Disable mitigation for bug in onnxruntime CSE triggered during value propagation

CHANGELOG.rst

@@ -7,6 +7,14 @@
 Change log
 ==========
 
+0.17.2 (2026-06-04)
+-------------------
+
+**Other change**
+
+- Disable onnxruntime optimization passes in value propagation as a mitigation for https://github.com/microsoft/onnxruntime/issues/28413 and to speed-up session creation.
+
+
 0.17.1 (2026-02-17)
 -------------------
 

src/spox/_value_prop.py

@@ -146,13 +146,14 @@ def _run_reference_implementation(
 def _run_onnxruntime(
     model: onnx.ModelProto, input_feed: dict[str, ORTValue]
 ) -> dict[str, ORTValue]:
-    import onnxruntime
+    import onnxruntime as ort
 
     # Silence possible warnings during execution (especially constant folding)
-    options = onnxruntime.SessionOptions()
+    options = ort.SessionOptions()
     options.log_severity_level = 3
+    options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
     try:
-        session = onnxruntime.InferenceSession(model.SerializeToString(), options)
+        session = ort.InferenceSession(model.SerializeToString(), options)
         output_names = [output.name for output in session.get_outputs()]
         output_feed = dict(zip(output_names, session.run(None, input_feed)))
     except Exception as e:

tests/test_value_propagation.py

@@ -9,7 +9,7 @@
 
 import spox
 import spox._future
-import spox.opset.ai.onnx.ml.v3 as ml
+import spox.opset.ai.onnx.ml.v4 as ml
 import spox.opset.ai.onnx.v22 as op
 from spox import Optional, Sequence, Tensor, Type, Var, argument
 from spox._graph import arguments, results
@@ -259,3 +259,17 @@ def test_strings():
 
     x, y = op.const(["foo"]), op.const(["bar"])
     np.testing.assert_equal(op.string_concat(x, y)._value.value, np.array(["foobar"]))  # type: ignore
+
+
+def test_value_prop_ort_cse_bug_mitigation():
+    """Test mitigation against ort CSE crash."""
+    # A constant string input so value propagation actually runs.
+    x = op.const(np.array(["a"], dtype=np.str_))
+
+    y = ml.label_encoder(
+        x,
+        keys_tensor=np.array(["a"], dtype=np.str_),
+        values_tensor=np.array(["b"], dtype=np.str_),
+        default_tensor=np.array(["MISSING"], dtype=np.str_),
+    )
+    assert y._value is not None