972730cde0
- Use the `linalg::Matrix` for storing gradients. - New API for the custom objective. - Custom objective for multi-class/multi-target is now required to return the correct shape. - Custom objective for Python can accept arrays with any strides. (row-major, column-major)
37 lines
1012 B
Plaintext
37 lines
1012 B
Plaintext
/**
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* Copyright 2018-2023, XGBoost Contributors
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*/
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#include <xgboost/linear_updater.h>
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#include <xgboost/gbm.h>
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#include "../helpers.h"
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#include "test_json_io.h"
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#include "../../../src/gbm/gblinear_model.h"
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namespace xgboost {
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TEST(Linear, GPUCoordinate) {
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size_t constexpr kRows = 10;
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size_t constexpr kCols = 10;
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auto mat = xgboost::RandomDataGenerator(kRows, kCols, 0).GenerateDMatrix();
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auto ctx = MakeCUDACtx(0);
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LearnerModelParam mparam{MakeMP(kCols, .5, 1)};
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auto updater = std::unique_ptr<xgboost::LinearUpdater>(
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xgboost::LinearUpdater::Create("gpu_coord_descent", &ctx));
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updater->Configure({{"eta", "1."}});
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auto gpair = linalg::Constant(&ctx, xgboost::GradientPair(-5, 1.0), mat->Info().num_row_, 1);
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xgboost::gbm::GBLinearModel model{&mparam};
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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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}
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TEST(GPUCoordinate, JsonIO) {
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TestUpdaterJsonIO("gpu_coord_descent");
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}
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} // namespace xgboost
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