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flask_server.py
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flask_server.py
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# Copyright (c) 2023 PaddlePaddle 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.
from __future__ import annotations
import json
import re
import time
from dataclasses import dataclass, field
from multiprocessing.shared_memory import SharedMemory
from predictor import BasePredictor, ModelArgument, PredictorArgument, create_predictor
from paddlenlp.trainer import PdArgumentParser
from paddlenlp.utils.log import logger
@dataclass
class ServerArgument:
port: int = field(default=8011, metadata={"help": "The port of ui service"})
flask_port: int = field(default=8010, metadata={"help": "The port of flask service"})
title: str = field(default="LLM", metadata={"help": "The title of gradio"})
def read_shared_memory(memory: SharedMemory):
"""read content from shared memory
Args:
memory (SharedMemory): the instance of shared Memory
"""
length = int(memory.buf[0]) * 256 + int(memory.buf[1])
if length == 0:
return ""
sentence = bytes(memory.buf[2 : length + 2]).decode()
return sentence
def write_shared_memory(memory: SharedMemory, sentence: str):
"""write content into shared memory
[0:2]: store the length of sentence
[2:]: store the content of sentence
Args:
memory (SharedMemory): the instance of shared Memory
sentence (str): the content which must be string
"""
buffer = bytearray(memory.buf.nbytes)
data = sentence.encode("utf-8")
buffer[0:2] = bytearray([len(data) // 256, len(data) % 256])
buffer[2 : len(data) + 2] = data
memory.buf[:] = buffer
SLEEP_SECOND = 0.5
SHARED_MEMORY_NAME = "shared_memory"
def create_shared_memory(name: int, rank: int):
"""create shared memory between multi-process
Args:
name (int): the name of memory block
rank (int): the rank of current process
"""
file = f"{SHARED_MEMORY_NAME}-{name}"
shared_memory = None
if rank != 0:
while True:
try:
shared_memory = SharedMemory(file, size=1024 * 100)
print("success create shared_memory")
break
except FileNotFoundError:
time.sleep(0.01)
print("sleep for create shared memory")
else:
shared_memory = SharedMemory(file, create=True, size=1024 * 100)
return shared_memory
def enforce_stop_tokens(text, stop) -> str:
"""Code by Langchain"""
"""Cut off the text as soon as any stop words occur."""
return re.split(re.escape(stop), text)[0]
class PredictorServer:
def __init__(self, args: ServerArgument, predictor: BasePredictor):
self.predictor = predictor
self.args = args
self.input_shared_memory = create_shared_memory("input", self.predictor.tensor_parallel_rank)
self.output_shared_memory = create_shared_memory("output", self.predictor.tensor_parallel_rank)
if self.predictor.tensor_parallel_rank == 0:
write_shared_memory(self.input_shared_memory, "")
write_shared_memory(self.output_shared_memory, "")
def predict(self, input_texts: str | list[str]):
return self.predictor.predict(input_texts)
def start_predict(self, data):
print("start to predict under data", data)
data = json.dumps(data, ensure_ascii=False)
write_shared_memory(self.input_shared_memory, data)
while True:
result = read_shared_memory(self.output_shared_memory)
if result:
write_shared_memory(self.output_shared_memory, "")
return result
else:
print("not found result, so to sleep ...")
time.sleep(0.5)
def start_flask_server(self):
from flask import Flask, jsonify, request
app = Flask(__name__)
@app.post("/api/chat")
def _server():
data = request.get_json()
logger.info(f"Request: {json.dumps(data, indent=2, ensure_ascii=False)}")
try:
pred_seq = self.start_predict(data)
output = {
"error_code": 0,
"error_msg": "Success",
"result": {"response": {"role": "bot", "utterance": pred_seq}},
}
except Exception as err:
logger.error(f"Server error: {err}")
output = {"error_code": 1000, "error_msg": f"Server error: {err}", "result": None}
logger.info(f"Response: {json.dumps(output, indent=2, ensure_ascii=False)}")
return jsonify(output)
app.run(host="0.0.0.0", port=self.args.flask_port)
def start_ui_service(self, args):
# do not support start ui service in one command
from multiprocessing import Process
from gradio_ui import main
p = Process(target=main, args=(args,))
p.daemon = True
p.start()
def main(args, server: PredictorServer):
from time import sleep
while True:
sleep(0.5)
content = read_shared_memory(server.input_shared_memory)
if content:
content = json.loads(content)
context = content.pop("context", "")
content.pop("extra_info", None)
generation_args = content
server.predictor.config.max_length = generation_args["max_length"]
server.predictor.config.top_p = generation_args["top_p"]
server.predictor.config.temperature = generation_args["temperature"]
server.predictor.config.top_k = generation_args["top_k"]
server.predictor.config.repetition_penalty = generation_args["repetition_penalty"]
for key, value in generation_args.items():
setattr(server.args, key, value)
result = server.predict(context)
result = result[0]
if not result:
result = "invalid response"
write_shared_memory(server.output_shared_memory, result)
write_shared_memory(server.input_shared_memory, "")
if __name__ == "__main__":
parser = PdArgumentParser((PredictorArgument, ModelArgument, ServerArgument))
predictor_args, model_args, server_args = parser.parse_args_into_dataclasses()
predictor = create_predictor(predictor_args, model_args)
server = PredictorServer(server_args, predictor)
if server.predictor.tensor_parallel_rank == 0:
server.start_ui_service(server_args)
from multiprocessing import Process
p = Process(
target=server.start_flask_server,
)
p.daemon = True
p.start()
main(server_args, server)