diff --git a/src/llmtuner/api/app.py b/src/llmtuner/api/app.py index 36918d1b..375ee61f 100644 --- a/src/llmtuner/api/app.py +++ b/src/llmtuner/api/app.py @@ -1,36 +1,29 @@ -import json import os from contextlib import asynccontextmanager -from typing import Any, Dict, Sequence - -from pydantic import BaseModel +from typing import Annotated, Optional 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 = { - 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 verify_api_key(auth: Annotated[Optional[HTTPAuthorizationCredentials], Depends(security)]): + if api_key and (auth is None or auth.credentials != api_key): + raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid API key.") - @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: - raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Cannot stream function calls.") - - generate = stream_chat_completion(input_messages, system, tools, request) + generate = create_stream_chat_completion_response(request, chat_model) return EventSourceResponse(generate, media_type="text/event-stream") + else: + return await create_chat_completion_response(request, chat_model) - 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) - 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) + @app.post( + "/v1/score/evaluation", + response_model=ScoreEvaluationResponse, + status_code=status.HTTP_200_OK, + dependencies=[Depends(verify_api_key)], + ) 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: - 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 await create_score_evaluation_response(request, chat_model) 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))) - uvicorn.run(app, host="0.0.0.0", port=int(os.environ.get("API_PORT", 8000)), workers=1) + api_host = os.environ.get("API_HOST", "0.0.0.0") + 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) diff --git a/src/llmtuner/api/chat.py b/src/llmtuner/api/chat.py new file mode 100644 index 00000000..c9c00f16 --- /dev/null +++ b/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) diff --git a/src/llmtuner/api/common.py b/src/llmtuner/api/common.py new file mode 100644 index 00000000..5ad9a071 --- /dev/null +++ b/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) diff --git a/src/llmtuner/api/protocol.py b/src/llmtuner/api/protocol.py index ece2132b..ae6e2e9b 100644 --- a/src/llmtuner/api/protocol.py +++ b/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] diff --git a/src/llmtuner/chat/chat_model.py b/src/llmtuner/chat/chat_model.py index 97ae87d7..281ef0c1 100644 --- a/src/llmtuner/chat/chat_model.py +++ b/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 diff --git a/src/llmtuner/eval/evaluator.py b/src/llmtuner/eval/evaluator.py index 4ea134c6..192f4815 100644 --- a/src/llmtuner/eval/evaluator.py +++ b/src/llmtuner/eval/evaluator.py @@ -118,6 +118,5 @@ class Evaluator: f.write(score_info) -def run_eval(): - evaluator = Evaluator() - evaluator.eval() +def run_eval() -> None: + Evaluator().eval() diff --git a/src/llmtuner/extras/callbacks.py b/src/llmtuner/extras/callbacks.py index a07c7059..a142928a 100644 --- a/src/llmtuner/extras/callbacks.py +++ b/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) diff --git a/src/llmtuner/train/tuner.py b/src/llmtuner/train/tuner.py index 6822ffb5..e1a997c1 100644 --- a/src/llmtuner/train/tuner.py +++ b/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: diff --git a/src/llmtuner/webui/components/eval.py b/src/llmtuner/webui/components/eval.py index 222f9314..60e22bb7 100644 --- a/src/llmtuner/webui/components/eval.py +++ b/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) diff --git a/src/llmtuner/webui/interface.py b/src/llmtuner/webui/interface.py index 459802f2..b293db90 100644 --- a/src/llmtuner/webui/interface.py +++ b/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() diff --git a/src/llmtuner/webui/locales.py b/src/llmtuner/webui/locales.py index 1c474f34..5bf925b7 100644 --- a/src/llmtuner/webui/locales.py +++ b/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.",