update api and support abort eval in webui
This commit is contained in:
hiyouga
parent
d4283bb6bf
commit
ed8f8be752
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@ -1,36 +1,29 @@
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import json
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import os
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from contextlib import asynccontextmanager
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from typing import Any, Dict, Sequence
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from pydantic import BaseModel
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from typing import Annotated, Optional
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from ..chat import ChatModel
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from ..data import Role as DataRole
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from ..extras.misc import torch_gc
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from ..extras.packages import is_fastapi_availble, is_starlette_available, is_uvicorn_available
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from .chat import (
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create_chat_completion_response,
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create_score_evaluation_response,
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create_stream_chat_completion_response,
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)
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from .protocol import (
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ChatCompletionMessage,
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ChatCompletionRequest,
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ChatCompletionResponse,
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ChatCompletionResponseChoice,
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ChatCompletionResponseStreamChoice,
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ChatCompletionResponseUsage,
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ChatCompletionStreamResponse,
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Finish,
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Function,
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FunctionCall,
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ModelCard,
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ModelList,
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Role,
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ScoreEvaluationRequest,
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ScoreEvaluationResponse,
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)
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if is_fastapi_availble():
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from fastapi import FastAPI, HTTPException, status
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from fastapi import Depends, FastAPI, HTTPException, status
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.security.http import HTTPAuthorizationCredentials, HTTPBearer
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if is_starlette_available():
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@ -47,23 +40,8 @@ async def lifespan(app: "FastAPI"): # collects GPU memory
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torch_gc()
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def dictify(data: "BaseModel") -> Dict[str, Any]:
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try: # pydantic v2
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return data.model_dump(exclude_unset=True)
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except AttributeError: # pydantic v1
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return data.dict(exclude_unset=True)
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def jsonify(data: "BaseModel") -> str:
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try: # pydantic v2
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return json.dumps(data.model_dump(exclude_unset=True), ensure_ascii=False)
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except AttributeError: # pydantic v1
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return data.json(exclude_unset=True, ensure_ascii=False)
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def create_app(chat_model: "ChatModel") -> "FastAPI":
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app = FastAPI(lifespan=lifespan)
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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@ -71,161 +49,58 @@ def create_app(chat_model: "ChatModel") -> "FastAPI":
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allow_methods=["*"],
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allow_headers=["*"],
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)
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api_key = os.environ.get("API_KEY", None)
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security = HTTPBearer(auto_error=False)
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role_mapping = {
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Role.USER: DataRole.USER.value,
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Role.ASSISTANT: DataRole.ASSISTANT.value,
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Role.SYSTEM: DataRole.SYSTEM.value,
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Role.FUNCTION: DataRole.FUNCTION.value,
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Role.TOOL: DataRole.OBSERVATION.value,
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}
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async def verify_api_key(auth: Annotated[Optional[HTTPAuthorizationCredentials], Depends(security)]):
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if api_key and (auth is None or auth.credentials != api_key):
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raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid API key.")
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@app.get("/v1/models", response_model=ModelList)
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@app.get(
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"/v1/models",
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response_model=ModelList,
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status_code=status.HTTP_200_OK,
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dependencies=[Depends(verify_api_key)],
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)
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async def list_models():
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model_card = ModelCard(id="gpt-3.5-turbo")
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return ModelList(data=[model_card])
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@app.post("/v1/chat/completions", response_model=ChatCompletionResponse, status_code=status.HTTP_200_OK)
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@app.post(
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"/v1/chat/completions",
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response_model=ChatCompletionResponse,
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status_code=status.HTTP_200_OK,
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dependencies=[Depends(verify_api_key)],
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)
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async def create_chat_completion(request: ChatCompletionRequest):
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if not chat_model.engine.can_generate:
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raise HTTPException(status_code=status.HTTP_405_METHOD_NOT_ALLOWED, detail="Not allowed")
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if len(request.messages) == 0:
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid length")
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if request.messages[0].role == Role.SYSTEM:
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system = request.messages.pop(0).content
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else:
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system = ""
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if len(request.messages) % 2 == 0:
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Only supports u/a/u/a/u...")
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input_messages = []
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for i, message in enumerate(request.messages):
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if i % 2 == 0 and message.role not in [Role.USER, Role.TOOL]:
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid role")
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elif i % 2 == 1 and message.role not in [Role.ASSISTANT, Role.FUNCTION]:
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid role")
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if message.role == Role.ASSISTANT and isinstance(message.tool_calls, list) and len(message.tool_calls):
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name = message.tool_calls[0].function.name
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arguments = message.tool_calls[0].function.arguments
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content = json.dumps({"name": name, "argument": arguments}, ensure_ascii=False)
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input_messages.append({"role": role_mapping[Role.FUNCTION], "content": content})
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else:
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input_messages.append({"role": role_mapping[message.role], "content": message.content})
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tool_list = request.tools
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if isinstance(tool_list, list) and len(tool_list):
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try:
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tools = json.dumps([dictify(tool.function) for tool in tool_list], ensure_ascii=False)
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except Exception:
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid tools")
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else:
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tools = ""
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if request.stream:
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if tools:
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Cannot stream function calls.")
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generate = stream_chat_completion(input_messages, system, tools, request)
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generate = create_stream_chat_completion_response(request, chat_model)
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return EventSourceResponse(generate, media_type="text/event-stream")
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else:
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return await create_chat_completion_response(request, chat_model)
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responses = await chat_model.achat(
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input_messages,
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system,
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tools,
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do_sample=request.do_sample,
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temperature=request.temperature,
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top_p=request.top_p,
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max_new_tokens=request.max_tokens,
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num_return_sequences=request.n,
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)
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prompt_length, response_length = 0, 0
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choices = []
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for i, response in enumerate(responses):
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if tools:
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result = chat_model.engine.template.format_tools.extract(response.response_text)
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else:
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result = response.response_text
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if isinstance(result, tuple):
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name, arguments = result
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function = Function(name=name, arguments=arguments)
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response_message = ChatCompletionMessage(
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role=Role.ASSISTANT, tool_calls=[FunctionCall(function=function)]
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)
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finish_reason = Finish.TOOL
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else:
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response_message = ChatCompletionMessage(role=Role.ASSISTANT, content=result)
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finish_reason = Finish.STOP if response.finish_reason == "stop" else Finish.LENGTH
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choices.append(
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ChatCompletionResponseChoice(index=i, message=response_message, finish_reason=finish_reason)
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)
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prompt_length = response.prompt_length
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response_length += response.response_length
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usage = ChatCompletionResponseUsage(
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prompt_tokens=prompt_length,
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completion_tokens=response_length,
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total_tokens=prompt_length + response_length,
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)
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return ChatCompletionResponse(model=request.model, choices=choices, usage=usage)
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async def stream_chat_completion(
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messages: Sequence[Dict[str, str]], system: str, tools: str, request: ChatCompletionRequest
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):
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choice_data = ChatCompletionResponseStreamChoice(
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index=0, delta=ChatCompletionMessage(role=Role.ASSISTANT, content=""), finish_reason=None
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)
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chunk = ChatCompletionStreamResponse(model=request.model, choices=[choice_data])
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yield jsonify(chunk)
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async for new_token in chat_model.astream_chat(
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messages,
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system,
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tools,
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do_sample=request.do_sample,
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temperature=request.temperature,
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top_p=request.top_p,
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max_new_tokens=request.max_tokens,
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):
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if len(new_token) == 0:
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continue
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choice_data = ChatCompletionResponseStreamChoice(
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index=0, delta=ChatCompletionMessage(content=new_token), finish_reason=None
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)
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chunk = ChatCompletionStreamResponse(model=request.model, choices=[choice_data])
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yield jsonify(chunk)
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choice_data = ChatCompletionResponseStreamChoice(
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index=0, delta=ChatCompletionMessage(), finish_reason=Finish.STOP
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)
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chunk = ChatCompletionStreamResponse(model=request.model, choices=[choice_data])
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yield jsonify(chunk)
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yield "[DONE]"
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@app.post("/v1/score/evaluation", response_model=ScoreEvaluationResponse, status_code=status.HTTP_200_OK)
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@app.post(
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"/v1/score/evaluation",
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response_model=ScoreEvaluationResponse,
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status_code=status.HTTP_200_OK,
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dependencies=[Depends(verify_api_key)],
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)
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async def create_score_evaluation(request: ScoreEvaluationRequest):
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if chat_model.engine.can_generate:
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raise HTTPException(status_code=status.HTTP_405_METHOD_NOT_ALLOWED, detail="Not allowed")
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if len(request.messages) == 0:
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid request")
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scores = await chat_model.aget_scores(request.messages, max_length=request.max_length)
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return ScoreEvaluationResponse(model=request.model, scores=scores)
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return await create_score_evaluation_response(request, chat_model)
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return app
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def run_api():
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def run_api() -> None:
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chat_model = ChatModel()
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app = create_app(chat_model)
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print("Visit http://localhost:{}/docs for API document.".format(os.environ.get("API_PORT", 8000)))
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uvicorn.run(app, host="0.0.0.0", port=int(os.environ.get("API_PORT", 8000)), workers=1)
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api_host = os.environ.get("API_HOST", "0.0.0.0")
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api_port = int(os.environ.get("API_PORT", "8000"))
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print("Visit http://localhost:{}/docs for API document.".format(api_port))
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uvicorn.run(app, host=api_host, port=api_port)
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@ -0,0 +1,176 @@
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import json
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import uuid
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from typing import TYPE_CHECKING, AsyncGenerator, Dict, List, Optional, Tuple
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from ..data import Role as DataRole
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from ..extras.packages import is_fastapi_availble
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from .common import dictify, jsonify
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from .protocol import (
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ChatCompletionMessage,
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ChatCompletionResponse,
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ChatCompletionResponseChoice,
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ChatCompletionResponseUsage,
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ChatCompletionStreamResponse,
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ChatCompletionStreamResponseChoice,
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Finish,
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Function,
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FunctionCall,
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Role,
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ScoreEvaluationResponse,
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)
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if is_fastapi_availble():
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from fastapi import HTTPException, status
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if TYPE_CHECKING:
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from ..chat import ChatModel
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from .protocol import ChatCompletionRequest, ScoreEvaluationRequest
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ROLE_MAPPING = {
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Role.USER: DataRole.USER.value,
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Role.ASSISTANT: DataRole.ASSISTANT.value,
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Role.SYSTEM: DataRole.SYSTEM.value,
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Role.FUNCTION: DataRole.FUNCTION.value,
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Role.TOOL: DataRole.OBSERVATION.value,
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}
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async def _process_request(request: "ChatCompletionRequest") -> Tuple[List[Dict[str, str]], str, str]:
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if len(request.messages) == 0:
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid length")
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if request.messages[0].role == Role.SYSTEM:
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system = request.messages.pop(0).content
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else:
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system = ""
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if len(request.messages) % 2 == 0:
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Only supports u/a/u/a/u...")
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input_messages = []
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for i, message in enumerate(request.messages):
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if i % 2 == 0 and message.role not in [Role.USER, Role.TOOL]:
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid role")
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elif i % 2 == 1 and message.role not in [Role.ASSISTANT, Role.FUNCTION]:
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid role")
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if message.role == Role.ASSISTANT and isinstance(message.tool_calls, list) and len(message.tool_calls):
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name = message.tool_calls[0].function.name
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arguments = message.tool_calls[0].function.arguments
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content = json.dumps({"name": name, "argument": arguments}, ensure_ascii=False)
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input_messages.append({"role": ROLE_MAPPING[Role.FUNCTION], "content": content})
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else:
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input_messages.append({"role": ROLE_MAPPING[message.role], "content": message.content})
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tool_list = request.tools
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if isinstance(tool_list, list) and len(tool_list):
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try:
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tools = json.dumps([dictify(tool.function) for tool in tool_list], ensure_ascii=False)
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except Exception:
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid tools")
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else:
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tools = ""
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return input_messages, system, tools
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async def create_chat_completion_response(
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request: "ChatCompletionRequest", chat_model: "ChatModel"
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) -> "ChatCompletionResponse":
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completion_id = "chatcmpl-{}".format(uuid.uuid4().hex)
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input_messages, system, tools = await _process_request(request)
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responses = await chat_model.achat(
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input_messages,
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system,
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tools,
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do_sample=request.do_sample,
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temperature=request.temperature,
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top_p=request.top_p,
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max_new_tokens=request.max_tokens,
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num_return_sequences=request.n,
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)
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prompt_length, response_length = 0, 0
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choices = []
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for i, response in enumerate(responses):
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if tools:
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result = chat_model.engine.template.format_tools.extract(response.response_text)
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else:
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result = response.response_text
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if isinstance(result, tuple):
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name, arguments = result
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function = Function(name=name, arguments=arguments)
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tool_call = FunctionCall(id="call_{}".format(uuid.uuid4().hex), function=function)
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response_message = ChatCompletionMessage(role=Role.ASSISTANT, tool_calls=[tool_call])
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finish_reason = Finish.TOOL
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else:
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response_message = ChatCompletionMessage(role=Role.ASSISTANT, content=result)
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finish_reason = Finish.STOP if response.finish_reason == "stop" else Finish.LENGTH
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choices.append(ChatCompletionResponseChoice(index=i, message=response_message, finish_reason=finish_reason))
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prompt_length = response.prompt_length
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response_length += response.response_length
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usage = ChatCompletionResponseUsage(
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prompt_tokens=prompt_length,
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completion_tokens=response_length,
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total_tokens=prompt_length + response_length,
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)
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return ChatCompletionResponse(id=completion_id, model=request.model, choices=choices, usage=usage)
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async def _create_stream_chat_completion_chunk(
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completion_id: str,
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model: str,
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delta: "ChatCompletionMessage",
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index: Optional[int] = 0,
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finish_reason: Optional["Finish"] = None,
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) -> str:
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choice_data = ChatCompletionStreamResponseChoice(index=index, delta=delta, finish_reason=finish_reason)
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chunk = ChatCompletionStreamResponse(id=completion_id, model=model, choices=[choice_data])
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return jsonify(chunk)
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async def create_stream_chat_completion_response(
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request: "ChatCompletionRequest", chat_model: "ChatModel"
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) -> AsyncGenerator[str, None]:
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completion_id = "chatcmpl-{}".format(uuid.uuid4().hex)
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input_messages, system, tools = await _process_request(request)
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if tools:
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Cannot stream function calls.")
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yield _create_stream_chat_completion_chunk(
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completion_id=completion_id, model=request.model, delta=ChatCompletionMessage(role=Role.ASSISTANT, content="")
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)
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async for new_token in chat_model.astream_chat(
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input_messages,
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system,
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tools,
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do_sample=request.do_sample,
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temperature=request.temperature,
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top_p=request.top_p,
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max_new_tokens=request.max_tokens,
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):
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yield _create_stream_chat_completion_chunk(
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completion_id=completion_id, model=request.model, delta=ChatCompletionMessage(content=new_token)
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)
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yield _create_stream_chat_completion_chunk(
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completion_id=completion_id, model=request.model, delta=ChatCompletionMessage(), finish_reason=Finish.STOP
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)
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yield "[DONE]"
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async def create_score_evaluation_response(
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request: "ScoreEvaluationRequest", chat_model: "ChatModel"
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) -> "ScoreEvaluationResponse":
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if len(request.messages) == 0:
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid request")
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scores = await chat_model.aget_scores(request.messages, max_length=request.max_length)
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return ScoreEvaluationResponse(model=request.model, scores=scores)
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|
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@ -0,0 +1,20 @@
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import json
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from typing import TYPE_CHECKING, Any, Dict
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if TYPE_CHECKING:
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from pydantic import BaseModel
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def dictify(data: "BaseModel") -> Dict[str, Any]:
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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)
|
||||
|
|
@ -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]
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
||||
|
|
|
|||
|
|
@ -118,6 +118,5 @@ class Evaluator:
|
|||
f.write(score_info)
|
||||
|
||||
|
||||
def run_eval():
|
||||
evaluator = Evaluator()
|
||||
evaluator.eval()
|
||||
def run_eval() -> None:
|
||||
Evaluator().eval()
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
|
|
|||
|
|
@ -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:
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
|
|
|||
|
|
@ -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()
|
||||
|
|
|
|||
|
|
@ -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.",
|
||||
|
|
|
|||
Loading…
Reference in New Issue