update api and support abort eval in webui

Former-commit-id: ed8f8be752ba2dcbaa6e8b1dc0a2e2821db1a5b8

src/llmtuner/api/app.py

@@ -1,36 +1,29 @@
-import json
 import os
 from contextlib import asynccontextmanager
-from typing import Any, Dict, Sequence
+from typing import Annotated, Optional
-
-from pydantic import BaseModel
 
 from ..chat import ChatModel
-from ..data import Role as DataRole
 from ..extras.misc import torch_gc
 from ..extras.packages import is_fastapi_availble, is_starlette_available, is_uvicorn_available
+from .chat import (
+    create_chat_completion_response,
+    create_score_evaluation_response,
+    create_stream_chat_completion_response,
+)
 from .protocol import (
-    ChatCompletionMessage,
     ChatCompletionRequest,
     ChatCompletionResponse,
-    ChatCompletionResponseChoice,
-    ChatCompletionResponseStreamChoice,
-    ChatCompletionResponseUsage,
-    ChatCompletionStreamResponse,
-    Finish,
-    Function,
-    FunctionCall,
     ModelCard,
     ModelList,
-    Role,
     ScoreEvaluationRequest,
     ScoreEvaluationResponse,
 )
 
 
 if is_fastapi_availble():
-    from fastapi import FastAPI, HTTPException, status
+    from fastapi import Depends, FastAPI, HTTPException, status
     from fastapi.middleware.cors import CORSMiddleware
+    from fastapi.security.http import HTTPAuthorizationCredentials, HTTPBearer
 
 
 if is_starlette_available():
@@ -47,23 +40,8 @@ async def lifespan(app: "FastAPI"):  # collects GPU memory
     torch_gc()
 
 
-def dictify(data: "BaseModel") -> Dict[str, Any]:
-    try:  # pydantic v2
-        return data.model_dump(exclude_unset=True)
-    except AttributeError:  # pydantic v1
-        return data.dict(exclude_unset=True)
-
-
-def jsonify(data: "BaseModel") -> str:
-    try:  # pydantic v2
-        return json.dumps(data.model_dump(exclude_unset=True), ensure_ascii=False)
-    except AttributeError:  # pydantic v1
-        return data.json(exclude_unset=True, ensure_ascii=False)
-
-
 def create_app(chat_model: "ChatModel") -> "FastAPI":
     app = FastAPI(lifespan=lifespan)
-
     app.add_middleware(
         CORSMiddleware,
         allow_origins=["*"],
@@ -71,161 +49,58 @@ def create_app(chat_model: "ChatModel") -> "FastAPI":
         allow_methods=["*"],
         allow_headers=["*"],
     )
+    api_key = os.environ.get("API_KEY", None)
+    security = HTTPBearer(auto_error=False)
 
-    role_mapping = {
+    async def verify_api_key(auth: Annotated[Optional[HTTPAuthorizationCredentials], Depends(security)]):
-        Role.USER: DataRole.USER.value,
+        if api_key and (auth is None or auth.credentials != api_key):
-        Role.ASSISTANT: DataRole.ASSISTANT.value,
+            raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid API key.")
-        Role.SYSTEM: DataRole.SYSTEM.value,
-        Role.FUNCTION: DataRole.FUNCTION.value,
-        Role.TOOL: DataRole.OBSERVATION.value,
-    }
 
-    @app.get("/v1/models", response_model=ModelList)
+    @app.get(
+        "/v1/models",
+        response_model=ModelList,
+        status_code=status.HTTP_200_OK,
+        dependencies=[Depends(verify_api_key)],
+    )
     async def list_models():
         model_card = ModelCard(id="gpt-3.5-turbo")
         return ModelList(data=[model_card])
 
-    @app.post("/v1/chat/completions", response_model=ChatCompletionResponse, status_code=status.HTTP_200_OK)
+    @app.post(
+        "/v1/chat/completions",
+        response_model=ChatCompletionResponse,
+        status_code=status.HTTP_200_OK,
+        dependencies=[Depends(verify_api_key)],
+    )
     async def create_chat_completion(request: ChatCompletionRequest):
         if not chat_model.engine.can_generate:
             raise HTTPException(status_code=status.HTTP_405_METHOD_NOT_ALLOWED, detail="Not allowed")
 
-        if len(request.messages) == 0:
-            raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid length")
-
-        if request.messages[0].role == Role.SYSTEM:
-            system = request.messages.pop(0).content
-        else:
-            system = ""
-
-        if len(request.messages) % 2 == 0:
-            raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Only supports u/a/u/a/u...")
-
-        input_messages = []
-        for i, message in enumerate(request.messages):
-            if i % 2 == 0 and message.role not in [Role.USER, Role.TOOL]:
-                raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid role")
-            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([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:
-            tools = ""
-
         if request.stream:
-            if tools:
+            generate = create_stream_chat_completion_response(request, chat_model)
-                raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Cannot stream function calls.")
-
-            generate = stream_chat_completion(input_messages, system, tools, request)
             return EventSourceResponse(generate, media_type="text/event-stream")
+        else:
+            return await create_chat_completion_response(request, chat_model)
 
-        responses = await chat_model.achat(
+    @app.post(
-            input_messages,
+        "/v1/score/evaluation",
-            system,
+        response_model=ScoreEvaluationResponse,
-            tools,
+        status_code=status.HTTP_200_OK,
-            do_sample=request.do_sample,
+        dependencies=[Depends(verify_api_key)],
-            temperature=request.temperature,
+    )
-            top_p=request.top_p,
-            max_new_tokens=request.max_tokens,
-            num_return_sequences=request.n,
-        )
-
-        prompt_length, response_length = 0, 0
-        choices = []
-        for i, response in enumerate(responses):
-            if tools:
-                result = chat_model.engine.template.format_tools.extract(response.response_text)
-            else:
-                result = response.response_text
-
-            if isinstance(result, tuple):
-                name, arguments = result
-                function = Function(name=name, arguments=arguments)
-                response_message = ChatCompletionMessage(
-                    role=Role.ASSISTANT, tool_calls=[FunctionCall(function=function)]
-                )
-                finish_reason = Finish.TOOL
-            else:
-                response_message = ChatCompletionMessage(role=Role.ASSISTANT, content=result)
-                finish_reason = Finish.STOP if response.finish_reason == "stop" else Finish.LENGTH
-
-            choices.append(
-                ChatCompletionResponseChoice(index=i, message=response_message, finish_reason=finish_reason)
-            )
-            prompt_length = response.prompt_length
-            response_length += response.response_length
-
-        usage = ChatCompletionResponseUsage(
-            prompt_tokens=prompt_length,
-            completion_tokens=response_length,
-            total_tokens=prompt_length + response_length,
-        )
-
-        return ChatCompletionResponse(model=request.model, choices=choices, usage=usage)
-
-    async def stream_chat_completion(
-        messages: Sequence[Dict[str, str]], system: str, tools: str, request: ChatCompletionRequest
-    ):
-        choice_data = ChatCompletionResponseStreamChoice(
-            index=0, delta=ChatCompletionMessage(role=Role.ASSISTANT, content=""), finish_reason=None
-        )
-        chunk = ChatCompletionStreamResponse(model=request.model, choices=[choice_data])
-        yield jsonify(chunk)
-
-        async for new_token in chat_model.astream_chat(
-            messages,
-            system,
-            tools,
-            do_sample=request.do_sample,
-            temperature=request.temperature,
-            top_p=request.top_p,
-            max_new_tokens=request.max_tokens,
-        ):
-            if len(new_token) == 0:
-                continue
-
-            choice_data = ChatCompletionResponseStreamChoice(
-                index=0, delta=ChatCompletionMessage(content=new_token), finish_reason=None
-            )
-            chunk = ChatCompletionStreamResponse(model=request.model, choices=[choice_data])
-            yield jsonify(chunk)
-
-        choice_data = ChatCompletionResponseStreamChoice(
-            index=0, delta=ChatCompletionMessage(), finish_reason=Finish.STOP
-        )
-        chunk = ChatCompletionStreamResponse(model=request.model, choices=[choice_data])
-        yield jsonify(chunk)
-        yield "[DONE]"
-
-    @app.post("/v1/score/evaluation", response_model=ScoreEvaluationResponse, status_code=status.HTTP_200_OK)
     async def create_score_evaluation(request: ScoreEvaluationRequest):
         if chat_model.engine.can_generate:
             raise HTTPException(status_code=status.HTTP_405_METHOD_NOT_ALLOWED, detail="Not allowed")
 
-        if len(request.messages) == 0:
+        return await create_score_evaluation_response(request, chat_model)
-            raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid request")
-
-        scores = await chat_model.aget_scores(request.messages, max_length=request.max_length)
-        return ScoreEvaluationResponse(model=request.model, scores=scores)
 
     return app
 
 
-def run_api():
+def run_api() -> None:
     chat_model = ChatModel()
     app = create_app(chat_model)
-    print("Visit http://localhost:{}/docs for API document.".format(os.environ.get("API_PORT", 8000)))
+    api_host = os.environ.get("API_HOST", "0.0.0.0")
-    uvicorn.run(app, host="0.0.0.0", port=int(os.environ.get("API_PORT", 8000)), workers=1)
+    api_port = int(os.environ.get("API_PORT", "8000"))
+    print("Visit http://localhost:{}/docs for API document.".format(api_port))
+    uvicorn.run(app, host=api_host, port=api_port)

src/llmtuner/api/chat.py

@@ -0,0 +1,176 @@
+import json
+import uuid
+from typing import TYPE_CHECKING, AsyncGenerator, Dict, List, Optional, Tuple
+
+from ..data import Role as DataRole
+from ..extras.packages import is_fastapi_availble
+from .common import dictify, jsonify
+from .protocol import (
+    ChatCompletionMessage,
+    ChatCompletionResponse,
+    ChatCompletionResponseChoice,
+    ChatCompletionResponseUsage,
+    ChatCompletionStreamResponse,
+    ChatCompletionStreamResponseChoice,
+    Finish,
+    Function,
+    FunctionCall,
+    Role,
+    ScoreEvaluationResponse,
+)
+
+
+if is_fastapi_availble():
+    from fastapi import HTTPException, status
+
+
+if TYPE_CHECKING:
+    from ..chat import ChatModel
+    from .protocol import ChatCompletionRequest, ScoreEvaluationRequest
+
+
+ROLE_MAPPING = {
+    Role.USER: DataRole.USER.value,
+    Role.ASSISTANT: DataRole.ASSISTANT.value,
+    Role.SYSTEM: DataRole.SYSTEM.value,
+    Role.FUNCTION: DataRole.FUNCTION.value,
+    Role.TOOL: DataRole.OBSERVATION.value,
+}
+
+
+async def _process_request(request: "ChatCompletionRequest") -> Tuple[List[Dict[str, str]], str, str]:
+    if len(request.messages) == 0:
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid length")
+
+    if request.messages[0].role == Role.SYSTEM:
+        system = request.messages.pop(0).content
+    else:
+        system = ""
+
+    if len(request.messages) % 2 == 0:
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Only supports u/a/u/a/u...")
+
+    input_messages = []
+    for i, message in enumerate(request.messages):
+        if i % 2 == 0 and message.role not in [Role.USER, Role.TOOL]:
+            raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid role")
+        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([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:
+        tools = ""
+
+    return input_messages, system, tools
+
+
+async def create_chat_completion_response(
+    request: "ChatCompletionRequest", chat_model: "ChatModel"
+) -> "ChatCompletionResponse":
+    completion_id = "chatcmpl-{}".format(uuid.uuid4().hex)
+    input_messages, system, tools = await _process_request(request)
+    responses = await chat_model.achat(
+        input_messages,
+        system,
+        tools,
+        do_sample=request.do_sample,
+        temperature=request.temperature,
+        top_p=request.top_p,
+        max_new_tokens=request.max_tokens,
+        num_return_sequences=request.n,
+    )
+
+    prompt_length, response_length = 0, 0
+    choices = []
+    for i, response in enumerate(responses):
+        if tools:
+            result = chat_model.engine.template.format_tools.extract(response.response_text)
+        else:
+            result = response.response_text
+
+        if isinstance(result, tuple):
+            name, arguments = result
+            function = Function(name=name, arguments=arguments)
+            tool_call = FunctionCall(id="call_{}".format(uuid.uuid4().hex), function=function)
+            response_message = ChatCompletionMessage(role=Role.ASSISTANT, tool_calls=[tool_call])
+            finish_reason = Finish.TOOL
+        else:
+            response_message = ChatCompletionMessage(role=Role.ASSISTANT, content=result)
+            finish_reason = Finish.STOP if response.finish_reason == "stop" else Finish.LENGTH
+
+        choices.append(ChatCompletionResponseChoice(index=i, message=response_message, finish_reason=finish_reason))
+        prompt_length = response.prompt_length
+        response_length += response.response_length
+
+    usage = ChatCompletionResponseUsage(
+        prompt_tokens=prompt_length,
+        completion_tokens=response_length,
+        total_tokens=prompt_length + response_length,
+    )
+
+    return ChatCompletionResponse(id=completion_id, model=request.model, choices=choices, usage=usage)
+
+
+async def _create_stream_chat_completion_chunk(
+    completion_id: str,
+    model: str,
+    delta: "ChatCompletionMessage",
+    index: Optional[int] = 0,
+    finish_reason: Optional["Finish"] = None,
+) -> str:
+    choice_data = ChatCompletionStreamResponseChoice(index=index, delta=delta, finish_reason=finish_reason)
+    chunk = ChatCompletionStreamResponse(id=completion_id, model=model, choices=[choice_data])
+    return jsonify(chunk)
+
+
+async def create_stream_chat_completion_response(
+    request: "ChatCompletionRequest", chat_model: "ChatModel"
+) -> AsyncGenerator[str, None]:
+    completion_id = "chatcmpl-{}".format(uuid.uuid4().hex)
+    input_messages, system, tools = await _process_request(request)
+    if tools:
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Cannot stream function calls.")
+
+    yield _create_stream_chat_completion_chunk(
+        completion_id=completion_id, model=request.model, delta=ChatCompletionMessage(role=Role.ASSISTANT, content="")
+    )
+    async for new_token in chat_model.astream_chat(
+        input_messages,
+        system,
+        tools,
+        do_sample=request.do_sample,
+        temperature=request.temperature,
+        top_p=request.top_p,
+        max_new_tokens=request.max_tokens,
+    ):
+        yield _create_stream_chat_completion_chunk(
+            completion_id=completion_id, model=request.model, delta=ChatCompletionMessage(content=new_token)
+        )
+
+    yield _create_stream_chat_completion_chunk(
+        completion_id=completion_id, model=request.model, delta=ChatCompletionMessage(), finish_reason=Finish.STOP
+    )
+    yield "[DONE]"
+
+
+async def create_score_evaluation_response(
+    request: "ScoreEvaluationRequest", chat_model: "ChatModel"
+) -> "ScoreEvaluationResponse":
+    if len(request.messages) == 0:
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid request")
+
+    scores = await chat_model.aget_scores(request.messages, max_length=request.max_length)
+    return ScoreEvaluationResponse(model=request.model, scores=scores)

src/llmtuner/api/common.py

@@ -0,0 +1,20 @@
+import json
+from typing import TYPE_CHECKING, Any, Dict
+
+
+if TYPE_CHECKING:
+    from pydantic import BaseModel
+
+
+def dictify(data: "BaseModel") -> Dict[str, Any]:
+    try:  # pydantic v2
+        return data.model_dump(exclude_unset=True)
+    except AttributeError:  # pydantic v1
+        return data.dict(exclude_unset=True)
+
+
+def jsonify(data: "BaseModel") -> str:
+    try:  # pydantic v2
+        return json.dumps(data.model_dump(exclude_unset=True), ensure_ascii=False)
+    except AttributeError:  # pydantic v1
+        return data.json(exclude_unset=True, ensure_ascii=False)

src/llmtuner/api/protocol.py

@@ -51,7 +51,7 @@ class FunctionAvailable(BaseModel):
 
 
 class FunctionCall(BaseModel):
-    id: Literal["call_default"] = "call_default"
+    id: str
     type: Literal["function"] = "function"
     function: Function
 
@@ -86,7 +86,7 @@ class ChatCompletionResponseChoice(BaseModel):
     finish_reason: Finish
 
 
-class ChatCompletionResponseStreamChoice(BaseModel):
+class ChatCompletionStreamResponseChoice(BaseModel):
     index: int
     delta: ChatCompletionMessage
     finish_reason: Optional[Finish] = None
@@ -99,7 +99,7 @@ class ChatCompletionResponseUsage(BaseModel):
 
 
 class ChatCompletionResponse(BaseModel):
-    id: Literal["chatcmpl-default"] = "chatcmpl-default"
+    id: str
     object: Literal["chat.completion"] = "chat.completion"
     created: int = Field(default_factory=lambda: int(time.time()))
     model: str
@@ -108,11 +108,11 @@ class ChatCompletionResponse(BaseModel):
 
 
 class ChatCompletionStreamResponse(BaseModel):
-    id: Literal["chatcmpl-default"] = "chatcmpl-default"
+    id: str
     object: Literal["chat.completion.chunk"] = "chat.completion.chunk"
     created: int = Field(default_factory=lambda: int(time.time()))
     model: str
-    choices: List[ChatCompletionResponseStreamChoice]
+    choices: List[ChatCompletionStreamResponseChoice]
 
 
 class ScoreEvaluationRequest(BaseModel):
@@ -122,7 +122,7 @@ class ScoreEvaluationRequest(BaseModel):
 
 
 class ScoreEvaluationResponse(BaseModel):
-    id: Literal["scoreeval-default"] = "scoreeval-default"
+    id: str
     object: Literal["score.evaluation"] = "score.evaluation"
     model: str
     scores: List[float]

src/llmtuner/chat/chat_model.py

@@ -98,7 +98,7 @@ class ChatModel:
         return await self.engine.get_scores(batch_input, **input_kwargs)
 
 
-def run_chat():
+def run_chat() -> None:
     try:
         import platform
 

src/llmtuner/eval/evaluator.py

@@ -118,6 +118,5 @@ class Evaluator:
                 f.write(score_info)
 
 
-def run_eval():
+def run_eval() -> None:
-    evaluator = Evaluator()
+    Evaluator().eval()
-    evaluator.eval()

src/llmtuner/extras/callbacks.py

@@ -2,6 +2,7 @@ import json
 import logging
 import os
 import signal
+import sys
 import time
 from concurrent.futures import ThreadPoolExecutor
 from datetime import timedelta
@@ -91,6 +92,18 @@ class LogCallback(TrainerCallback):
             self.thread_pool.shutdown(wait=True)
             self.thread_pool = None
 
+    def on_init_end(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
+        r"""
+        Event called at the end of the initialization of the `Trainer`.
+        """
+        if (
+            args.should_save
+            and os.path.exists(os.path.join(args.output_dir, TRAINER_LOG))
+            and args.overwrite_output_dir
+        ):
+            logger.warning("Previous trainer log in this folder will be deleted.")
+            os.remove(os.path.join(args.output_dir, TRAINER_LOG))
+
     def on_train_begin(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
         r"""
         Event called at the beginning of training.
@@ -100,14 +113,6 @@ class LogCallback(TrainerCallback):
             self._reset(max_steps=state.max_steps)
             self._create_thread_pool(output_dir=args.output_dir)
 
-        if (
-            args.should_save
-            and os.path.exists(os.path.join(args.output_dir, TRAINER_LOG))
-            and args.overwrite_output_dir
-        ):
-            logger.warning("Previous trainer log in this folder will be deleted.")
-            os.remove(os.path.join(args.output_dir, TRAINER_LOG))
-
     def on_train_end(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
         r"""
         Event called at the end of training.
@@ -126,9 +131,6 @@ class LogCallback(TrainerCallback):
         r"""
         Event called at the end of a training step.
         """
-        if args.should_save:
-            self._timing(cur_steps=state.global_step)
-
         if self.aborted:
             control.should_epoch_stop = True
             control.should_training_stop = True
@@ -152,6 +154,7 @@ class LogCallback(TrainerCallback):
         if not args.should_save:
             return
 
+        self._timing(cur_steps=state.global_step)
         logs = dict(
             current_steps=self.cur_steps,
             total_steps=self.max_steps,
@@ -183,8 +186,17 @@ class LogCallback(TrainerCallback):
         r"""
         Event called after a prediction step.
         """
+        if self.do_train:
+            return
+
+        if self.aborted:
+            sys.exit(0)
+
+        if not args.should_save:
+            return
+
         eval_dataloader = kwargs.pop("eval_dataloader", None)
-        if args.should_save and has_length(eval_dataloader) and not self.do_train:
+        if has_length(eval_dataloader):
             if self.max_steps == 0:
                 self._reset(max_steps=len(eval_dataloader))
                 self._create_thread_pool(output_dir=args.output_dir)

src/llmtuner/train/tuner.py

@@ -23,7 +23,7 @@ if TYPE_CHECKING:
 logger = get_logger(__name__)
 
 
-def run_exp(args: Optional[Dict[str, Any]] = None, callbacks: List["TrainerCallback"] = []):
+def run_exp(args: Optional[Dict[str, Any]] = None, callbacks: List["TrainerCallback"] = []) -> None:
     model_args, data_args, training_args, finetuning_args, generating_args = get_train_args(args)
     callbacks.append(LogCallback(training_args.output_dir))
 
@@ -43,7 +43,7 @@ def run_exp(args: Optional[Dict[str, Any]] = None, callbacks: List["TrainerCallb
         raise ValueError("Unknown task.")
 
 
-def export_model(args: Optional[Dict[str, Any]] = None):
+def export_model(args: Optional[Dict[str, Any]] = None) -> None:
     model_args, data_args, finetuning_args, _ = get_infer_args(args)
 
     if model_args.export_dir is None:

src/llmtuner/webui/components/eval.py

@@ -48,6 +48,7 @@ def create_eval_tab(engine: "Engine") -> Dict[str, "Component"]:
     with gr.Row():
         cmd_preview_btn = gr.Button()
         start_btn = gr.Button(variant="primary")
+        stop_btn = gr.Button(variant="stop")
 
     with gr.Row():
         resume_btn = gr.Checkbox(visible=False, interactive=False)
@@ -61,6 +62,7 @@ def create_eval_tab(engine: "Engine") -> Dict[str, "Component"]:
         dict(
             cmd_preview_btn=cmd_preview_btn,
             start_btn=start_btn,
+            stop_btn=stop_btn,
             resume_btn=resume_btn,
             progress_bar=progress_bar,
             output_box=output_box,
@@ -69,6 +71,7 @@ def create_eval_tab(engine: "Engine") -> Dict[str, "Component"]:
 
     cmd_preview_btn.click(engine.runner.preview_eval, input_elems, output_elems, concurrency_limit=None)
     start_btn.click(engine.runner.run_eval, input_elems, output_elems)
+    stop_btn.click(engine.runner.set_abort)
     resume_btn.change(engine.runner.monitor, outputs=output_elems, concurrency_limit=None)
 
     dataset_dir.change(list_dataset, [dataset_dir], [dataset], queue=False)

src/llmtuner/webui/interface.py

@@ -68,9 +68,9 @@ def create_web_demo() -> gr.Blocks:
     return demo
 
 
-def run_web_ui():
+def run_web_ui() -> None:
     create_ui().queue().launch()
 
 
-def run_web_demo():
+def run_web_demo() -> None:
     create_web_demo().queue().launch()

src/llmtuner/webui/locales.py

@@ -1449,7 +1449,7 @@ ALERTS = {
     "info_aborting": {
         "en": "Aborted, wait for terminating...",
         "ru": "Прервано, ожидание завершения...",
-        "zh": "训练中断，正在等待线程结束……",
+        "zh": "训练中断，正在等待进程结束……",
     },
     "info_aborted": {
         "en": "Ready.",