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Replaced std::vector with HostDeviceVector in MetaInfo and SparsePage. (#3446)

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* Replaced std::vector with HostDeviceVector in MetaInfo and SparsePage.

- added distributions to HostDeviceVector
- using HostDeviceVector for labels, weights and base margings in MetaInfo
- using HostDeviceVector for offset and data in SparsePage
- other necessary refactoring

* Added const version of HostDeviceVector API calls.

- const versions added to calls that can trigger data transfers, e.g. DevicePointer()
- updated the code that uses HostDeviceVector
- objective functions now accept const HostDeviceVector<bst_float>& for predictions

* Updated src/linear/updater_gpu_coordinate.cu.

* Added read-only state for HostDeviceVector sync.

- this means no copies are performed if both host and devices access
  the HostDeviceVector read-only

* Fixed linter and test errors.

- updated the lz4 plugin
- added ConstDeviceSpan to HostDeviceVector
- using device % dh::NVisibleDevices() for the physical device number,
  e.g. in calls to cudaSetDevice()

* Fixed explicit template instantiation errors for HostDeviceVector.

- replaced HostDeviceVector<unsigned int> with HostDeviceVector<int>

* Fixed HostDeviceVector tests that require multiple GPUs.

- added a mock set device handler; when set, it is called instead of cudaSetDevice()
This commit is contained in:
Andy Adinets
2018-08-30 04:28:47 +02:00
committed by Rory Mitchell
parent 58d783df16
commit 72cd1517d6
45 changed files with 1141 additions and 560 deletions
Download Patch File Download Diff File Expand all files Collapse all files

24
src/predictor/gpu_predictor.cu
View File

@@ -58,28 +58,30 @@ struct DeviceMatrix {
DeviceMatrix(DMatrix* dmat, int device_idx, bool silent) : p_mat(dmat) {
dh::safe_cuda(cudaSetDevice(device_idx));
auto info = dmat->Info();
const auto& info = dmat->Info();
ba.Allocate(device_idx, silent, &row_ptr, info.num_row_ + 1, &data,
info.num_nonzero_);
auto iter = dmat->RowIterator();
iter->BeforeFirst();
size_t data_offset = 0;
while (iter->Next()) {
auto &batch = iter->Value();
const auto& batch = iter->Value();
const auto& offset_vec = batch.offset.HostVector();
const auto& data_vec = batch.data.HostVector();
// Copy row ptr
dh::safe_cuda(cudaMemcpy(
row_ptr.Data() + batch.base_rowid, batch.offset.data(),
sizeof(size_t) * batch.offset.size(), cudaMemcpyHostToDevice));
row_ptr.Data() + batch.base_rowid, offset_vec.data(),
sizeof(size_t) * offset_vec.size(), cudaMemcpyHostToDevice));
if (batch.base_rowid > 0) {
auto begin_itr = row_ptr.tbegin() + batch.base_rowid;
auto end_itr = begin_itr + batch.Size() + 1;
IncrementOffset(begin_itr, end_itr, batch.base_rowid);
}
dh::safe_cuda(cudaMemcpy(data.Data() + data_offset, batch.data.data(),
sizeof(Entry) * batch.data.size(),
dh::safe_cuda(cudaMemcpy(data.Data() + data_offset, data_vec.data(),
sizeof(Entry) * data_vec.size(),
cudaMemcpyHostToDevice));
// Copy data
data_offset += batch.data.size();
data_offset += batch.data.Size();
}
}
};
@@ -374,10 +376,10 @@ class GPUPredictor : public xgboost::Predictor {
HostDeviceVector<bst_float>* out_preds,
const gbm::GBTreeModel& model) const {
size_t n = model.param.num_output_group * info.num_row_;
const std::vector<bst_float>& base_margin = info.base_margin_;
const HostDeviceVector<bst_float>& base_margin = info.base_margin_;
out_preds->Reshard(devices);
out_preds->Resize(n);
if (base_margin.size() != 0) {
if (base_margin.Size() != 0) {
CHECK_EQ(out_preds->Size(), n);
out_preds->Copy(base_margin);
} else {
@@ -391,11 +393,11 @@ class GPUPredictor : public xgboost::Predictor {
ntree_limit * model.param.num_output_group >= model.trees.size()) {
auto it = cache_.find(dmat);
if (it != cache_.end()) {
HostDeviceVector<bst_float>& y = it->second.predictions;
const HostDeviceVector<bst_float>& y = it->second.predictions;
if (y.Size() != 0) {
out_preds->Reshard(devices);
out_preds->Resize(y.Size());
out_preds->Copy(&y);
out_preds->Copy(y);
return true;
}
}