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Add error-path tests for InferenceSession Run/Load/Initialize validation #29555

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Merged
Gopalakrishnan Nallasamy (GopalakrishnanN) merged 2 commits into from
Jul 9, 2026
Merged

Add error-path tests for InferenceSession Run/Load/Initialize validation #29555

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ac9e46c
Add error-path tests for InferenceSession Run/Load/Initialize validat…
Jul 6, 2026
028b992
Update error-path test header comment to reflect all covered branches
Jul 8, 2026
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165 changes: 165 additions & 0 deletions onnxruntime/test/framework/inference_session_test.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -353,6 +353,171 @@ TEST(InferenceSessionTests, RequestLoadCancellation) {
}
}

// Error-path coverage for InferenceSession validation / early-return branches.
// "Invalid input name" is already covered by TestOptionalInputs; the tests below cover the
// remaining gaps in Run/Initialize/Load: Run-before-Initialize, Initialize-before-Load, an
// invalid output name, input type/rank and output type mismatches, feed name/value count
// mismatch, and load failures (malformed model bytes and a nonexistent model path).
TEST(InferenceSessionTests, RunBeforeInitializeReturnsError) {
SessionOptions so;
so.session_logid = "InferenceSessionTests.RunBeforeInitializeReturnsError";
InferenceSession session_object{so, GetEnvironment()};
ASSERT_STATUS_OK(session_object.Load(MODEL_URI));
// Intentionally do NOT call Initialize().

std::vector<int64_t> dims_x = {3, 2};
std::vector<float> values_x = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f};
OrtValue ml_value;
CreateMLValue<float>(TestCPUExecutionProvider()->CreatePreferredAllocators()[0], dims_x, values_x, &ml_value);
NameMLValMap feeds;
feeds.insert(std::make_pair("X", ml_value));
std::vector<std::string> output_names{"Y"};
std::vector<OrtValue> fetches;

RunOptions run_options;
ASSERT_STATUS_NOT_OK_AND_HAS_SUBSTR(session_object.Run(run_options, feeds, output_names, &fetches),
"not initialized");
}

TEST(InferenceSessionTests, RunWithInvalidOutputNameReturnsError) {
SessionOptions so;
so.session_logid = "InferenceSessionTests.RunWithInvalidOutputNameReturnsError";
InferenceSession session_object{so, GetEnvironment()};
ASSERT_STATUS_OK(session_object.Load(MODEL_URI));
ASSERT_STATUS_OK(session_object.Initialize());

std::vector<int64_t> dims_x = {3, 2};
std::vector<float> values_x = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f};
OrtValue ml_value;
CreateMLValue<float>(TestCPUExecutionProvider()->CreatePreferredAllocators()[0], dims_x, values_x, &ml_value);
NameMLValMap feeds;
feeds.insert(std::make_pair("X", ml_value));
std::vector<std::string> output_names{"not_a_real_output"};
std::vector<OrtValue> fetches;

RunOptions run_options;
ASSERT_STATUS_NOT_OK_AND_HAS_SUBSTR(session_object.Run(run_options, feeds, output_names, &fetches),
"Invalid output name");
}

TEST(InferenceSessionTests, LoadMalformedModelFromArrayReturnsError) {
SessionOptions so;
so.session_logid = "InferenceSessionTests.LoadMalformedModelFromArrayReturnsError";
InferenceSession session_object{so, GetEnvironment()};

// Bytes that are not a valid ModelProto: Load must fail gracefully (return an error, not crash).
const std::string garbage = "this is definitely not a valid onnx model proto";
ASSERT_FALSE(session_object.Load(garbage.data(), static_cast<int>(garbage.size())).IsOK());
}

TEST(InferenceSessionTests, RunWithWrongInputTypeReturnsError) {
SessionOptions so;
so.session_logid = "InferenceSessionTests.RunWithWrongInputTypeReturnsError";
InferenceSession session_object{so, GetEnvironment()};
ASSERT_STATUS_OK(session_object.Load(MODEL_URI));
ASSERT_STATUS_OK(session_object.Initialize());

// Model input "X" is float; feed int32 to trigger the element-type check (CheckTypes).
std::vector<int64_t> dims_x = {3, 2};
std::vector<int32_t> values_x = {1, 2, 3, 4, 5, 6};
OrtValue ml_value;
CreateMLValue<int32_t>(TestCPUExecutionProvider()->CreatePreferredAllocators()[0], dims_x, values_x, &ml_value);
NameMLValMap feeds;
feeds.insert(std::make_pair("X", ml_value));
std::vector<std::string> output_names{"Y"};
std::vector<OrtValue> fetches;

RunOptions run_options;
ASSERT_STATUS_NOT_OK_AND_HAS_SUBSTR(session_object.Run(run_options, feeds, output_names, &fetches),
"Unexpected input data type");
}

TEST(InferenceSessionTests, RunWithWrongInputRankReturnsError) {
SessionOptions so;
so.session_logid = "InferenceSessionTests.RunWithWrongInputRankReturnsError";
InferenceSession session_object{so, GetEnvironment()};
ASSERT_STATUS_OK(session_object.Load(MODEL_URI));
ASSERT_STATUS_OK(session_object.Initialize());

// Model input "X" has rank 2; feed a rank-1 tensor to trigger the rank check (CheckShapes).
std::vector<int64_t> bad_dims = {6};
std::vector<float> values_x = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f};
OrtValue ml_value;
CreateMLValue<float>(TestCPUExecutionProvider()->CreatePreferredAllocators()[0], bad_dims, values_x, &ml_value);
NameMLValMap feeds;
feeds.insert(std::make_pair("X", ml_value));
std::vector<std::string> output_names{"Y"};
std::vector<OrtValue> fetches;

RunOptions run_options;
ASSERT_STATUS_NOT_OK_AND_HAS_SUBSTR(session_object.Run(run_options, feeds, output_names, &fetches),
"Invalid rank for input");
}

TEST(InferenceSessionTests, RunWithMismatchedFeedCountReturnsError) {
SessionOptions so;
so.session_logid = "InferenceSessionTests.RunWithMismatchedFeedCountReturnsError";
InferenceSession session_object{so, GetEnvironment()};
ASSERT_STATUS_OK(session_object.Load(MODEL_URI));
ASSERT_STATUS_OK(session_object.Initialize());

std::vector<int64_t> dims_x = {3, 2};
std::vector<float> values_x = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f};
OrtValue ml_value;
CreateMLValue<float>(TestCPUExecutionProvider()->CreatePreferredAllocators()[0], dims_x, values_x, &ml_value);

// Two feed names but only one feed value -> count-mismatch branch in ValidateInputsOutputs.
std::vector<std::string> feed_names{"X", "X"};
std::vector<OrtValue> feeds{ml_value};
std::vector<std::string> output_names{"Y"};
std::vector<OrtValue> fetches;

RunOptions run_options;
ASSERT_STATUS_NOT_OK_AND_HAS_SUBSTR(session_object.Run(run_options, feed_names, feeds, output_names, &fetches),
"feed names has");
}

TEST(InferenceSessionTests, InitializeBeforeLoadReturnsError) {
SessionOptions so;
so.session_logid = "InferenceSessionTests.InitializeBeforeLoadReturnsError";
InferenceSession session_object{so, GetEnvironment()};
// Initialize() without a prior successful Load().
ASSERT_STATUS_NOT_OK_AND_HAS_SUBSTR(session_object.Initialize(), "Model was not loaded");
}

TEST(InferenceSessionTests, LoadNonexistentModelReturnsError) {
SessionOptions so;
so.session_logid = "InferenceSessionTests.LoadNonexistentModelReturnsError";
InferenceSession session_object{so, GetEnvironment()};
// Loading a path that does not exist must fail gracefully (return an error, not crash).
ASSERT_FALSE(session_object.Load(ORT_TSTR("testdata/this_model_does_not_exist.onnx")).IsOK());
}

TEST(InferenceSessionTests, RunWithWrongOutputTypeReturnsError) {
SessionOptions so;
so.session_logid = "InferenceSessionTests.RunWithWrongOutputTypeReturnsError";
InferenceSession session_object{so, GetEnvironment()};
ASSERT_STATUS_OK(session_object.Load(MODEL_URI));
ASSERT_STATUS_OK(session_object.Initialize());

std::vector<int64_t> dims_x = {3, 2};
std::vector<float> values_x = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f};
OrtValue x_value;
CreateMLValue<float>(TestCPUExecutionProvider()->CreatePreferredAllocators()[0], dims_x, values_x, &x_value);
NameMLValMap feeds;
feeds.insert(std::make_pair("X", x_value));

// Pre-allocate the fetch for float output "Y" as int32 to trigger the output type check.
std::vector<std::string> output_names{"Y"};
std::vector<OrtValue> fetches;
fetches.resize(1);
AllocateMLValue<int32_t>(TestCPUExecutionProvider()->CreatePreferredAllocators()[0], dims_x, &fetches[0]);

RunOptions run_options;
ASSERT_STATUS_NOT_OK_AND_HAS_SUBSTR(session_object.Run(run_options, feeds, output_names, &fetches),
"Unexpected output data type");
}

TEST(InferenceSessionTests, CheckRunLogger) {
if constexpr (!SessionOptions::DEFAULT_USE_PER_SESSION_THREADS) {
GTEST_SKIP() << "Skipping the test";
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