feat: Add logprobs support to chat completions (#1311)

* Add logprobs return in ChatCompletionResponse

* Fix duplicate field

* Set default to false

* Simplify check

* Add server example

---------

Co-authored-by: Andrei Betlen <abetlen@gmail.com>

Author: windspirit95

llama_cpp/llama.py

@@ -1653,6 +1653,7 @@ def create_chat_completion(
             top_k=top_k,
             min_p=min_p,
             typical_p=typical_p,
+            logprobs=top_logprobs if logprobs else None,
             stream=stream,
             stop=stop,
             seed=seed,

llama_cpp/llama_chat_format.py

@@ -231,6 +231,7 @@ def _convert_text_completion_to_chat(
                     "role": "assistant",
                     "content": completion["choices"][0]["text"],
                 },
+                "logprobs": completion["choices"][0]["logprobs"],
                 "finish_reason": completion["choices"][0]["finish_reason"],
             }
         ],
@@ -254,6 +255,7 @@ def _convert_text_completion_chunks_to_chat(
                         "delta": {
                             "role": "assistant",
                         },
+                        "logprobs": None,
                         "finish_reason": None,
                     }
                 ],
@@ -273,6 +275,7 @@ def _convert_text_completion_chunks_to_chat(
                         if chunk["choices"][0]["finish_reason"] is None
                         else {}
                     ),
+                    "logprobs": chunk["choices"][0]["logprobs"],
                     "finish_reason": chunk["choices"][0]["finish_reason"],
                 }
             ],
@@ -487,6 +490,7 @@ def chat_completion_handler(
         temperature: float = 0.2,
         top_p: float = 0.95,
         top_k: int = 40,
+        logprobs: int = 0,
         min_p: float = 0.05,
         typical_p: float = 1.0,
         stream: bool = False,
@@ -576,6 +580,7 @@ def chat_completion_handler(
             top_k=top_k,
             min_p=min_p,
             typical_p=typical_p,
+            logprobs=logprobs,
             stream=stream,
             stop=stop,
             seed=seed,

llama_cpp/llama_types.py

@@ -84,6 +84,7 @@ class ChatCompletionFunction(TypedDict):
 class ChatCompletionResponseChoice(TypedDict):
     index: int
     message: "ChatCompletionResponseMessage"
+    logprobs: Optional[CompletionLogprobs]
     finish_reason: Optional[str]
 
 

llama_cpp/server/app.py

@@ -405,6 +405,18 @@ async def create_chat_completion(
                     }
                 },
             },
+            "logprobs": {
+                "summary": "Logprobs",
+                "value": {
+                    "model": "gpt-3.5-turbo",
+                    "messages": [
+                        {"role": "system", "content": "You are a helpful assistant."},
+                        {"role": "user", "content": "What is the capital of France?"},
+                    ],
+                    "logprobs": True,
+                    "top_logprobs": 10
+                },
+            },
         }
     ),
     llama_proxy: LlamaProxy = Depends(get_llama_proxy),

llama_cpp/server/types.py

@@ -130,7 +130,6 @@ class CreateCompletionRequest(BaseModel):
     presence_penalty: Optional[float] = presence_penalty_field
     frequency_penalty: Optional[float] = frequency_penalty_field
     logit_bias: Optional[Dict[str, float]] = Field(None)
-    logprobs: Optional[int] = Field(None)
     seed: Optional[int] = Field(None)
 
     # ignored or currently unsupported
@@ -209,6 +208,15 @@ class CreateChatCompletionRequest(BaseModel):
         default=None,
         description="The maximum number of tokens to generate. Defaults to inf",
     )
+    logprobs: Optional[bool] = Field(
+        default=False,
+        description="Whether to output the logprobs or not. Default is True"
+    )
+    top_logprobs: Optional[int] = Field(
+        default=None,
+        ge=0,
+        description="The number of logprobs to generate. If None, no logprobs are generated. logprobs need to set to True.",
+    )
     temperature: float = temperature_field
     top_p: float = top_p_field
     min_p: float = min_p_field