feat(batching): support batching for ColPali functions

python/cocoindex/functions/colpali.py

@@ -2,7 +2,7 @@
 
 import functools
 from dataclasses import dataclass
-from typing import Any, Optional, TYPE_CHECKING, Literal
+from typing import Any, TYPE_CHECKING, Literal
 import numpy as np
 
 from .. import op
@@ -22,18 +22,11 @@ class ColPaliModelInfo:
     dimension: int
 
 
-@functools.lru_cache(maxsize=None)
+@functools.cache
 def _get_colpali_model_and_processor(model_name: str) -> ColPaliModelInfo:
     """Load and cache ColPali model and processor with shared device setup."""
     try:
-        from colpali_engine import (  # type: ignore[import-untyped]
-            ColPali,
-            ColPaliProcessor,
-            ColQwen2,
-            ColQwen2Processor,
-            ColSmol,
-            ColSmolProcessor,
-        )
+        import colpali_engine as ce  # type: ignore[import-untyped]
         import torch
     except ImportError as e:
         raise ImportError(
@@ -42,29 +35,30 @@ def _get_colpali_model_and_processor(model_name: str) -> ColPaliModelInfo:
         ) from e
 
     device = "cuda" if torch.cuda.is_available() else "cpu"
+    lower_model_name = model_name.lower()
 
     # Determine model type from name
-    if "colpali" in model_name.lower():
-        model = ColPali.from_pretrained(
+    if lower_model_name.startswith("colpali"):
+        model = ce.ColPali.from_pretrained(
             model_name, torch_dtype=torch.bfloat16, device_map=device
         )
-        processor = ColPaliProcessor.from_pretrained(model_name)
-    elif "colqwen" in model_name.lower():
-        model = ColQwen2.from_pretrained(
+        processor = ce.ColPaliProcessor.from_pretrained(model_name)
+    elif lower_model_name.startswith("colqwen2.5"):
+        model = ce.ColQwen2_5.from_pretrained(
             model_name, torch_dtype=torch.bfloat16, device_map=device
         )
-        processor = ColQwen2Processor.from_pretrained(model_name)
-    elif "colsmol" in model_name.lower():
-        model = ColSmol.from_pretrained(
+        processor = ce.ColQwen2_5_Processor.from_pretrained(model_name)
+    elif lower_model_name.startswith("colqwen"):
+        model = ce.ColQwen2.from_pretrained(
             model_name, torch_dtype=torch.bfloat16, device_map=device
         )
-        processor = ColSmolProcessor.from_pretrained(model_name)
+        processor = ce.ColQwen2Processor.from_pretrained(model_name)
     else:
         # Fallback to ColPali for backwards compatibility
-        model = ColPali.from_pretrained(
+        model = ce.ColPali.from_pretrained(
             model_name, torch_dtype=torch.bfloat16, device_map=device
         )
-        processor = ColPaliProcessor.from_pretrained(model_name)
+        processor = ce.ColPaliProcessor.from_pretrained(model_name)
 
     # Detect dimension
     dimension = _detect_colpali_dimension(model, processor, device)
@@ -130,6 +124,7 @@ class ColPaliEmbedImage(op.FunctionSpec):
 @op.executor_class(
     gpu=True,
     cache=True,
+    batching=True,
     behavior_version=1,
 )
 class ColPaliEmbedImageExecutor:
@@ -146,7 +141,7 @@ def analyze(self) -> type:
         dimension = self._model_info.dimension
         return Vector[Vector[np.float32, Literal[dimension]]]  # type: ignore
 
-    def __call__(self, img_bytes: bytes) -> Any:
+    def __call__(self, img_bytes_list: list[bytes]) -> Any:
         try:
             from PIL import Image
             import torch
@@ -160,8 +155,11 @@ def __call__(self, img_bytes: bytes) -> Any:
         processor = self._model_info.processor
         device = self._model_info.device
 
-        pil_image = Image.open(io.BytesIO(img_bytes)).convert("RGB")
-        inputs = processor.process_images([pil_image]).to(device)
+        pil_images = [
+            Image.open(io.BytesIO(img_bytes)).convert("RGB")
+            for img_bytes in img_bytes_list
+        ]
+        inputs = processor.process_images(pil_images).to(device)
         with torch.no_grad():
             embeddings = model(**inputs)
 
@@ -171,10 +169,8 @@ def __call__(self, img_bytes: bytes) -> Any:
                 f"Expected 3D tensor [batch, patches, hidden_dim], got shape {embeddings.shape}"
             )
 
-        # Keep patch-level embeddings: [batch, patches, hidden_dim] -> [patches, hidden_dim]
-        patch_embeddings = embeddings[0]  # Remove batch dimension
-
-        return patch_embeddings.cpu().to(torch.float32).numpy()
+        # [patches, hidden_dim]
+        return embeddings.cpu().to(torch.float32).numpy()
 
 
 class ColPaliEmbedQuery(op.FunctionSpec):
@@ -207,6 +203,7 @@ class ColPaliEmbedQuery(op.FunctionSpec):
     gpu=True,
     cache=True,
     behavior_version=1,
+    batching=True,
 )
 class ColPaliEmbedQueryExecutor:
     """Executor for ColVision query embedding (ColPali, ColQwen2, ColSmol, etc.)."""
@@ -222,7 +219,7 @@ def analyze(self) -> type:
         dimension = self._model_info.dimension
         return Vector[Vector[np.float32, Literal[dimension]]]  # type: ignore
 
-    def __call__(self, query: str) -> Any:
+    def __call__(self, queries: list[str]) -> Any:
         try:
             import torch
         except ImportError as e:
@@ -234,7 +231,7 @@ def __call__(self, query: str) -> Any:
         processor = self._model_info.processor
         device = self._model_info.device
 
-        inputs = processor.process_queries([query]).to(device)
+        inputs = processor.process_queries(queries).to(device)
         with torch.no_grad():
             embeddings = model(**inputs)
 
@@ -244,7 +241,5 @@ def __call__(self, query: str) -> Any:
                 f"Expected 3D tensor [batch, tokens, hidden_dim], got shape {embeddings.shape}"
             )
 
-        # Keep token-level embeddings: [batch, tokens, hidden_dim] -> [tokens, hidden_dim]
-        token_embeddings = embeddings[0]  # Remove batch dimension
-
-        return token_embeddings.cpu().to(torch.float32).numpy()
+        # [tokens, hidden_dim]
+        return embeddings.cpu().to(torch.float32).numpy()