mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-12-15 11:20:35 +08:00
modity code structure
This commit is contained in:
hiyouga
218
src/api_demo.py
218
src/api_demo.py
@@ -4,225 +4,11 @@
|
||||
# Visit http://localhost:8000/docs for document.
|
||||
|
||||
|
||||
import time
|
||||
import torch
|
||||
import uvicorn
|
||||
from threading import Thread
|
||||
from pydantic import BaseModel, Field
|
||||
from fastapi import FastAPI, HTTPException
|
||||
from fastapi.middleware.cors import CORSMiddleware
|
||||
from contextlib import asynccontextmanager
|
||||
from transformers import TextIteratorStreamer
|
||||
from sse_starlette import EventSourceResponse
|
||||
from typing import Any, Dict, List, Literal, Optional
|
||||
|
||||
from utils import (
|
||||
Template,
|
||||
load_pretrained,
|
||||
prepare_infer_args,
|
||||
get_logits_processor
|
||||
)
|
||||
|
||||
|
||||
@asynccontextmanager
|
||||
async def lifespan(app: FastAPI): # collects GPU memory
|
||||
yield
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
torch.cuda.ipc_collect()
|
||||
|
||||
|
||||
app = FastAPI(lifespan=lifespan)
|
||||
|
||||
|
||||
app.add_middleware(
|
||||
CORSMiddleware,
|
||||
allow_origins=["*"],
|
||||
allow_credentials=True,
|
||||
allow_methods=["*"],
|
||||
allow_headers=["*"],
|
||||
)
|
||||
|
||||
|
||||
class ModelCard(BaseModel):
|
||||
id: str
|
||||
object: Optional[str] = "model"
|
||||
created: Optional[int] = Field(default_factory=lambda: int(time.time()))
|
||||
owned_by: Optional[str] = "owner"
|
||||
root: Optional[str] = None
|
||||
parent: Optional[str] = None
|
||||
permission: Optional[list] = []
|
||||
|
||||
|
||||
class ModelList(BaseModel):
|
||||
object: Optional[str] = "list"
|
||||
data: Optional[List[ModelCard]] = []
|
||||
|
||||
|
||||
class ChatMessage(BaseModel):
|
||||
role: Literal["user", "assistant", "system"]
|
||||
content: str
|
||||
|
||||
|
||||
class DeltaMessage(BaseModel):
|
||||
role: Optional[Literal["user", "assistant", "system"]] = None
|
||||
content: Optional[str] = None
|
||||
|
||||
|
||||
class ChatCompletionRequest(BaseModel):
|
||||
model: str
|
||||
messages: List[ChatMessage]
|
||||
temperature: Optional[float] = None
|
||||
top_p: Optional[float] = None
|
||||
n: Optional[int] = 1
|
||||
max_tokens: Optional[int] = None
|
||||
stream: Optional[bool] = False
|
||||
|
||||
|
||||
class ChatCompletionResponseChoice(BaseModel):
|
||||
index: int
|
||||
message: ChatMessage
|
||||
finish_reason: Literal["stop", "length"]
|
||||
|
||||
|
||||
class ChatCompletionResponseStreamChoice(BaseModel):
|
||||
index: int
|
||||
delta: DeltaMessage
|
||||
finish_reason: Optional[Literal["stop", "length"]] = None
|
||||
|
||||
|
||||
class ChatCompletionResponseUsage(BaseModel):
|
||||
prompt_tokens: int
|
||||
completion_tokens: int
|
||||
total_tokens: int
|
||||
|
||||
|
||||
class ChatCompletionResponse(BaseModel):
|
||||
id: Optional[str] = "chatcmpl-default"
|
||||
object: Literal["chat.completion"]
|
||||
created: Optional[int] = Field(default_factory=lambda: int(time.time()))
|
||||
model: str
|
||||
choices: List[ChatCompletionResponseChoice]
|
||||
usage: ChatCompletionResponseUsage
|
||||
|
||||
|
||||
class ChatCompletionStreamResponse(BaseModel):
|
||||
id: Optional[str] = "chatcmpl-default"
|
||||
object: Literal["chat.completion.chunk"]
|
||||
created: Optional[int] = Field(default_factory=lambda: int(time.time()))
|
||||
model: str
|
||||
choices: List[ChatCompletionResponseStreamChoice]
|
||||
|
||||
|
||||
@app.get("/v1/models", response_model=ModelList)
|
||||
async def list_models():
|
||||
global model_args
|
||||
model_card = ModelCard(id="gpt-3.5-turbo")
|
||||
return ModelList(data=[model_card])
|
||||
|
||||
|
||||
@app.post("/v1/chat/completions", response_model=ChatCompletionResponse)
|
||||
async def create_chat_completion(request: ChatCompletionRequest):
|
||||
global model, tokenizer, source_prefix, generating_args
|
||||
|
||||
if request.messages[-1].role != "user":
|
||||
raise HTTPException(status_code=400, detail="Invalid request")
|
||||
query = request.messages[-1].content
|
||||
|
||||
prev_messages = request.messages[:-1]
|
||||
if len(prev_messages) > 0 and prev_messages[0].role == "system":
|
||||
prefix = prev_messages.pop(0).content
|
||||
else:
|
||||
prefix = source_prefix
|
||||
|
||||
history = []
|
||||
if len(prev_messages) % 2 == 0:
|
||||
for i in range(0, len(prev_messages), 2):
|
||||
if prev_messages[i].role == "user" and prev_messages[i+1].role == "assistant":
|
||||
history.append([prev_messages[i].content, prev_messages[i+1].content])
|
||||
|
||||
inputs = tokenizer([prompt_template.get_prompt(query, history, prefix)], return_tensors="pt")
|
||||
inputs = inputs.to(model.device)
|
||||
|
||||
gen_kwargs = generating_args.to_dict()
|
||||
gen_kwargs.update({
|
||||
"input_ids": inputs["input_ids"],
|
||||
"temperature": request.temperature if request.temperature else gen_kwargs["temperature"],
|
||||
"top_p": request.top_p if request.top_p else gen_kwargs["top_p"],
|
||||
"logits_processor": get_logits_processor()
|
||||
})
|
||||
|
||||
if request.max_tokens:
|
||||
gen_kwargs.pop("max_length", None)
|
||||
gen_kwargs["max_new_tokens"] = request.max_tokens
|
||||
|
||||
if request.stream:
|
||||
generate = predict(gen_kwargs, request.model)
|
||||
return EventSourceResponse(generate, media_type="text/event-stream")
|
||||
|
||||
generation_output = model.generate(**gen_kwargs)
|
||||
outputs = generation_output.tolist()[0][len(inputs["input_ids"][0]):]
|
||||
response = tokenizer.decode(outputs, skip_special_tokens=True)
|
||||
|
||||
usage = ChatCompletionResponseUsage(
|
||||
prompt_tokens=len(inputs["input_ids"][0]),
|
||||
completion_tokens=len(outputs),
|
||||
total_tokens=len(inputs["input_ids"][0]) + len(outputs)
|
||||
)
|
||||
|
||||
choice_data = ChatCompletionResponseChoice(
|
||||
index=0,
|
||||
message=ChatMessage(role="assistant", content=response),
|
||||
finish_reason="stop"
|
||||
)
|
||||
|
||||
return ChatCompletionResponse(model=request.model, choices=[choice_data], usage=usage, object="chat.completion")
|
||||
|
||||
|
||||
async def predict(gen_kwargs: Dict[str, Any], model_id: str):
|
||||
global model, tokenizer
|
||||
|
||||
streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
|
||||
gen_kwargs["streamer"] = streamer
|
||||
|
||||
thread = Thread(target=model.generate, kwargs=gen_kwargs)
|
||||
thread.start()
|
||||
|
||||
choice_data = ChatCompletionResponseStreamChoice(
|
||||
index=0,
|
||||
delta=DeltaMessage(role="assistant"),
|
||||
finish_reason=None
|
||||
)
|
||||
chunk = ChatCompletionStreamResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
|
||||
yield chunk.json(exclude_unset=True, ensure_ascii=False)
|
||||
|
||||
for new_text in streamer:
|
||||
if len(new_text) == 0:
|
||||
continue
|
||||
|
||||
choice_data = ChatCompletionResponseStreamChoice(
|
||||
index=0,
|
||||
delta=DeltaMessage(content=new_text),
|
||||
finish_reason=None
|
||||
)
|
||||
chunk = ChatCompletionStreamResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
|
||||
yield chunk.json(exclude_unset=True, ensure_ascii=False)
|
||||
|
||||
choice_data = ChatCompletionResponseStreamChoice(
|
||||
index=0,
|
||||
delta=DeltaMessage(),
|
||||
finish_reason="stop"
|
||||
)
|
||||
chunk = ChatCompletionStreamResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
|
||||
yield chunk.json(exclude_unset=True, ensure_ascii=False)
|
||||
yield "[DONE]"
|
||||
from llmtuner import create_app
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
model_args, data_args, finetuning_args, generating_args = prepare_infer_args()
|
||||
model, tokenizer = load_pretrained(model_args, finetuning_args)
|
||||
|
||||
prompt_template = Template(data_args.prompt_template)
|
||||
source_prefix = data_args.source_prefix if data_args.source_prefix else ""
|
||||
|
||||
app = create_app()
|
||||
uvicorn.run(app, host="0.0.0.0", port=8000, workers=1)
|
||||
|
||||
Reference in New Issue
Block a user