wip: start reviewing

fastembed/rerank/cross_encoder/onnx_text_cross_encoder.py

@@ -1,6 +1,15 @@
-from typing import Any, Iterable, Optional, Sequence, Type
+from typing import Iterable, Any, Sequence, Optional, Type
+
 
 import numpy as np
+
+from fastembed.common import OnnxProvider
+from fastembed.rerank.cross_encoder.onnx_text_model import (
+    OnnxCrossEncoderModel,
+    TextRerankerWorker,
+)
+from fastembed.rerank.cross_encoder.text_cross_encoder_base import TextCrossEncoderBase
+from fastembed.common.utils import define_cache_dir
 from loguru import logger
 
 from fastembed.common import OnnxProvider
@@ -12,6 +21,7 @@
 )
 from fastembed.rerank.cross_encoder.text_cross_encoder_base import TextCrossEncoderBase
 
+
 supported_onnx_models = [
     {
         "model": "Xenova/ms-marco-MiniLM-L-6-v2",

fastembed/rerank/cross_encoder/onnx_text_model.py

@@ -1,7 +1,7 @@
 import os
 from multiprocessing import get_all_start_methods
 from pathlib import Path
-from typing import Any, Iterable, Optional, Sequence
+from typing import Sequence, Optional, Iterable, Any, Type
 
 import numpy as np
 from tokenizers import Encoding
@@ -18,9 +18,13 @@
 from fastembed.parallel_processor import ParallelWorkerPool
 
 
-class OnnxCrossEncoderModel(OnnxModel):
+class OnnxCrossEncoderModel(OnnxModel[float]):
     ONNX_OUTPUT_NAMES: Optional[list[str]] = None
 
+    @classmethod
+    def _get_worker_class(cls) -> Type["TextRerankerWorker"]:
+        raise NotImplementedError("Subclasses must implement this method")
+
     def _load_onnx_model(
         self,
         model_dir: Path,
@@ -40,10 +44,8 @@ def _load_onnx_model(
         )
         self.tokenizer, _ = load_tokenizer(model_dir=model_dir)
 
-    def tokenize(
-        self, pairs: Iterable[tuple[str, str]], **kwargs: Any
-    ) -> list[Encoding]:
-        return self.tokenizer.encode_batch([pair for pair in pairs], **kwargs)
+    def tokenize(self, pairs: list[tuple[str, str]], **kwargs: Any) -> list[Encoding]:
+        return self.tokenizer.encode_batch(pairs, **kwargs)
 
     def _build_onnx_input(self, tokenized_input):
         inputs = {
@@ -59,10 +61,8 @@ def _build_onnx_input(self, tokenized_input):
             )
         return inputs
 
-    def onnx_embed(
-        self, query: str, documents: list[str], **kwargs: Any
-    ) -> OnnxOutputContext:
-        pairs = ((query, doc) for doc in documents)
+    def onnx_embed(self, query: str, documents: list[str], **kwargs: Any) -> OnnxOutputContext:
+        pairs = [(query, doc) for doc in documents]
         return self.onnx_embed_pairs(pairs, **kwargs)
 
     def onnx_embed_pairs(self, pairs: list[tuple[str, str]], **kwargs: Any):
@@ -72,15 +72,15 @@ def onnx_embed_pairs(self, pairs: list[tuple[str, str]], **kwargs: Any):
         outputs = self.model.run(self.ONNX_OUTPUT_NAMES, onnx_input)
         relevant_output = outputs[0]
         scores = relevant_output[:, 0]
-        return OnnxOutputContext(model_output=scores.tolist())
+        return OnnxOutputContext(model_output=scores)
 
     def _rerank_documents(
         self, query: str, documents: Iterable[str], batch_size: int, **kwargs: Any
     ) -> Iterable[float]:
         if not hasattr(self, "model") or self.model is None:
             self.load_onnx_model()
         for batch in iter_batch(documents, batch_size):
-            yield from self.onnx_embed(query, batch, **kwargs).model_output
+            yield from self._post_process_onnx_output(self.onnx_embed(query, batch, **kwargs))
 
     def _rerank_pairs(
         self,
@@ -96,9 +96,6 @@ def _rerank_pairs(
     ) -> Iterable[float]:
         is_small = False
 
-        if not hasattr(self, "model") or self.model is None:
-            self.load_onnx_model()
-
         if isinstance(pairs, tuple):
             pairs = [pairs]
             is_small = True
@@ -111,7 +108,7 @@ def _rerank_pairs(
             if not hasattr(self, "model") or self.model is None:
                 self.load_onnx_model()
             for batch in iter_batch(pairs, batch_size):
-                yield from self.onnx_embed_pairs(batch, **kwargs).model_output
+                yield from self._post_process_onnx_output(self.onnx_embed_pairs(batch, **kwargs))
         else:
             if parallel == 0:
                 parallel = os.cpu_count()
@@ -138,7 +135,7 @@ def _rerank_pairs(
                     self.onnx_embed_pairs(batch, **kwargs)
                 )
 
-    def _post_process_onnx_output(self, output: OnnxOutputContext) -> Iterable[T]:
+    def _post_process_onnx_output(self, output: OnnxOutputContext) -> Iterable[float]:
         return output.model_output
 
     def _preprocess_onnx_input(

fastembed/rerank/cross_encoder/text_cross_encoder_base.py

@@ -1,4 +1,4 @@
-from typing import Any, Iterable, Optional
+from typing import Iterable, Optional, Any
 
 from fastembed.common.model_management import ModelManagement
 
@@ -23,7 +23,7 @@ def rerank(
         batch_size: int = 64,
         **kwargs,
     ) -> Iterable[float]:
-        """Reranks a list of documents given a query.
+        """Rerank a list of documents given a query.
 
         Args:
             query (str): The query to rerank the documents.