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Remove internal use of gpu_id. (#9568)

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This commit is contained in:
Jiaming Yuan
2023-09-20 23:29:51 +08:00
committed by GitHub
parent 38ac52dd87
commit 8c676c889d
121 changed files with 1012 additions and 1044 deletions
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42
tests/cpp/tree/test_gpu_hist.cu
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@@ -29,7 +29,7 @@ TEST(GpuHist, DeviceHistogram) {
constexpr int kNNodes = 4;
constexpr size_t kStopGrowing = kNNodes * kNBins * 2u;
DeviceHistogramStorage<kStopGrowing> histogram;
histogram.Init(0, kNBins);
histogram.Init(FstCU(), kNBins);
for (int i = 0; i < kNNodes; ++i) {
histogram.AllocateHistograms({i});
}
@@ -102,12 +102,12 @@ void TestBuildHist(bool use_shared_memory_histograms) {
bst_float hess = dist(&gen);
gp = GradientPair(grad, hess);
}
gpair.SetDevice(0);
gpair.SetDevice(DeviceOrd::CUDA(0));
thrust::host_vector<common::CompressedByteT> h_gidx_buffer (page->gidx_buffer.HostVector());
maker.row_partitioner = std::make_unique<RowPartitioner>(0, kNRows);
maker.row_partitioner = std::make_unique<RowPartitioner>(FstCU(), kNRows);
maker.hist.Init(0, page->Cuts().TotalBins());
maker.hist.Init(FstCU(), page->Cuts().TotalBins());
maker.hist.AllocateHistograms({0});
maker.gpair = gpair.DeviceSpan();
@@ -116,8 +116,8 @@ void TestBuildHist(bool use_shared_memory_histograms) {
maker.InitFeatureGroupsOnce();
BuildGradientHistogram(ctx.CUDACtx(), page->GetDeviceAccessor(0),
maker.feature_groups->DeviceAccessor(0), gpair.DeviceSpan(),
BuildGradientHistogram(ctx.CUDACtx(), page->GetDeviceAccessor(DeviceOrd::CUDA(0)),
maker.feature_groups->DeviceAccessor(DeviceOrd::CUDA(0)), gpair.DeviceSpan(),
maker.row_partitioner->GetRows(0), maker.hist.GetNodeHistogram(0),
*maker.quantiser, !use_shared_memory_histograms);
@@ -198,7 +198,7 @@ void TestHistogramIndexImpl() {
// histogram index
const auto &maker = hist_maker.maker;
auto grad = GenerateRandomGradients(kNRows);
grad.SetDevice(0);
grad.SetDevice(DeviceOrd::CUDA(0));
maker->Reset(&grad, hist_maker_dmat.get(), kNCols);
std::vector<common::CompressedByteT> h_gidx_buffer(maker->page->gidx_buffer.HostVector());
@@ -264,17 +264,17 @@ TEST(GpuHist, UniformSampling) {
// Create an in-memory DMatrix.
std::unique_ptr<DMatrix> dmat(CreateSparsePageDMatrixWithRC(kRows, kCols, 0, true));
linalg::Matrix<GradientPair> gpair({kRows}, Context{}.MakeCUDA().Ordinal());
linalg::Matrix<GradientPair> gpair({kRows}, Context{}.MakeCUDA().Device());
gpair.Data()->Copy(GenerateRandomGradients(kRows));
// Build a tree using the in-memory DMatrix.
RegTree tree;
HostDeviceVector<bst_float> preds(kRows, 0.0, 0);
HostDeviceVector<bst_float> preds(kRows, 0.0, DeviceOrd::CUDA(0));
Context ctx(MakeCUDACtx(0));
UpdateTree(&ctx, &gpair, dmat.get(), 0, &tree, &preds, 1.0, "uniform", kRows);
// Build another tree using sampling.
RegTree tree_sampling;
HostDeviceVector<bst_float> preds_sampling(kRows, 0.0, 0);
HostDeviceVector<bst_float> preds_sampling(kRows, 0.0, DeviceOrd::CUDA(0));
UpdateTree(&ctx, &gpair, dmat.get(), 0, &tree_sampling, &preds_sampling, kSubsample, "uniform",
kRows);
@@ -295,18 +295,18 @@ TEST(GpuHist, GradientBasedSampling) {
// Create an in-memory DMatrix.
std::unique_ptr<DMatrix> dmat(CreateSparsePageDMatrixWithRC(kRows, kCols, 0, true));
linalg::Matrix<GradientPair> gpair({kRows}, MakeCUDACtx(0).Ordinal());
linalg::Matrix<GradientPair> gpair({kRows}, MakeCUDACtx(0).Device());
gpair.Data()->Copy(GenerateRandomGradients(kRows));
// Build a tree using the in-memory DMatrix.
RegTree tree;
HostDeviceVector<bst_float> preds(kRows, 0.0, 0);
HostDeviceVector<bst_float> preds(kRows, 0.0, DeviceOrd::CUDA(0));
Context ctx(MakeCUDACtx(0));
UpdateTree(&ctx, &gpair, dmat.get(), 0, &tree, &preds, 1.0, "uniform", kRows);
// Build another tree using sampling.
RegTree tree_sampling;
HostDeviceVector<bst_float> preds_sampling(kRows, 0.0, 0);
HostDeviceVector<bst_float> preds_sampling(kRows, 0.0, DeviceOrd::CUDA(0));
UpdateTree(&ctx, &gpair, dmat.get(), 0, &tree_sampling, &preds_sampling, kSubsample,
"gradient_based", kRows);
@@ -333,16 +333,16 @@ TEST(GpuHist, ExternalMemory) {
std::unique_ptr<DMatrix> dmat(CreateSparsePageDMatrix(kRows, kCols, 1, tmpdir.path + "/cache"));
Context ctx(MakeCUDACtx(0));
linalg::Matrix<GradientPair> gpair({kRows}, ctx.Ordinal());
linalg::Matrix<GradientPair> gpair({kRows}, ctx.Device());
gpair.Data()->Copy(GenerateRandomGradients(kRows));
// Build a tree using the in-memory DMatrix.
RegTree tree;
HostDeviceVector<bst_float> preds(kRows, 0.0, 0);
HostDeviceVector<bst_float> preds(kRows, 0.0, DeviceOrd::CUDA(0));
UpdateTree(&ctx, &gpair, dmat.get(), 0, &tree, &preds, 1.0, "uniform", kRows);
// Build another tree using multiple ELLPACK pages.
RegTree tree_ext;
HostDeviceVector<bst_float> preds_ext(kRows, 0.0, 0);
HostDeviceVector<bst_float> preds_ext(kRows, 0.0, DeviceOrd::CUDA(0));
UpdateTree(&ctx, &gpair, dmat_ext.get(), kPageSize, &tree_ext, &preds_ext, 1.0, "uniform", kRows);
// Make sure the predictions are the same.
@@ -371,20 +371,20 @@ TEST(GpuHist, ExternalMemoryWithSampling) {
CreateSparsePageDMatrix(kRows, kCols, kRows / kPageSize, tmpdir.path + "/cache"));
Context ctx(MakeCUDACtx(0));
linalg::Matrix<GradientPair> gpair({kRows}, ctx.Ordinal());
linalg::Matrix<GradientPair> gpair({kRows}, ctx.Device());
gpair.Data()->Copy(GenerateRandomGradients(kRows));
// Build a tree using the in-memory DMatrix.
auto rng = common::GlobalRandom();
RegTree tree;
HostDeviceVector<bst_float> preds(kRows, 0.0, 0);
HostDeviceVector<bst_float> preds(kRows, 0.0, DeviceOrd::CUDA(0));
UpdateTree(&ctx, &gpair, dmat.get(), 0, &tree, &preds, kSubsample, kSamplingMethod, kRows);
// Build another tree using multiple ELLPACK pages.
common::GlobalRandom() = rng;
RegTree tree_ext;
HostDeviceVector<bst_float> preds_ext(kRows, 0.0, 0);
HostDeviceVector<bst_float> preds_ext(kRows, 0.0, DeviceOrd::CUDA(0));
UpdateTree(&ctx, &gpair, dmat_ext.get(), kPageSize, &tree_ext, &preds_ext, kSubsample,
kSamplingMethod, kRows);
@@ -436,7 +436,7 @@ RegTree GetHistTree(Context const* ctx, DMatrix* dmat) {
TrainParam param;
param.UpdateAllowUnknown(Args{});
linalg::Matrix<GradientPair> gpair({dmat->Info().num_row_}, ctx->Ordinal());
linalg::Matrix<GradientPair> gpair({dmat->Info().num_row_}, ctx->Device());
gpair.Data()->Copy(GenerateRandomGradients(dmat->Info().num_row_));
std::vector<HostDeviceVector<bst_node_t>> position(1);
@@ -486,7 +486,7 @@ RegTree GetApproxTree(Context const* ctx, DMatrix* dmat) {
TrainParam param;
param.UpdateAllowUnknown(Args{});
linalg::Matrix<GradientPair> gpair({dmat->Info().num_row_}, ctx->Ordinal());
linalg::Matrix<GradientPair> gpair({dmat->Info().num_row_}, ctx->Device());
gpair.Data()->Copy(GenerateRandomGradients(dmat->Info().num_row_));
std::vector<HostDeviceVector<bst_node_t>> position(1);