# Copyright (c) 2021 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. import os import tarfile import socket import paddle_serving_server as paddle_serving_server from paddle_serving_server.rpc_service import MultiLangServerServiceServicer from .proto import server_configure_pb2 as server_sdk from .proto import general_model_config_pb2 as m_config from .proto import multi_lang_general_model_service_pb2_grpc import google.protobuf.text_format import time from .version import serving_server_version, version_suffix, device_type from contextlib import closing import argparse import sys if sys.platform.startswith('win') is False: import fcntl import shutil import platform import numpy as np import grpc import sys import collections from multiprocessing import Pool, Process from concurrent import futures class Server(object): def __init__(self): self.server_handle_ = None self.infer_service_conf = None self.model_toolkit_conf = []#The quantity is equal to the InferOp quantity,Engine--OP self.resource_conf = None self.memory_optimization = False self.ir_optimization = False self.model_conf = collections.OrderedDict()# save the serving_server_conf.prototxt content (feed and fetch information) this is a map for multi-model in a workflow self.workflow_fn = "workflow.prototxt"#only one for one Service,Workflow--Op self.resource_fn = "resource.prototxt"#only one for one Service,model_toolkit_fn and general_model_config_fn is recorded in this file self.infer_service_fn = "infer_service.prototxt"#only one for one Service,Service--Workflow self.model_toolkit_fn = []#["general_infer_0/model_toolkit.prototxt"]The quantity is equal to the InferOp quantity,Engine--OP self.general_model_config_fn = []#["general_infer_0/general_model.prototxt"]The quantity is equal to the InferOp quantity,Feed and Fetch --OP self.subdirectory = []#The quantity is equal to the InferOp quantity, and name = node.name = engine.name self.cube_config_fn = "cube.conf" self.workdir = "" self.max_concurrency = 0 self.num_threads = 2 self.port = 8080 self.reload_interval_s = 10 self.max_body_size = 64 * 1024 * 1024 self.module_path = os.path.dirname(paddle_serving_server.__file__) self.cur_path = os.getcwd() self.use_local_bin = False self.mkl_flag = False self.device = "cpu" self.gpuid = 0 self.use_trt = False self.use_lite = False self.use_xpu = False self.model_config_paths = collections.OrderedDict() # save the serving_server_conf.prototxt path (feed and fetch information) this is a map for multi-model in a workflow self.product_name = None self.container_id = None def get_fetch_list(self,infer_node_idx = -1 ): fetch_names = [var.alias_name for var in list(self.model_conf.values())[infer_node_idx].fetch_var] return fetch_names def set_max_concurrency(self, concurrency): self.max_concurrency = concurrency def set_num_threads(self, threads): self.num_threads = threads def set_max_body_size(self, body_size): if body_size >= self.max_body_size: self.max_body_size = body_size else: print( "max_body_size is less than default value, will use default value in service." ) def use_encryption_model(self, flag=False): self.encryption_model = flag def set_port(self, port): self.port = port def set_reload_interval(self, interval): self.reload_interval_s = interval def set_op_sequence(self, op_seq): self.workflow_conf = op_seq def set_op_graph(self, op_graph): self.workflow_conf = op_graph def set_memory_optimize(self, flag=False): self.memory_optimization = flag def set_ir_optimize(self, flag=False): self.ir_optimization = flag def set_product_name(self, product_name=None): if product_name == None: raise ValueError("product_name can't be None.") self.product_name = product_name def set_container_id(self, container_id): if container_id == None: raise ValueError("container_id can't be None.") self.container_id = container_id def check_local_bin(self): if "SERVING_BIN" in os.environ: self.use_local_bin = True self.bin_path = os.environ["SERVING_BIN"] def check_cuda(self): if os.system("ls /dev/ | grep nvidia > /dev/null") == 0: pass else: raise SystemExit( "GPU not found, please check your environment or use cpu version by \"pip install paddle_serving_server\"" ) def set_device(self, device="cpu"): self.device = device def set_gpuid(self, gpuid=0): self.gpuid = gpuid def set_trt(self): self.use_trt = True def set_lite(self): self.use_lite = True def set_xpu(self): self.use_xpu = True def _prepare_engine(self, model_config_paths, device, use_encryption_model): if self.model_toolkit_conf == None: self.model_toolkit_conf = [] for engine_name, model_config_path in model_config_paths.items(): engine = server_sdk.EngineDesc() engine.name = engine_name # engine.reloadable_meta = model_config_path + "/fluid_time_file" engine.reloadable_meta = model_config_path + "/fluid_time_file" os.system("touch {}".format(engine.reloadable_meta)) engine.reloadable_type = "timestamp_ne" engine.runtime_thread_num = 0 engine.batch_infer_size = 0 engine.enable_batch_align = 0 engine.model_dir = model_config_path engine.enable_memory_optimization = self.memory_optimization engine.enable_ir_optimization = self.ir_optimization engine.use_trt = self.use_trt engine.use_lite = self.use_lite engine.use_xpu = self.use_xpu if os.path.exists('{}/__params__'.format(model_config_path)): engine.combined_model = True else: engine.combined_model = False if use_encryption_model: engine.encrypted_model = True engine.type = "PADDLE_INFER" self.model_toolkit_conf.append(server_sdk.ModelToolkitConf()) self.model_toolkit_conf[-1].engines.extend([engine]) def _prepare_infer_service(self, port): if self.infer_service_conf == None: self.infer_service_conf = server_sdk.InferServiceConf() self.infer_service_conf.port = port infer_service = server_sdk.InferService() infer_service.name = "GeneralModelService" infer_service.workflows.extend(["workflow1"]) self.infer_service_conf.services.extend([infer_service]) def _prepare_resource(self, workdir, cube_conf): self.workdir = workdir if self.resource_conf == None: self.resource_conf = server_sdk.ResourceConf() for idx, op_general_model_config_fn in enumerate(self.general_model_config_fn): with open("{}/{}".format(workdir, op_general_model_config_fn), "w") as fout: fout.write(str(list(self.model_conf.values())[idx])) for workflow in self.workflow_conf.workflows: for node in workflow.nodes: if "dist_kv" in node.name: self.resource_conf.cube_config_path = workdir self.resource_conf.cube_config_file = self.cube_config_fn if cube_conf == None: raise ValueError( "Please set the path of cube.conf while use dist_kv op." ) shutil.copy(cube_conf, workdir) if "quant" in node.name: self.resource_conf.cube_quant_bits = 8 self.resource_conf.model_toolkit_path.extend([workdir]) self.resource_conf.model_toolkit_file.extend([self.model_toolkit_fn[idx]]) self.resource_conf.general_model_path.extend([workdir]) self.resource_conf.general_model_file.extend([op_general_model_config_fn]) #TODO:figure out the meaning of product_name and container_id. if self.product_name != None: self.resource_conf.auth_product_name = self.product_name if self.container_id != None: self.resource_conf.auth_container_id = self.container_id def _write_pb_str(self, filepath, pb_obj): with open(filepath, "w") as fout: fout.write(str(pb_obj)) def load_model_config(self, model_config_paths_args): # At present, Serving needs to configure the model path in # the resource.prototxt file to determine the input and output # format of the workflow. To ensure that the input and output # of multiple models are the same. if isinstance(model_config_paths_args, str): model_config_paths_args = [model_config_paths_args] for single_model_config in model_config_paths_args: 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 isinstance(model_config_paths_args, list): # If there is only one model path, use the default infer_op. # Because there are several infer_op type, we need to find # it from workflow_conf. default_engine_types = [ 'GeneralInferOp', 'GeneralDistKVInferOp', 'GeneralDistKVQuantInferOp','GeneralDetectionOp', ] # now only support single-workflow. # TODO:support multi-workflow model_config_paths_list_idx = 0 for node in self.workflow_conf.workflows[0].nodes: if node.type in default_engine_types: if node.name is None: raise Exception( "You have set the engine_name of Op. Please use the form {op: model_path} to configure model path" ) f = open("{}/serving_server_conf.prototxt".format( model_config_paths_args[model_config_paths_list_idx]), 'r') self.model_conf[node.name] = google.protobuf.text_format.Merge(str(f.read()), m_config.GeneralModelConfig()) self.model_config_paths[node.name] = model_config_paths_args[model_config_paths_list_idx] self.general_model_config_fn.append(node.name+"/general_model.prototxt") self.model_toolkit_fn.append(node.name+"/model_toolkit.prototxt") self.subdirectory.append(node.name) model_config_paths_list_idx += 1 if model_config_paths_list_idx == len(model_config_paths_args): break #Right now, this is not useful. elif isinstance(model_config_paths_args, dict): self.model_config_paths = collections.OrderedDict() for node_str, path in model_config_paths_args.items(): node = server_sdk.DAGNode() google.protobuf.text_format.Parse(node_str, node) self.model_config_paths[node.name] = path print("You have specified multiple model paths, please ensure " "that the input and output of multiple models are the same.") f = open("{}/serving_server_conf.prototxt".format(path), 'r') self.model_conf[node.name] = google.protobuf.text_format.Merge( str(f.read()), m_config.GeneralModelConfig()) else: raise Exception("The type of model_config_paths must be str or list or " "dict({op: model_path}), not {}.".format( type(model_config_paths_args))) # check config here # print config here def use_mkl(self, flag): self.mkl_flag = flag def get_device_version(self): avx_flag = False mkl_flag = self.mkl_flag r = os.system("cat /proc/cpuinfo | grep avx > /dev/null 2>&1") if r == 0: avx_flag = True if avx_flag: if mkl_flag: device_version = "cpu-avx-mkl" else: device_version = "cpu-avx-openblas" else: if mkl_flag: print( "Your CPU does not support AVX, server will running with noavx-openblas mode." ) device_version = "cpu-noavx-openblas" return device_version def get_serving_bin_name(self): if device_type == "0": device_version = self.get_device_version() elif device_type == "1": if version_suffix == "101" or version_suffix == "102": device_version = "gpu-" + version_suffix else: device_version = "gpu-cuda" + version_suffix elif device_type == "2": device_version = "xpu-" + platform.machine() return device_version def download_bin(self): os.chdir(self.module_path) need_download = False #acquire lock version_file = open("{}/version.py".format(self.module_path), "r") folder_name = "serving-%s-%s" % (self.get_serving_bin_name(), serving_server_version) tar_name = "%s.tar.gz" % folder_name bin_url = "https://paddle-serving.bj.bcebos.com/bin/%s" % tar_name self.server_path = os.path.join(self.module_path, folder_name) download_flag = "{}/{}.is_download".format(self.module_path, folder_name) fcntl.flock(version_file, fcntl.LOCK_EX) if os.path.exists(download_flag): os.chdir(self.cur_path) self.bin_path = self.server_path + "/serving" return if not os.path.exists(self.server_path): os.system("touch {}/{}.is_download".format(self.module_path, folder_name)) print('Frist time run, downloading PaddleServing components ...') r = os.system('wget ' + bin_url + ' --no-check-certificate') if r != 0: if os.path.exists(tar_name): os.remove(tar_name) raise SystemExit( 'Download failed, please check your network or permission of {}.' .format(self.module_path)) else: try: print('Decompressing files ..') tar = tarfile.open(tar_name) tar.extractall() tar.close() except: if os.path.exists(exe_path): os.remove(exe_path) raise SystemExit( 'Decompressing failed, please check your permission of {} or disk space left.' .format(self.module_path)) finally: os.remove(tar_name) #release lock version_file.close() os.chdir(self.cur_path) self.bin_path = self.server_path + "/serving" def prepare_server(self, workdir=None, port=9292, device="cpu", use_encryption_model=False, cube_conf=None): if workdir == None: workdir = "./tmp" os.system("mkdir -p {}".format(workdir)) else: os.system("mkdir -p {}".format(workdir)) for subdir in self.subdirectory: os.system("mkdir -p {}/{}".format(workdir, subdir)) os.system("touch {}/{}/fluid_time_file".format(workdir, subdir)) if not self.port_is_available(port): raise SystemExit("Port {} is already used".format(port)) self.set_port(port) self._prepare_resource(workdir, cube_conf) self._prepare_engine(self.model_config_paths, device, use_encryption_model) self._prepare_infer_service(port) self.workdir = workdir infer_service_fn = "{}/{}".format(workdir, self.infer_service_fn) self._write_pb_str(infer_service_fn, self.infer_service_conf) workflow_fn = "{}/{}".format(workdir, self.workflow_fn) self._write_pb_str(workflow_fn, self.workflow_conf) resource_fn = "{}/{}".format(workdir, self.resource_fn) self._write_pb_str(resource_fn, self.resource_conf) for idx,single_model_toolkit_fn in enumerate(self.model_toolkit_fn): model_toolkit_fn = "{}/{}".format(workdir, single_model_toolkit_fn) self._write_pb_str(model_toolkit_fn, self.model_toolkit_conf[idx]) def port_is_available(self, port): with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock: sock.settimeout(2) result = sock.connect_ex(('0.0.0.0', port)) if result != 0: return True else: return False def run_server(self): # just run server with system command # currently we do not load cube self.check_local_bin() if not self.use_local_bin: self.download_bin() # wait for other process to download server bin while not os.path.exists(self.server_path): time.sleep(1) else: print("Use local bin : {}".format(self.bin_path)) #self.check_cuda() # Todo: merge CPU and GPU code, remove device to model_toolkit if self.device == "cpu" or self.device == "arm": command = "{} " \ "-enable_model_toolkit " \ "-inferservice_path {} " \ "-inferservice_file {} " \ "-max_concurrency {} " \ "-num_threads {} " \ "-port {} " \ "-reload_interval_s {} " \ "-resource_path {} " \ "-resource_file {} " \ "-workflow_path {} " \ "-workflow_file {} " \ "-bthread_concurrency {} " \ "-max_body_size {} ".format( self.bin_path, self.workdir, self.infer_service_fn, self.max_concurrency, self.num_threads, self.port, self.reload_interval_s, self.workdir, self.resource_fn, self.workdir, self.workflow_fn, self.num_threads, self.max_body_size) else: command = "{} " \ "-enable_model_toolkit " \ "-inferservice_path {} " \ "-inferservice_file {} " \ "-max_concurrency {} " \ "-num_threads {} " \ "-port {} " \ "-reload_interval_s {} " \ "-resource_path {} " \ "-resource_file {} " \ "-workflow_path {} " \ "-workflow_file {} " \ "-bthread_concurrency {} " \ "-gpuid {} " \ "-max_body_size {} ".format( self.bin_path, self.workdir, self.infer_service_fn, self.max_concurrency, self.num_threads, self.port, self.reload_interval_s, self.workdir, self.resource_fn, self.workdir, self.workflow_fn, self.num_threads, self.gpuid, self.max_body_size) print("Going to Run Comand") print(command) os.system(command) class MultiLangServer(object): def __init__(self): self.bserver_ = Server() self.worker_num_ = 4 self.body_size_ = 64 * 1024 * 1024 self.concurrency_ = 100000 self.is_multi_model_ = False # for model ensemble, which is not useful right now. def set_max_concurrency(self, concurrency): self.concurrency_ = concurrency self.bserver_.set_max_concurrency(concurrency) def set_device(self, device="cpu"): self.device = device def set_num_threads(self, threads): self.worker_num_ = threads self.bserver_.set_num_threads(threads) def set_max_body_size(self, body_size): self.bserver_.set_max_body_size(body_size) if body_size >= self.body_size_: self.body_size_ = body_size else: print( "max_body_size is less than default value, will use default value in service." ) def use_encryption_model(self, flag=False): self.encryption_model = flag def set_port(self, port): self.gport_ = port def set_reload_interval(self, interval): self.bserver_.set_reload_interval(interval) def set_op_sequence(self, op_seq): self.bserver_.set_op_sequence(op_seq) def set_op_graph(self, op_graph): self.bserver_.set_op_graph(op_graph) def use_mkl(self, flag): self.bserver_.use_mkl(flag) def set_memory_optimize(self, flag=False): self.bserver_.set_memory_optimize(flag) def set_ir_optimize(self, flag=False): self.bserver_.set_ir_optimize(flag) def set_gpuid(self, gpuid=0): self.bserver_.set_gpuid(gpuid) def load_model_config(self, server_config_dir_paths, client_config_path=None): if isinstance(server_config_dir_paths, str): server_config_dir_paths = [server_config_dir_paths] elif isinstance(server_config_dir_paths, list): pass else: raise Exception("The type of model_config_paths must be str or list" ", not {}.".format( type(server_config_dir_paths))) for single_model_config in server_config_dir_paths: 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.") self.bserver_.load_model_config(server_config_dir_paths) if client_config_path is None: #now dict is not useful. if isinstance(server_config_dir_paths, dict): self.is_multi_model_ = True client_config_path = [] for server_config_path_items in list(server_config_dir_paths.items()): client_config_path.append( server_config_path_items[1] ) elif isinstance(server_config_dir_paths, list): self.is_multi_model_ = False client_config_path = server_config_dir_paths else: raise Exception("The type of model_config_paths must be str or list or " "dict({op: model_path}), not {}.".format( type(server_config_dir_paths))) if isinstance(client_config_path, str): client_config_path = [client_config_path] elif isinstance(client_config_path, list): pass else:# dict is not support right now. raise Exception("The type of client_config_path must be str or list or " "dict({op: model_path}), not {}.".format( type(client_config_path))) if len(client_config_path) != len(server_config_dir_paths): raise Warning("The len(client_config_path) is {}, != len(server_config_dir_paths) {}." .format( len(client_config_path), len(server_config_dir_paths) ) ) self.bclient_config_path_list = client_config_path def prepare_server(self, workdir=None, port=9292, device="cpu", use_encryption_model=False, cube_conf=None): if not self._port_is_available(port): raise SystemExit("Prot {} is already used".format(port)) default_port = 12000 self.port_list_ = [] for i in range(1000): if default_port + i != port and self._port_is_available(default_port + i): self.port_list_.append(default_port + i) break self.bserver_.prepare_server( workdir=workdir, port=self.port_list_[0], device=device, use_encryption_model=use_encryption_model, cube_conf=cube_conf) self.set_port(port) def _launch_brpc_service(self, bserver): bserver.run_server() def _port_is_available(self, port): with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock: sock.settimeout(2) result = sock.connect_ex(('0.0.0.0', port)) return result != 0 def run_server(self): p_bserver = Process( target=self._launch_brpc_service, args=(self.bserver_, )) p_bserver.start() options = [('grpc.max_send_message_length', self.body_size_), ('grpc.max_receive_message_length', self.body_size_)] server = grpc.server( futures.ThreadPoolExecutor(max_workers=self.worker_num_), options=options, maximum_concurrent_rpcs=self.concurrency_) multi_lang_general_model_service_pb2_grpc.add_MultiLangGeneralModelServiceServicer_to_server( MultiLangServerServiceServicer( self.bclient_config_path_list, self.is_multi_model_, ["0.0.0.0:{}".format(self.port_list_[0])]), server) server.add_insecure_port('[::]:{}'.format(self.gport_)) server.start() p_bserver.join() server.wait_for_termination()