Repository navigation
Expand file tree
/
Copy pathserve_openai_api.py
More file actions
164 lines (144 loc) · 6.06 KB
/
Copy pathserve_openai_api.py
File metadata and controls
164 lines (144 loc) · 6.06 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
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] == '�') 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)