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一、Why MindSpore Serving

大模型时代，作为一个开发人员更多的是关注一个大模型如何训练好、如何调整模型参数、如何才能得到一个更高的模型精度。而作为一个整体项目，只有项目落地才能有其真正的价值。那么如何才能够使得大模型实现落地？如何才能使大模型项目中的文件以app的形式呈现给用户？

解决这个问题的一个组件就是Serving（服务），它主要解决的问题有：

• 模型如何提交给服务；
• 服务如何部署；
• 服务如何呈现给用户；
• 如何应用各种复杂场景等待

MindSpore Serving就是为了实现将大模型部署到生产环境而产生的。

MindSpore Serving是一个轻量级、高性能的服务模块，旨在帮助MindSpore开发者在生产环境中高效部署在线推理服务。当用户使用MindSpore完成模型训练后，导出MindIR，即可使用MindSpore Serving创建该大模型的推理服务。

MindSpore Serving实现的是一个模型服务化的部署，也就是说模型以线上的形式部署在服务器和云上，客户通过浏览器或者客户端去访问这个服务，将需要进行推理的输入内容发送给服务器，然后服务器将推理的结果返回给用户。

二、Component

MindSpore Serving由三部分组成，分别是客户端（Client）、Master和Worker。

• 客户端是用户节点，提供了gRPC和RESTful的访问。

• Master是一个管理节点，管理所有Worker的信息，包括Worker有哪些模型的信息；Master也是一个分化节点，接收到了客户端的请求之后，会根据请求的内容，结合当前管理的Worker节点的信息进行分发，将请求分发给不同的Worker执行。

• Worker是一个执行节点，会执行加载、模型的更新，在接收到Master转发的请求之后，会将请求进行组装和拆分，然后做前处理、推理和后处理，执行完之后将结果返回给Master，Master再将结果返回给客户端。

三、Features

1．简单易用：
对客户端提供了gRPC和RESTful的服务，同时又提供了服务的拉起、服务的部署和客户端的访问，提供了简单的python接口，通过python接口，用户可以很方便的定制和访问部署服务，只需要一行命令就能够完成一件事。

2．提供定制化的服务：
对于模型来说输入和输出一般是固定的，而对于用户来说输入和输出可能是多变的，这就需要一个预处理模块，将模型的输入转为一个模型可以识别的输入。同时还需要一个后处理模块，给用户提供定制化的服务，针对模型可以定制方法classifly_top，用户根据需要去写前处理和后处理的操作。对于客户端来说只要指定模型名和方法名就能实现推理的结果。

3．支持批处理：
主要是针对具有batchsize维度的文本来说。batchsize实现了文本的并行，在硬件资源足够的情况下，batchsize可以很大地提高性能。对于MindSpore Serving来说，用户一次性发送的请求是不确定的，因此Serving分割和组合一个或者多个请求以匹配用户模型的batchsize。例如batchsize=2，但是有三个请求发过来，这时候就会将两个请求合并处理，到后面再拆分，这样就实现了三个请求的并行，提高了效率。

4. 高性能扩展：
   MindSpore Serving所使用的算子引擎框架是MindSpore框架，具有自动融合和自动并行的高性能，再加上MindSpore Serving本身具有一个高性能的底层通信能力，客户端可以进行多实例组装，模型支持批处理，多模型之间支持并发，预处理和后处理支持多线程的处理。客户端和Worker可以实现扩展的，因此它也实现了一个高扩展性。

四、Demo

基于昇腾910B3

start_agent.py

from agent.agent_multi_post_method import *
from multiprocessing import Queuefrom config.serving_config import AgentConfig, ModelNameif __name__ == "__main__":startup_queue = Queue(1024)startup_agents(AgentConfig.ctx_setting,AgentConfig.inc_setting,AgentConfig.post_model_setting,len(AgentConfig.AgentPorts),AgentConfig.prefill_model,AgentConfig.decode_model,AgentConfig.argmax_model,AgentConfig.topk_model,startup_queue)started_agents = 0while True:value = startup_queue.get()print("agent : %f started" % value)started_agents = started_agents + 1if started_agents >= len(AgentConfig.AgentPorts):print("all agents started")break# server_app_post.init_server_app()# server_app_post.warmup_model(ModelName)# server_app_post.run_server_app()

client/server_app_post.py

import asyncio
import json
import logging
import signal
import sys
import uuid
from multiprocessing import Processimport uvicorn
from fastapi import FastAPI
from fastapi.responses import StreamingResponse
from sse_starlette.sse import EventSourceResponse, ServerSentEventfrom client.client_utils import ClientRequest, Parameters
from config.serving_config import SERVER_APP_HOST, SERVER_APP_PORT
from server.llm_server_post import LLMServerlogging.basicConfig(level=logging.DEBUG,filename='./output/server_app.log',filemode='w',format='%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s')app = FastAPI()
llm_server = Noneasync def get_full_res(request, results):all_texts = ''async for result in results:prompt_ = result.promptanswer_texts = [output.text for output in result.outputs]text = answer_texts[0]if text is None:text = ""all_texts += textret = {"generated_text": all_texts,}yield (json.dumps(ret, ensure_ascii=False) + '\n').encode("utf-8")async def get_full_res_sse(request, results):all_texts = ''async for result in results:answer_texts = [output.text for output in result.outputs]text = answer_texts[0]if text is None:text = ""all_texts += textret = {"event": "message", "retry": 30000, "generated_text": all_texts}yield json.dumps(ret, ensure_ascii=False)async def get_stream_res(request, results):all_texts = ''index = 0async for result in results:prompt_ = result.promptanswer_texts = [output.text for output in result.outputs]text = answer_texts[0]if text is None:text = ""else:index += 1all_texts += textret = {"token": {"text": text,"index": index},}print(ret, index)yield ("data:" + json.dumps(ret, ensure_ascii=False) + '\n').encode("utf-8")print(all_texts)return_full_text = request.parameters.return_full_textif return_full_text:ret = {"generated_text": all_texts,}yield ("data:" + json.dumps(ret, ensure_ascii=False) + '\n').encode("utf-8")async def get_stream_res_sse(request, results):all_texts = ""index = 0async for result in results:answer_texts = [output.text for output in result.outputs]text = answer_texts[0]if text is None:text = ""else:index += 1all_texts += textret = {"event": "message", "retry": 30000, "data": text}yield json.dumps(ret, ensure_ascii=False)print(all_texts)if request.parameters.return_full_text:ret = {"event": "message", "retry": 30000, "data": all_texts}yield json.dumps(ret, ensure_ascii=False)def send_request(request: ClientRequest):print('request: ', request)request_id = str(uuid.uuid1())if request.parameters is None:request.parameters = Parameters()if request.parameters.do_sample is None:request.parameters.do_sample = Falseif request.parameters.top_k is None:request.parameters.top_k = 3if request.parameters.top_p is None:request.parameters.top_p = 1.0if request.parameters.temperature is None:request.parameters.temperature = 1.0if request.parameters.repetition_penalty is None:request.parameters.repetition_penalty = 1.0if request.parameters.max_new_tokens is None:request.parameters.max_new_tokens = 300if request.parameters.return_protocol is None:request.parameters.return_protocol = "sse"if request.parameters.top_k < 0:request.parameters.top_k = 0if request.parameters.top_p < 0.01:request.parameters.top_p = 0.01if request.parameters.top_p > 1.0:request.parameters.top_p = 1.0params = {"prompt": request.inputs,"do_sample": request.parameters.do_sample,"top_k": request.parameters.top_k,"top_p": request.parameters.top_p,"temperature": request.parameters.temperature,"repetition_penalty": request.parameters.repetition_penalty,"max_token_len": request.parameters.max_new_tokens}print('generate_answer...')global llm_serverresults = llm_server.generate_answer(request_id, **params)return results@app.post("/models/llama2")
async def async_generator(request: ClientRequest):results = send_request(request)if request.stream:if request.parameters.return_protocol == "sse":print('get_stream_res_sse...')return EventSourceResponse(get_stream_res_sse(request, results),media_type="text/event-stream",ping_message_factory=lambda: ServerSentEvent(**{"comment": "You can't see this ping"}),ping=600)else:print('get_stream_res...')return StreamingResponse(get_stream_res(request, results))else:print('get_full_res...')return StreamingResponse(get_full_res(request, results))@app.post("/models/llama2/generate")
async def async_full_generator(request: ClientRequest):results = send_request(request)print('get_full_res...')return StreamingResponse(get_full_res(request, results))@app.post("/models/llama2/generate_stream")
async def async_stream_generator(request: ClientRequest):results = send_request(request)if request.parameters.return_protocol == "sse":print('get_stream_res_sse...')return EventSourceResponse(get_stream_res_sse(request, results),media_type="text/event-stream",ping_message_factory=lambda: ServerSentEvent(**{"comment": "You can't see this ping"}),ping=600)else:print('get_stream_res...')return StreamingResponse(get_stream_res(request, results))def update_internlm_request(request: ClientRequest):if request.inputs:request.inputs = "<s><|User|>:{}<eoh>\n<|Bot|>:".format(request.inputs)@app.post("/models/internlm")
async def async_internlm_generator(request: ClientRequest):# update_internlm_request(request)return await async_generator(request)@app.post("/models/internlm/generate")
async def async_internlm_full_generator(request: ClientRequest):# update_internlm_request(request)return await async_full_generator(request)@app.post("/models/internlm/generate_stream")
async def async_internlm_stream_generator(request: ClientRequest):# update_internlm_request(request)return await async_stream_generator(request)def init_server_app():global llm_serverllm_server = LLMServer()print('init server app finish')async def warmup(request: ClientRequest):request.parameters = Parameters(max_new_tokens=3)results = send_request(request)print('warmup get_stream_res...')async for item in get_stream_res(request, results):print(item)def warmup_llama2():request = ClientRequest(inputs="test")asyncio.run(warmup(request))print('warmup llama2 finish')def warmup_internlm():request = ClientRequest(inputs="test")update_internlm_request(request)asyncio.run(warmup(request))print('warmup internlm finish')def run_server_app():print('server port is: ', SERVER_APP_PORT)uvicorn.run(app, host=SERVER_APP_HOST, port=SERVER_APP_PORT)WARMUP_MODEL_MAP = {"llama": warmup_llama2,"internlm": warmup_internlm,
}def warmup_model(model_name):model_prefix = model_name.split('_')[0]if model_prefix in WARMUP_MODEL_MAP.keys():func = WARMUP_MODEL_MAP[model_prefix]warmup_process = Process(target=func)warmup_process.start()warmup_process.join()print("mindspore serving is started.")else:print("model not support warmup : ", model_name)async def _get_batch_size():global llm_serverbatch_size = llm_server.get_bs_current()ret = {'event': "message", "retry": 30000, "data": batch_size}yield json.dumps(ret, ensure_ascii=False)async def _get_request_numbers():global llm_serverqueue_size = llm_server.get_queue_current()ret = {'event': "message", "retry": 30000, "data": queue_size}yield json.dumps(ret, ensure_ascii=False)@app.get("/serving/get_bs")
async def get_batch_size():return EventSourceResponse(_get_batch_size(),media_type="text/event-stream",ping_message_factory=lambda: ServerSentEvent(**{"comment": "You can't see this ping"}),ping=600)@app.get("/serving/get_request_numbers")
async def get_request_numbers():return EventSourceResponse(_get_request_numbers(),media_type="text/event-stream",ping_message_factory=lambda: ServerSentEvent(**{"comment": "You can't see this ping"}),ping=600)def sig_term_handler(signal, frame):print("catch SIGTERM")global llm_serverllm_server.stop()print("----serving exit----")sys.exit(0)if __name__ == "__main__":signal.signal(signal.SIGTERM, sig_term_handler)init_server_app()# warmup_model(ModelName)run_server_app()