fix #3077

README.md

@@ -264,8 +264,8 @@ huggingface-cli login
 | ------------ | ------- | --------- |
 | python       | 3.8     | 3.10      |
 | torch        | 1.13.1  | 2.2.0     |
-| transformers | 4.37.2  | 4.39.1    |
-| datasets     | 2.14.3  | 2.17.1    |
+| transformers | 4.37.2  | 4.39.2    |
+| datasets     | 2.14.3  | 2.18.0    |
 | accelerate   | 0.27.2  | 0.28.0    |
 | peft         | 0.9.0   | 0.10.0    |
 | trl          | 0.8.1   | 0.8.1     |

README_zh.md

@@ -264,8 +264,8 @@ huggingface-cli login
 | ------------ | ------- | --------- |
 | python       | 3.8     | 3.10      |
 | torch        | 1.13.1  | 2.2.0     |
-| transformers | 4.37.2  | 4.39.1    |
-| datasets     | 2.14.3  | 2.17.1    |
+| transformers | 4.37.2  | 4.39.2    |
+| datasets     | 2.14.3  | 2.18.0    |
 | accelerate   | 0.27.2  | 0.28.0    |
 | peft         | 0.9.0   | 0.10.0    |
 | trl          | 0.8.1   | 0.8.1     |

src/llmtuner/api/app.py

@@ -108,12 +108,18 @@ def create_app(chat_model: "ChatModel") -> "FastAPI":
             elif i % 2 == 1 and message.role not in [Role.ASSISTANT, Role.FUNCTION]:
                 raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid role")
 
+            if message.role == Role.ASSISTANT and isinstance(message.tool_calls, list) and len(message.tool_calls):
+                name = message.tool_calls[0].function.name
+                arguments = message.tool_calls[0].function.arguments
+                content = json.dumps({"name": name, "argument": arguments}, ensure_ascii=False)
+                input_messages.append({"role": role_mapping[Role.FUNCTION], "content": content})
+            else:
                 input_messages.append({"role": role_mapping[message.role], "content": message.content})
 
         tool_list = request.tools
         if isinstance(tool_list, list) and len(tool_list):
             try:
-                tools = json.dumps([tool["function"] for tool in tool_list], ensure_ascii=False)
+                tools = json.dumps([dictify(tool.function) for tool in tool_list], ensure_ascii=False)
             except Exception:
                 raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid tools")
         else:

src/llmtuner/api/protocol.py

@@ -1,6 +1,6 @@
 import time
 from enum import Enum, unique
-from typing import List, Optional
+from typing import Any, Dict, List, Optional
 
 from pydantic import BaseModel, Field
 from typing_extensions import Literal
@@ -39,6 +39,17 @@ class Function(BaseModel):
     arguments: str
 
 
+class FunctionDefinition(BaseModel):
+    name: str
+    description: str
+    parameters: Dict[str, Any]
+
+
+class FunctionAvailable(BaseModel):
+    type: Literal["function", "code_interpreter"] = "function"
+    function: Optional[FunctionDefinition] = None
+
+
 class FunctionCall(BaseModel):
     id: Literal["call_default"] = "call_default"
     type: Literal["function"] = "function"
@@ -47,7 +58,8 @@ class FunctionCall(BaseModel):
 
 class ChatMessage(BaseModel):
     role: Role
-    content: str
+    content: Optional[str] = None
+    tool_calls: Optional[List[FunctionCall]] = None
 
 
 class ChatCompletionMessage(BaseModel):
@@ -59,7 +71,7 @@ class ChatCompletionMessage(BaseModel):
 class ChatCompletionRequest(BaseModel):
     model: str
     messages: List[ChatMessage]
-    tools: list = []
+    tools: Optional[List[FunctionAvailable]] = None
     do_sample: bool = True
     temperature: Optional[float] = None
     top_p: Optional[float] = None

src/llmtuner/extras/patches/llama_patch.py

@@ -193,6 +193,6 @@ def llama_flash_attn_forward(
 
 
 def apply_llama_patch() -> None:
-    require_version("transformers==4.39.1", "To fix: pip install transformers==4.39.1")
+    require_version("transformers==4.39.2", "To fix: pip install transformers==4.39.2")
     LlamaAttention.forward = llama_torch_attn_forward
     LlamaFlashAttention2.forward = llama_flash_attn_forward

src/llmtuner/model/patcher.py

@@ -331,7 +331,7 @@ def patch_model(
     ):
         gen_config.do_sample = True
 
-    if model_args.resize_vocab:
+    if is_trainable and model_args.resize_vocab:
         _resize_embedding_layer(model, tokenizer)
 
     if is_trainable:

tests/test_toolcall.py

@@ -15,7 +15,7 @@ def calculate_gpa(grades: Sequence[str], hours: Sequence[int]) -> float:
     for grade, hour in zip(grades, hours):
         total_score += grade_to_score[grade] * hour
         total_hour += hour
-    return total_score / total_hour
+    return round(total_score / total_hour, 2)
 
 
 def main():
@@ -45,16 +45,19 @@ def main():
     messages = []
     messages.append({"role": "user", "content": "My grades are A, A, B, and C. The credit hours are 3, 4, 3, and 2."})
     result = client.chat.completions.create(messages=messages, model="test", tools=tools)
+    if result.choices[0].message.tool_calls is None:
+        raise ValueError("Cannot retrieve function call from the response.")
+
+    messages.append(result.choices[0].message)
     tool_call = result.choices[0].message.tool_calls[0].function
+    print(tool_call)
+    # Function(arguments='{"grades": ["A", "A", "B", "C"], "hours": [3, 4, 3, 2]}', name='calculate_gpa')
     name, arguments = tool_call.name, json.loads(tool_call.arguments)
-    messages.append(
-        {"role": "function", "content": json.dumps({"name": name, "argument": arguments}, ensure_ascii=False)}
-    )
     tool_result = tool_map[name](**arguments)
     messages.append({"role": "tool", "content": json.dumps({"gpa": tool_result}, ensure_ascii=False)})
     result = client.chat.completions.create(messages=messages, model="test", tools=tools)
     print(result.choices[0].message.content)
-    # Based on your grades and credit hours, your calculated Grade Point Average (GPA) is 3.4166666666666665.
+    # Based on the grades and credit hours you provided, your Grade Point Average (GPA) is 3.42.
 
 
 if __name__ == "__main__":