f0064c07ab
* Refactor configuration [Part II].
* General changes:
** Remove `Init` methods to avoid ambiguity.
** Remove `Configure(std::map<>)` to avoid redundant copying and prepare for
parameter validation. (`std::vector` is returned from `InitAllowUnknown`).
** Add name to tree updaters for easier debugging.
* Learner changes:
** Make `LearnerImpl` the only source of configuration.
All configurations are stored and carried out by `LearnerImpl::Configure()`.
** Remove booster in C API.
Originally kept for "compatibility reason", but did not state why. So here
we just remove it.
** Add a `metric_names_` field in `LearnerImpl`.
** Remove `LazyInit`. Configuration will always be lazy.
** Run `Configure` before every iteration.
* Predictor changes:
** Allocate both cpu and gpu predictor.
** Remove cpu_predictor from gpu_predictor.
`GBTree` is now used to dispatch the predictor.
** Remove some GPU Predictor tests.
* IO
No IO changes. The binary model format stability is tested by comparing
hashing value of save models between two commits
70 lines
2.3 KiB
Plaintext
70 lines
2.3 KiB
Plaintext
// Copyright by Contributors
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#include <xgboost/linear_updater.h>
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#include "../helpers.h"
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#include "xgboost/gbm.h"
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namespace xgboost {
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TEST(Linear, GPUCoordinate) {
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auto mat = xgboost::CreateDMatrix(10, 10, 0);
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auto lparam = CreateEmptyGenericParam(0, 1);
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lparam.n_gpus = 1;
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auto updater = std::unique_ptr<xgboost::LinearUpdater>(
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xgboost::LinearUpdater::Create("gpu_coord_descent", &lparam));
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updater->Configure({{"eta", "1."}});
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xgboost::HostDeviceVector<xgboost::GradientPair> gpair(
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(*mat)->Info().num_row_, xgboost::GradientPair(-5, 1.0));
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xgboost::gbm::GBLinearModel model;
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model.param.num_feature = (*mat)->Info().num_col_;
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model.param.num_output_group = 1;
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model.LazyInitModel();
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updater->Update(&gpair, (*mat).get(), &model, gpair.Size());
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ASSERT_EQ(model.bias()[0], 5.0f);
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delete mat;
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}
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#if defined(XGBOOST_USE_NCCL)
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TEST(Linear, MGPU_GPUCoordinate) {
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{
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auto mat = xgboost::CreateDMatrix(10, 10, 0);
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auto lparam = CreateEmptyGenericParam(0, -1);
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lparam.n_gpus = -1;
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auto updater = std::unique_ptr<xgboost::LinearUpdater>(
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xgboost::LinearUpdater::Create("gpu_coord_descent", &lparam));
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updater->Configure({{"eta", "1."}});
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xgboost::HostDeviceVector<xgboost::GradientPair> gpair(
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(*mat)->Info().num_row_, xgboost::GradientPair(-5, 1.0));
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xgboost::gbm::GBLinearModel model;
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model.param.num_feature = (*mat)->Info().num_col_;
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model.param.num_output_group = 1;
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model.LazyInitModel();
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updater->Update(&gpair, (*mat).get(), &model, gpair.Size());
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ASSERT_EQ(model.bias()[0], 5.0f);
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delete mat;
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}
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{
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auto lparam = CreateEmptyGenericParam(1, -1);
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lparam.n_gpus = -1;
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auto mat = xgboost::CreateDMatrix(10, 10, 0);
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auto updater = std::unique_ptr<xgboost::LinearUpdater>(
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xgboost::LinearUpdater::Create("gpu_coord_descent", &lparam));
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updater->Configure({{"eta", "1."}});
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xgboost::HostDeviceVector<xgboost::GradientPair> gpair(
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(*mat)->Info().num_row_, xgboost::GradientPair(-5, 1.0));
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xgboost::gbm::GBLinearModel model;
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model.param.num_feature = (*mat)->Info().num_col_;
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model.param.num_output_group = 1;
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model.LazyInitModel();
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updater->Update(&gpair, (*mat).get(), &model, gpair.Size());
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ASSERT_EQ(model.bias()[0], 5.0f);
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delete mat;
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}
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}
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#endif
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} // namespace xgboost |