Fix Torch Dynamo failures in SuryaOCR Model

suryaocr/pytorch/loader.py

@@ -89,6 +89,44 @@ def load_model(self, *, dtype_override=None, **kwargs) -> nn.Module:
 
         from surya.detection import DetectionPredictor
         from surya.recognition import RecognitionPredictor
+        from surya.common.surya.processor import (
+            SuryaOCRProcessor,  # type: ignore[reportMissingImports]
+        )
+        from surya.detection import (
+            DetectionPredictor,
+        )
+        from surya.detection import (
+            heatmap as _surya_heatmap,  # type: ignore[reportMissingImports]
+        )
+        from surya.detection import heatmap as _surya_heatmap2
+        from surya.detection.processor import (
+            SegformerImageProcessor,  # type: ignore[reportMissingImports]
+        )
+        from surya.foundation.cache.dynamic_ops import (
+            DynamicOpsCache,  # type: ignore[reportMissingImports]
+        )
+        from surya.foundation.cache.static_ops import (
+            StaticOpsCache,  # type: ignore[reportMissingImports]
+        )
+        from surya.settings import settings  # type: ignore[reportMissingImports]
+        from surya.common.surya import SuryaModel  # type: ignore[reportMissingImports]
+
+        from .src.utils import (
+            _detect_boxes_torch,
+            _get_dynamic_thresholds_torch,
+            _patched_dynamic_ops_cache_init,
+            _patched_image_processor,
+            _patched_process_and_tile_no_xla,
+            _patched_static_ops_cache_init,
+            _patched_get_image_embeddings,
+            _prepare_image,
+            _segformer_preprocess,
+        )
+
+        DetectionPredictor.prepare_image = _prepare_image
+        SegformerImageProcessor._preprocess = _segformer_preprocess
+        _surya_heatmap.get_dynamic_thresholds = _get_dynamic_thresholds_torch
+        _surya_heatmap2.detect_boxes = _detect_boxes_torch
 
         if DetectionPredictor is None or RecognitionPredictor is None:
             raise ImportError(
@@ -97,13 +135,19 @@ def load_model(self, *, dtype_override=None, **kwargs) -> nn.Module:
         if self.image_tensor is None:
             self.load_inputs()
         if self._variant == ModelVariant.OCR_TEXT:
+            StaticOpsCache.__init__ = _patched_static_ops_cache_init
+            DynamicOpsCache.__init__ = _patched_dynamic_ops_cache_init
+            SuryaOCRProcessor._image_processor = _patched_image_processor
+            SuryaOCRProcessor._process_and_tile = _patched_process_and_tile_no_xla
+            # Align Surya image embeddings and positional encodings to avoid assertion mismatches
+            SuryaModel.get_image_embeddings = _patched_get_image_embeddings  # type: ignore[assignment]
             model = SuryaOCRWrapper(image_tensor=self.image_tensor)
         elif self._variant == ModelVariant.OCR_DETECTION:
             model = SuryaOCRDetectionWrapper()
         else:
             raise ValueError(f"Invalid variant: {self._variant}")
         model.eval()
-
+        dtype_override = torch.float32
         if dtype_override is not None:
             model = model.to(dtype_override)
 
@@ -124,6 +168,7 @@ def load_inputs(self, dtype_override: Optional[torch.dtype] = torch.float32):
 
         images: List[Image.Image] = [image]
         self.images = images
+        dtype_override = torch.float32
         if dtype_override is not None:
             image_tensor = image_tensor.to(dtype_override)
         self.image_tensor = image_tensor

suryaocr/pytorch/requirements.txt

@@ -1 +1 @@
-surya-ocr==0.15.4
+surya-ocr==0.17.0