fix: missing logprobs in response, incorrect response type for functionary, minor type issues. Closes #1328 Closes #1314

llama_cpp/llama_chat_format.py

@@ -6,7 +6,7 @@ import ctypes
 import dataclasses
 import random
 import string
-from typing import Any, Dict, Iterator, List, Literal, Optional, Tuple, Union, Protocol
+from typing import Any, Dict, Iterator, List, Literal, Optional, Tuple, Union, Protocol, cast
 
 import jinja2
 
@@ -338,6 +338,7 @@ def _convert_completion_to_chat_function(
                             }
                         ],
                     },
+                    "logprobs": None,
                     "finish_reason": "tool_calls",
                 }
             ],
@@ -1191,7 +1192,6 @@ def format_mistral_instruct(
         elif (
             message["role"] == "assistant"
             and message["content"] is not None
-            and isinstance(message["content"], str)
         ):
             prompt += " [/INST]" + message["content"] + eos
     prompt += " [/INST]"
@@ -1263,7 +1263,7 @@ def format_gemma(
     **kwargs: Any,
 ) -> ChatFormatterResponse:
     system_message = _get_system_message(messages)
-    if system_message is not None and system_message != "":
+    if system_message != "":
         logger.debug(
             "`role='system'` messages are not allowed on Google's Gemma models."
         )
@@ -1628,6 +1628,7 @@ def functionary_chat_handler(
                         }
                     ],
                 },
+                "logprobs": None,
                 "finish_reason": "tool_calls",
             }
         ],
@@ -1909,14 +1910,14 @@ def functionary_v1_v2_chat_handler(
         return grammar
 
     def create_completion(stop):
-        completion: llama_types.Completion = llama.create_completion(
+        completion = cast(llama_types.Completion, llama.create_completion(
             prompt=prompt,
             temperature=temperature,
             top_p=top_p,
             top_k=top_k,
             min_p=min_p,
             typical_p=typical_p,
-            stream=stream,
+            stream=False,
             stop=stop,
             max_tokens=max_tokens,
             presence_penalty=presence_penalty,
@@ -1929,7 +1930,7 @@ def functionary_v1_v2_chat_handler(
             model=model,
             logits_processor=logits_processor,
             grammar=grammar,
-        )
+        ))
 
         return completion
 
@@ -2050,7 +2051,7 @@ def functionary_v1_v2_chat_handler(
     assert "usage" in completion
     assert len(function_calls) == len(function_bodies)
 
-    tool_calls = []
+    tool_calls: List[llama_types.ChatCompletionMessageToolCall] = []
     for function_call, function_body in zip(function_calls, function_bodies):
         tool_calls.append(
             {
@@ -2070,6 +2071,12 @@ def functionary_v1_v2_chat_handler(
         )
 
     # TODO: support stream mode
+    function_call_dict: Union[Dict[str, str], Dict[Literal["function_call"], llama_types.ChatCompletionRequestAssistantMessageFunctionCall]] = { 
+        "function_call": {
+            "name": tool_calls[0]["function"]["name"],
+            "arguments": tool_calls[0]["function"]["arguments"],
+        }
+    } if len(tool_calls) == 1 else {}
     return llama_types.CreateChatCompletionResponse(
         id="chat" + completion["id"],
         object="chat.completion",
@@ -2078,14 +2085,12 @@ def functionary_v1_v2_chat_handler(
         choices=[
             {
                 "index": 0,
+                "logprobs": None,
                 "message": {
                     "role": "assistant",
                     "content": None if content == "" else content,
-                    "function_call": {
-                        "name": tool_calls[0]["function"]["name"],
-                        "arguments": tool_calls[0]["function"]["arguments"],
-                    } if len(tool_calls) > 0 else None,
-                    "tool_calls": tool_calls if len(tool_calls) > 0 else None,
+                    "tool_calls": tool_calls,
+                    **function_call_dict,
                 },
                 "finish_reason": "tool_calls" if len(tool_calls) > 0 else "stop",
             }
@@ -2565,8 +2570,8 @@ def chatml_function_calling(
     tool_name = text[len("functions.") :]
     tool = next((tool for tool in tools if tool["function"]["name"] == tool_name), None)
     if not stream:
-        completions = []
-        completions_tool_name = []
+        completions: List[llama_types.CreateCompletionResponse] = []
+        completions_tool_name: List[str] = []
         while tool is not None:
             prompt += f"functions.{tool_name}:\n"
             try:
@@ -2603,6 +2608,7 @@ def chatml_function_calling(
                 logits_processor=logits_processor,
                 grammar=grammar,
             )
+            completion_or_chunks = cast(llama_types.CreateCompletionResponse, completion_or_chunks)
             completions.append(completion_or_chunks)
             completions_tool_name.append(tool_name)
             prompt += completion_or_chunks["choices"][0]["text"]
@@ -2631,6 +2637,7 @@ def chatml_function_calling(
                     follow_up_gbnf_tool_grammar, verbose=llama.verbose
                 ),
             )
+            response = cast(llama_types.CreateCompletionResponse, response)
 
             tool_name = response["choices"][0]["text"][len("functions.") :]
             tool = next(
@@ -2638,7 +2645,7 @@ def chatml_function_calling(
             )
 
         # Merge completions
-        function_call = { 
+        function_call_dict: Union[Dict[str, str], Dict[Literal["function_call"], llama_types.ChatCompletionRequestAssistantMessageFunctionCall]] = { 
             "function_call": {
                 "name": tool_name,
                 "arguments": completions[0]["choices"][0]["text"],
@@ -2653,6 +2660,7 @@ def chatml_function_calling(
                 {
                     "finish_reason": "tool_calls",
                     "index": 0,
+                    "logprobs": None,
                     "message": {
                         "role": "assistant",
                         "content": None,
@@ -2673,20 +2681,22 @@ def chatml_function_calling(
                                 zip(completions_tool_name, completions)
                             )
                         ],
-                        **function_call
+                        **function_call_dict
                     },
                 }
             ],
             "usage": {
                 "completion_tokens": sum(
-                    completion["usage"]["completion_tokens"]
+                    completion["usage"]["completion_tokens"] if "usage" in completion else 0
                     for completion in completions
                 ),
                 "prompt_tokens": sum(
-                    completion["usage"]["prompt_tokens"] for completion in completions
+                    completion["usage"]["prompt_tokens"] if "usage" in completion else 0
+                    for completion in completions
                 ),
                 "total_tokens": sum(
-                    completion["usage"]["total_tokens"] for completion in completions
+                    completion["usage"]["total_tokens"] if "usage" in completion else 0
+                    for completion in completions
                 ),
             },
         }