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Use the new DeviceOrd in the linalg module. (#9527)

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This commit is contained in:
Jiaming Yuan
2023-08-29 13:37:29 +08:00
committed by GitHub
parent 942b957eef
commit ddf2e68821
43 changed files with 252 additions and 273 deletions
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8
src/predictor/cpu_predictor.cc
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@@ -663,7 +663,7 @@ class CPUPredictor : public Predictor {
std::size_t n_samples = p_fmat->Info().num_row_;
std::size_t n_groups = model.learner_model_param->OutputLength();
CHECK_EQ(out_preds->size(), n_samples * n_groups);
linalg::TensorView<float, 2> out_predt{*out_preds, {n_samples, n_groups}, ctx_->gpu_id};
auto out_predt = linalg::MakeTensorView(ctx_, *out_preds, n_samples, n_groups);
if (!p_fmat->PageExists<SparsePage>()) {
std::vector<Entry> workspace(p_fmat->Info().num_col_ * kUnroll * n_threads);
@@ -732,7 +732,7 @@ class CPUPredictor : public Predictor {
std::vector<RegTree::FVec> thread_temp;
InitThreadTemp(n_threads * kBlockSize, &thread_temp);
std::size_t n_groups = model.learner_model_param->OutputLength();
linalg::TensorView<float, 2> out_predt{predictions, {m->NumRows(), n_groups}, Context::kCpuId};
auto out_predt = linalg::MakeTensorView(ctx_, predictions, m->NumRows(), n_groups);
PredictBatchByBlockOfRowsKernel<AdapterView<Adapter>, kBlockSize>(
AdapterView<Adapter>(m.get(), missing, common::Span<Entry>{workspace}, n_threads), model,
tree_begin, tree_end, &thread_temp, n_threads, out_predt);
@@ -878,8 +878,8 @@ class CPUPredictor : public Predictor {
common::ParallelFor(ntree_limit, n_threads, [&](bst_omp_uint i) {
FillNodeMeanValues(model.trees[i].get(), &(mean_values[i]));
});
auto base_margin = info.base_margin_.View(Context::kCpuId);
auto base_score = model.learner_model_param->BaseScore(Context::kCpuId)(0);
auto base_margin = info.base_margin_.View(ctx_->Device());
auto base_score = model.learner_model_param->BaseScore(ctx_->Device())(0);
// start collecting the contributions
for (const auto &batch : p_fmat->GetBatches<SparsePage>()) {
auto page = batch.GetView();