Add /v1/internal/logits endpoint (#4650)

extensions/openai/script.py

@@ -16,6 +16,7 @@ from sse_starlette import EventSourceResponse
 import extensions.openai.completions as OAIcompletions
 import extensions.openai.embeddings as OAIembeddings
 import extensions.openai.images as OAIimages
+import extensions.openai.logits as OAIlogits
 import extensions.openai.models as OAImodels
 import extensions.openai.moderations as OAImoderations
 from extensions.openai.errors import ServiceUnavailableError
@@ -38,6 +39,8 @@ from .typing import (
     EncodeRequest,
     EncodeResponse,
     LoadModelRequest,
+    LogitsRequest,
+    LogitsResponse,
     ModelInfoResponse,
     TokenCountResponse,
     to_dict
@@ -242,6 +245,16 @@ async def handle_token_count(request_data: EncodeRequest):
     return JSONResponse(response)
 
 
+@app.post("/v1/internal/logits", response_model=LogitsResponse, dependencies=check_key)
+async def handle_logits(request_data: LogitsRequest):
+    '''
+    Given a prompt, returns the top 50 most likely logits as a dict.
+    The keys are the tokens, and the values are the probabilities.
+    '''
+    response = OAIlogits._get_next_logits(to_dict(request_data))
+    return JSONResponse(response)
+
+
 @app.post("/v1/internal/stop-generation", dependencies=check_key)
 async def handle_stop_generation(request: Request):
     stop_everything_event()

extensions/openai/typing.py

@@ -126,15 +126,15 @@ class EncodeRequest(BaseModel):
     text: str
 
 
-class DecodeRequest(BaseModel):
-    tokens: List[int]
-
-
 class EncodeResponse(BaseModel):
     tokens: List[int]
     length: int
 
 
+class DecodeRequest(BaseModel):
+    tokens: List[int]
+
+
 class DecodeResponse(BaseModel):
     text: str
 
@@ -143,6 +143,24 @@ class TokenCountResponse(BaseModel):
     length: int
 
 
+class LogitsRequestParams(BaseModel):
+    prompt: str
+    use_samplers: bool = False
+    frequency_penalty: float | None = 0
+    max_tokens: int | None = 16
+    presence_penalty: float | None = 0
+    temperature: float | None = 1
+    top_p: float | None = 1
+
+
+class LogitsRequest(GenerationOptions, LogitsRequestParams):
+    pass
+
+
+class LogitsResponse(BaseModel):
+    logits: dict
+
+
 class ModelInfoResponse(BaseModel):
     model_name: str
     lora_names: List[str]