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vraspar merged 4 commits into from
Mar 6, 2026
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Add missing attribute array validation for TreeEnsembleClassifier #27571

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Add tests for mismatched class and node attribute array sizes in Tree…
vraspar Mar 5, 2026
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vraspar Mar 5, 2026
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vraspar Mar 5, 2026
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fix lint issues
vraspar Mar 6, 2026
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34 changes: 17 additions & 17 deletions onnxruntime/core/providers/cpu/ml/tree_ensemble_attribute.h
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Original file line number Diff line number Diff line change
Expand Up @@ -69,24 +69,24 @@ struct TreeEnsembleAttributesV3 {
target_class_nodeids = info.GetAttrsOrDefault<int64_t>("target_nodeids");
target_class_treeids = info.GetAttrsOrDefault<int64_t>("target_treeids");
target_class_weights = info.GetAttrsOrDefault<float>("target_weights");

ORT_ENFORCE(n_targets_or_classes > 0);
ORT_ENFORCE(nodes_falsenodeids.size() == nodes_featureids.size());
ORT_ENFORCE(nodes_falsenodeids.size() == nodes_modes_string.size());
ORT_ENFORCE(nodes_falsenodeids.size() == nodes_nodeids.size());
ORT_ENFORCE(nodes_falsenodeids.size() == nodes_treeids.size());
ORT_ENFORCE(nodes_falsenodeids.size() == nodes_truenodeids.size());
ORT_ENFORCE(nodes_falsenodeids.size() == nodes_values.size() ||
nodes_falsenodeids.size() == nodes_values_as_tensor.size());
ORT_ENFORCE(target_class_ids.size() == target_class_nodeids.size());
ORT_ENFORCE(target_class_ids.size() == target_class_treeids.size());
ORT_ENFORCE(target_class_weights.empty() || target_class_ids.size() == target_class_weights.size());
ORT_ENFORCE(base_values.empty() || base_values_as_tensor.empty());
ORT_ENFORCE(nodes_hitrates.empty() || nodes_hitrates_as_tensor.empty());
ORT_ENFORCE(nodes_values.empty() || nodes_values_as_tensor.empty());
ORT_ENFORCE(target_class_weights.empty() || target_class_weights_as_tensor.empty());
ORT_ENFORCE(nodes_modes_string.size() < std::numeric_limits<uint32_t>::max());
}

ORT_ENFORCE(n_targets_or_classes > 0);
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ORT_ENFORCE(nodes_falsenodeids.size() == nodes_featureids.size());
ORT_ENFORCE(nodes_falsenodeids.size() == nodes_modes.size());
ORT_ENFORCE(nodes_falsenodeids.size() == nodes_nodeids.size());
ORT_ENFORCE(nodes_falsenodeids.size() == nodes_treeids.size());
ORT_ENFORCE(nodes_falsenodeids.size() == nodes_truenodeids.size());
ORT_ENFORCE(nodes_falsenodeids.size() == nodes_values.size() ||
nodes_falsenodeids.size() == nodes_values_as_tensor.size());
ORT_ENFORCE(target_class_ids.size() == target_class_nodeids.size());
ORT_ENFORCE(target_class_ids.size() == target_class_treeids.size());
ORT_ENFORCE(target_class_weights.empty() || target_class_ids.size() == target_class_weights.size());
ORT_ENFORCE(base_values.empty() || base_values_as_tensor.empty());
ORT_ENFORCE(nodes_hitrates.empty() || nodes_hitrates_as_tensor.empty());
ORT_ENFORCE(nodes_values.empty() || nodes_values_as_tensor.empty());
ORT_ENFORCE(target_class_weights.empty() || target_class_weights_as_tensor.empty());
ORT_ENFORCE(nodes_modes.size() < std::numeric_limits<uint32_t>::max());
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}

std::string aggregate_function;
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80 changes: 80 additions & 0 deletions onnxruntime/test/providers/cpu/ml/tree_ensembler_classifier_test.cc
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Expand Up @@ -367,5 +367,85 @@ TEST(MLOpTest, TreeEnsembleClassifierBinaryProbabilities) {
test.Run();
}

TEST(MLOpTest, TreeEnsembleClassifierMismatchedClassArrays) {
OpTester test("TreeEnsembleClassifier", 1, onnxruntime::kMLDomain);

// Use a minimal valid tree: one root (BRANCH_LEQ) with two leaf children.
std::vector<int64_t> lefts = {1, -1, -1};
std::vector<int64_t> rights = {2, -1, -1};
std::vector<int64_t> treeids = {0, 0, 0};
std::vector<int64_t> nodeids = {0, 1, 2};
std::vector<int64_t> featureids = {0, -2, -2};
std::vector<float> thresholds = {0.5f, -2.f, -2.f};
std::vector<std::string> modes = {"BRANCH_LEQ", "LEAF", "LEAF"};
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// Intentionally mismatched: class_nodeids has fewer elements than class_ids.
std::vector<int64_t> class_treeids = {0, 0};
std::vector<int64_t> class_nodeids = {1}; // only 1 element — mismatch!
std::vector<int64_t> class_classids = {0, 1};
std::vector<float> class_weights = {1.f, 1.f};
std::vector<int64_t> classes = {0, 1};

test.AddAttribute("nodes_truenodeids", lefts);
test.AddAttribute("nodes_falsenodeids", rights);
test.AddAttribute("nodes_treeids", treeids);
test.AddAttribute("nodes_nodeids", nodeids);
test.AddAttribute("nodes_featureids", featureids);
test.AddAttribute("nodes_values", thresholds);
test.AddAttribute("nodes_modes", modes);
test.AddAttribute("class_treeids", class_treeids);
test.AddAttribute("class_nodeids", class_nodeids);
test.AddAttribute("class_ids", class_classids);
test.AddAttribute("class_weights", class_weights);
test.AddAttribute("classlabels_int64s", classes);

std::vector<float> X = {1.f};
test.AddInput<float>("X", {1, 1}, X);
test.AddOutput<int64_t>("Y", {1}, {0});
test.AddOutput<float>("Z", {1, 2}, {0.f, 0.f});

test.Run(OpTester::ExpectResult::kExpectFailure, "");
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}


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TEST(MLOpTest, TreeEnsembleClassifierMismatchedNodeArrays) {
OpTester test("TreeEnsembleClassifier", 1, onnxruntime::kMLDomain);

// nodes_falsenodeids has 3 elements, but nodes_featureids has only 2 — mismatch!
std::vector<int64_t> lefts = {1, -1, -1};
std::vector<int64_t> rights = {2, -1, -1};
std::vector<int64_t> treeids = {0, 0, 0};
std::vector<int64_t> nodeids = {0, 1, 2};
std::vector<int64_t> featureids = {0, -2}; // only 2 elements — mismatch!
std::vector<float> thresholds = {0.5f, -2.f, -2.f};
std::vector<std::string> modes = {"BRANCH_LEQ", "LEAF", "LEAF"};

std::vector<int64_t> class_treeids = {0, 0};
std::vector<int64_t> class_nodeids = {1, 2};
std::vector<int64_t> class_classids = {0, 1};
std::vector<float> class_weights = {1.f, 1.f};
std::vector<int64_t> classes = {0, 1};

test.AddAttribute("nodes_truenodeids", lefts);
test.AddAttribute("nodes_falsenodeids", rights);
test.AddAttribute("nodes_treeids", treeids);
test.AddAttribute("nodes_nodeids", nodeids);
test.AddAttribute("nodes_featureids", featureids);
test.AddAttribute("nodes_values", thresholds);
test.AddAttribute("nodes_modes", modes);
test.AddAttribute("class_treeids", class_treeids);
test.AddAttribute("class_nodeids", class_nodeids);
test.AddAttribute("class_ids", class_classids);
test.AddAttribute("class_weights", class_weights);
test.AddAttribute("classlabels_int64s", classes);

std::vector<float> X = {1.f};
test.AddInput<float>("X", {1, 1}, X);
test.AddOutput<int64_t>("Y", {1}, {0});
test.AddOutput<float>("Z", {1, 2}, {0.f, 0.f});

test.Run(OpTester::ExpectResult::kExpectFailure, "");
}

} // namespace test
} // namespace onnxruntime
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