Spaces:
Paused
Paused
File size: 5,270 Bytes
ab2ded1 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 |
#
# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import argparse
import pickle
import random
import time
from copy import deepcopy
from multiprocessing.connection import Listener
from threading import Thread
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
def torch_gc():
try:
import torch
if torch.cuda.is_available():
# with torch.cuda.device(DEVICE):
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
elif torch.backends.mps.is_available():
try:
from torch.mps import empty_cache
empty_cache()
except Exception as e:
pass
except Exception:
pass
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:
torch_gc()
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 chat_streamly(messages, gen_conf):
global tokenizer
model = Model()
try:
torch_gc()
conf = deepcopy(gen_conf)
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)
streamer = TextStreamer(tokenizer)
conf["inputs"] = model_inputs.input_ids
conf["streamer"] = streamer
conf["max_new_tokens"] = conf["max_tokens"]
del conf["max_tokens"]
thread = Thread(target=model.generate, kwargs=conf)
thread.start()
for _, new_text in enumerate(streamer):
yield new_text
except Exception as e:
yield "**ERROR**: " + 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)
handler.register_function(chat_streamly)
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')
|