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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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39
tests/cpp/helpers.cc
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@@ -119,8 +119,10 @@ void CheckObjFunction(std::unique_ptr<xgboost::ObjFunction> const& obj,
std::vector<xgboost::bst_float> out_hess) {
xgboost::MetaInfo info;
info.num_row_ = labels.size();
info.labels = xgboost::linalg::Tensor<float, 2>{
labels.cbegin(), labels.cend(), {labels.size(), static_cast<std::size_t>(1)}, -1};
info.labels = xgboost::linalg::Tensor<float, 2>{labels.cbegin(),
labels.cend(),
{labels.size(), static_cast<std::size_t>(1)},
xgboost::DeviceOrd::CPU()};
info.weights_.HostVector() = weights;
CheckObjFunctionImpl(obj, preds, labels, weights, info, out_grad, out_hess);
@@ -155,8 +157,10 @@ void CheckRankingObjFunction(std::unique_ptr<xgboost::ObjFunction> const& obj,
std::vector<xgboost::bst_float> out_hess) {
xgboost::MetaInfo info;
info.num_row_ = labels.size();
info.labels = xgboost::linalg::Matrix<float>{
labels.cbegin(), labels.cend(), {labels.size(), static_cast<std::size_t>(1)}, -1};
info.labels = xgboost::linalg::Matrix<float>{labels.cbegin(),
labels.cend(),
{labels.size(), static_cast<std::size_t>(1)},
xgboost::DeviceOrd::CPU()};
info.weights_.HostVector() = weights;
info.group_ptr_ = groups;
@@ -171,8 +175,9 @@ xgboost::bst_float GetMetricEval(xgboost::Metric* metric,
xgboost::DataSplitMode data_split_mode) {
return GetMultiMetricEval(
metric, preds,
xgboost::linalg::Tensor<float, 2>{labels.begin(), labels.end(), {labels.size()}, -1}, weights,
groups, data_split_mode);
xgboost::linalg::Tensor<float, 2>{
labels.begin(), labels.end(), {labels.size()}, xgboost::DeviceOrd::CPU()},
weights, groups, data_split_mode);
}
double GetMultiMetricEval(xgboost::Metric* metric,
@@ -215,7 +220,7 @@ void RandomDataGenerator::GenerateLabels(std::shared_ptr<DMatrix> p_fmat) const
p_fmat->Info().labels.Data());
CHECK_EQ(p_fmat->Info().labels.Size(), this->rows_ * this->n_targets_);
p_fmat->Info().labels.Reshape(this->rows_, this->n_targets_);
if (device_ != Context::kCpuId) {
if (device_.IsCUDA()) {
p_fmat->Info().labels.SetDevice(device_);
}
}
@@ -236,7 +241,7 @@ void RandomDataGenerator::GenerateDense(HostDeviceVector<float> *out) const {
v = dist(&lcg);
}
}
if (device_ >= 0) {
if (device_.IsCUDA()) {
out->SetDevice(device_);
out->DeviceSpan();
}
@@ -258,7 +263,7 @@ std::string RandomDataGenerator::GenerateArrayInterface(
std::pair<std::vector<std::string>, std::string> MakeArrayInterfaceBatch(
HostDeviceVector<float> const* storage, std::size_t n_samples, bst_feature_t n_features,
std::size_t batches, std::int32_t device) {
std::size_t batches, DeviceOrd device) {
std::vector<std::string> result(batches);
std::vector<Json> objects;
@@ -267,7 +272,7 @@ std::pair<std::vector<std::string>, std::string> MakeArrayInterfaceBatch(
auto make_interface = [storage, device, n_features](std::size_t offset, std::size_t rows) {
Json array_interface{Object()};
array_interface["data"] = std::vector<Json>(2);
if (device >= 0) {
if (device.IsCUDA()) {
array_interface["data"][0] =
Integer(reinterpret_cast<int64_t>(storage->DevicePointer() + offset));
array_interface["stream"] = Null{};
@@ -359,7 +364,7 @@ void RandomDataGenerator::GenerateCSR(
h_rptr.emplace_back(rptr);
}
if (device_ >= 0) {
if (device_.IsCUDA()) {
value->SetDevice(device_);
value->DeviceSpan();
row_ptr->SetDevice(device_);
@@ -400,7 +405,7 @@ void RandomDataGenerator::GenerateCSR(
out->Info().labels.Reshape(this->rows_, this->n_targets_);
}
}
if (device_ >= 0) {
if (device_.IsCUDA()) {
out->Info().labels.SetDevice(device_);
out->Info().feature_types.SetDevice(device_);
for (auto const& page : out->GetBatches<SparsePage>()) {
@@ -423,7 +428,7 @@ void RandomDataGenerator::GenerateCSR(
CHECK_GE(this->n_batches_, 1)
<< "Must set the n_batches before generating an external memory DMatrix.";
std::unique_ptr<ArrayIterForTest> iter;
if (device_ == Context::kCpuId) {
if (device_.IsCPU()) {
iter = std::make_unique<NumpyArrayIterForTest>(this->sparsity_, rows_, cols_, n_batches_);
} else {
#if defined(XGBOOST_USE_CUDA)
@@ -487,7 +492,7 @@ int CudaArrayIterForTest::Next() {
NumpyArrayIterForTest::NumpyArrayIterForTest(float sparsity, size_t rows, size_t cols,
size_t batches)
: ArrayIterForTest{sparsity, rows, cols, batches} {
rng_->Device(Context::kCpuId);
rng_->Device(DeviceOrd::CPU());
std::tie(batches_, interface_) = rng_->GenerateArrayInterfaceBatch(&data_, n_batches_);
this->Reset();
}
@@ -644,8 +649,8 @@ std::unique_ptr<GradientBooster> CreateTrainedGBM(std::string name, Args kwargs,
labels[i] = i;
}
p_dmat->Info().labels =
linalg::Tensor<float, 2>{labels.cbegin(), labels.cend(), {labels.size()}, -1};
linalg::Matrix<GradientPair> gpair({kRows}, ctx->Ordinal());
linalg::Tensor<float, 2>{labels.cbegin(), labels.cend(), {labels.size()}, DeviceOrd::CPU()};
linalg::Matrix<GradientPair> gpair({kRows}, ctx->Device());
auto h_gpair = gpair.HostView();
for (size_t i = 0; i < kRows; ++i) {
h_gpair(i) = GradientPair{static_cast<float>(i), 1};
@@ -674,7 +679,7 @@ ArrayIterForTest::ArrayIterForTest(Context const* ctx, HostDeviceVector<float> c
CHECK_EQ(this->data_.Size(), rows_ * cols_ * n_batches);
this->data_.Copy(data);
std::tie(batches_, interface_) =
MakeArrayInterfaceBatch(&data_, rows_, cols_, n_batches_, ctx->gpu_id);
MakeArrayInterfaceBatch(&data_, rows_, cols_, n_batches_, ctx->Device());
}
ArrayIterForTest::~ArrayIterForTest() { XGDMatrixFree(proxy_); }