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temp merge, disable 1 line, SetValid

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2023-10-12 16:16:44 -07:00
parent 2e7e9d3b2d 85d3017ca5
commit ea19555474
492 changed files with 15533 additions and 9376 deletions
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59
src/tree/gpu_hist/row_partitioner.cuh
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@@ -116,13 +116,7 @@ template <typename RowIndexT, typename OpT, typename OpDataT>
void SortPositionBatch(common::Span<const PerNodeData<OpDataT>> d_batch_info,
common::Span<RowIndexT> ridx, common::Span<RowIndexT> ridx_tmp,
common::Span<bst_uint> d_counts, std::size_t total_rows, OpT op,
dh::device_vector<int8_t>* tmp,
#if defined(XGBOOST_USE_HIP)
hipStream_t stream
#elif defined(XGBOOST_USE_CUDA)
cudaStream_t stream
#endif
) {
dh::device_vector<int8_t>* tmp) {
dh::LDGIterator<PerNodeData<OpDataT>> batch_info_itr(d_batch_info.data());
WriteResultsFunctor<OpDataT> write_results{batch_info_itr, ridx.data(), ridx_tmp.data(),
d_counts.data()};
@@ -135,29 +129,28 @@ void SortPositionBatch(common::Span<const PerNodeData<OpDataT>> d_batch_info,
int batch_idx;
std::size_t item_idx;
AssignBatch(batch_info_itr, idx, &batch_idx, &item_idx);
auto op_res = op(ridx[item_idx], batch_info_itr[batch_idx].data);
auto op_res = op(ridx[item_idx], batch_idx, batch_info_itr[batch_idx].data);
return IndexFlagTuple{static_cast<bst_uint>(item_idx), op_res, batch_idx, op_res};
});
size_t temp_bytes = 0;
if (tmp->empty()) {
#if defined(XGBOOST_USE_CUDA)
cub::DeviceScan::InclusiveScan(nullptr, temp_bytes, input_iterator, discard_write_iterator,
IndexFlagOp(), total_rows, stream);
IndexFlagOp(), total_rows);
#elif defined(XGBOOST_USE_HIP)
rocprim::inclusive_scan(nullptr, temp_bytes, input_iterator, discard_write_iterator,
total_rows, IndexFlagOp(), stream);
total_rows,IndexFlagOp());
#endif
tmp->resize(temp_bytes);
}
temp_bytes = tmp->size();
#if defined(XGBOOST_USE_CUDA)
cub::DeviceScan::InclusiveScan(tmp->data().get(), temp_bytes, input_iterator,
discard_write_iterator, IndexFlagOp(), total_rows, stream);
discard_write_iterator, IndexFlagOp(), total_rows);
#elif defined(XGBOOST_USE_HIP)
rocprim::inclusive_scan(tmp->data().get(), temp_bytes, input_iterator, discard_write_iterator,
total_rows, IndexFlagOp(), stream);
rocprim::inclusive_scan(tmp->data().get(), temp_bytes, input_iterator,
discard_write_iterator, total_rows, IndexFlagOp());
#endif
constexpr int kBlockSize = 256;
@@ -167,7 +160,7 @@ void SortPositionBatch(common::Span<const PerNodeData<OpDataT>> d_batch_info,
const int grid_size = xgboost::common::DivRoundUp(total_rows, kBlockSize * kItemsThread);
SortPositionCopyKernel<kBlockSize, RowIndexT, OpDataT>
<<<grid_size, kBlockSize, 0, stream>>>(batch_info_itr, ridx, ridx_tmp, total_rows);
<<<grid_size, kBlockSize, 0>>>(batch_info_itr, ridx, ridx_tmp, total_rows);
}
struct NodePositionInfo {
@@ -240,12 +233,6 @@ class RowPartitioner {
dh::PinnedMemory pinned_;
dh::PinnedMemory pinned2_;
#if defined(XGBOOST_USE_HIP)
hipStream_t stream_;
#else
cudaStream_t stream_;
#endif
public:
RowPartitioner(int device_idx, size_t num_rows);
~RowPartitioner();
@@ -303,11 +290,11 @@ class RowPartitioner {
#if defined(XGBOOST_USE_HIP)
dh::safe_cuda(hipMemcpyAsync(d_batch_info.data().get(), h_batch_info.data(),
h_batch_info.size() * sizeof(PerNodeData<OpDataT>),
hipMemcpyDefault, stream_));
hipMemcpyDefault));
#else
dh::safe_cuda(cudaMemcpyAsync(d_batch_info.data().get(), h_batch_info.data(),
h_batch_info.size() * sizeof(PerNodeData<OpDataT>),
cudaMemcpyDefault, stream_));
cudaMemcpyDefault));
#endif
// Temporary arrays
@@ -317,23 +304,17 @@ class RowPartitioner {
// Partition the rows according to the operator
SortPositionBatch<RowIndexT, UpdatePositionOpT, OpDataT>(
dh::ToSpan(d_batch_info), dh::ToSpan(ridx_), dh::ToSpan(ridx_tmp_), dh::ToSpan(d_counts),
total_rows, op, &tmp_, stream_);
#if defined(XGBOOST_USE_HIP)
dh::safe_cuda(hipMemcpyAsync(h_counts.data(), d_counts.data().get(), h_counts.size_bytes(),
hipMemcpyDefault, stream_));
#else
total_rows, op, &tmp_);
#if defined(XGBOOST_USE_CUDA)
dh::safe_cuda(cudaMemcpyAsync(h_counts.data(), d_counts.data().get(), h_counts.size_bytes(),
cudaMemcpyDefault, stream_));
cudaMemcpyDefault));
#elif defined(XGBOOST_USE_HIP)
dh::safe_cuda(hipMemcpyAsync(h_counts.data(), d_counts.data().get(), h_counts.size_bytes(),
hipMemcpyDefault));
#endif
// TODO(Rory): this synchronisation hurts performance a lot
// Future optimisation should find a way to skip this
#if defined(XGBOOST_USE_HIP)
dh::safe_cuda(hipStreamSynchronize(stream_));
#else
dh::safe_cuda(cudaStreamSynchronize(stream_));
#endif
dh::DefaultStream().Sync();
// Update segments
for (size_t i = 0; i < nidx.size(); i++) {
@@ -370,18 +351,18 @@ class RowPartitioner {
#if defined(XGBOOST_USE_HIP)
dh::safe_cuda(hipMemcpyAsync(d_node_info_storage.data().get(), ridx_segments_.data(),
sizeof(NodePositionInfo) * ridx_segments_.size(),
hipMemcpyDefault, stream_));
hipMemcpyDefault));
#else
dh::safe_cuda(cudaMemcpyAsync(d_node_info_storage.data().get(), ridx_segments_.data(),
sizeof(NodePositionInfo) * ridx_segments_.size(),
cudaMemcpyDefault, stream_));
cudaMemcpyDefault));
#endif
constexpr int kBlockSize = 512;
const int kItemsThread = 8;
const int grid_size = xgboost::common::DivRoundUp(ridx_.size(), kBlockSize * kItemsThread);
common::Span<const RowIndexT> d_ridx(ridx_.data().get(), ridx_.size());
FinalisePositionKernel<kBlockSize><<<grid_size, kBlockSize, 0, stream_>>>(
FinalisePositionKernel<kBlockSize><<<grid_size, kBlockSize, 0>>>(
dh::ToSpan(d_node_info_storage), d_ridx, d_out_position, op);
}
};