;
::sycl::buffer<bst_float, 1> preds_buf(preds.HostPointer(), preds.Size());
::sycl::buffer<bst_float, 1> labels_buf(info.labels.Data()->HostPointer(), info.labels.Size());
::sycl::buffer<GradientPair, 1> out_gpair_buf(out_gpair->Data()->HostPointer(),
out_gpair->Size());
::sycl::buffer<bst_float, 1> weights_buf(is_null_weight ? NULL : info.weights_.HostPointer(),
is_null_weight ? 1 : info.weights_.Size());
auto scale_pos_weight = param_.scale_pos_weight;
if (!is_null_weight) {
CHECK_EQ(info.weights_.Size(), info.labels.Shape(0))
<< "Number of weights should be equal to number of data points.";
}
int flag = 1;
{
::sycl::buffer<int, 1> flag_buf(&flag, 1);
qu_.submit([&](::sycl::handler& cgh) {
auto preds_acc = preds_buf.get_access<::sycl::access::mode::read>(cgh);
auto labels_acc = labels_buf.get_access<::sycl::access::mode::read>(cgh);
auto weights_acc = weights_buf.get_access<::sycl::access::mode::read>(cgh);
auto out_gpair_acc = out_gpair_buf.get_access<::sycl::access::mode::write>(cgh);
auto flag_buf_acc = flag_buf.get_access<::sycl::access::mode::write>(cgh);
cgh.parallel_for<>(::sycl::range<1>(ndata), [=](::sycl::id<1> pid) {
int idx = pid[0];
bst_float p = Loss::PredTransform(preds_acc[idx]);
bst_float w = is_null_weight ? 1.0f : weights_acc[idx/n_targets];
bst_float label = labels_acc[idx];
if (label == 1.0f) {
w *= scale_pos_weight;
}
if (!Loss::CheckLabel(label)) {
// If there is an incorrect label, the host code will know.
flag_buf_acc[0] = 0;
}
out_gpair_acc[idx] = GradientPair(Loss::FirstOrderGradient(p, label) * w,
Loss::SecondOrderGradient(p, label) * w);
});
}).wait();
}
// flag_buf is destroyed, content is copyed to the "flag"
if (flag == 0) {
LOG(FATAL) << Loss::LabelErrorMsg();
}
}
public:
const char* DefaultEvalMetric() const override {
return Loss::DefaultEvalMetric();
}
void PredTransform(HostDeviceVector<float> *io_preds) const override {
size_t const ndata = io_preds->Size();
if (ndata == 0) return;
::sycl::buffer<bst_float, 1> io_preds_buf(io_preds->HostPointer(), io_preds->Size());
qu_.submit([&](::sycl::handler& cgh) {
auto io_preds_acc = io_preds_buf.get_access<::sycl::access::mode::read_write>(cgh);
cgh.parallel_for<>(::sycl::range<1>(ndata), [=](::sycl::id<1> pid) {
int idx = pid[0];
io_preds_acc[idx] = Loss::PredTransform(io_preds_acc[idx]);
});
}).wait();
}
float ProbToMargin(float base_score) const override {
return Loss::ProbToMargin(base_score);
}
struct ObjInfo Task() const override {
return Loss::Info();
};
uint32_t Targets(MetaInfo const& info) const override {
// Multi-target regression.
return std::max(static_cast<size_t>(1), info.labels.Shape(1));
}
void SaveConfig(Json* p_out) const override {
auto& out = *p_out;
out["name"] = String(Loss::Name());
out["reg_loss_param"] = ToJson(param_);
}
void LoadConfig(Json const& in) override {
FromJson(in["reg_loss_param"], &param_);
}
protected:
xgboost::obj::RegLossParam param_;
sycl::DeviceManager device_manager;
mutable ::sycl::queue qu_;
};
XGBOOST_REGISTER_OBJECTIVE(SquaredLossRegression,
std::string(xgboost::obj::LinearSquareLoss::Name()) + "_sycl")
.describe("Regression with squared error with SYCL backend.")
.set_body([]() { return new RegLossObj<xgboost::obj::LinearSquareLoss>(); });
XGBOOST_REGISTER_OBJECTIVE(SquareLogError,
std::string(xgboost::obj::SquaredLogError::Name()) + "_sycl")
.describe("Regression with root mean squared logarithmic error with SYCL backend.")
.set_body([]() { return new RegLossObj<xgboost::obj::SquaredLogError>(); });
XGBOOST_REGISTER_OBJECTIVE(LogisticRegression,
std::string(xgboost::obj::LogisticRegression::Name()) + "_sycl")
.describe("Logistic regression for probability regression task with SYCL backend.")
.set_body([]() { return new RegLossObj<xgboost::obj::LogisticRegression>(); });
XGBOOST_REGISTER_OBJECTIVE(LogisticClassification,
std::string(xgboost::obj::LogisticClassification::Name()) + "_sycl")
.describe("Logistic regression for binary classification task with SYCL backend.")
.set_body([]() { return new RegLossObj<xgboost::obj::LogisticClassification>(); });
XGBOOST_REGISTER_OBJECTIVE(LogisticRaw,
std::string(xgboost::obj::LogisticRaw::Name()) + "_sycl")
.describe("Logistic regression for classification, output score "
"before logistic transformation with SYCL backend.")
.set_body([]() { return new RegLossObj<xgboost::obj::LogisticRaw>(); });
} // namespace obj
} // namespace sycl
} // namespace xgboost

11
src/objective/multiclass_obj.cu
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@ -21,6 +21,8 @@
#include "../common/math.h" #include "../common/math.h"
#include "../common/transform.h" #include "../common/transform.h"
#include "multiclass_param.h"
namespace xgboost { namespace xgboost {
namespace obj { namespace obj {
@ -28,15 +30,6 @@ namespace obj {
DMLC_REGISTRY_FILE_TAG(multiclass_obj_gpu); DMLC_REGISTRY_FILE_TAG(multiclass_obj_gpu);
#endif // defined(XGBOOST_USE_CUDA) #endif // defined(XGBOOST_USE_CUDA)
struct SoftmaxMultiClassParam : public XGBoostParameter<SoftmaxMultiClassParam> {
int num_class;
// declare parameters
DMLC_DECLARE_PARAMETER(SoftmaxMultiClassParam) {
DMLC_DECLARE_FIELD(num_class).set_lower_bound(1)
.describe("Number of output class in the multi-class classification.");
}
};
class SoftmaxMultiClassObj : public ObjFunction { class SoftmaxMultiClassObj : public ObjFunction {
public: public:
explicit SoftmaxMultiClassObj(bool output_prob) explicit SoftmaxMultiClassObj(bool output_prob)

25
src/objective/multiclass_param.h Normal file
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@ -0,0 +1,25 @@
/*!
* Copyright 2015-2023 by Contributors
* \file multiclass_param.h
* \brief Definition of multi-class classification parameters.
*/
#ifndef XGBOOST_OBJECTIVE_MULTICLASS_PARAM_H_
#define XGBOOST_OBJECTIVE_MULTICLASS_PARAM_H_
#include "xgboost/parameter.h"
namespace xgboost {
namespace obj {
struct SoftmaxMultiClassParam : public XGBoostParameter<SoftmaxMultiClassParam> {
int num_class;
// declare parameters
DMLC_DECLARE_PARAMETER(SoftmaxMultiClassParam) {
DMLC_DECLARE_FIELD(num_class).set_lower_bound(1)
.describe("Number of output class in the multi-class classification.");
}
};
} // namespace obj
} // namespace xgboost
#endif // XGBOOST_OBJECTIVE_MULTICLASS_PARAM_H_

22
src/objective/objective.cc
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@ -18,7 +18,11 @@ DMLC_REGISTRY_ENABLE(::xgboost::ObjFunctionReg);
namespace xgboost { namespace xgboost {
// implement factory functions // implement factory functions
ObjFunction* ObjFunction::Create(const std::string& name, Context const* ctx) { ObjFunction* ObjFunction::Create(const std::string& name, Context const* ctx) {
auto *e = ::dmlc::Registry< ::xgboost::ObjFunctionReg>::Get()->Find(name); std::string obj_name = name;
if (ctx->IsSycl()) {
obj_name = GetSyclImplementationName(obj_name);
}
auto *e = ::dmlc::Registry< ::xgboost::ObjFunctionReg>::Get()->Find(obj_name);
if (e == nullptr) { if (e == nullptr) {
std::stringstream ss; std::stringstream ss;
for (const auto& entry : ::dmlc::Registry< ::xgboost::ObjFunctionReg>::List()) { for (const auto& entry : ::dmlc::Registry< ::xgboost::ObjFunctionReg>::List()) {
@ -32,6 +36,22 @@ ObjFunction* ObjFunction::Create(const std::string& name, Context const* ctx) {
return pobj; return pobj;
} }
/* If the objective function has sycl-specific implementation,
* returns the specific implementation name.
* Otherwise return the orginal name without modifications.
*/
std::string ObjFunction::GetSyclImplementationName(const std::string& name) {
const std::string sycl_postfix = "_sycl";
auto *e = ::dmlc::Registry< ::xgboost::ObjFunctionReg>::Get()->Find(name + sycl_postfix);
if (e != nullptr) {
// Function has specific sycl implementation
return name + sycl_postfix;
} else {
// Function hasn't specific sycl implementation
return name;
}
}
void ObjFunction::InitEstimation(MetaInfo const&, linalg::Tensor<float, 1>* base_score) const { void ObjFunction::InitEstimation(MetaInfo const&, linalg::Tensor<float, 1>* base_score) const {
CHECK(base_score); CHECK(base_score);
base_score->Reshape(1); base_score->Reshape(1);

11
src/objective/regression_obj.cu
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@ -35,6 +35,8 @@
#include "xgboost/span.h" #include "xgboost/span.h"
#include "xgboost/tree_model.h" // RegTree #include "xgboost/tree_model.h" // RegTree
#include "regression_param.h"
#if defined(XGBOOST_USE_CUDA) #if defined(XGBOOST_USE_CUDA)
#include "../common/cuda_context.cuh" // for CUDAContext #include "../common/cuda_context.cuh" // for CUDAContext
#include "../common/device_helpers.cuh" #include "../common/device_helpers.cuh"
@ -53,14 +55,7 @@ void CheckRegInputs(MetaInfo const& info, HostDeviceVector<bst_float> const& pre
DMLC_REGISTRY_FILE_TAG(regression_obj_gpu); DMLC_REGISTRY_FILE_TAG(regression_obj_gpu);
#endif // defined(XGBOOST_USE_CUDA) #endif // defined(XGBOOST_USE_CUDA)
struct RegLossParam : public XGBoostParameter<RegLossParam> {
float scale_pos_weight;
// declare parameters
DMLC_DECLARE_PARAMETER(RegLossParam) {
DMLC_DECLARE_FIELD(scale_pos_weight).set_default(1.0f).set_lower_bound(0.0f)
.describe("Scale the weight of positive examples by this factor");
}
};
template<typename Loss> template<typename Loss>
class RegLossObj : public FitIntercept { class RegLossObj : public FitIntercept {

25
src/objective/regression_param.h Normal file
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@ -0,0 +1,25 @@
/*!
* Copyright 2015-2023 by Contributors
* \file multiclass_param.h
* \brief Definition of single-value regression and classification parameters.
*/
#ifndef XGBOOST_OBJECTIVE_REGRESSION_PARAM_H_
#define XGBOOST_OBJECTIVE_REGRESSION_PARAM_H_
#include "xgboost/parameter.h"
namespace xgboost {
namespace obj {
struct RegLossParam : public XGBoostParameter<RegLossParam> {
float scale_pos_weight;
// declare parameters
DMLC_DECLARE_PARAMETER(RegLossParam) {
DMLC_DECLARE_FIELD(scale_pos_weight).set_default(1.0f).set_lower_bound(0.0f)
.describe("Scale the weight of positive examples by this factor");
}
};
} // namespace obj
} // namespace xgboost
#endif // XGBOOST_OBJECTIVE_REGRESSION_PARAM_H_

19
tests/cpp/objective/test_multiclass_obj.cc
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@ -1,18 +1,18 @@
/*! /*!
* Copyright 2018-2019 XGBoost contributors * Copyright 2018-2023 XGBoost contributors
*/ */
#include <xgboost/objective.h> #include <xgboost/objective.h>
#include <xgboost/context.h> #include <xgboost/context.h>
#include "../../src/common/common.h" #include "../../src/common/common.h"
#include "../helpers.h" #include "../helpers.h"
#include "test_multiclass_obj.h"
namespace xgboost { namespace xgboost {
TEST(Objective, DeclareUnifiedTest(SoftmaxMultiClassObjGPair)) { void TestSoftmaxMultiClassObjGPair(const Context* ctx) {
Context ctx = MakeCUDACtx(GPUIDX);
std::vector<std::pair<std::string, std::string>> args {{"num_class", "3"}}; std::vector<std::pair<std::string, std::string>> args {{"num_class", "3"}};
std::unique_ptr<ObjFunction> obj { std::unique_ptr<ObjFunction> obj {
ObjFunction::Create("multi:softmax", &ctx) ObjFunction::Create("multi:softmax", ctx)
}; };
obj->Configure(args); obj->Configure(args);
@ -35,12 +35,11 @@ TEST(Objective, DeclareUnifiedTest(SoftmaxMultiClassObjGPair)) {
ASSERT_NO_THROW(obj->DefaultEvalMetric()); ASSERT_NO_THROW(obj->DefaultEvalMetric());
} }
TEST(Objective, DeclareUnifiedTest(SoftmaxMultiClassBasic)) { void TestSoftmaxMultiClassBasic(const Context* ctx) {
auto ctx = MakeCUDACtx(GPUIDX);
std::vector<std::pair<std::string, std::string>> args{ std::vector<std::pair<std::string, std::string>> args{
std::pair<std::string, std::string>("num_class", "3")}; std::pair<std::string, std::string>("num_class", "3")};
std::unique_ptr<ObjFunction> obj{ObjFunction::Create("multi:softmax", &ctx)}; std::unique_ptr<ObjFunction> obj{ObjFunction::Create("multi:softmax", ctx)};
obj->Configure(args); obj->Configure(args);
CheckConfigReload(obj, "multi:softmax"); CheckConfigReload(obj, "multi:softmax");
@ -56,13 +55,12 @@ TEST(Objective, DeclareUnifiedTest(SoftmaxMultiClassBasic)) {
} }
} }
TEST(Objective, DeclareUnifiedTest(SoftprobMultiClassBasic)) { void TestSoftprobMultiClassBasic(const Context* ctx) {
Context ctx = MakeCUDACtx(GPUIDX);
std::vector<std::pair<std::string, std::string>> args { std::vector<std::pair<std::string, std::string>> args {
std::pair<std::string, std::string>("num_class", "3")}; std::pair<std::string, std::string>("num_class", "3")};
std::unique_ptr<ObjFunction> obj { std::unique_ptr<ObjFunction> obj {
ObjFunction::Create("multi:softprob", &ctx) ObjFunction::Create("multi:softprob", ctx)
}; };
obj->Configure(args); obj->Configure(args);
CheckConfigReload(obj, "multi:softprob"); CheckConfigReload(obj, "multi:softprob");
@ -77,4 +75,5 @@ TEST(Objective, DeclareUnifiedTest(SoftprobMultiClassBasic)) {
EXPECT_NEAR(preds[i], out_preds[i], 0.01f); EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
} }
} }
} // namespace xgboost } // namespace xgboost

19
tests/cpp/objective/test_multiclass_obj.h Normal file
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@ -0,0 +1,19 @@
/**
* Copyright 2020-2023 by XGBoost Contributors
*/
#ifndef XGBOOST_TEST_MULTICLASS_OBJ_H_
#define XGBOOST_TEST_MULTICLASS_OBJ_H_
#include <xgboost/context.h> // for Context
namespace xgboost {
void TestSoftmaxMultiClassObjGPair(const Context* ctx);
void TestSoftmaxMultiClassBasic(const Context* ctx);
void TestSoftprobMultiClassBasic(const Context* ctx);
} // namespace xgboost
#endif // XGBOOST_TEST_MULTICLASS_OBJ_H_

25
tests/cpp/objective/test_multiclass_obj_cpu.cc Normal file
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@ -0,0 +1,25 @@
/*!
* Copyright 2018-2023 XGBoost contributors
*/
#include <gtest/gtest.h>
#include <xgboost/context.h>
#include "../helpers.h"
#include "test_multiclass_obj.h"
namespace xgboost {
TEST(Objective, DeclareUnifiedTest(SoftmaxMultiClassObjGPair)) {
Context ctx = MakeCUDACtx(GPUIDX);
TestSoftmaxMultiClassObjGPair(&ctx);
}
TEST(Objective, DeclareUnifiedTest(SoftmaxMultiClassBasic)) {
auto ctx = MakeCUDACtx(GPUIDX);
TestSoftmaxMultiClassBasic(&ctx);
}
TEST(Objective, DeclareUnifiedTest(SoftprobMultiClassBasic)) {
Context ctx = MakeCUDACtx(GPUIDX);
TestSoftprobMultiClassBasic(&ctx);
}
} // namespace xgboost

2
tests/cpp/objective/test_multiclass_obj_gpu.cu
View File

@ -1 +1 @@
#include "test_multiclass_obj.cc" #include "test_multiclass_obj_cpu.cc"

414
tests/cpp/objective/test_regression_obj.cc
View File

@ -14,13 +14,15 @@
#include "xgboost/data.h" #include "xgboost/data.h"
#include "xgboost/linalg.h" #include "xgboost/linalg.h"
#include "test_regression_obj.h"
namespace xgboost { namespace xgboost {
TEST(Objective, DeclareUnifiedTest(LinearRegressionGPair)) { void TestLinearRegressionGPair(const Context* ctx) {
Context ctx = MakeCUDACtx(GPUIDX); std::string obj_name = "reg:squarederror";
std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj{ObjFunction::Create("reg:squarederror", &ctx)}; std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj{ObjFunction::Create(obj_name, ctx)};
obj->Configure(args); obj->Configure(args);
CheckObjFunction(obj, CheckObjFunction(obj,
@ -38,13 +40,13 @@ TEST(Objective, DeclareUnifiedTest(LinearRegressionGPair)) {
ASSERT_NO_THROW(obj->DefaultEvalMetric()); ASSERT_NO_THROW(obj->DefaultEvalMetric());
} }
TEST(Objective, DeclareUnifiedTest(SquaredLog)) { void TestSquaredLog(const Context* ctx) {
Context ctx = MakeCUDACtx(GPUIDX); std::string obj_name = "reg:squaredlogerror";
std::vector<std::pair<std::string, std::string>> args; std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj{ObjFunction::Create("reg:squaredlogerror", &ctx)}; std::unique_ptr<ObjFunction> obj{ObjFunction::Create(obj_name, ctx)};
obj->Configure(args); obj->Configure(args);
CheckConfigReload(obj, "reg:squaredlogerror"); CheckConfigReload(obj, obj_name);
CheckObjFunction(obj, CheckObjFunction(obj,
{0.1f, 0.2f, 0.4f, 0.8f, 1.6f}, // pred {0.1f, 0.2f, 0.4f, 0.8f, 1.6f}, // pred
@ -61,42 +63,13 @@ TEST(Objective, DeclareUnifiedTest(SquaredLog)) {
ASSERT_EQ(obj->DefaultEvalMetric(), std::string{"rmsle"}); ASSERT_EQ(obj->DefaultEvalMetric(), std::string{"rmsle"});
} }
TEST(Objective, DeclareUnifiedTest(PseudoHuber)) { void TestLogisticRegressionGPair(const Context* ctx) {
Context ctx = MakeCUDACtx(GPUIDX); std::string obj_name = "reg:logistic";
Args args;
std::unique_ptr<ObjFunction> obj{ObjFunction::Create("reg:pseudohubererror", &ctx)};
obj->Configure(args);
CheckConfigReload(obj, "reg:pseudohubererror");
CheckObjFunction(obj, {0.1f, 0.2f, 0.4f, 0.8f, 1.6f}, // pred
{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // labels
{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // weights
{-0.668965f, -0.624695f, -0.514496f, -0.196116f, 0.514496f}, // out_grad
{0.410660f, 0.476140f, 0.630510f, 0.9428660f, 0.630510f}); // out_hess
CheckObjFunction(obj, {0.1f, 0.2f, 0.4f, 0.8f, 1.6f}, // pred
{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // labels
{}, // empty weights
{-0.668965f, -0.624695f, -0.514496f, -0.196116f, 0.514496f}, // out_grad
{0.410660f, 0.476140f, 0.630510f, 0.9428660f, 0.630510f}); // out_hess
ASSERT_EQ(obj->DefaultEvalMetric(), std::string{"mphe"});
obj->Configure({{"huber_slope", "0.1"}});
CheckConfigReload(obj, "reg:pseudohubererror");
CheckObjFunction(obj, {0.1f, 0.2f, 0.4f, 0.8f, 1.6f}, // pred
{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // labels
{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // weights
{-0.099388f, -0.099228f, -0.098639f, -0.089443f, 0.098639f}, // out_grad
{0.0013467f, 0.001908f, 0.004443f, 0.089443f, 0.004443f}); // out_hess
}
TEST(Objective, DeclareUnifiedTest(LogisticRegressionGPair)) {
Context ctx = MakeCUDACtx(GPUIDX);
std::vector<std::pair<std::string, std::string>> args; std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj{ObjFunction::Create("reg:logistic", &ctx)}; std::unique_ptr<ObjFunction> obj{ObjFunction::Create(obj_name, ctx)};
obj->Configure(args); obj->Configure(args);
CheckConfigReload(obj, "reg:logistic"); CheckConfigReload(obj, obj_name);
CheckObjFunction(obj, CheckObjFunction(obj,
{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1}, // preds { 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1}, // preds
@ -106,13 +79,13 @@ TEST(Objective, DeclareUnifiedTest(LogisticRegressionGPair)) {
{0.25f, 0.24f, 0.20f, 0.19f, 0.25f, 0.24f, 0.20f, 0.19f}); // out_hess {0.25f, 0.24f, 0.20f, 0.19f, 0.25f, 0.24f, 0.20f, 0.19f}); // out_hess
} }
TEST(Objective, DeclareUnifiedTest(LogisticRegressionBasic)) { void TestLogisticRegressionBasic(const Context* ctx) {
Context ctx = MakeCUDACtx(GPUIDX); std::string obj_name = "reg:logistic";
std::vector<std::pair<std::string, std::string>> args; std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj{ObjFunction::Create("reg:logistic", &ctx)}; std::unique_ptr<ObjFunction> obj{ObjFunction::Create(obj_name, ctx)};
obj->Configure(args); obj->Configure(args);
CheckConfigReload(obj, "reg:logistic"); CheckConfigReload(obj, obj_name);
// test label validation // test label validation
EXPECT_ANY_THROW(CheckObjFunction(obj, {0}, {10}, {1}, {0}, {0})) EXPECT_ANY_THROW(CheckObjFunction(obj, {0}, {10}, {1}, {0}, {0}))
@ -135,12 +108,10 @@ TEST(Objective, DeclareUnifiedTest(LogisticRegressionBasic)) {
} }
} }
TEST(Objective, DeclareUnifiedTest(LogisticRawGPair)) { void TestsLogisticRawGPair(const Context* ctx) {
Context ctx = MakeCUDACtx(GPUIDX); std::string obj_name = "binary:logitraw";
std::vector<std::pair<std::string, std::string>> args; std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj { std::unique_ptr<ObjFunction> obj {ObjFunction::Create(obj_name, ctx)};
ObjFunction::Create("binary:logitraw", &ctx)
};
obj->Configure(args); obj->Configure(args);
CheckObjFunction(obj, CheckObjFunction(obj,
@ -151,347 +122,4 @@ TEST(Objective, DeclareUnifiedTest(LogisticRawGPair)) {
{0.25f, 0.24f, 0.20f, 0.19f, 0.25f, 0.24f, 0.20f, 0.19f}); {0.25f, 0.24f, 0.20f, 0.19f, 0.25f, 0.24f, 0.20f, 0.19f});
} }
TEST(Objective, DeclareUnifiedTest(PoissonRegressionGPair)) {
Context ctx = MakeCUDACtx(GPUIDX);
std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj {
ObjFunction::Create("count:poisson", &ctx)
};
args.emplace_back("max_delta_step", "0.1f");
obj->Configure(args);
CheckObjFunction(obj,
{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
{ 0, 0, 0, 0, 1, 1, 1, 1},
{ 1, 1, 1, 1, 1, 1, 1, 1},
{ 1, 1.10f, 2.45f, 2.71f, 0, 0.10f, 1.45f, 1.71f},
{1.10f, 1.22f, 2.71f, 3.00f, 1.10f, 1.22f, 2.71f, 3.00f});
CheckObjFunction(obj,
{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
{ 0, 0, 0, 0, 1, 1, 1, 1},
{}, // Empty weight
{ 1, 1.10f, 2.45f, 2.71f, 0, 0.10f, 1.45f, 1.71f},
{1.10f, 1.22f, 2.71f, 3.00f, 1.10f, 1.22f, 2.71f, 3.00f});
}
TEST(Objective, DeclareUnifiedTest(PoissonRegressionBasic)) {
Context ctx = MakeCUDACtx(GPUIDX);
std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj {
ObjFunction::Create("count:poisson", &ctx)
};
obj->Configure(args);
CheckConfigReload(obj, "count:poisson");
// test label validation
EXPECT_ANY_THROW(CheckObjFunction(obj, {0}, {-1}, {1}, {0}, {0}))
<< "Expected error when label < 0 for PoissonRegression";
// test ProbToMargin
EXPECT_NEAR(obj->ProbToMargin(0.1f), -2.30f, 0.01f);
EXPECT_NEAR(obj->ProbToMargin(0.5f), -0.69f, 0.01f);
EXPECT_NEAR(obj->ProbToMargin(0.9f), -0.10f, 0.01f);
// test PredTransform
HostDeviceVector<bst_float> io_preds = {0, 0.1f, 0.5f, 0.9f, 1};
std::vector<bst_float> out_preds = {1, 1.10f, 1.64f, 2.45f, 2.71f};
obj->PredTransform(&io_preds);
auto& preds = io_preds.HostVector();
for (int i = 0; i < static_cast<int>(io_preds.Size()); ++i) {
EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
}
}
TEST(Objective, DeclareUnifiedTest(GammaRegressionGPair)) {
Context ctx = MakeCUDACtx(GPUIDX);
std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj {
ObjFunction::Create("reg:gamma", &ctx)
};
obj->Configure(args);
CheckObjFunction(obj,
{0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
{2, 2, 2, 2, 1, 1, 1, 1},
{1, 1, 1, 1, 1, 1, 1, 1},
{-1, -0.809, 0.187, 0.264, 0, 0.09f, 0.59f, 0.63f},
{2, 1.809, 0.813, 0.735, 1, 0.90f, 0.40f, 0.36f});
CheckObjFunction(obj,
{0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
{2, 2, 2, 2, 1, 1, 1, 1},
{}, // Empty weight
{-1, -0.809, 0.187, 0.264, 0, 0.09f, 0.59f, 0.63f},
{2, 1.809, 0.813, 0.735, 1, 0.90f, 0.40f, 0.36f});
}
TEST(Objective, DeclareUnifiedTest(GammaRegressionBasic)) {
Context ctx = MakeCUDACtx(GPUIDX);
std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj{ObjFunction::Create("reg:gamma", &ctx)};
obj->Configure(args);
CheckConfigReload(obj, "reg:gamma");
// test label validation
EXPECT_ANY_THROW(CheckObjFunction(obj, {0}, {0}, {1}, {0}, {0}))
<< "Expected error when label = 0 for GammaRegression";
EXPECT_ANY_THROW(CheckObjFunction(obj, {-1}, {-1}, {1}, {-1}, {-3}))
<< "Expected error when label < 0 for GammaRegression";
// test ProbToMargin
EXPECT_NEAR(obj->ProbToMargin(0.1f), -2.30f, 0.01f);
EXPECT_NEAR(obj->ProbToMargin(0.5f), -0.69f, 0.01f);
EXPECT_NEAR(obj->ProbToMargin(0.9f), -0.10f, 0.01f);
// test PredTransform
HostDeviceVector<bst_float> io_preds = {0, 0.1f, 0.5f, 0.9f, 1};
std::vector<bst_float> out_preds = {1, 1.10f, 1.64f, 2.45f, 2.71f};
obj->PredTransform(&io_preds);
auto& preds = io_preds.HostVector();
for (int i = 0; i < static_cast<int>(io_preds.Size()); ++i) {
EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
}
}
TEST(Objective, DeclareUnifiedTest(TweedieRegressionGPair)) {
Context ctx = MakeCUDACtx(GPUIDX);
std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj{ObjFunction::Create("reg:tweedie", &ctx)};
args.emplace_back("tweedie_variance_power", "1.1f");
obj->Configure(args);
CheckObjFunction(obj,
{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
{ 0, 0, 0, 0, 1, 1, 1, 1},
{ 1, 1, 1, 1, 1, 1, 1, 1},
{ 1, 1.09f, 2.24f, 2.45f, 0, 0.10f, 1.33f, 1.55f},
{0.89f, 0.98f, 2.02f, 2.21f, 1, 1.08f, 2.11f, 2.30f});
CheckObjFunction(obj,
{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
{ 0, 0, 0, 0, 1, 1, 1, 1},
{}, // Empty weight.
{ 1, 1.09f, 2.24f, 2.45f, 0, 0.10f, 1.33f, 1.55f},
{0.89f, 0.98f, 2.02f, 2.21f, 1, 1.08f, 2.11f, 2.30f});
ASSERT_EQ(obj->DefaultEvalMetric(), std::string{"tweedie-nloglik@1.1"});
}
#if defined(__CUDACC__)
TEST(Objective, CPU_vs_CUDA) {
Context ctx = MakeCUDACtx(GPUIDX);
std::unique_ptr<ObjFunction> obj{ObjFunction::Create("reg:squarederror", &ctx)};
linalg::Matrix<GradientPair> cpu_out_preds;
linalg::Matrix<GradientPair> cuda_out_preds;
constexpr size_t kRows = 400;
constexpr size_t kCols = 100;
auto pdmat = RandomDataGenerator(kRows, kCols, 0).Seed(0).GenerateDMatrix();
HostDeviceVector<float> preds;
preds.Resize(kRows);
auto& h_preds = preds.HostVector();
for (size_t i = 0; i < h_preds.size(); ++i) {
h_preds[i] = static_cast<float>(i);
}
auto& info = pdmat->Info();
info.labels.Reshape(kRows);
auto& h_labels = info.labels.Data()->HostVector();
for (size_t i = 0; i < h_labels.size(); ++i) {
h_labels[i] = 1 / static_cast<float>(i+1);
}
{
// CPU
ctx = ctx.MakeCPU();
obj->GetGradient(preds, info, 0, &cpu_out_preds);
}
{
// CUDA
ctx = ctx.MakeCUDA(0);
obj->GetGradient(preds, info, 0, &cuda_out_preds);
}
auto h_cpu_out = cpu_out_preds.HostView();
auto h_cuda_out = cuda_out_preds.HostView();
float sgrad = 0;
float shess = 0;
for (size_t i = 0; i < kRows; ++i) {
sgrad += std::pow(h_cpu_out(i).GetGrad() - h_cuda_out(i).GetGrad(), 2);
shess += std::pow(h_cpu_out(i).GetHess() - h_cuda_out(i).GetHess(), 2);
}
ASSERT_NEAR(sgrad, 0.0f, kRtEps);
ASSERT_NEAR(shess, 0.0f, kRtEps);
}
#endif
TEST(Objective, DeclareUnifiedTest(TweedieRegressionBasic)) {
Context ctx = MakeCUDACtx(GPUIDX);
std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj{ObjFunction::Create("reg:tweedie", &ctx)};
obj->Configure(args);
CheckConfigReload(obj, "reg:tweedie");
// test label validation
EXPECT_ANY_THROW(CheckObjFunction(obj, {0}, {-1}, {1}, {0}, {0}))
<< "Expected error when label < 0 for TweedieRegression";
// test ProbToMargin
EXPECT_NEAR(obj->ProbToMargin(0.1f), -2.30f, 0.01f);
EXPECT_NEAR(obj->ProbToMargin(0.5f), -0.69f, 0.01f);
EXPECT_NEAR(obj->ProbToMargin(0.9f), -0.10f, 0.01f);
// test PredTransform
HostDeviceVector<bst_float> io_preds = {0, 0.1f, 0.5f, 0.9f, 1};
std::vector<bst_float> out_preds = {1, 1.10f, 1.64f, 2.45f, 2.71f};
obj->PredTransform(&io_preds);
auto& preds = io_preds.HostVector();
for (int i = 0; i < static_cast<int>(io_preds.Size()); ++i) {
EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
}
}
// CoxRegression not implemented in GPU code, no need for testing.
#if !defined(__CUDACC__)
TEST(Objective, CoxRegressionGPair) {
Context ctx = MakeCUDACtx(GPUIDX);
std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj{ObjFunction::Create("survival:cox", &ctx)};
obj->Configure(args);
CheckObjFunction(obj,
{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
{ 0, -2, -2, 2, 3, 5, -10, 100},
{ 1, 1, 1, 1, 1, 1, 1, 1},
{ 0, 0, 0, -0.799f, -0.788f, -0.590f, 0.910f, 1.006f},
{ 0, 0, 0, 0.160f, 0.186f, 0.348f, 0.610f, 0.639f});
}
#endif
TEST(Objective, DeclareUnifiedTest(AbsoluteError)) {
Context ctx = MakeCUDACtx(GPUIDX);
std::unique_ptr<ObjFunction> obj{ObjFunction::Create("reg:absoluteerror", &ctx)};
obj->Configure({});
CheckConfigReload(obj, "reg:absoluteerror");
MetaInfo info;
std::vector<float> labels{0.f, 3.f, 2.f, 5.f, 4.f, 7.f};
info.labels.Reshape(6, 1);
info.labels.Data()->HostVector() = labels;
info.num_row_ = labels.size();
HostDeviceVector<float> predt{1.f, 2.f, 3.f, 4.f, 5.f, 6.f};
info.weights_.HostVector() = {1.f, 1.f, 1.f, 1.f, 1.f, 1.f};
CheckObjFunction(obj, predt.HostVector(), labels, info.weights_.HostVector(),
{1.f, -1.f, 1.f, -1.f, 1.f, -1.f}, info.weights_.HostVector());
RegTree tree;
tree.ExpandNode(0, /*split_index=*/1, 2, true, 0.0f, 2.f, 3.f, 4.f, 2.f, 1.f, 1.f);
HostDeviceVector<bst_node_t> position(labels.size(), 0);
auto& h_position = position.HostVector();
for (size_t i = 0; i < labels.size(); ++i) {
if (i < labels.size() / 2) {
h_position[i] = 1; // left
} else {
h_position[i] = 2; // right
}
}
auto& h_predt = predt.HostVector();
for (size_t i = 0; i < h_predt.size(); ++i) {
h_predt[i] = labels[i] + i;
}
tree::TrainParam param;
param.Init(Args{});
auto lr = param.learning_rate;
obj->UpdateTreeLeaf(position, info, param.learning_rate, predt, 0, &tree);
ASSERT_EQ(tree[1].LeafValue(), -1.0f * lr);
ASSERT_EQ(tree[2].LeafValue(), -4.0f * lr);
}
TEST(Objective, DeclareUnifiedTest(AbsoluteErrorLeaf)) {
Context ctx = MakeCUDACtx(GPUIDX);
bst_target_t constexpr kTargets = 3, kRows = 16;
std::unique_ptr<ObjFunction> obj{ObjFunction::Create("reg:absoluteerror", &ctx)};
obj->Configure({});
MetaInfo info;
info.num_row_ = kRows;
info.labels.Reshape(16, kTargets);
HostDeviceVector<float> predt(info.labels.Size());
for (bst_target_t t{0}; t < kTargets; ++t) {
auto h_labels = info.labels.HostView().Slice(linalg::All(), t);
std::iota(linalg::begin(h_labels), linalg::end(h_labels), 0);
auto h_predt =
linalg::MakeTensorView(&ctx, predt.HostSpan(), kRows, kTargets).Slice(linalg::All(), t);
for (size_t i = 0; i < h_predt.Size(); ++i) {
h_predt(i) = h_labels(i) + i;
}
HostDeviceVector<bst_node_t> position(h_labels.Size(), 0);
auto& h_position = position.HostVector();
for (int32_t i = 0; i < 3; ++i) {
h_position[i] = ~i; // negation for sampled nodes.
}
for (size_t i = 3; i < 8; ++i) {
h_position[i] = 3;
}
// empty leaf for node 4
for (size_t i = 8; i < 13; ++i) {
h_position[i] = 5;
}
for (size_t i = 13; i < h_labels.Size(); ++i) {
h_position[i] = 6;
}
RegTree tree;
tree.ExpandNode(0, /*split_index=*/1, 2, true, 0.0f, 2.f, 3.f, 4.f, 2.f, 1.f, 1.f);
tree.ExpandNode(1, /*split_index=*/1, 2, true, 0.0f, 2.f, 3.f, 4.f, 2.f, 1.f, 1.f);
tree.ExpandNode(2, /*split_index=*/1, 2, true, 0.0f, 2.f, 3.f, 4.f, 2.f, 1.f, 1.f);
ASSERT_EQ(tree.GetNumLeaves(), 4);
auto empty_leaf = tree[4].LeafValue();
tree::TrainParam param;
param.Init(Args{});
auto lr = param.learning_rate;
obj->UpdateTreeLeaf(position, info, lr, predt, t, &tree);
ASSERT_EQ(tree[3].LeafValue(), -5.0f * lr);
ASSERT_EQ(tree[4].LeafValue(), empty_leaf * lr);
ASSERT_EQ(tree[5].LeafValue(), -10.0f * lr);
ASSERT_EQ(tree[6].LeafValue(), -14.0f * lr);
}
}
TEST(Adaptive, DeclareUnifiedTest(MissingLeaf)) {
std::vector<bst_node_t> missing{1, 3};
std::vector<bst_node_t> h_nidx = {2, 4, 5};
std::vector<size_t> h_nptr = {0, 4, 8, 16};
obj::detail::FillMissingLeaf(missing, &h_nidx, &h_nptr);
ASSERT_EQ(h_nidx[0], missing[0]);
ASSERT_EQ(h_nidx[2], missing[1]);
ASSERT_EQ(h_nidx[1], 2);
ASSERT_EQ(h_nidx[3], 4);
ASSERT_EQ(h_nidx[4], 5);
ASSERT_EQ(h_nptr[0], 0);
ASSERT_EQ(h_nptr[1], 0); // empty
ASSERT_EQ(h_nptr[2], 4);
ASSERT_EQ(h_nptr[3], 4); // empty
ASSERT_EQ(h_nptr[4], 8);
ASSERT_EQ(h_nptr[5], 16);
}
} // namespace xgboost } // namespace xgboost

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/**
* Copyright 2020-2023 by XGBoost Contributors
*/
#ifndef XGBOOST_TEST_REGRESSION_OBJ_H_
#define XGBOOST_TEST_REGRESSION_OBJ_H_
#include <xgboost/context.h> // for Context
namespace xgboost {
void TestLinearRegressionGPair(const Context* ctx);
void TestSquaredLog(const Context* ctx);
void TestLogisticRegressionGPair(const Context* ctx);
void TestLogisticRegressionBasic(const Context* ctx);
void TestsLogisticRawGPair(const Context* ctx);
} // namespace xgboost
#endif // XGBOOST_TEST_REGRESSION_OBJ_H_

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/*!
* Copyright 2018-2023 XGBoost contributors
*/
#include <gtest/gtest.h>
#include <xgboost/context.h>
#include <xgboost/objective.h>
#include "../../../src/objective/adaptive.h"
#include "../../../src/tree/param.h" // for TrainParam
#include "../helpers.h"
#include "test_regression_obj.h"
namespace xgboost {
TEST(Objective, DeclareUnifiedTest(LinearRegressionGPair)) {
Context ctx = MakeCUDACtx(GPUIDX);
TestLinearRegressionGPair(&ctx);
}
TEST(Objective, DeclareUnifiedTest(SquaredLog)) {
Context ctx = MakeCUDACtx(GPUIDX);
TestSquaredLog(&ctx);
}
TEST(Objective, DeclareUnifiedTest(PseudoHuber)) {
Context ctx = MakeCUDACtx(GPUIDX);
Args args;
std::unique_ptr<ObjFunction> obj{ObjFunction::Create("reg:pseudohubererror", &ctx)};
obj->Configure(args);
CheckConfigReload(obj, "reg:pseudohubererror");
CheckObjFunction(obj, {0.1f, 0.2f, 0.4f, 0.8f, 1.6f}, // pred
{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // labels
{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // weights
{-0.668965f, -0.624695f, -0.514496f, -0.196116f, 0.514496f}, // out_grad
{0.410660f, 0.476140f, 0.630510f, 0.9428660f, 0.630510f}); // out_hess
CheckObjFunction(obj, {0.1f, 0.2f, 0.4f, 0.8f, 1.6f}, // pred
{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // labels
{}, // empty weights
{-0.668965f, -0.624695f, -0.514496f, -0.196116f, 0.514496f}, // out_grad
{0.410660f, 0.476140f, 0.630510f, 0.9428660f, 0.630510f}); // out_hess
ASSERT_EQ(obj->DefaultEvalMetric(), std::string{"mphe"});
obj->Configure({{"huber_slope", "0.1"}});
CheckConfigReload(obj, "reg:pseudohubererror");
CheckObjFunction(obj, {0.1f, 0.2f, 0.4f, 0.8f, 1.6f}, // pred
{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // labels
{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // weights
{-0.099388f, -0.099228f, -0.098639f, -0.089443f, 0.098639f}, // out_grad
{0.0013467f, 0.001908f, 0.004443f, 0.089443f, 0.004443f}); // out_hess
}
TEST(Objective, DeclareUnifiedTest(LogisticRegressionGPair)) {
Context ctx = MakeCUDACtx(GPUIDX);
TestLogisticRegressionGPair(&ctx);
}
TEST(Objective, DeclareUnifiedTest(LogisticRegressionBasic)) {
Context ctx = MakeCUDACtx(GPUIDX);
TestLogisticRegressionBasic(&ctx);
}
TEST(Objective, DeclareUnifiedTest(LogisticRawGPair)) {
Context ctx = MakeCUDACtx(GPUIDX);
TestsLogisticRawGPair(&ctx);
}
TEST(Objective, DeclareUnifiedTest(PoissonRegressionGPair)) {
Context ctx = MakeCUDACtx(GPUIDX);
std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj {
ObjFunction::Create("count:poisson", &ctx)
};
args.emplace_back("max_delta_step", "0.1f");
obj->Configure(args);
CheckObjFunction(obj,
{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
{ 0, 0, 0, 0, 1, 1, 1, 1},
{ 1, 1, 1, 1, 1, 1, 1, 1},
{ 1, 1.10f, 2.45f, 2.71f, 0, 0.10f, 1.45f, 1.71f},
{1.10f, 1.22f, 2.71f, 3.00f, 1.10f, 1.22f, 2.71f, 3.00f});
CheckObjFunction(obj,
{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
{ 0, 0, 0, 0, 1, 1, 1, 1},
{}, // Empty weight
{ 1, 1.10f, 2.45f, 2.71f, 0, 0.10f, 1.45f, 1.71f},
{1.10f, 1.22f, 2.71f, 3.00f, 1.10f, 1.22f, 2.71f, 3.00f});
}
TEST(Objective, DeclareUnifiedTest(PoissonRegressionBasic)) {
Context ctx = MakeCUDACtx(GPUIDX);
std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj {
ObjFunction::Create("count:poisson", &ctx)
};
obj->Configure(args);
CheckConfigReload(obj, "count:poisson");
// test label validation
EXPECT_ANY_THROW(CheckObjFunction(obj, {0}, {-1}, {1}, {0}, {0}))
<< "Expected error when label < 0 for PoissonRegression";
// test ProbToMargin
EXPECT_NEAR(obj->ProbToMargin(0.1f), -2.30f, 0.01f);
EXPECT_NEAR(obj->ProbToMargin(0.5f), -0.69f, 0.01f);
EXPECT_NEAR(obj->ProbToMargin(0.9f), -0.10f, 0.01f);
// test PredTransform
HostDeviceVector<bst_float> io_preds = {0, 0.1f, 0.5f, 0.9f, 1};
std::vector<bst_float> out_preds = {1, 1.10f, 1.64f, 2.45f, 2.71f};
obj->PredTransform(&io_preds);
auto& preds = io_preds.HostVector();
for (int i = 0; i < static_cast<int>(io_preds.Size()); ++i) {
EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
}
}
TEST(Objective, DeclareUnifiedTest(GammaRegressionGPair)) {
Context ctx = MakeCUDACtx(GPUIDX);
std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj {
ObjFunction::Create("reg:gamma", &ctx)
};
obj->Configure(args);
CheckObjFunction(obj,
{0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
{2, 2, 2, 2, 1, 1, 1, 1},
{1, 1, 1, 1, 1, 1, 1, 1},
{-1, -0.809, 0.187, 0.264, 0, 0.09f, 0.59f, 0.63f},
{2, 1.809, 0.813, 0.735, 1, 0.90f, 0.40f, 0.36f});
CheckObjFunction(obj,
{0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
{2, 2, 2, 2, 1, 1, 1, 1},
{}, // Empty weight
{-1, -0.809, 0.187, 0.264, 0, 0.09f, 0.59f, 0.63f},
{2, 1.809, 0.813, 0.735, 1, 0.90f, 0.40f, 0.36f});
}
TEST(Objective, DeclareUnifiedTest(GammaRegressionBasic)) {
Context ctx = MakeCUDACtx(GPUIDX);
std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj{ObjFunction::Create("reg:gamma", &ctx)};
obj->Configure(args);
CheckConfigReload(obj, "reg:gamma");
// test label validation
EXPECT_ANY_THROW(CheckObjFunction(obj, {0}, {0}, {1}, {0}, {0}))
<< "Expected error when label = 0 for GammaRegression";
EXPECT_ANY_THROW(CheckObjFunction(obj, {-1}, {-1}, {1}, {-1}, {-3}))
<< "Expected error when label < 0 for GammaRegression";
// test ProbToMargin
EXPECT_NEAR(obj->ProbToMargin(0.1f), -2.30f, 0.01f);
EXPECT_NEAR(obj->ProbToMargin(0.5f), -0.69f, 0.01f);
EXPECT_NEAR(obj->ProbToMargin(0.9f), -0.10f, 0.01f);
// test PredTransform
HostDeviceVector<bst_float> io_preds = {0, 0.1f, 0.5f, 0.9f, 1};
std::vector<bst_float> out_preds = {1, 1.10f, 1.64f, 2.45f, 2.71f};
obj->PredTransform(&io_preds);
auto& preds = io_preds.HostVector();
for (int i = 0; i < static_cast<int>(io_preds.Size()); ++i) {
EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
}
}
TEST(Objective, DeclareUnifiedTest(TweedieRegressionGPair)) {
Context ctx = MakeCUDACtx(GPUIDX);
std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj{ObjFunction::Create("reg:tweedie", &ctx)};
args.emplace_back("tweedie_variance_power", "1.1f");
obj->Configure(args);
CheckObjFunction(obj,
{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
{ 0, 0, 0, 0, 1, 1, 1, 1},
{ 1, 1, 1, 1, 1, 1, 1, 1},
{ 1, 1.09f, 2.24f, 2.45f, 0, 0.10f, 1.33f, 1.55f},
{0.89f, 0.98f, 2.02f, 2.21f, 1, 1.08f, 2.11f, 2.30f});
CheckObjFunction(obj,
{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
{ 0, 0, 0, 0, 1, 1, 1, 1},
{}, // Empty weight.
{ 1, 1.09f, 2.24f, 2.45f, 0, 0.10f, 1.33f, 1.55f},
{0.89f, 0.98f, 2.02f, 2.21f, 1, 1.08f, 2.11f, 2.30f});
ASSERT_EQ(obj->DefaultEvalMetric(), std::string{"tweedie-nloglik@1.1"});
}
#if defined(__CUDACC__)
TEST(Objective, CPU_vs_CUDA) {
Context ctx = MakeCUDACtx(GPUIDX);
std::unique_ptr<ObjFunction> obj{ObjFunction::Create("reg:squarederror", &ctx)};
linalg::Matrix<GradientPair> cpu_out_preds;
linalg::Matrix<GradientPair> cuda_out_preds;
constexpr size_t kRows = 400;
constexpr size_t kCols = 100;
auto pdmat = RandomDataGenerator(kRows, kCols, 0).Seed(0).GenerateDMatrix();
HostDeviceVector<float> preds;
preds.Resize(kRows);
auto& h_preds = preds.HostVector();
for (size_t i = 0; i < h_preds.size(); ++i) {
h_preds[i] = static_cast<float>(i);
}
auto& info = pdmat->Info();
info.labels.Reshape(kRows);
auto& h_labels = info.labels.Data()->HostVector();
for (size_t i = 0; i < h_labels.size(); ++i) {
h_labels[i] = 1 / static_cast<float>(i+1);
}
{
// CPU
ctx = ctx.MakeCPU();
obj->GetGradient(preds, info, 0, &cpu_out_preds);
}
{
// CUDA
ctx = ctx.MakeCUDA(0);
obj->GetGradient(preds, info, 0, &cuda_out_preds);
}
auto h_cpu_out = cpu_out_preds.HostView();
auto h_cuda_out = cuda_out_preds.HostView();
float sgrad = 0;
float shess = 0;
for (size_t i = 0; i < kRows; ++i) {
sgrad += std::pow(h_cpu_out(i).GetGrad() - h_cuda_out(i).GetGrad(), 2);
shess += std::pow(h_cpu_out(i).GetHess() - h_cuda_out(i).GetHess(), 2);
}
ASSERT_NEAR(sgrad, 0.0f, kRtEps);
ASSERT_NEAR(shess, 0.0f, kRtEps);
}
#endif
TEST(Objective, DeclareUnifiedTest(TweedieRegressionBasic)) {
Context ctx = MakeCUDACtx(GPUIDX);
std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj{ObjFunction::Create("reg:tweedie", &ctx)};
obj->Configure(args);
CheckConfigReload(obj, "reg:tweedie");
// test label validation
EXPECT_ANY_THROW(CheckObjFunction(obj, {0}, {-1}, {1}, {0}, {0}))
<< "Expected error when label < 0 for TweedieRegression";
// test ProbToMargin
EXPECT_NEAR(obj->ProbToMargin(0.1f), -2.30f, 0.01f);
EXPECT_NEAR(obj->ProbToMargin(0.5f), -0.69f, 0.01f);
EXPECT_NEAR(obj->ProbToMargin(0.9f), -0.10f, 0.01f);
// test PredTransform
HostDeviceVector<bst_float> io_preds = {0, 0.1f, 0.5f, 0.9f, 1};
std::vector<bst_float> out_preds = {1, 1.10f, 1.64f, 2.45f, 2.71f};
obj->PredTransform(&io_preds);
auto& preds = io_preds.HostVector();
for (int i = 0; i < static_cast<int>(io_preds.Size()); ++i) {
EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
}
}
// CoxRegression not implemented in GPU code, no need for testing.
#if !defined(__CUDACC__)
TEST(Objective, CoxRegressionGPair) {
Context ctx = MakeCUDACtx(GPUIDX);
std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj{ObjFunction::Create("survival:cox", &ctx)};
obj->Configure(args);
CheckObjFunction(obj,
{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
{ 0, -2, -2, 2, 3, 5, -10, 100},
{ 1, 1, 1, 1, 1, 1, 1, 1},
{ 0, 0, 0, -0.799f, -0.788f, -0.590f, 0.910f, 1.006f},
{ 0, 0, 0, 0.160f, 0.186f, 0.348f, 0.610f, 0.639f});
}
#endif
TEST(Objective, DeclareUnifiedTest(AbsoluteError)) {
Context ctx = MakeCUDACtx(GPUIDX);
std::unique_ptr<ObjFunction> obj{ObjFunction::Create("reg:absoluteerror", &ctx)};
obj->Configure({});
CheckConfigReload(obj, "reg:absoluteerror");
MetaInfo info;
std::vector<float> labels{0.f, 3.f, 2.f, 5.f, 4.f, 7.f};
info.labels.Reshape(6, 1);
info.labels.Data()->HostVector() = labels;
info.num_row_ = labels.size();
HostDeviceVector<float> predt{1.f, 2.f, 3.f, 4.f, 5.f, 6.f};
info.weights_.HostVector() = {1.f, 1.f, 1.f, 1.f, 1.f, 1.f};
CheckObjFunction(obj, predt.HostVector(), labels, info.weights_.HostVector(),
{1.f, -1.f, 1.f, -1.f, 1.f, -1.f}, info.weights_.HostVector());
RegTree tree;
tree.ExpandNode(0, /*split_index=*/1, 2, true, 0.0f, 2.f, 3.f, 4.f, 2.f, 1.f, 1.f);
HostDeviceVector<bst_node_t> position(labels.size(), 0);
auto& h_position = position.HostVector();
for (size_t i = 0; i < labels.size(); ++i) {
if (i < labels.size() / 2) {
h_position[i] = 1; // left
} else {
h_position[i] = 2; // right
}
}
auto& h_predt = predt.HostVector();
for (size_t i = 0; i < h_predt.size(); ++i) {
h_predt[i] = labels[i] + i;
}
tree::TrainParam param;
param.Init(Args{});
auto lr = param.learning_rate;
obj->UpdateTreeLeaf(position, info, param.learning_rate, predt, 0, &tree);
ASSERT_EQ(tree[1].LeafValue(), -1.0f * lr);
ASSERT_EQ(tree[2].LeafValue(), -4.0f * lr);
}
TEST(Objective, DeclareUnifiedTest(AbsoluteErrorLeaf)) {
Context ctx = MakeCUDACtx(GPUIDX);
bst_target_t constexpr kTargets = 3, kRows = 16;
std::unique_ptr<ObjFunction> obj{ObjFunction::Create("reg:absoluteerror", &ctx)};
obj->Configure({});
MetaInfo info;
info.num_row_ = kRows;
info.labels.Reshape(16, kTargets);
HostDeviceVector<float> predt(info.labels.Size());
for (bst_target_t t{0}; t < kTargets; ++t) {
auto h_labels = info.labels.HostView().Slice(linalg::All(), t);
std::iota(linalg::begin(h_labels), linalg::end(h_labels), 0);
auto h_predt =
linalg::MakeTensorView(&ctx, predt.HostSpan(), kRows, kTargets).Slice(linalg::All(), t);
for (size_t i = 0; i < h_predt.Size(); ++i) {
h_predt(i) = h_labels(i) + i;
}
HostDeviceVector<bst_node_t> position(h_labels.Size(), 0);
auto& h_position = position.HostVector();
for (int32_t i = 0; i < 3; ++i) {
h_position[i] = ~i; // negation for sampled nodes.
}
for (size_t i = 3; i < 8; ++i) {
h_position[i] = 3;
}
// empty leaf for node 4
for (size_t i = 8; i < 13; ++i) {
h_position[i] = 5;
}
for (size_t i = 13; i < h_labels.Size(); ++i) {
h_position[i] = 6;
}
RegTree tree;
tree.ExpandNode(0, /*split_index=*/1, 2, true, 0.0f, 2.f, 3.f, 4.f, 2.f, 1.f, 1.f);
tree.ExpandNode(1, /*split_index=*/1, 2, true, 0.0f, 2.f, 3.f, 4.f, 2.f, 1.f, 1.f);
tree.ExpandNode(2, /*split_index=*/1, 2, true, 0.0f, 2.f, 3.f, 4.f, 2.f, 1.f, 1.f);
ASSERT_EQ(tree.GetNumLeaves(), 4);
auto empty_leaf = tree[4].LeafValue();
tree::TrainParam param;
param.Init(Args{});
auto lr = param.learning_rate;
obj->UpdateTreeLeaf(position, info, lr, predt, t, &tree);
ASSERT_EQ(tree[3].LeafValue(), -5.0f * lr);
ASSERT_EQ(tree[4].LeafValue(), empty_leaf * lr);
ASSERT_EQ(tree[5].LeafValue(), -10.0f * lr);
ASSERT_EQ(tree[6].LeafValue(), -14.0f * lr);
}
}
TEST(Adaptive, DeclareUnifiedTest(MissingLeaf)) {
std::vector<bst_node_t> missing{1, 3};
std::vector<bst_node_t> h_nidx = {2, 4, 5};
std::vector<size_t> h_nptr = {0, 4, 8, 16};
obj::detail::FillMissingLeaf(missing, &h_nidx, &h_nptr);
ASSERT_EQ(h_nidx[0], missing[0]);
ASSERT_EQ(h_nidx[2], missing[1]);
ASSERT_EQ(h_nidx[1], 2);
ASSERT_EQ(h_nidx[3], 4);
ASSERT_EQ(h_nidx[4], 5);
ASSERT_EQ(h_nptr[0], 0);
ASSERT_EQ(h_nptr[1], 0); // empty
ASSERT_EQ(h_nptr[2], 4);
ASSERT_EQ(h_nptr[3], 4); // empty
ASSERT_EQ(h_nptr[4], 8);
ASSERT_EQ(h_nptr[5], 16);
}
} // namespace xgboost

2
tests/cpp/objective/test_regression_obj_gpu.cu
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@ -3,4 +3,4 @@
*/ */
// Dummy file to keep the CUDA tests. // Dummy file to keep the CUDA tests.
#include "test_regression_obj.cc" #include "test_regression_obj_cpu.cc"

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/*!
* Copyright 2018-2023 XGBoost contributors
*/
#include <gtest/gtest.h>
#include <xgboost/context.h>
#include "../objective/test_multiclass_obj.h"
namespace xgboost {
TEST(SyclObjective, SoftmaxMultiClassObjGPair) {
Context ctx;
ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
TestSoftmaxMultiClassObjGPair(&ctx);
}
TEST(SyclObjective, SoftmaxMultiClassBasic) {
Context ctx;
ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
TestSoftmaxMultiClassObjGPair(&ctx);
}
TEST(SyclObjective, SoftprobMultiClassBasic) {
Context ctx;
ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
TestSoftprobMultiClassBasic(&ctx);
}
} // namespace xgboost

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/*!
* Copyright 2017-2019 XGBoost contributors
*/
#include <gtest/gtest.h>
#include <xgboost/objective.h>
#include <xgboost/context.h>
#include "../helpers.h"
#include "../objective/test_regression_obj.h"
namespace xgboost {
TEST(SyclObjective, LinearRegressionGPair) {
Context ctx;
ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
TestLinearRegressionGPair(&ctx);
}
TEST(SyclObjective, SquaredLog) {
Context ctx;
ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
TestSquaredLog(&ctx);
}
TEST(SyclObjective, LogisticRegressionGPair) {
Context ctx;
ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
TestLogisticRegressionGPair(&ctx);
}
TEST(SyclObjective, LogisticRegressionBasic) {
Context ctx;
ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
TestLogisticRegressionBasic(&ctx);
}
TEST(SyclObjective, LogisticRawGPair) {
Context ctx;
ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
TestsLogisticRawGPair(&ctx);
}
TEST(SyclObjective, CPUvsSycl) {
Context ctx;
ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
ObjFunction * obj_sycl =
ObjFunction::Create("reg:squarederror_sycl", &ctx);
ctx = ctx.MakeCPU();
ObjFunction * obj_cpu =
ObjFunction::Create("reg:squarederror", &ctx);
linalg::Matrix<GradientPair> cpu_out_preds;
linalg::Matrix<GradientPair> sycl_out_preds;
constexpr size_t kRows = 400;
constexpr size_t kCols = 100;
auto pdmat = RandomDataGenerator(kRows, kCols, 0).Seed(0).GenerateDMatrix();
HostDeviceVector<float> preds;
preds.Resize(kRows);
auto& h_preds = preds.HostVector();
for (size_t i = 0; i < h_preds.size(); ++i) {
h_preds[i] = static_cast<float>(i);
}
auto& info = pdmat->Info();
info.labels.Reshape(kRows, 1);
auto& h_labels = info.labels.Data()->HostVector();
for (size_t i = 0; i < h_labels.size(); ++i) {
h_labels[i] = 1 / static_cast<float>(i+1);
}
{
// CPU
obj_cpu->GetGradient(preds, info, 0, &cpu_out_preds);
}
{
// sycl
obj_sycl->GetGradient(preds, info, 0, &sycl_out_preds);
}
auto h_cpu_out = cpu_out_preds.HostView();
auto h_sycl_out = sycl_out_preds.HostView();
float sgrad = 0;
float shess = 0;
for (size_t i = 0; i < kRows; ++i) {
sgrad += std::pow(h_cpu_out(i).GetGrad() - h_sycl_out(i).GetGrad(), 2);
shess += std::pow(h_cpu_out(i).GetHess() - h_sycl_out(i).GetHess(), 2);
}
ASSERT_NEAR(sgrad, 0.0f, kRtEps);
ASSERT_NEAR(shess, 0.0f, kRtEps);
delete obj_cpu;
delete obj_sycl;
}
} // namespace xgboost