fix hiyouga#294

src/glmtuner/api/app.py

@@ -10,6 +10,8 @@
 from glmtuner.extras.misc import torch_gc
 from glmtuner.chat.stream_chat import ChatModel
 from glmtuner.api.protocol import (
+    Role,
+    Finish,
     ModelCard,
     ModelList,
     ChatMessage,
@@ -49,18 +51,18 @@ async def list_models():
 
     @app.post("/v1/chat/completions", response_model=ChatCompletionResponse)
     async def create_chat_completion(request: ChatCompletionRequest):
-        if request.messages[-1].role != "user":
+        if request.messages[-1].role != Role.USER:
             raise HTTPException(status_code=400, detail="Invalid request")
         query = request.messages[-1].content
 
         prev_messages = request.messages[:-1]
-        if len(prev_messages) > 0 and prev_messages[0].role == "system":
+        if len(prev_messages) > 0 and prev_messages[0].role == Role.SYSTEM:
             query = prev_messages.pop(0).content + query
 
         history = []
         if len(prev_messages) % 2 == 0:
             for i in range(0, len(prev_messages), 2):
-                if prev_messages[i].role == "user" and prev_messages[i+1].role == "assistant":
+                if prev_messages[i].role == Role.USER and prev_messages[i+1].role == Role.ASSISTANT:
                     history.append([prev_messages[i].content, prev_messages[i+1].content])
 
         if request.stream:
@@ -79,19 +81,19 @@ async def create_chat_completion(request: ChatCompletionRequest):
 
         choice_data = ChatCompletionResponseChoice(
             index=0,
-            message=ChatMessage(role="assistant", content=response),
-            finish_reason="stop"
+            message=ChatMessage(role=Role.ASSISTANT, content=response),
+            finish_reason=Finish.STOP
         )
 
-        return ChatCompletionResponse(model=request.model, choices=[choice_data], usage=usage, object="chat.completion")
+        return ChatCompletionResponse(model=request.model, choices=[choice_data], usage=usage)
 
     async def predict(query: str, history: List[Tuple[str, str]], request: ChatCompletionRequest):
         choice_data = ChatCompletionResponseStreamChoice(
             index=0,
-            delta=DeltaMessage(role="assistant"),
+            delta=DeltaMessage(role=Role.ASSISTANT),
             finish_reason=None
         )
-        chunk = ChatCompletionStreamResponse(model=request.model, choices=[choice_data], object="chat.completion.chunk")
+        chunk = ChatCompletionStreamResponse(model=request.model, choices=[choice_data])
         yield json.dumps(chunk, ensure_ascii=False)
 
         for new_text in chat_model.stream_chat(
@@ -105,15 +107,15 @@ async def predict(query: str, history: List[Tuple[str, str]], request: ChatCompl
                 delta=DeltaMessage(content=new_text),
                 finish_reason=None
             )
-            chunk = ChatCompletionStreamResponse(model=request.model, choices=[choice_data], object="chat.completion.chunk")
+            chunk = ChatCompletionStreamResponse(model=request.model, choices=[choice_data])
             yield json.dumps(chunk, ensure_ascii=False)
 
         choice_data = ChatCompletionResponseStreamChoice(
             index=0,
             delta=DeltaMessage(),
-            finish_reason="stop"
+            finish_reason=Finish.STOP
         )
-        chunk = ChatCompletionStreamResponse(model=request.model, choices=[choice_data], object="chat.completion.chunk")
+        chunk = ChatCompletionStreamResponse(model=request.model, choices=[choice_data])
         yield json.dumps(chunk, ensure_ascii=False)
         yield "[DONE]"
 

src/glmtuner/api/protocol.py

@@ -1,6 +1,18 @@
 import time
+from enum import Enum
 from pydantic import BaseModel, Field
-from typing import List, Literal, Optional
+from typing import List, Optional
+
+
+class Role(str, Enum):
+    USER = "user"
+    ASSISTANT = "assistant"
+    SYSTEM = "system"
+
+
+class Finish(str, Enum):
+    STOP = "stop"
+    LENGTH = "length"
 
 
 class ModelCard(BaseModel):
@@ -19,12 +31,12 @@ class ModelList(BaseModel):
 
 
 class ChatMessage(BaseModel):
-    role: Literal["user", "assistant", "system"]
+    role: Role
     content: str
 
 
 class DeltaMessage(BaseModel):
-    role: Optional[Literal["user", "assistant", "system"]] = None
+    role: Optional[Role] = None
     content: Optional[str] = None
 
 
@@ -41,13 +53,13 @@ class ChatCompletionRequest(BaseModel):
 class ChatCompletionResponseChoice(BaseModel):
     index: int
     message: ChatMessage
-    finish_reason: Literal["stop", "length"]
+    finish_reason: Finish
 
 
 class ChatCompletionResponseStreamChoice(BaseModel):
     index: int
     delta: DeltaMessage
-    finish_reason: Optional[Literal["stop", "length"]] = None
+    finish_reason: Optional[Finish] = None
 
 
 class ChatCompletionResponseUsage(BaseModel):
@@ -58,7 +70,7 @@ class ChatCompletionResponseUsage(BaseModel):
 
 class ChatCompletionResponse(BaseModel):
     id: Optional[str] = "chatcmpl-default"
-    object: Literal["chat.completion"]
+    object: Optional[str] = "chat.completion"
     created: Optional[int] = Field(default_factory=lambda: int(time.time()))
     model: str
     choices: List[ChatCompletionResponseChoice]
@@ -67,7 +79,7 @@ class ChatCompletionResponse(BaseModel):
 
 class ChatCompletionStreamResponse(BaseModel):
     id: Optional[str] = "chatcmpl-default"
-    object: Literal["chat.completion.chunk"]
+    object: Optional[str] = "chat.completion.chunk"
     created: Optional[int] = Field(default_factory=lambda: int(time.time()))
     model: str
     choices: List[ChatCompletionResponseStreamChoice]

src/glmtuner/extras/callbacks.py

@@ -47,6 +47,9 @@ def on_log(self, args: TrainingArguments, state: TrainerState, control: TrainerC
         r"""
         Event called after logging the last logs.
         """
+        if not state.is_world_process_zero:
+            return
+
         cur_time = time.time()
         cur_steps = state.log_history[-1].get("step")
         elapsed_time = cur_time - self.start_time