Use DFS to get a valid but smaller new node order

src/model_loader/lightgbm.cc

@@ -526,22 +526,24 @@ inline std::unique_ptr<treelite::Model> ParseStream(std::istream& fi) {
   for (auto const& lgb_tree : lgb_trees_) {
     builder->StartTree();
 
-    // Assign node ID's so that a breadth-wise traversal would yield
+    // Assign node ID's so that a depth-wise traversal would yield
     // the monotonic sequence 0, 1, 2, ...
     // We re-arrange nodes here, since LightGBM uses negative indices to distinguish leaf nodes
     // from internal nodes.
-    std::queue<std::pair<int, int>> Q;  // (old ID, new ID) pair
+    std::deque<std::pair<int, int>> Q;  // (old ID, new ID) pair
+    int dfs_index = 1;
     if (lgb_tree.num_leaves == 0) {
       continue;
     } else if (lgb_tree.num_leaves == 1) {
       // A constant-value tree with a single root node that's also a leaf
-      Q.emplace(-1, 0);
+      Q.emplace_front(-1, dfs_index);
     } else {
-      Q.emplace(0, 0);
+      Q.emplace_front(0, dfs_index);
     }
+    dfs_index++;
     while (!Q.empty()) {
       auto [old_node_id, new_node_id] = Q.front();
-      Q.pop();
+      Q.pop_front();
       builder->StartNode(new_node_id);
       if (old_node_id < 0) {  // leaf
         builder->LeafScalar(lgb_tree.leaf_value[~old_node_id]);
@@ -554,9 +556,9 @@ inline std::unique_ptr<treelite::Model> ParseStream(std::istream& fi) {
         auto const split_index = static_cast<std::int32_t>(lgb_tree.split_feature[old_node_id]);
         auto const missing_type = GetMissingType(lgb_tree.decision_type[old_node_id]);
         int const left_child_old_id = lgb_tree.left_child[old_node_id];
-        int const left_child_new_id = new_node_id * 2 + 1;
+        int const left_child_new_id = dfs_index++;
         int const right_child_old_id = lgb_tree.right_child[old_node_id];
-        int const right_child_new_id = new_node_id * 2 + 2;
+        int const right_child_new_id = dfs_index++;
 
         if (GetDecisionType(lgb_tree.decision_type[old_node_id], kCategoricalMask)) {
           // Categorical split
@@ -591,8 +593,8 @@ inline std::unique_ptr<treelite::Model> ParseStream(std::istream& fi) {
         if (!lgb_tree.split_gain.empty()) {
           builder->Gain(lgb_tree.split_gain[old_node_id]);
         }
-        Q.emplace(left_child_old_id, left_child_new_id);
-        Q.emplace(right_child_old_id, right_child_new_id);
+        Q.emplace_front(left_child_old_id, left_child_new_id);
+        Q.emplace_front(right_child_old_id, right_child_new_id);
       }
       builder->EndNode();
     }

tests/examples/deep_lightgbm/model.txt

@@ -0,0 +1,36 @@
+tree
+version=v4
+num_class=1
+num_tree_per_iteration=1
+label_index=0
+max_feature_idx=0
+objective=regression
+feature_names=this
+feature_infos=[0:100]
+tree_sizes=1119
+
+Tree=0
+num_leaves=32
+num_cat=0
+split_feature=0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+split_gain=0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
+threshold=1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 -31
+right_child=-1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 -13 -14 -15 -16 -17 -18 -19 -20 -21 -22 -23 -24 -25 -26 -27 -28 -29 -30 -32
+leaf_value=31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 0 1
+leaf_weight=1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
+leaf_count=1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
+internal_value=0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+internal_weight=1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
+internal_count=1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
+is_linear=0
+shrinkage=1
+
+
+end of trees
+
+feature_importances:
+this=31
+
+pandas_categorical:null

tests/python/test_lightgbm_integration.py

@@ -10,8 +10,9 @@
 
 try:
     from hypothesis import given, settings
+    from hypothesis.extra.numpy import arrays
     from hypothesis.strategies import data as hypothesis_callback
-    from hypothesis.strategies import integers, just, sampled_from
+    from hypothesis.strategies import integers, just, sampled_from, tuples
 except ImportError:
     pytest.skip("hypothesis not installed; skipping", allow_module_level=True)
 
@@ -259,3 +260,25 @@ def test_lightgbm_sparse_categorical_model():
     expected_pred = load_txt(dataset_db[dataset].expected_margin).reshape((-1, 1, 1))
     out_pred = treelite.gtil.predict(tl_model, X, pred_margin=True)
     np.testing.assert_almost_equal(out_pred, expected_pred, decimal=5)
+
+
+@given(
+    X=arrays(
+        dtype=sampled_from([np.float32, np.float64]),
+        shape=tuples(integers(min_value=10, max_value=100), just(1)),
+    )
+)
+def test_lightgbm_deep_tree(X):
+    """Test LightGBM model with depth 32+"""
+    path = (
+        pathlib.Path(__file__).parent.parent
+        / "examples"
+        / "deep_lightgbm"
+        / "model.txt"
+    )
+    bst = lgb.Booster(model_file=path)
+    expected_pred = bst.predict(X).reshape((X.shape[0], 1, -1))
+
+    tl_model = treelite.frontend.load_lightgbm_model(path)
+    out_pred = treelite.gtil.predict(tl_model, X)
+    np.testing.assert_almost_equal(out_pred, expected_pred, decimal=5)