use extended graph optimization for jinav2 (#24079)

The CUDA execution provider returns an identical vector for every image when jina-clip-v2 is built below ORT_ENABLE_EXTENDED, so every thumbnail embedding written on a GPU was the same normalized garbage and semantic search returned the same results for any query. Reproduced on two different NVIDIA cards, across onnxruntime 1.22 and 1.24, and on both the 0.17 and 0.18 CUDA stacks, so it isn't specific to any of those. ORT_ENABLE_ALL isn't an option because it fails to build on CPU with a SimplifiedLayerNormFusion error, leaving EXTENDED as the only level that works on both providers. jinav1 is unaffected and stays on BASIC.

frigate/detectors/detection_runners.py

@@ -25,25 +25,31 @@ def is_arm64_platform() -> bool:
     return machine in ("aarch64", "arm64", "armv8", "armv7l")
 
 
-def get_ort_session_options(
-    is_complex_model: bool = False,
-) -> ort.SessionOptions | None:
+def get_ort_session_options(model_type: str | None = None) -> ort.SessionOptions | None:
     """Get ONNX Runtime session options with appropriate settings.
 
     Args:
-        is_complex_model: Whether the model needs basic optimization to avoid graph fusion issues.
+        model_type: Model being loaded, used to pin its graph optimization level.
 
     Returns:
-        SessionOptions with appropriate optimization level, or None for default settings.
+        SessionOptions with a pinned optimization level, or None for default settings.
     """
-    if is_complex_model:
-        sess_options = ort.SessionOptions()
-        sess_options.graph_optimization_level = (
-            ort.GraphOptimizationLevel.ORT_ENABLE_BASIC
-        )
-        return sess_options
+    # Import here to avoid circular imports
+    from frigate.embeddings.types import EnrichmentModelTypeEnum
 
-    return None
+    if model_type == EnrichmentModelTypeEnum.jina_v2.value:
+        # below EXTENDED the CUDA EP returns an identical vector for every image,
+        # and ORT_ENABLE_ALL fails to build on CPU with a SimplifiedLayerNormFusion error
+        level = ort.GraphOptimizationLevel.ORT_ENABLE_EXTENDED
+    elif model_type == EnrichmentModelTypeEnum.jina_v1.value:
+        # aggressive optimizations create or expect nodes that don't exist
+        level = ort.GraphOptimizationLevel.ORT_ENABLE_BASIC
+    else:
+        return None
+
+    sess_options = ort.SessionOptions()
+    sess_options.graph_optimization_level = level
+    return sess_options
 
 
 # Import OpenVINO only when needed to avoid circular dependencies
@@ -115,21 +121,6 @@ class BaseModelRunner(ABC):
 class ONNXModelRunner(BaseModelRunner):
     """Run ONNX models using ONNX Runtime."""
 
-    @staticmethod
-    def is_cpu_complex_model(model_type: str) -> bool:
-        """Check if model needs basic optimization level to avoid graph fusion issues.
-
-        Some models (like Jina-CLIP) have issues with aggressive optimizations like
-        SimplifiedLayerNormFusion that create or expect nodes that don't exist.
-        """
-        # Import here to avoid circular imports
-        from frigate.embeddings.types import EnrichmentModelTypeEnum
-
-        return model_type in [
-            EnrichmentModelTypeEnum.jina_v1.value,
-            EnrichmentModelTypeEnum.jina_v2.value,
-        ]
-
     @staticmethod
     def is_migraphx_complex_model(model_type: str) -> bool:
         # Import here to avoid circular imports
@@ -626,9 +617,7 @@ def get_optimized_runner(
     return ONNXModelRunner(
         ort.InferenceSession(
             model_path,
-            sess_options=get_ort_session_options(
-                ONNXModelRunner.is_cpu_complex_model(model_type)
-            ),
+            sess_options=get_ort_session_options(model_type),
             providers=providers,
             provider_options=options,
         ),

frigate/test/test_detection_runners.py

@@ -0,0 +1,41 @@
+"""Tests for ONNX Runtime session option selection."""
+
+import unittest
+
+import onnxruntime as ort
+
+from frigate.detectors.detection_runners import get_ort_session_options
+from frigate.detectors.detector_config import ModelTypeEnum
+from frigate.embeddings.types import EnrichmentModelTypeEnum
+
+
+class TestGetOrtSessionOptions(unittest.TestCase):
+    def test_jina_v2_uses_extended(self):
+        """jina-clip-v2 returns an identical vector for every image on the CUDA
+        execution provider at anything below EXTENDED."""
+        options = get_ort_session_options(EnrichmentModelTypeEnum.jina_v2.value)
+
+        self.assertIsNotNone(options)
+        self.assertEqual(
+            options.graph_optimization_level,
+            ort.GraphOptimizationLevel.ORT_ENABLE_EXTENDED,
+        )
+
+    def test_jina_v1_uses_basic(self):
+        options = get_ort_session_options(EnrichmentModelTypeEnum.jina_v1.value)
+
+        self.assertIsNotNone(options)
+        self.assertEqual(
+            options.graph_optimization_level,
+            ort.GraphOptimizationLevel.ORT_ENABLE_BASIC,
+        )
+
+    def test_other_models_use_defaults(self):
+        for model_type in [
+            None,
+            EnrichmentModelTypeEnum.paddleocr.value,
+            EnrichmentModelTypeEnum.arcface.value,
+            ModelTypeEnum.rfdetr.value,
+        ]:
+            with self.subTest(model_type=model_type):
+                self.assertIsNone(get_ort_session_options(model_type))