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Use matrix for gradient. (#9508)

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- 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)
This commit is contained in:
Jiaming Yuan
2023-08-24 05:29:52 +08:00
committed by GitHub
parent 6103dca0bb
commit 972730cde0
77 changed files with 1052 additions and 651 deletions
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18
tests/cpp/tree/test_gpu_hist.cu
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@@ -214,7 +214,7 @@ TEST(GpuHist, TestHistogramIndex) {
TestHistogramIndexImpl();
}
void UpdateTree(Context const* ctx, HostDeviceVector<GradientPair>* gpair, DMatrix* dmat,
void UpdateTree(Context const* ctx, linalg::Matrix<GradientPair>* gpair, DMatrix* dmat,
size_t gpu_page_size, RegTree* tree, HostDeviceVector<bst_float>* preds,
float subsample = 1.0f, const std::string& sampling_method = "uniform",
int max_bin = 2) {
@@ -264,7 +264,8 @@ TEST(GpuHist, UniformSampling) {
// Create an in-memory DMatrix.
std::unique_ptr<DMatrix> dmat(CreateSparsePageDMatrixWithRC(kRows, kCols, 0, true));
auto gpair = GenerateRandomGradients(kRows);
linalg::Matrix<GradientPair> gpair({kRows}, Context{}.MakeCUDA().Ordinal());
gpair.Data()->Copy(GenerateRandomGradients(kRows));
// Build a tree using the in-memory DMatrix.
RegTree tree;
@@ -294,7 +295,8 @@ TEST(GpuHist, GradientBasedSampling) {
// Create an in-memory DMatrix.
std::unique_ptr<DMatrix> dmat(CreateSparsePageDMatrixWithRC(kRows, kCols, 0, true));
auto gpair = GenerateRandomGradients(kRows);
linalg::Matrix<GradientPair> gpair({kRows}, MakeCUDACtx(0).Ordinal());
gpair.Data()->Copy(GenerateRandomGradients(kRows));
// Build a tree using the in-memory DMatrix.
RegTree tree;
@@ -330,11 +332,12 @@ TEST(GpuHist, ExternalMemory) {
// Create a single batch DMatrix.
std::unique_ptr<DMatrix> dmat(CreateSparsePageDMatrix(kRows, kCols, 1, tmpdir.path + "/cache"));
auto gpair = GenerateRandomGradients(kRows);
Context ctx(MakeCUDACtx(0));
linalg::Matrix<GradientPair> gpair({kRows}, ctx.Ordinal());
gpair.Data()->Copy(GenerateRandomGradients(kRows));
// Build a tree using the in-memory DMatrix.
RegTree tree;
Context ctx(MakeCUDACtx(0));
HostDeviceVector<bst_float> preds(kRows, 0.0, 0);
UpdateTree(&ctx, &gpair, dmat.get(), 0, &tree, &preds, 1.0, "uniform", kRows);
// Build another tree using multiple ELLPACK pages.
@@ -367,12 +370,13 @@ TEST(GpuHist, ExternalMemoryWithSampling) {
std::unique_ptr<DMatrix> dmat_ext(
CreateSparsePageDMatrix(kRows, kCols, kRows / kPageSize, tmpdir.path + "/cache"));
auto gpair = GenerateRandomGradients(kRows);
Context ctx(MakeCUDACtx(0));
linalg::Matrix<GradientPair> gpair({kRows}, ctx.Ordinal());
gpair.Data()->Copy(GenerateRandomGradients(kRows));
// Build a tree using the in-memory DMatrix.
auto rng = common::GlobalRandom();
Context ctx(MakeCUDACtx(0));
RegTree tree;
HostDeviceVector<bst_float> preds(kRows, 0.0, 0);
UpdateTree(&ctx, &gpair, dmat.get(), 0, &tree, &preds, kSubsample, kSamplingMethod, kRows);