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import argparse
import pickle
import random
import time
from multiprocessing.connection import Listener
from threading import Thread
from transformers import AutoModelForCausalLM, AutoTokenizer
class RPCHandler:
def __init__(self):
self._functions = {}
def register_function(self, func):
self._functions[func.__name__] = func
def handle_connection(self, connection):
try:
while True:
# Receive a message
func_name, args, kwargs = pickle.loads(connection.recv())
# Run the RPC and send a response
try:
r = self._functions[func_name](*args, **kwargs)
connection.send(pickle.dumps(r))
except Exception as e:
connection.send(pickle.dumps(e))
except EOFError:
pass
def rpc_server(hdlr, address, authkey):
sock = Listener(address, authkey=authkey)
while True:
try:
client = sock.accept()
t = Thread(target=hdlr.handle_connection, args=(client,))
t.daemon = True
t.start()
except Exception as e:
print("【EXCEPTION】:", str(e))
models = []
tokenizer = None
def chat(messages, gen_conf):
global tokenizer
model = Model()
try:
conf = {
"max_new_tokens": int(
gen_conf.get(
"max_tokens", 256)), "temperature": float(
gen_conf.get(
"temperature", 0.1))}
print(messages, conf)
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
model_inputs.input_ids,
**conf
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
return tokenizer.batch_decode(
generated_ids, skip_special_tokens=True)[0]
except Exception as e:
return str(e)
def Model():
global models
random.seed(time.time())
return random.choice(models)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model_name", type=str, help="Model name")
parser.add_argument(
"--port",
default=7860,
type=int,
help="RPC serving port")
args = parser.parse_args()
handler = RPCHandler()
handler.register_function(chat)
models = []
for _ in range(1):
m = AutoModelForCausalLM.from_pretrained(args.model_name,
device_map="auto",
torch_dtype='auto')
models.append(m)
tokenizer = AutoTokenizer.from_pretrained(args.model_name)
# Run the server
rpc_server(handler, ('0.0.0.0', args.port),
authkey=b'infiniflow-token4kevinhu')
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