# 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. import paddle_serving_client import os from .proto import sdk_configure_pb2 as sdk from .proto import general_model_config_pb2 as m_config import google.protobuf.text_format import time import sys int_type = 0 float_type = 1 class SDKConfig(object): def __init__(self): self.sdk_desc = sdk.SDKConf() self.endpoints = [] def set_server_endpoints(self, endpoints): self.endpoints = endpoints def gen_desc(self): predictor_desc = sdk.Predictor() predictor_desc.name = "general_model" predictor_desc.service_name = \ "baidu.paddle_serving.predictor.general_model.GeneralModelService" predictor_desc.endpoint_router = "WeightedRandomRender" predictor_desc.weighted_random_render_conf.variant_weight_list = "100" variant_desc = sdk.VariantConf() variant_desc.tag = "var1" variant_desc.naming_conf.cluster = "list://{}".format(":".join( self.endpoints)) predictor_desc.variants.extend([variant_desc]) self.sdk_desc.predictors.extend([predictor_desc]) self.sdk_desc.default_variant_conf.tag = "default" self.sdk_desc.default_variant_conf.connection_conf.connect_timeout_ms = 2000 self.sdk_desc.default_variant_conf.connection_conf.rpc_timeout_ms = 20000 self.sdk_desc.default_variant_conf.connection_conf.connect_retry_count = 2 self.sdk_desc.default_variant_conf.connection_conf.max_connection_per_host = 100 self.sdk_desc.default_variant_conf.connection_conf.hedge_request_timeout_ms = -1 self.sdk_desc.default_variant_conf.connection_conf.hedge_fetch_retry_count = 2 self.sdk_desc.default_variant_conf.connection_conf.connection_type = "pooled" self.sdk_desc.default_variant_conf.naming_conf.cluster_filter_strategy = "Default" self.sdk_desc.default_variant_conf.naming_conf.load_balance_strategy = "la" self.sdk_desc.default_variant_conf.rpc_parameter.compress_type = 0 self.sdk_desc.default_variant_conf.rpc_parameter.package_size = 20 self.sdk_desc.default_variant_conf.rpc_parameter.protocol = "baidu_std" self.sdk_desc.default_variant_conf.rpc_parameter.max_channel_per_request = 3 return self.sdk_desc class Client(object): def __init__(self): self.feed_names_ = [] self.fetch_names_ = [] self.client_handle_ = None self.result_handle_ = None self.feed_shapes_ = {} self.feed_types_ = {} self.feed_names_to_idx_ = {} self.rpath() self.pid = os.getpid() def rpath(self): lib_path = os.path.dirname(paddle_serving_client.__file__) client_path = os.path.join(lib_path, 'serving_client.so') lib_path = os.path.join(lib_path, 'lib') os.popen('patchelf --set-rpath {} {}'.format(lib_path, client_path)) def load_client_config(self, path): from .serving_client import PredictorClient from .serving_client import PredictorRes model_conf = m_config.GeneralModelConfig() f = open(path, 'r') model_conf = google.protobuf.text_format.Merge( str(f.read()), model_conf) # load configuraion here # get feed vars, fetch vars # get feed shapes, feed types # map feed names to index self.result_handle_ = PredictorRes() self.client_handle_ = PredictorClient() self.client_handle_.init(path) read_env_flags = ["profile_client", "profile_server"] self.client_handle_.init_gflags([sys.argv[ 0]] + ["--tryfromenv=" + ",".join(read_env_flags)]) self.feed_names_ = [var.alias_name for var in model_conf.feed_var] self.fetch_names_ = [var.alias_name for var in model_conf.fetch_var] self.feed_names_to_idx_ = {} self.fetch_names_to_type_ = {} self.fetch_names_to_idx_ = {} self.lod_tensor_set = set() self.feed_tensor_len = {} for i, var in enumerate(model_conf.feed_var): self.feed_names_to_idx_[var.alias_name] = i self.feed_types_[var.alias_name] = var.feed_type self.feed_shapes_[var.alias_name] = var.shape if var.is_lod_tensor: self.lod_tensor_set.add(var.alias_name) else: counter = 1 for dim in self.feed_shapes_[var.alias_name]: counter *= dim self.feed_tensor_len[var.alias_name] = counter for i, var in enumerate(model_conf.fetch_var): self.fetch_names_to_idx_[var.alias_name] = i self.fetch_names_to_type_[var.alias_name] = var.fetch_type return def connect(self, endpoints): # check whether current endpoint is available # init from client config # create predictor here predictor_sdk = SDKConfig() predictor_sdk.set_server_endpoints(endpoints) sdk_desc = predictor_sdk.gen_desc() print(sdk_desc) self.client_handle_.create_predictor_by_desc(sdk_desc.SerializeToString( )) def get_feed_names(self): return self.feed_names_ def get_fetch_names(self): return self.fetch_names_ def shape_check(self, feed, key): seq_shape = 1 if key in self.lod_tensor_set: return if len(feed[key]) != self.feed_tensor_len[key]: raise SystemExit("The shape of feed tensor {} not match.".format( key)) def predict(self, feed={}, fetch=[]): int_slot = [] float_slot = [] int_feed_names = [] float_feed_names = [] fetch_names = [] for key in feed: self.shape_check(feed, key) if key not in self.feed_names_: continue if self.feed_types_[key] == int_type: int_feed_names.append(key) int_slot.append(feed[key]) elif self.feed_types_[key] == float_type: float_feed_names.append(key) float_slot.append(feed[key]) for key in fetch: if key in self.fetch_names_: fetch_names.append(key) ret = self.client_handle_.predict(float_slot, float_feed_names, int_slot, int_feed_names, fetch_names, self.result_handle_, self.pid) result_map = {} for i, name in enumerate(fetch_names): if self.fetch_names_to_type_[name] == int_type: result_map[name] = self.result_handle_.get_int64_by_name(name)[ 0] elif self.fetch_names_to_type_[name] == float_type: result_map[name] = self.result_handle_.get_float_by_name(name)[ 0] return result_map def batch_predict(self, feed_batch=[], fetch=[]): int_slot_batch = [] float_slot_batch = [] int_feed_names = [] float_feed_names = [] fetch_names = [] counter = 0 batch_size = len(feed_batch) for feed in feed_batch: int_slot = [] float_slot = [] for key in feed: if key not in self.feed_names_: continue if self.feed_types_[key] == int_type: if counter == 0: int_feed_names.append(key) int_slot.append(feed[key]) elif self.feed_types_[key] == float_type: if counter == 0: float_feed_names.append(key) float_slot.append(feed[key]) counter += 1 int_slot_batch.append(int_slot) float_slot_batch.append(float_slot) for key in fetch: if key in self.fetch_names_: fetch_names.append(key) result_batch = self.result_handle_ res = self.client_handle_.batch_predict( float_slot_batch, float_feed_names, int_slot_batch, int_feed_names, fetch_names, result_batch, self.pid) result_map_batch = [] for index in range(batch_size): result_map = {} for i, name in enumerate(fetch_names): if self.fetch_names_to_type_[name] == int_type: result_map[name] = result_batch.get_int64_by_name(name)[ index] elif self.fetch_names_to_type_[name] == float_type: result_map[name] = result_batch.get_float_by_name(name)[ index] result_map_batch.append(result_map) return result_map_batch def release(self): self.client_handle_.destroy_predictor() self.client_handle_ = None