More tests for cpu predictor with column split (#9270)

src/predictor/cpu_predictor.cc

@@ -430,8 +430,7 @@ class ColumnSplitHelper {
         << "column-split prediction is only supported for distributed training";
 
     for (auto const &batch : p_fmat->GetBatches<SparsePage>()) {
-      CHECK_EQ(out_preds->size(),
+      CHECK_EQ(out_preds->size(), p_fmat->Info().num_row_ * (tree_end_ - tree_begin_));
-               p_fmat->Info().num_row_ * model_.learner_model_param->num_output_group);
       PredictBatchKernel<SparsePageView, kBlockOfRowsSize, true>(SparsePageView{&batch}, out_preds);
     }
   }
@@ -543,8 +542,12 @@ class ColumnSplitHelper {
     for (size_t tree_id = tree_begin_; tree_id < tree_end_; ++tree_id) {
       auto const gid = model_.tree_info[tree_id];
       for (size_t i = 0; i < block_size; ++i) {
-        preds[(predict_offset + i) * num_group + gid] +=
+        auto const result = PredictOneTree<predict_leaf>(tree_id, batch_offset + i);
-            PredictOneTree<predict_leaf>(tree_id, batch_offset + i);
+        if constexpr (predict_leaf) {
+          preds[(predict_offset + i) * (tree_end_ - tree_begin_) + tree_id] = result;
+        } else {
+          preds[(predict_offset + i) * num_group + gid] += result;
+        }
       }
     }
   }
@@ -645,6 +648,9 @@ class CPUPredictor : public Predictor {
   void PredictDMatrix(DMatrix *p_fmat, std::vector<bst_float> *out_preds,
                       gbm::GBTreeModel const &model, int32_t tree_begin, int32_t tree_end) const {
     if (p_fmat->Info().IsColumnSplit()) {
+      CHECK(!model.learner_model_param->IsVectorLeaf())
+          << "Predict DMatrix with column split" << MTNotImplemented();
+
       ColumnSplitHelper helper(this->ctx_->Threads(), model, tree_begin, tree_end);
       helper.PredictDMatrix(p_fmat, out_preds);
       return;
@@ -743,6 +749,8 @@ class CPUPredictor : public Predictor {
                       unsigned tree_end) const override {
     auto proxy = dynamic_cast<data::DMatrixProxy *>(p_m.get());
     CHECK(proxy)<< "Inplace predict accepts only DMatrixProxy as input.";
+    CHECK(!p_m->Info().IsColumnSplit())
+        << "Inplace predict support for column-wise data split is not yet implemented.";
     auto x = proxy->Adapter();
     if (x.type() == typeid(std::shared_ptr<data::DenseAdapter>)) {
       this->DispatchedInplacePredict<data::DenseAdapter, kBlockOfRowsSize>(
@@ -773,6 +781,9 @@ class CPUPredictor : public Predictor {
     out_preds->resize(model.learner_model_param->num_output_group);
 
     if (is_column_split) {
+      CHECK(!model.learner_model_param->IsVectorLeaf())
+          << "Predict instance with column split" << MTNotImplemented();
+
       ColumnSplitHelper helper(this->ctx_->Threads(), model, 0, ntree_limit);
       helper.PredictInstance(inst, out_preds);
       return;
@@ -802,6 +813,9 @@ class CPUPredictor : public Predictor {
     preds.resize(info.num_row_ * ntree_limit);
 
     if (p_fmat->Info().IsColumnSplit()) {
+      CHECK(!model.learner_model_param->IsVectorLeaf())
+          << "Predict leaf with column split" << MTNotImplemented();
+
       ColumnSplitHelper helper(n_threads, model, 0, ntree_limit);
       helper.PredictLeaf(p_fmat, &preds);
       return;

tests/cpp/predictor/test_cpu_predictor.cc

@@ -117,7 +117,7 @@ void TestColumnSplit() {
 }
 }  // anonymous namespace
 
-TEST(CpuPredictor, ColumnSplitBasic) {
+TEST(CpuPredictor, BasicColumnSplit) {
   auto constexpr kWorldSize = 2;
   RunWithInMemoryCommunicator(kWorldSize, TestColumnSplit);
 }
@@ -126,6 +126,10 @@ TEST(CpuPredictor, IterationRange) {
   TestIterationRange("cpu_predictor");
 }
 
+TEST(CpuPredictor, IterationRangeColmnSplit) {
+  TestIterationRangeColumnSplit("cpu_predictor");
+}
+
 TEST(CpuPredictor, ExternalMemory) {
   size_t constexpr kPageSize = 64, kEntriesPerCol = 3;
   size_t constexpr kEntries = kPageSize * kEntriesPerCol * 2;
@@ -223,10 +227,18 @@ TEST(CPUPredictor, CategoricalPrediction) {
   TestCategoricalPrediction("cpu_predictor");
 }
 
+TEST(CPUPredictor, CategoricalPredictionColumnSplit) {
+  TestCategoricalPredictionColumnSplit("cpu_predictor");
+}
+
 TEST(CPUPredictor, CategoricalPredictLeaf) {
   TestCategoricalPredictLeaf(StringView{"cpu_predictor"});
 }
 
+TEST(CPUPredictor, CategoricalPredictLeafColumnSplit) {
+  TestCategoricalPredictLeafColumnSplit(StringView{"cpu_predictor"});
+}
+
 TEST(CpuPredictor, UpdatePredictionCache) {
   TestUpdatePredictionCache(false);
   TestUpdatePredictionCache(true);
@@ -236,11 +248,20 @@ TEST(CpuPredictor, LesserFeatures) {
   TestPredictionWithLesserFeatures("cpu_predictor");
 }
 
+TEST(CpuPredictor, LesserFeaturesColumnSplit) {
+  TestPredictionWithLesserFeaturesColumnSplit("cpu_predictor");
+}
+
 TEST(CpuPredictor, Sparse) {
   TestSparsePrediction(0.2, "cpu_predictor");
   TestSparsePrediction(0.8, "cpu_predictor");
 }
 
+TEST(CpuPredictor, SparseColumnSplit) {
+  TestSparsePredictionColumnSplit(0.2, "cpu_predictor");
+  TestSparsePredictionColumnSplit(0.8, "cpu_predictor");
+}
+
 TEST(CpuPredictor, Multi) {
   Context ctx;
   ctx.nthread = 1;

tests/cpp/predictor/test_gpu_predictor.cu

@@ -209,7 +209,6 @@ TEST(GPUPredictor, IterationRange) {
   TestIterationRange("gpu_predictor");
 }
 
-
 TEST(GPUPredictor, CategoricalPrediction) {
   TestCategoricalPrediction("gpu_predictor");
 }

tests/cpp/predictor/test_predictor.cc

@@ -153,27 +153,31 @@ void TestInplacePrediction(std::shared_ptr<DMatrix> x, std::string predictor, bs
   learner->Configure();
 }
 
-void TestPredictionWithLesserFeatures(std::string predictor_name) {
+namespace {
-  size_t constexpr kRows = 256, kTrainCols = 256, kTestCols = 4, kIters = 4;
+std::unique_ptr<Learner> LearnerForTest(std::shared_ptr<DMatrix> dmat, size_t iters,
-  auto m_train = RandomDataGenerator(kRows, kTrainCols, 0.5).GenerateDMatrix(true);
+                                        size_t forest = 1) {
-  auto m_test = RandomDataGenerator(kRows, kTestCols, 0.5).GenerateDMatrix(false);
+  std::unique_ptr<Learner> learner{Learner::Create({dmat})};
-  std::unique_ptr<Learner> learner{Learner::Create({m_train})};
+  learner->SetParams(Args{{"num_parallel_tree", std::to_string(forest)}});
-
+  for (size_t i = 0; i < iters; ++i) {
-  for (size_t i = 0; i < kIters; ++i) {
+    learner->UpdateOneIter(i, dmat);
-    learner->UpdateOneIter(i, m_train);
+  }
+  return learner;
 }
 
+void VerifyPredictionWithLesserFeatures(Learner *learner, std::string const &predictor_name,
+                                        size_t rows, std::shared_ptr<DMatrix> const &m_test,
+                                        std::shared_ptr<DMatrix> const &m_invalid) {
   HostDeviceVector<float> prediction;
   learner->SetParam("predictor", predictor_name);
   learner->Configure();
   Json config{Object()};
   learner->SaveConfig(&config);
-  ASSERT_EQ(get<String>(config["learner"]["gradient_booster"]["gbtree_train_param"]["predictor"]), predictor_name);
+  ASSERT_EQ(get<String>(config["learner"]["gradient_booster"]["gbtree_train_param"]["predictor"]),
+            predictor_name);
 
   learner->Predict(m_test, false, &prediction, 0, 0);
-  ASSERT_EQ(prediction.Size(), kRows);
+  ASSERT_EQ(prediction.Size(), rows);
 
-  auto m_invalid = RandomDataGenerator(kRows, kTrainCols + 1, 0.5).GenerateDMatrix(false);
   ASSERT_THROW({ learner->Predict(m_invalid, false, &prediction, 0, 0); }, dmlc::Error);
 
 #if defined(XGBOOST_USE_CUDA)
@@ -192,6 +196,42 @@ void TestPredictionWithLesserFeatures(std::string predictor_name) {
   }
 #endif  // defined(XGBOOST_USE_CUDA)
 }
+}  // anonymous namespace
+
+void TestPredictionWithLesserFeatures(std::string predictor_name) {
+  size_t constexpr kRows = 256, kTrainCols = 256, kTestCols = 4, kIters = 4;
+  auto m_train = RandomDataGenerator(kRows, kTrainCols, 0.5).GenerateDMatrix(true);
+  auto learner = LearnerForTest(m_train, kIters);
+  auto m_test = RandomDataGenerator(kRows, kTestCols, 0.5).GenerateDMatrix(false);
+  auto m_invalid = RandomDataGenerator(kRows, kTrainCols + 1, 0.5).GenerateDMatrix(false);
+  VerifyPredictionWithLesserFeatures(learner.get(), predictor_name, kRows, m_test, m_invalid);
+}
+
+namespace {
+void VerifyPredictionWithLesserFeaturesColumnSplit(Learner *learner,
+                                                   std::string const &predictor_name, size_t rows,
+                                                   std::shared_ptr<DMatrix> m_test,
+                                                   std::shared_ptr<DMatrix> m_invalid) {
+  auto const world_size = collective::GetWorldSize();
+  auto const rank = collective::GetRank();
+  std::shared_ptr<DMatrix> sliced_test{m_test->SliceCol(world_size, rank)};
+  std::shared_ptr<DMatrix> sliced_invalid{m_invalid->SliceCol(world_size, rank)};
+
+  VerifyPredictionWithLesserFeatures(learner, predictor_name, rows, sliced_test, sliced_invalid);
+}
+}  // anonymous namespace
+
+void TestPredictionWithLesserFeaturesColumnSplit(std::string predictor_name) {
+  size_t constexpr kRows = 256, kTrainCols = 256, kTestCols = 4, kIters = 4;
+  auto m_train = RandomDataGenerator(kRows, kTrainCols, 0.5).GenerateDMatrix(true);
+  auto learner = LearnerForTest(m_train, kIters);
+  auto m_test = RandomDataGenerator(kRows, kTestCols, 0.5).GenerateDMatrix(false);
+  auto m_invalid = RandomDataGenerator(kRows, kTrainCols + 1, 0.5).GenerateDMatrix(false);
+
+  auto constexpr kWorldSize = 2;
+  RunWithInMemoryCommunicator(kWorldSize, VerifyPredictionWithLesserFeaturesColumnSplit,
+                              learner.get(), predictor_name, kRows, m_test, m_invalid);
+}
 
 void GBTreeModelForTest(gbm::GBTreeModel *model, uint32_t split_ind,
                         bst_cat_t split_cat, float left_weight,
@@ -212,7 +252,7 @@ void GBTreeModelForTest(gbm::GBTreeModel *model, uint32_t split_ind,
   model->CommitModelGroup(std::move(trees), 0);
 }
 
-void TestCategoricalPrediction(std::string name) {
+void TestCategoricalPrediction(std::string name, bool is_column_split) {
   size_t constexpr kCols = 10;
   PredictionCacheEntry out_predictions;
 
@@ -236,6 +276,9 @@ void TestCategoricalPrediction(std::string name) {
 
   std::vector<FeatureType> types(10, FeatureType::kCategorical);
   m->Info().feature_types.HostVector() = types;
+  if (is_column_split) {
+    m = std::shared_ptr<DMatrix>{m->SliceCol(collective::GetWorldSize(), collective::GetRank())};
+  }
 
   predictor->InitOutPredictions(m->Info(), &out_predictions.predictions, model);
   predictor->PredictBatch(m.get(), &out_predictions, model, 0);
@@ -246,13 +289,21 @@ void TestCategoricalPrediction(std::string name) {
 
   row[split_ind] = split_cat + 1;
   m = GetDMatrixFromData(row, 1, kCols);
+  if (is_column_split) {
+    m = std::shared_ptr<DMatrix>{m->SliceCol(collective::GetWorldSize(), collective::GetRank())};
+  }
   out_predictions.version = 0;
   predictor->InitOutPredictions(m->Info(), &out_predictions.predictions, model);
   predictor->PredictBatch(m.get(), &out_predictions, model, 0);
   ASSERT_EQ(out_predictions.predictions.HostVector()[0], left_weight + score);
 }
 
-void TestCategoricalPredictLeaf(StringView name) {
+void TestCategoricalPredictionColumnSplit(std::string name) {
+  auto constexpr kWorldSize = 2;
+  RunWithInMemoryCommunicator(kWorldSize, TestCategoricalPrediction, name, true);
+}
+
+void TestCategoricalPredictLeaf(StringView name, bool is_column_split) {
   size_t constexpr kCols = 10;
   PredictionCacheEntry out_predictions;
 
@@ -275,6 +326,9 @@ void TestCategoricalPredictLeaf(StringView name) {
   std::vector<float> row(kCols);
   row[split_ind] = split_cat;
   auto m = GetDMatrixFromData(row, 1, kCols);
+  if (is_column_split) {
+    m = std::shared_ptr<DMatrix>{m->SliceCol(collective::GetWorldSize(), collective::GetRank())};
+  }
 
   predictor->PredictLeaf(m.get(), &out_predictions.predictions, model);
   CHECK_EQ(out_predictions.predictions.Size(), 1);
@@ -283,25 +337,25 @@ void TestCategoricalPredictLeaf(StringView name) {
 
   row[split_ind] = split_cat + 1;
   m = GetDMatrixFromData(row, 1, kCols);
+  if (is_column_split) {
+    m = std::shared_ptr<DMatrix>{m->SliceCol(collective::GetWorldSize(), collective::GetRank())};
+  }
   out_predictions.version = 0;
   predictor->InitOutPredictions(m->Info(), &out_predictions.predictions, model);
   predictor->PredictLeaf(m.get(), &out_predictions.predictions, model);
   ASSERT_EQ(out_predictions.predictions.HostVector()[0], 1);
 }
 
+void TestCategoricalPredictLeafColumnSplit(StringView name) {
+  auto constexpr kWorldSize = 2;
+  RunWithInMemoryCommunicator(kWorldSize, TestCategoricalPredictLeaf, name, true);
+}
 
 void TestIterationRange(std::string name) {
-  size_t constexpr kRows = 1000, kCols = 20, kClasses = 4, kForest = 3;
+  size_t constexpr kRows = 1000, kCols = 20, kClasses = 4, kForest = 3, kIters = 10;
   auto dmat = RandomDataGenerator(kRows, kCols, 0).GenerateDMatrix(true, true, kClasses);
-  std::unique_ptr<Learner> learner{Learner::Create({dmat})};
+  auto learner = LearnerForTest(dmat, kIters, kForest);
-
+  learner->SetParams(Args{{"predictor", name}});
-  learner->SetParams(Args{{"num_parallel_tree", std::to_string(kForest)},
-                          {"predictor", name}});
-
-  size_t kIters = 10;
-  for (size_t i = 0; i < kIters; ++i) {
-    learner->UpdateOneIter(i, dmat);
-  }
 
   bool bound = false;
   std::unique_ptr<Learner> sliced {learner->Slice(0, 3, 1, &bound)};
@@ -363,15 +417,82 @@ void TestIterationRange(std::string name) {
   }
 }
 
-void TestSparsePrediction(float sparsity, std::string predictor) {
+namespace {
-  size_t constexpr kRows = 512, kCols = 128;
+void VerifyIterationRangeColumnSplit(DMatrix *dmat, Learner *learner, Learner *sliced,
-  auto Xy = RandomDataGenerator(kRows, kCols, sparsity).GenerateDMatrix(true);
+                                     std::vector<float> const &expected_margin_ranged,
-  std::unique_ptr<Learner> learner{Learner::Create({Xy})};
+                                     std::vector<float> const &expected_margin_sliced,
-  learner->Configure();
+                                     std::vector<float> const &expected_leaf_ranged,
-  for (size_t i = 0; i < 4; ++i) {
+                                     std::vector<float> const &expected_leaf_sliced) {
-    learner->UpdateOneIter(i, Xy);
+  auto const world_size = collective::GetWorldSize();
+  auto const rank = collective::GetRank();
+  std::shared_ptr<DMatrix> Xy{dmat->SliceCol(world_size, rank)};
+
+  HostDeviceVector<float> out_predt_sliced;
+  HostDeviceVector<float> out_predt_ranged;
+
+  // margin
+  {
+    sliced->Predict(Xy, true, &out_predt_sliced, 0, 0, false, false, false, false, false);
+    learner->Predict(Xy, true, &out_predt_ranged, 0, 3, false, false, false, false, false);
+    auto const &h_sliced = out_predt_sliced.HostVector();
+    auto const &h_range = out_predt_ranged.HostVector();
+    ASSERT_EQ(h_sliced.size(), expected_margin_sliced.size());
+    ASSERT_EQ(h_sliced, expected_margin_sliced);
+    ASSERT_EQ(h_range.size(), expected_margin_ranged.size());
+    ASSERT_EQ(h_range, expected_margin_ranged);
   }
 
+  // Leaf
+  {
+    sliced->Predict(Xy, false, &out_predt_sliced, 0, 0, false, true, false, false, false);
+    learner->Predict(Xy, false, &out_predt_ranged, 0, 3, false, true, false, false, false);
+    auto const &h_sliced = out_predt_sliced.HostVector();
+    auto const &h_range = out_predt_ranged.HostVector();
+    ASSERT_EQ(h_sliced.size(), expected_leaf_sliced.size());
+    ASSERT_EQ(h_sliced, expected_leaf_sliced);
+    ASSERT_EQ(h_range.size(), expected_leaf_ranged.size());
+    ASSERT_EQ(h_range, expected_leaf_ranged);
+  }
+}
+}  // anonymous namespace
+
+void TestIterationRangeColumnSplit(std::string name) {
+  size_t constexpr kRows = 1000, kCols = 20, kClasses = 4, kForest = 3, kIters = 10;
+  auto dmat = RandomDataGenerator(kRows, kCols, 0).GenerateDMatrix(true, true, kClasses);
+  auto learner = LearnerForTest(dmat, kIters, kForest);
+  learner->SetParams(Args{{"predictor", name}});
+
+  bool bound = false;
+  std::unique_ptr<Learner> sliced{learner->Slice(0, 3, 1, &bound)};
+  ASSERT_FALSE(bound);
+
+  // margin
+  HostDeviceVector<float> margin_predt_sliced;
+  HostDeviceVector<float> margin_predt_ranged;
+  sliced->Predict(dmat, true, &margin_predt_sliced, 0, 0, false, false, false, false, false);
+  learner->Predict(dmat, true, &margin_predt_ranged, 0, 3, false, false, false, false, false);
+  auto const &margin_sliced = margin_predt_sliced.HostVector();
+  auto const &margin_ranged = margin_predt_ranged.HostVector();
+
+  // Leaf
+  HostDeviceVector<float> leaf_predt_sliced;
+  HostDeviceVector<float> leaf_predt_ranged;
+  sliced->Predict(dmat, false, &leaf_predt_sliced, 0, 0, false, true, false, false, false);
+  learner->Predict(dmat, false, &leaf_predt_ranged, 0, 3, false, true, false, false, false);
+  auto const &leaf_sliced = leaf_predt_sliced.HostVector();
+  auto const &leaf_ranged = leaf_predt_ranged.HostVector();
+
+  auto constexpr kWorldSize = 2;
+  RunWithInMemoryCommunicator(kWorldSize, VerifyIterationRangeColumnSplit, dmat.get(),
+                              learner.get(), sliced.get(), margin_ranged, margin_sliced,
+                              leaf_ranged, leaf_sliced);
+}
+
+void TestSparsePrediction(float sparsity, std::string predictor) {
+  size_t constexpr kRows = 512, kCols = 128, kIters = 4;
+  auto Xy = RandomDataGenerator(kRows, kCols, sparsity).GenerateDMatrix(true);
+  auto learner = LearnerForTest(Xy, kIters);
+
   HostDeviceVector<float> sparse_predt;
 
   Json model{Object{}};
@@ -419,6 +540,43 @@ void TestSparsePrediction(float sparsity, std::string predictor) {
   }
 }
 
+namespace {
+void VerifySparsePredictionColumnSplit(DMatrix *dmat, Learner *learner,
+                                       std::vector<float> const &expected_predt) {
+  std::shared_ptr<DMatrix> sliced{
+      dmat->SliceCol(collective::GetWorldSize(), collective::GetRank())};
+  HostDeviceVector<float> sparse_predt;
+  learner->Predict(sliced, false, &sparse_predt, 0, 0);
+
+  auto const &predt = sparse_predt.HostVector();
+  ASSERT_EQ(predt.size(), expected_predt.size());
+  for (size_t i = 0; i < predt.size(); ++i) {
+    ASSERT_FLOAT_EQ(predt[i], expected_predt[i]);
+  }
+}
+}  // anonymous namespace
+
+void TestSparsePredictionColumnSplit(float sparsity, std::string predictor) {
+  size_t constexpr kRows = 512, kCols = 128, kIters = 4;
+  auto Xy = RandomDataGenerator(kRows, kCols, sparsity).GenerateDMatrix(true);
+  auto learner = LearnerForTest(Xy, kIters);
+
+  HostDeviceVector<float> sparse_predt;
+
+  Json model{Object{}};
+  learner->SaveModel(&model);
+
+  learner.reset(Learner::Create({Xy}));
+  learner->LoadModel(model);
+
+  learner->SetParam("predictor", predictor);
+  learner->Predict(Xy, false, &sparse_predt, 0, 0);
+
+  auto constexpr kWorldSize = 2;
+  RunWithInMemoryCommunicator(kWorldSize, VerifySparsePredictionColumnSplit, Xy.get(),
+                              learner.get(), sparse_predt.HostVector());
+}
+
 void TestVectorLeafPrediction(Context const *ctx) {
   std::unique_ptr<Predictor> cpu_predictor =
       std::unique_ptr<Predictor>(Predictor::Create("cpu_predictor", ctx));

tests/cpp/predictor/test_predictor.h

@@ -86,14 +86,24 @@ void TestInplacePrediction(std::shared_ptr<DMatrix> x, std::string predictor, bs
 
 void TestPredictionWithLesserFeatures(std::string preditor_name);
 
-void TestCategoricalPrediction(std::string name);
+void TestPredictionWithLesserFeaturesColumnSplit(std::string preditor_name);
 
-void TestCategoricalPredictLeaf(StringView name);
+void TestCategoricalPrediction(std::string name, bool is_column_split = false);
+
+void TestCategoricalPredictionColumnSplit(std::string name);
+
+void TestCategoricalPredictLeaf(StringView name, bool is_column_split = false);
+
+void TestCategoricalPredictLeafColumnSplit(StringView name);
 
 void TestIterationRange(std::string name);
 
+void TestIterationRangeColumnSplit(std::string name);
+
 void TestSparsePrediction(float sparsity, std::string predictor);
 
+void TestSparsePredictionColumnSplit(float sparsity, std::string predictor);
+
 void TestVectorLeafPrediction(Context const* ctx);
 }  // namespace xgboost