Decoder-only Scoring API

python/sglang/srt/entrypoints/engine.py

@@ -472,6 +472,79 @@ def save_remote_model(self, **kwargs):
     def save_sharded_model(self, **kwargs):
         self.collective_rpc("save_sharded_model", **kwargs)
 
+    def score(
+        self,
+        query: Optional[Union[str, List[int]]] = None,
+        items: Optional[Union[str, List[str], List[List[int]]]] = None,
+        label_token_ids: Optional[List[int]] = None,
+        apply_softmax: bool = False,
+        item_first: bool = False,
+    ) -> List[List[float]]:
+        """
+        Score the probability of specified token IDs appearing after the given (query + item) pair. For example:
+        query = "<|user|>Is the following city the capital of France? "
+        items = ["Paris <|assistant|>", "London <|assistant|>", "Berlin <|assistant|>"]
+        label_token_ids = [2332, 1223] # Token IDs for "Yes" and "No"
+        item_first = False
+
+        This would pass the following prompts to the model:
+        "<|user|>Is the following city the capital of France? Paris <|assistant|>"
+        "<|user|>Is the following city the capital of France? London <|assistant|>"
+        "<|user|>Is the following city the capital of France? Berlin <|assistant|>"
+        The api would then return the probabilities of the model producing "Yes" and "No" as the next token.
+        The output would look like:
+        [[0.9, 0.1], [0.2, 0.8], [0.1, 0.9]]
+
+
+        Args:
+            query: The query text or pre-tokenized query token IDs. Must be provided.
+            items: The item text(s) or pre-tokenized item token IDs. Must be provided.
+            label_token_ids: List of token IDs to compute probabilities for. If None, no token probabilities will be computed.
+            apply_softmax: Whether to normalize probabilities using softmax.
+            item_first: If True, prepend items to query. Otherwise append items to query.
+
+        Returns:
+            List of dictionaries mapping token IDs to their probabilities for each item.
+            Each dictionary in the list corresponds to one item input.
+
+        Raises:
+            ValueError: If query is not provided, or if items is not provided,
+                      or if token IDs are out of vocabulary, or if logprobs are not available for the specified tokens.
+        """
+        loop = asyncio.get_event_loop()
+        return loop.run_until_complete(
+            self.tokenizer_manager.score_request(
+                query=query,
+                items=items,
+                label_token_ids=label_token_ids,
+                apply_softmax=apply_softmax,
+                item_first=item_first,
+                request=None,
+            )
+        )
+
+    async def async_score(
+        self,
+        query: Optional[Union[str, List[int]]] = None,
+        items: Optional[Union[str, List[str], List[List[int]]]] = None,
+        label_token_ids: Optional[List[int]] = None,
+        apply_softmax: bool = False,
+        item_first: bool = False,
+    ) -> List[List[float]]:
+        """
+        Asynchronous version of score method.
+
+        See score() for detailed documentation.
+        """
+        return await self.tokenizer_manager.score_request(
+            query=query,
+            items=items,
+            label_token_ids=label_token_ids,
+            apply_softmax=apply_softmax,
+            item_first=item_first,
+            request=None,
+        )
+
 
 def _set_envs_and_config(server_args: ServerArgs):
     # Set global environments

python/sglang/srt/entrypoints/http_server.py

@@ -82,6 +82,7 @@
     v1_retrieve_batch,
     v1_retrieve_file,
     v1_retrieve_file_content,
+    v1_score,
 )
 from sglang.srt.openai_api.protocol import ModelCard, ModelList
 from sglang.srt.reasoning_parser import ReasoningParser
@@ -720,6 +721,12 @@ async def vertex_generate(vertex_req: VertexGenerateReqInput, raw_request: Reque
     return ORJSONResponse({"predictions": ret})
 
 
+@app.post("/v1/score")
+async def v1_score_request(raw_request: Request):
+    """Endpoint for the decoder-only scoring API. See Engine.score() for detailed documentation."""
+    return await v1_score(_global_state.tokenizer_manager, raw_request)
+
+
 def _create_error_response(e):
     return ORJSONResponse(
         {"error": {"message": str(e)}}, status_code=HTTPStatus.BAD_REQUEST

python/sglang/srt/managers/tokenizer_manager.py

@@ -18,6 +18,7 @@
 import dataclasses
 import json
 import logging
+import math
 import os
 import pickle
 import signal
@@ -42,6 +43,7 @@
 )
 
 import fastapi
+import torch
 import uvloop
 import zmq
 import zmq.asyncio
@@ -1433,6 +1435,100 @@ def _handle_update_weights_from_disk_req_output(self, recv_obj):
             if len(self.model_update_tmp) == self.server_args.dp_size:
                 self.model_update_result.set_result(self.model_update_tmp)
 
+    async def score_request(
+        self,
+        query: Optional[Union[str, List[int]]] = None,
+        items: Optional[Union[str, List[str], List[List[int]]]] = None,
+        label_token_ids: Optional[List[int]] = None,
+        apply_softmax: bool = False,
+        item_first: bool = False,
+        request: Optional[Any] = None,
+    ) -> List[List[float]]:
+        """
+        See Engine.score() for more details.
+        """
+        if label_token_ids is None:
+            raise ValueError("label_token_ids must be provided")
+
+        if self.tokenizer is not None:
+            vocab_size = self.tokenizer.vocab_size
+            for token_id in label_token_ids:
+                if token_id >= vocab_size:
+                    raise ValueError(
+                        f"Token ID {token_id} is out of vocabulary (vocab size: {vocab_size})"
+                    )
+
+        # Handle string or tokenized query/items
+        if isinstance(query, str) and (
+            isinstance(items, str)
+            or (isinstance(items, list) and (not items or isinstance(items[0], str)))
+        ):
+            # Both query and items are text
+            items_list = [items] if isinstance(items, str) else items
+            if item_first:
+                prompts = [f"{item}{query}" for item in items_list]
+            else:
+                prompts = [f"{query}{item}" for item in items_list]
+            batch_request = GenerateReqInput(
+                text=prompts,
+                return_logprob=True,
+                token_ids_logprob=label_token_ids,
+                stream=False,
+                sampling_params={"max_new_tokens": 1},
+            )
+        elif (
+            isinstance(query, list)
+            and isinstance(items, list)
+            and items
+            and isinstance(items[0], list)
+        ):
+            # Both query and items are token IDs
+            if item_first:
+                input_ids_list = [item + query for item in items]
+            else:
+                input_ids_list = [query + item for item in items]
+            batch_request = GenerateReqInput(
+                input_ids=input_ids_list,
+                return_logprob=True,
+                token_ids_logprob=label_token_ids,
+                stream=False,
+                sampling_params={"max_new_tokens": 1},
+            )
+        else:
+            raise ValueError(
+                "Invalid combination of query/items types for score_request."
+            )
+
+        results = await self.generate_request(batch_request, request).__anext__()
+        scores = []
+
+        for result in results:
+            # Get logprobs for each token
+            logprobs = {}
+            for logprob, token_id, _ in result["meta_info"].get(
+                "output_token_ids_logprobs", []
+            )[0]:
+                if token_id in label_token_ids:
+                    logprobs[token_id] = logprob
+
+            # Get scores in order of label_token_ids
+            score_list = [
+                logprobs.get(token_id, float("-inf")) for token_id in label_token_ids
+            ]
+
+            # Apply softmax to logprobs if needed
+            if apply_softmax:
+                score_list = torch.softmax(torch.tensor(score_list), dim=0).tolist()
+            else:
+                # Convert logprobs to probabilities if not using softmax
+                score_list = [
+                    math.exp(x) if x != float("-inf") else 0.0 for x in score_list
+                ]
+
+            scores.append(score_list)
+
+        return scores
+
 
 async def print_exception_wrapper(func):
     """

python/sglang/srt/openai_api/adapter.py

@@ -69,6 +69,8 @@
     FunctionResponse,
     LogProbs,
     MultimodalEmbeddingInput,
+    ScoringRequest,
+    ScoringResponse,
     ToolCall,
     TopLogprob,
     UsageInfo,
@@ -1928,3 +1930,31 @@ def append_top_logprobs(top_logprobs):
         append_top_logprobs(output_top_logprobs)
 
     return ret_logprobs
+
+
+async def v1_score(tokenizer_manager, raw_request):
+    try:
+        # Parse request
+        request_data = await raw_request.json()
+        request = ScoringRequest(**request_data)
+
+        # Use tokenizer_manager's score_request method directly
+        scores = await tokenizer_manager.score_request(
+            query=request.query,
+            items=request.items,
+            label_token_ids=request.label_token_ids,
+            apply_softmax=request.apply_softmax,
+            item_first=request.item_first,
+            request=request,
+        )
+
+        # Create response with just the scores, without usage info
+        response = ScoringResponse(
+            scores=scores,
+            model=request.model,
+        )
+        return response
+
+    except Exception as e:
+        logger.error(f"Error in v1_score: {str(e)}")
+        return create_error_response(str(e))

python/sglang/srt/openai_api/protocol.py

@@ -489,3 +489,27 @@ class EmbeddingResponse(BaseModel):
     model: str
     object: str = "list"
     usage: Optional[UsageInfo] = None
+
+
+class ScoringRequest(BaseModel):
+    query: Optional[Union[str, List[int]]] = (
+        None  # Query text or pre-tokenized token IDs
+    )
+    items: Optional[Union[str, List[str], List[List[int]]]] = (
+        None  # Item text(s) or pre-tokenized token IDs
+    )
+    label_token_ids: Optional[List[int]] = (
+        None  # Token IDs to compute probabilities for
+    )
+    apply_softmax: bool = False
+    item_first: bool = False
+    model: str
+
+
+class ScoringResponse(BaseModel):
+    scores: List[
+        List[float]
+    ]  # List of lists of probabilities, each in the order of label_token_ids
+    model: str
+    usage: Optional[UsageInfo] = None
+    object: str = "scoring"