serve.py

# Copyright (c) 2020 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.
"""
Usage:
    Host a trained paddle model with one line command
    Example:
        python -m paddle_serving_server.serve --model ./serving_server_model --port 9292
"""
import argparse
import os
import json
import base64
import time
from multiprocessing import Process
from flask import Flask, request
import sys
if sys.version_info.major == 2:
    from BaseHTTPServer import BaseHTTPRequestHandler, HTTPServer
elif sys.version_info.major == 3:
    from http.server import BaseHTTPRequestHandler, HTTPServer


def serve_args():
    parser = argparse.ArgumentParser("serve")
    parser.add_argument(
        "--thread", type=int, default=2, help="Concurrency of server")
    parser.add_argument(
        "--port", type=int, default=9292, help="Port of the starting gpu")
    parser.add_argument(
        "--device", type=str, default="gpu", help="Type of device")
    parser.add_argument(
        "--gpu_ids", type=str, default="", nargs="+", help="gpu ids")
    parser.add_argument(
        "--op_num", type=int, default=0, nargs="+", help="Number of each op")
    parser.add_argument(
        "--op_max_batch",
        type=int,
        default=32,
        nargs="+",
        help="Max batch of each op")
    parser.add_argument(
        "--model", type=str, default="", nargs="+", help="Model for serving")
    parser.add_argument(
        "--workdir",
        type=str,
        default="workdir",
        help="Working dir of current service")
    parser.add_argument(
        "--name", type=str, default="None", help="Default service name")
    parser.add_argument(
        "--use_mkl", default=False, action="store_true", help="Use MKL")
    parser.add_argument(
        "--precision",
        type=str,
        default="fp32",
        help="precision mode(fp32, int8, fp16, bf16)")
    parser.add_argument(
        "--use_calib",
        default=False,
        action="store_true",
        help="Use TensorRT Calibration")
    parser.add_argument(
        "--mem_optim_off",
        default=False,
        action="store_true",
        help="Memory optimize")
    parser.add_argument(
        "--ir_optim", default=False, action="store_true", help="Graph optimize")
    parser.add_argument(
        "--max_body_size",
        type=int,
        default=512 * 1024 * 1024,
        help="Limit sizes of messages")
    parser.add_argument(
        "--use_encryption_model",
        default=False,
        action="store_true",
        help="Use encryption model")
    parser.add_argument(
        "--use_multilang",
        default=False,
        action="store_true",
        help="Use Multi-language-service")
    parser.add_argument(
        "--use_trt", default=False, action="store_true", help="Use TensorRT")
    parser.add_argument(
        "--use_lite", default=False, action="store_true", help="Use PaddleLite")
    parser.add_argument(
        "--use_xpu", default=False, action="store_true", help="Use XPU")
    parser.add_argument(
        "--product_name",
        type=str,
        default=None,
        help="product_name for authentication")
    parser.add_argument(
        "--container_id",
        type=str,
        default=None,
        help="container_id for authentication")
    parser.add_argument(
        "--gpu_multi_stream",
        default=False,
        action="store_true",
        help="Use gpu_multi_stream")
    return parser.parse_args()


def start_gpu_card_model(gpu_mode, port, args):  # pylint: disable=doc-string-missing

    device = "gpu"
    if gpu_mode == False:
        device = "cpu"

    thread_num = args.thread
    model = args.model
    mem_optim = args.mem_optim_off is False
    ir_optim = args.ir_optim
    use_mkl = args.use_mkl
    max_body_size = args.max_body_size
    use_multilang = args.use_multilang
    workdir = "{}_{}".format(args.workdir, port)

    if model == "":
        print("You must specify your serving model")
        exit(-1)

    for single_model_config in args.model:
        if os.path.isdir(single_model_config):
            pass
        elif os.path.isfile(single_model_config):
            raise ValueError("The input of --model should be a dir not file.")
    import paddle_serving_server as serving
    op_maker = serving.OpMaker()
    op_seq_maker = serving.OpSeqMaker()
    read_op = op_maker.create('general_reader')
    op_seq_maker.add_op(read_op)
    for idx, single_model in enumerate(model):
        infer_op_name = "general_infer"
        # 目前由于ocr的节点Det模型依赖于opencv的第三方库
        # 只有使用ocr的时候，才会加入opencv的第三方库并编译GeneralDetectionOp
        # 故此处做特殊处理，当不满足下述情况时，所添加的op默认为GeneralInferOp
        if len(model) == 2 and idx == 0 and single_model == "ocr_det_model":
            infer_op_name = "general_detection"
        else:
            infer_op_name = "general_infer"
        general_infer_op = op_maker.create(infer_op_name)
        op_seq_maker.add_op(general_infer_op)

    general_response_op = op_maker.create('general_response')
    op_seq_maker.add_op(general_response_op)

    if use_multilang:
        server = serving.MultiLangServer()
    else:
        server = serving.Server()
    server.set_op_sequence(op_seq_maker.get_op_sequence())
    server.set_num_threads(thread_num)
    server.use_mkl(use_mkl)
    server.set_precision(args.precision)
    server.set_use_calib(args.use_calib)
    server.set_memory_optimize(mem_optim)
    server.set_ir_optimize(ir_optim)
    server.set_max_body_size(max_body_size)

    if args.use_trt and device == "gpu":
        server.set_trt()
        server.set_ir_optimize(True)

    if args.gpu_multi_stream and device == "gpu":
        server.set_gpu_multi_stream()

    if args.op_num:
        server.set_op_num(args.op_num)

    if args.op_max_batch:
        server.set_op_max_batch(args.op_max_batch)

    if args.use_lite:
        server.set_lite()

    server.set_device(device)
    if args.use_xpu:
        server.set_xpu()

    if args.product_name != None:
        server.set_product_name(args.product_name)
    if args.container_id != None:
        server.set_container_id(args.container_id)

    server.load_model_config(model)
    server.prepare_server(
        workdir=workdir,
        port=port,
        device=device,
        use_encryption_model=args.use_encryption_model)
    if gpu_mode == True:
        server.set_gpuid(args.gpu_ids)
    server.run_server()


def start_multi_card(args, serving_port=None):  # pylint: disable=doc-string-missing
    gpus = []
    if serving_port == None:
        serving_port = args.port

    if args.gpu_ids == "":
        gpus = []
    else:
        #check the gpu_id is valid or not.
        gpus = args.gpu_ids
        if isinstance(gpus, str):
            gpus = [gpus]
        if "CUDA_VISIBLE_DEVICES" in os.environ:
            env_gpus = os.environ["CUDA_VISIBLE_DEVICES"].split(",")
            for op_gpus_str in gpus:
                op_gpu_list = op_gpus_str.split(",")
                for ids in op_gpu_list:
                    if ids not in env_gpus:
                        print("gpu_ids is not in CUDA_VISIBLE_DEVICES.")
                        exit(-1)

    if args.use_lite:
        print("run using paddle-lite.")
        start_gpu_card_model(False, serving_port, args)
    elif len(gpus) <= 0:
        print("gpu_ids not set, going to run cpu service.")
        start_gpu_card_model(False, serving_port, args)
    else:
        start_gpu_card_model(True, serving_port, args)


class MainService(BaseHTTPRequestHandler):
    def get_available_port(self):
        default_port = 12000
        for i in range(1000):
            if port_is_available(default_port + i):
                return default_port + i

    def start_serving(self):
        start_multi_card(args, serving_port)

    def get_key(self, post_data):
        if "key" not in post_data:
            return False
        else:
            key = base64.b64decode(post_data["key"].encode())
            for single_model_config in args.model:
                if os.path.isfile(single_model_config):
                    raise ValueError(
                        "The input of --model should be a dir not file.")
                with open(single_model_config + "/key", "wb") as f:
                    f.write(key)
            return True

    def check_key(self, post_data):
        if "key" not in post_data:
            return False
        else:
            key = base64.b64decode(post_data["key"].encode())
            for single_model_config in args.model:
                if os.path.isfile(single_model_config):
                    raise ValueError(
                        "The input of --model should be a dir not file.")
                with open(single_model_config + "/key", "rb") as f:
                    cur_key = f.read()
                if key != cur_key:
                    return False
            return True

    def start(self, post_data):
        post_data = json.loads(post_data.decode('utf-8'))
        global p_flag
        if not p_flag:
            if args.use_encryption_model:
                print("waiting key for model")
                if not self.get_key(post_data):
                    print("not found key in request")
                    return False
            global serving_port
            global p
            serving_port = self.get_available_port()
            p = Process(target=self.start_serving)
            p.start()
            time.sleep(3)
            if p.is_alive():
                p_flag = True
            else:
                return False
        else:
            if p.is_alive():
                if not self.check_key(post_data):
                    return False
            else:
                return False
        return True

    def do_POST(self):
        content_length = int(self.headers['Content-Length'])
        post_data = self.rfile.read(content_length)
        if self.start(post_data):
            response = {"endpoint_list": [serving_port]}
        else:
            response = {"message": "start serving failed"}
        self.send_response(200)
        self.send_header('Content-type', 'application/json')
        self.end_headers()
        self.wfile.write(json.dumps(response).encode())


if __name__ == "__main__":

    args = serve_args()
    for single_model_config in args.model:
        if os.path.isdir(single_model_config):
            pass
        elif os.path.isfile(single_model_config):
            raise ValueError("The input of --model should be a dir not file.")

    if args.name == "None":
        from .web_service import port_is_available
        if args.use_encryption_model:
            p_flag = False
            p = None
            serving_port = 0
            server = HTTPServer(('localhost', int(args.port)), MainService)
            print(
                'Starting encryption server, waiting for key from client, use <Ctrl-C> to stop'
            )
            server.serve_forever()
        else:
            start_multi_card(args)
    else:
        from .web_service import WebService
        web_service = WebService(name=args.name)
        web_service.load_model_config(args.model)

        if args.gpu_ids == "":
            gpus = []
        else:
            #check the gpu_id is valid or not.
            gpus = args.gpu_ids
            if isinstance(gpus, str):
                gpus = [gpus]
            if "CUDA_VISIBLE_DEVICES" in os.environ:
                env_gpus = os.environ["CUDA_VISIBLE_DEVICES"].split(",")
                for op_gpus_str in gpus:
                    op_gpu_list = op_gpus_str.split(",")
                    for ids in op_gpu_list:
                        if ids not in env_gpus:
                            print("gpu_ids is not in CUDA_VISIBLE_DEVICES.")
                            exit(-1)

        if len(gpus) > 0:
            web_service.set_gpus(gpus)
        workdir = "{}_{}".format(args.workdir, args.port)
        web_service.prepare_server(
            workdir=workdir,
            port=args.port,
            device=args.device,
            use_lite=args.use_lite,
            use_xpu=args.use_xpu,
            ir_optim=args.ir_optim,
            thread_num=args.thread,
            precision=args.precision,
            use_calib=args.use_calib,
            use_trt=args.use_trt,
            gpu_multi_stream=args.gpu_multi_stream,
            op_num=args.op_num,
            op_max_batch=args.op_max_batch)
        web_service.run_rpc_service()

        app_instance = Flask(__name__)

        @app_instance.before_first_request
        def init():
            web_service._launch_web_service()

        service_name = "/" + web_service.name + "/prediction"

        @app_instance.route(service_name, methods=["POST"])
        def run():
            return web_service.get_prediction(request)

        app_instance.run(host="0.0.0.0",
                         port=web_service.port,
                         threaded=False,
                         processes=4)