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enable ROCm on latest XGBoost

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This commit is contained in:
Hui Liu
2023-10-23 11:07:08 -07:00
parent fb19e15ce3 3b86260b50
commit 15421e40d9
328 changed files with 8028 additions and 3642 deletions
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55
src/common/quantile.cuh
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@@ -41,7 +41,7 @@ class SketchContainer {
bst_row_t num_rows_;
bst_feature_t num_columns_;
int32_t num_bins_;
int32_t device_;
DeviceOrd device_;
// Double buffer as neither prune nor merge can be performed inplace.
dh::device_vector<SketchEntry> entries_a_;
@@ -93,35 +93,32 @@ class SketchContainer {
* \param num_rows Total number of rows in known dataset (typically the rows in current worker).
* \param device GPU ID.
*/
SketchContainer(HostDeviceVector<FeatureType> const &feature_types,
int32_t max_bin, bst_feature_t num_columns,
bst_row_t num_rows, int32_t device)
: num_rows_{num_rows},
num_columns_{num_columns}, num_bins_{max_bin}, device_{device} {
CHECK_GE(device, 0);
// Initialize Sketches for this dmatrix
this->columns_ptr_.SetDevice(device_);
this->columns_ptr_.Resize(num_columns + 1);
this->columns_ptr_b_.SetDevice(device_);
this->columns_ptr_b_.Resize(num_columns + 1);
SketchContainer(HostDeviceVector<FeatureType> const& feature_types, int32_t max_bin,
bst_feature_t num_columns, bst_row_t num_rows, DeviceOrd device)
: num_rows_{num_rows}, num_columns_{num_columns}, num_bins_{max_bin}, device_{device} {
CHECK(device.IsCUDA());
// Initialize Sketches for this dmatrix
this->columns_ptr_.SetDevice(device_);
this->columns_ptr_.Resize(num_columns + 1);
this->columns_ptr_b_.SetDevice(device_);
this->columns_ptr_b_.Resize(num_columns + 1);
this->feature_types_.Resize(feature_types.Size());
this->feature_types_.Copy(feature_types);
// Pull to device.
this->feature_types_.SetDevice(device);
this->feature_types_.ConstDeviceSpan();
this->feature_types_.ConstHostSpan();
this->feature_types_.Resize(feature_types.Size());
this->feature_types_.Copy(feature_types);
// Pull to device.
this->feature_types_.SetDevice(device);
this->feature_types_.ConstDeviceSpan();
this->feature_types_.ConstHostSpan();
auto d_feature_types = feature_types_.ConstDeviceSpan();
has_categorical_ =
!d_feature_types.empty() &&
thrust::any_of(dh::tbegin(d_feature_types), dh::tend(d_feature_types),
common::IsCatOp{});
auto d_feature_types = feature_types_.ConstDeviceSpan();
has_categorical_ =
!d_feature_types.empty() &&
thrust::any_of(dh::tbegin(d_feature_types), dh::tend(d_feature_types), common::IsCatOp{});
timer_.Init(__func__);
}
timer_.Init(__func__);
}
/* \brief Return GPU ID for this container. */
int32_t DeviceIdx() const { return device_; }
[[nodiscard]] DeviceOrd DeviceIdx() const { return device_; }
/* \brief Whether the predictor matrix contains categorical features. */
bool HasCategorical() const { return has_categorical_; }
/* \brief Accumulate weights of duplicated entries in input. */
@@ -175,9 +172,7 @@ class SketchContainer {
template <typename KeyComp = thrust::equal_to<size_t>>
size_t Unique(KeyComp key_comp = thrust::equal_to<size_t>{}) {
timer_.Start(__func__);
dh::safe_cuda(cudaSetDevice(device_));
dh::safe_cuda(cudaSetDevice(device_.ordinal));
this->columns_ptr_.SetDevice(device_);
Span<OffsetT> d_column_scan = this->columns_ptr_.DeviceSpan();
CHECK_EQ(d_column_scan.size(), num_columns_ + 1);
@@ -195,7 +190,7 @@ class SketchContainer {
d_column_scan.data() + d_column_scan.size(), entries.data(),
entries.data() + entries.size(), scan_out.DevicePointer(),
entries.data(), detail::SketchUnique{}, key_comp);
#else
#elif defined(XGBOOST_USE_CUDA)
size_t n_uniques = dh::SegmentedUnique(
thrust::cuda::par(alloc), d_column_scan.data(),
d_column_scan.data() + d_column_scan.size(), entries.data(),