2025-02-10 00:14:11 +08:00

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import argparse
import json
import os
import sys
__package__ = "scripts"
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
import time
import torch
import warnings
import uvicorn
from fastapi import FastAPI, HTTPException
from fastapi.responses import StreamingResponse
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForCausalLM
from model.LMConfig import LMConfig
from model.model import MiniMindLM
from model.model_lora import apply_lora, load_lora
warnings.filterwarnings('ignore')
app = FastAPI()
def init_model(args):
tokenizer = AutoTokenizer.from_pretrained('../model/minimind_tokenizer')
if args.load == 0:
moe_path = '_moe' if args.use_moe else ''
modes = {0: 'pretrain', 1: 'full_sft', 2: 'rlhf', 3: 'reason'}
ckp = f'../{args.out_dir}/{modes[args.model_mode]}_{args.dim}{moe_path}.pth'
model = MiniMindLM(LMConfig(
dim=args.dim,
n_layers=args.n_layers,
max_seq_len=args.max_seq_len,
use_moe=args.use_moe
))
state_dict = torch.load(ckp, map_location=device)
model.load_state_dict({k: v for k, v in state_dict.items() if 'mask' not in k}, strict=True)
if args.lora_name != 'None':
apply_lora(model)
load_lora(model, f'../{args.out_dir}/{args.lora_name}_{args.dim}.pth')
else:
model = AutoModelForCausalLM.from_pretrained(
'./MiniMind2',
trust_remote_code=True
)
print(f'MiniMind模型参数量: {sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.2f}M(illion)')
return model.eval().to(device), tokenizer
class ChatRequest(BaseModel):
model: str
messages: list
temperature: float = 0.7
top_p: int = 0.92
max_tokens: int = 8192
stream: bool = False
def generate_stream_response(messages, temperature, top_p, max_tokens):
try:
new_prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)[-max_tokens:]
x = tokenizer(new_prompt).data['input_ids']
x = (torch.tensor(x, dtype=torch.long, device=device)[None, ...])
with torch.no_grad():
res_y = model.generate(
x,
eos_token_id=tokenizer.eos_token_id,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
stream=True,
rp=1.,
pad_token_id=tokenizer.pad_token_id
)
history_idx = 0
for y in res_y:
answer = tokenizer.decode(y[0].tolist(), skip_special_tokens=True)
if (answer and answer[-1] == '<EFBFBD>') or not answer:
continue
delta = answer[history_idx:]
history_idx = len(answer)
json_data = {
'id': f'chatcmpl-{int(time.time())}',
'object': 'chat.completion.chunk',
'created': int(time.time()),
'model': 'minimind',
'choices': [{'index': 0, 'delta': {'content': delta}, 'finish_reason': None}]
}
yield f"data: {json.dumps(json_data)}\n\n"
except Exception as e:
yield f"data: {json.dumps({'error': str(e)})}\n\n"
@app.post("/v1/chat/completions")
async def chat_completions(request: ChatRequest):
try:
if request.stream:
return StreamingResponse(
generate_stream_response(
messages=request.messages,
temperature=request.temperature,
top_p=request.top_p,
max_tokens=request.max_tokens
),
media_type="text/event-stream"
)
else:
new_prompt = tokenizer.apply_chat_template(
request.messages,
tokenize=False,
add_generation_prompt=True
)[-request.max_tokens:]
x = tokenizer(new_prompt).data['input_ids']
x = (torch.tensor(x, dtype=torch.long, device=device)[None, ...])
with torch.no_grad():
res_y = model.generate(
x,
eos_token_id=tokenizer.eos_token_id,
max_new_tokens=request.max_tokens,
temperature=request.temperature,
top_p=request.top_p,
stream=False,
rp=1.,
pad_token_id=tokenizer.pad_token_id
)
answer = tokenizer.decode(res_y.squeeze()[x.shape[1]:].tolist(), skip_special_tokens=True)
return {
"id": f"chatcmpl-{int(time.time())}",
"object": "chat.completion",
"created": int(time.time()),
"model": "minimind",
"choices": [
{
"index": 0,
"message": {"role": "assistant", "content": answer},
"finish_reason": "stop"
}
]
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Server for MiniMind")
parser.add_argument('--out_dir', default='out', type=str)
parser.add_argument('--lora_name', default='None', type=str)
parser.add_argument('--dim', default=512, type=int)
parser.add_argument('--n_layers', default=8, type=int)
parser.add_argument('--max_seq_len', default=8192, type=int)
parser.add_argument('--use_moe', default=False, type=bool)
parser.add_argument('--load', default=0, type=int, help="0: 从原生torch权重1: 利用transformers加载")
parser.add_argument('--model_mode', default=1, type=int, help="0: 预训练模型1: SFT-Chat模型2: RLHF-Chat模型3: Reason模型")
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model, tokenizer = init_model(parser.parse_args())
uvicorn.run(app, host="0.0.0.0", port=8998)