Colqwen omni

src/vidore_benchmark/__init__.py

@@ -9,6 +9,7 @@
     ColPaliRetriever,
     ColQwen2_5_Retriever,
     ColQwen2Retriever,
+    ColQwenOmniRetriever,
     DSEQwen2Retriever,
     DummyVisionRetriever,
     HFEndpointRetriever,

src/vidore_benchmark/retrievers/__init__.py

@@ -8,6 +8,7 @@
 from .colpali_retriever import ColPaliRetriever
 from .colqwen2_5_retriever import ColQwen2_5_Retriever
 from .colqwen2_retriever import ColQwen2Retriever
+from .colqwenomni_retriever import ColQwenOmniRetriever
 from .dse_qwen2_retriever import DSEQwen2Retriever
 from .dummy_vision_retriever import DummyVisionRetriever
 from .hf_api_retriever import HFEndpointRetriever

src/vidore_benchmark/retrievers/colqwen2_5_retriever.py

@@ -21,7 +21,7 @@
 
 
 @register_vision_retriever("colqwen2_5")
-class ColQwen2_5_Retriever(BaseVisionRetriever): #noqa: N801
+class ColQwen2_5_Retriever(BaseVisionRetriever):  # noqa: N801
     """
     ColQwen2.5 retriever that implements the model from "ColPali: Efficient Document Retrieval
     with Vision Language Models".

src/vidore_benchmark/retrievers/colqwenomni_retriever.py

@@ -0,0 +1,117 @@
+from __future__ import annotations
+
+import logging
+from typing import List, Optional, Union
+
+import torch
+from dotenv import load_dotenv
+from PIL import Image
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+from transformers.utils.import_utils import is_flash_attn_2_available
+
+from vidore_benchmark.retrievers.base_vision_retriever import BaseVisionRetriever
+from vidore_benchmark.retrievers.registry_utils import register_vision_retriever
+from vidore_benchmark.utils.data_utils import ListDataset
+from vidore_benchmark.utils.torch_utils import get_torch_device
+
+logger = logging.getLogger(__name__)
+
+load_dotenv(override=True)
+
+
+@register_vision_retriever("colqwen-omni")
+class ColQwenOmniRetriever(BaseVisionRetriever):
+    """
+    ColQwenOmni retriever that implements the model from "ColPali: Efficient Document Retrieval
+    with Vision Language Models". Based on the ColQwen2.5 Omni model.
+    """
+
+    def __init__(
+        self,
+        pretrained_model_name_or_path: str = "vidore/colqwen-omni-v0.1",
+        device: str = "auto",
+        num_workers: int = 0,
+        **kwargs,
+    ):
+        super().__init__(use_visual_embedding=True)
+
+        try:
+            from colpali_engine.models import ColQwen2_5Omni, ColQwen2_5OmniProcessor
+        except ImportError:
+            raise ImportError(
+                'Install the missing dependencies with `pip install "vidore-benchmark[colpali-engine]"` '
+                "to use ColQwenOmniRetriever."
+            )
+
+        self.device = get_torch_device(device)
+        self.num_workers = num_workers
+
+        # Load the model and LORA adapter
+        self.model = ColQwen2_5Omni.from_pretrained(
+            pretrained_model_name_or_path,
+            torch_dtype=torch.bfloat16,
+            device_map=self.device,
+            attn_implementation="flash_attention_2" if is_flash_attn_2_available() else None,
+        ).eval()
+
+        # Load the processor
+        self.processor = ColQwen2_5OmniProcessor.from_pretrained(pretrained_model_name_or_path)
+
+    def process_images(self, images: List[Image.Image], **kwargs):
+        return self.processor.process_images(images=images).to(self.device)
+
+    def process_queries(self, queries: List[str], **kwargs):
+        return self.processor.process_queries(queries=queries).to(self.device)
+
+    def forward_queries(self, queries: List[str], batch_size: int, **kwargs) -> List[torch.Tensor]:
+        dataloader = DataLoader(
+            dataset=ListDataset[str](queries),
+            batch_size=batch_size,
+            shuffle=False,
+            collate_fn=self.process_queries,
+            num_workers=self.num_workers,
+        )
+
+        query_embeddings: List[torch.Tensor] = []
+
+        with torch.no_grad():
+            for batch_query in tqdm(dataloader, desc="Forward pass queries...", leave=False):
+                embeddings_query = self.model(**batch_query).to("cpu")
+                query_embeddings.extend(list(torch.unbind(embeddings_query)))
+
+        return query_embeddings
+
+    def forward_passages(self, passages: List[Image.Image], batch_size: int, **kwargs) -> List[torch.Tensor]:
+        dataloader = DataLoader(
+            dataset=ListDataset[Image.Image](passages),
+            batch_size=batch_size,
+            shuffle=False,
+            collate_fn=self.process_images,
+            num_workers=self.num_workers,
+        )
+
+        passage_embeddings: List[torch.Tensor] = []
+
+        with torch.no_grad():
+            for batch_doc in tqdm(dataloader, desc="Forward pass documents...", leave=False):
+                embeddings_doc = self.model(**batch_doc).to("cpu")
+                passage_embeddings.extend(list(torch.unbind(embeddings_doc)))
+
+        return passage_embeddings
+
+    def get_scores(
+        self,
+        query_embeddings: Union[torch.Tensor, List[torch.Tensor]],
+        passage_embeddings: Union[torch.Tensor, List[torch.Tensor]],
+        batch_size: Optional[int] = 128,
+    ) -> torch.Tensor:
+        if batch_size is None:
+            raise ValueError("`batch_size` must be provided for ColQwenRetriever's scoring")
+        scores = self.processor.score(
+            query_embeddings,
+            passage_embeddings,
+            batch_size=batch_size,
+            device="cpu",
+        )
+        return scores