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Objective function evaluation on GPU with minimal PCIe transfers (#2935)

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* Added GPU objective function and no-copy interface.

- xgboost::HostDeviceVector<T> syncs automatically between host and device
- no-copy interfaces have been added
- default implementations just sync the data to host
  and call the implementations with std::vector
- GPU objective function, predictor, histogram updater process data
  directly on GPU
This commit is contained in:
Thejaswi
2018-01-12 14:03:39 +05:30
committed by Rory Mitchell
parent a187ed6c8f
commit 84ab74f3a5
23 changed files with 1036 additions and 127 deletions
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69
tests/cpp/objective/test_regression_obj_gpu.cu Normal file
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/*!
* Copyright 2017 XGBoost contributors
*/
#include <xgboost/objective.h>
#include "../helpers.h"
TEST(Objective, GPULinearRegressionGPair) {
xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("gpu:reg:linear");
std::vector<std::pair<std::string, std::string> > args;
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},
{0, 0.1f, 0.9f, 1.0f, -1.0f, -0.9f, -0.1f, 0},
{1, 1, 1, 1, 1, 1, 1, 1});
ASSERT_NO_THROW(obj->DefaultEvalMetric());
}
TEST(Objective, GPULogisticRegressionGPair) {
xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("gpu:reg:logistic");
std::vector<std::pair<std::string, std::string> > args;
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},
{ 0.5f, 0.52f, 0.71f, 0.73f, -0.5f, -0.47f, -0.28f, -0.26f},
{0.25f, 0.24f, 0.20f, 0.19f, 0.25f, 0.24f, 0.20f, 0.19f});
}
TEST(Objective, GPULogisticRegressionBasic) {
xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("gpu:reg:logistic");
std::vector<std::pair<std::string, std::string> > args;
obj->Configure(args);
// test label validation
EXPECT_ANY_THROW(CheckObjFunction(obj, {0}, {10}, {1}, {0}, {0}))
<< "Expected error when label not in range [0,1f] for LogisticRegression";
// test ProbToMargin
EXPECT_NEAR(obj->ProbToMargin(0.1f), -2.197f, 0.01f);
EXPECT_NEAR(obj->ProbToMargin(0.5f), 0, 0.01f);
EXPECT_NEAR(obj->ProbToMargin(0.9f), 2.197f, 0.01f);
EXPECT_ANY_THROW(obj->ProbToMargin(10))
<< "Expected error when base_score not in range [0,1f] for LogisticRegression";
// test PredTransform
std::vector<xgboost::bst_float> preds = {0, 0.1f, 0.5f, 0.9f, 1};
std::vector<xgboost::bst_float> out_preds = {0.5f, 0.524f, 0.622f, 0.710f, 0.731f};
obj->PredTransform(&preds);
for (int i = 0; i < static_cast<int>(preds.size()); ++i) {
EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
}
}
TEST(Objective, GPULogisticRawGPair) {
xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("gpu:binary:logitraw");
std::vector<std::pair<std::string, std::string> > args;
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},
{ 0.5f, 0.52f, 0.71f, 0.73f, -0.5f, -0.47f, -0.28f, -0.26f},
{0.25f, 0.24f, 0.20f, 0.19f, 0.25f, 0.24f, 0.20f, 0.19f});
}