# 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. # pylint: disable=doc-string-missing 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 numpy as np import requests import json import base64 import time import sys sys.path.append( os.path.join(os.path.abspath(os.path.dirname(__file__)), 'proto')) #param 'type'(which is in feed_var or fetch_var) = 0 means dataType is int64 #param 'type'(which is in feed_var or fetch_var) = 1 means dataType is float32 #param 'type'(which is in feed_var or fetch_var) = 2 means dataType is int32 #param 'type'(which is in feed_var or fetch_var) = 5 means dataType is float16 #param 'type'(which is in feed_var or fetch_var) = 7 means dataType is uint8 #param 'type'(which is in feed_var or fetch_var) = 8 means dataType is int8 #param 'type'(which is in feed_var or fetch_var) = 20 means dataType is string(also called bytes in proto) int64_type = 0 float32_type = 1 int32_type = 2 float16_type = 5 uint8_type = 7 int8_type = 8 bytes_type = 20 #int_type,float_type,string_type are the set of each subdivision classes. int_type = set([int64_type, int32_type]) float_type = set([float32_type]) string_type = set([bytes_type, float16_type, uint8_type, int8_type]) class _NOPProfiler(object): def record(self, name): pass def print_profile(self): pass class _TimeProfiler(object): def __init__(self): self.pid = os.getpid() self.print_head = 'PROFILE\tpid:{}\t'.format(self.pid) self.time_record = [self.print_head] def record(self, name): self.time_record.append('{}:{} '.format( name, int(round(time.time() * 1000000)))) def print_profile(self): self.time_record.append('\n') sys.stderr.write(''.join(self.time_record)) self.time_record = [self.print_head] _is_profile = int(os.environ.get('FLAGS_profile_client', 0)) _Profiler = _TimeProfiler if _is_profile else _NOPProfiler class SDKConfig(object): def __init__(self): self.sdk_desc = sdk.SDKConf() self.tag_list = [] self.cluster_list = [] self.variant_weight_list = [] self.rpc_timeout_ms = 200000 self.load_balance_strategy = "la" def add_server_variant(self, tag, cluster, variant_weight): self.tag_list.append(tag) self.cluster_list.append(cluster) self.variant_weight_list.append(variant_weight) def set_load_banlance_strategy(self, strategy): self.load_balance_strategy = strategy def gen_desc(self, rpc_timeout_ms): 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 = "|".join( self.variant_weight_list) for idx, tag in enumerate(self.tag_list): variant_desc = sdk.VariantConf() variant_desc.tag = tag variant_desc.naming_conf.cluster = "list://{}".format(",".join( self.cluster_list[idx])) 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 = rpc_timeout_ms 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.feed_shapes_ = {} self.feed_types_ = {} self.feed_names_to_idx_ = {} self.pid = os.getpid() self.predictor_sdk_ = None self.producers = [] self.consumer = None self.profile_ = _Profiler() self.all_numpy_input = True self.has_numpy_input = False self.rpc_timeout_ms = 200000 from .serving_client import PredictorRes self.predictorres_constructor = PredictorRes def load_client_config(self, model_config_path_list): if isinstance(model_config_path_list, str): model_config_path_list = [model_config_path_list] elif isinstance(model_config_path_list, list): pass file_path_list = [] for single_model_config in model_config_path_list: if os.path.isdir(single_model_config): file_path_list.append("{}/serving_client_conf.prototxt".format( single_model_config)) elif os.path.isfile(single_model_config): file_path_list.append(single_model_config) from .serving_client import PredictorClient model_conf = m_config.GeneralModelConfig() f = open(file_path_list[0], '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.client_handle_ = PredictorClient() self.client_handle_.init(file_path_list) if "FLAGS_max_body_size" not in os.environ: os.environ["FLAGS_max_body_size"] = str(512 * 1024 * 1024) read_env_flags = ["profile_client", "profile_server", "max_body_size"] 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.feed_names_to_idx_ = {} #this is not useful self.lod_tensor_set = set() self.feed_tensor_len = {} #this is only used for shape check self.key = None 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 if len(file_path_list) > 1: model_conf = m_config.GeneralModelConfig() f = open(file_path_list[-1], 'r') model_conf = google.protobuf.text_format.Merge( str(f.read()), model_conf) self.fetch_names_ = [var.alias_name for var in model_conf.fetch_var] self.fetch_names_to_type_ = {} self.fetch_names_to_idx_ = {} 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 if var.is_lod_tensor: self.lod_tensor_set.add(var.alias_name) return def add_variant(self, tag, cluster, variant_weight): if self.predictor_sdk_ is None: self.predictor_sdk_ = SDKConfig() self.predictor_sdk_.add_server_variant(tag, cluster, str(variant_weight)) def set_rpc_timeout_ms(self, rpc_timeout): if not isinstance(rpc_timeout, int): raise ValueError("rpc_timeout must be int type.") else: self.rpc_timeout_ms = rpc_timeout def use_key(self, key_filename): with open(key_filename, "rb") as f: self.key = f.read() def get_serving_port(self, endpoints): if self.key is not None: req = json.dumps({"key": base64.b64encode(self.key).decode()}) else: req = json.dumps({}) r = requests.post("http://" + endpoints[0], req) result = r.json() print(result) if "endpoint_list" not in result: raise ValueError("server not ready") else: endpoints = [ endpoints[0].split(":")[0] + ":" + str(result["endpoint_list"][0]) ] return endpoints def connect(self, endpoints=None, encryption=False): # check whether current endpoint is available # init from client config # create predictor here if endpoints is None: if self.predictor_sdk_ is None: raise ValueError( "You must set the endpoints parameter or use add_variant function to create a variant." ) else: if encryption: endpoints = self.get_serving_port(endpoints) if self.predictor_sdk_ is None: self.add_variant('default_tag_{}'.format(id(self)), endpoints, 100) else: print( "parameter endpoints({}) will not take effect, because you use the add_variant function.". format(endpoints)) sdk_desc = self.predictor_sdk_.gen_desc(self.rpc_timeout_ms) 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): if key in self.lod_tensor_set: return if isinstance(feed[key], list) and len(feed[key]) != self.feed_tensor_len[key]: raise ValueError("The shape of feed tensor {} not match.".format( key)) if type(feed[key]).__module__ == np.__name__ and np.size(feed[ key]) != self.feed_tensor_len[key]: #raise SystemExit("The shape of feed tensor {} not match.".format( # key)) pass def predict(self, feed=None, fetch=None, batch=False, need_variant_tag=False, log_id=0): self.profile_.record('py_prepro_0') # fetch 可以为空，此时会取所有的输出结果 if feed is None: raise ValueError("You should specify feed for prediction") fetch_list = [] if isinstance(fetch, str): fetch_list = [fetch] elif isinstance(fetch, list): fetch_list = fetch # fetch 可以为空，此时会取所有的输出结果 elif fetch == None: pass else: raise ValueError("Fetch only accepts string or list of string") feed_batch = [] if isinstance(feed, dict): feed_batch.append(feed) elif isinstance(feed, list): # feed = [dict] if len(feed) == 1 and isinstance(feed[0], dict): feed_batch = feed else: # if input is a list and the number of feed_var is 1. # create a temp_dict { key = feed_var_name, value = list} # put the temp_dict into the feed_batch. if len(self.feed_names_) != 1: raise ValueError( "input is a list, but we got 0 or 2+ feed_var, don`t know how to divide the feed list" ) temp_dict = {} temp_dict[self.feed_names_[0]] = feed feed_batch.append(temp_dict) else: raise ValueError("Feed only accepts dict and list of dict") # batch_size must be 1, cause batch is already in Tensor. if len(feed_batch) != 1: raise ValueError("len of feed_batch can only be 1.") int32_slot = [] int32_feed_names = [] int32_shape = [] int32_lod_slot_batch = [] int64_slot = [] int64_feed_names = [] int64_shape = [] int64_lod_slot_batch = [] float_slot = [] float_feed_names = [] float_lod_slot_batch = [] float_shape = [] string_slot = [] string_feed_names = [] string_lod_slot_batch = [] string_shape = [] fetch_names = [] for key in fetch_list: if key in self.fetch_names_: fetch_names.append(key) feed_dict = feed_batch[0] for key in feed_dict: if ".lod" not in key and key not in self.feed_names_: raise ValueError("Wrong feed name: {}.".format(key)) if ".lod" in key: continue self.shape_check(feed_dict, key) if self.feed_types_[key] in int_type: shape_lst = [] if batch == False: feed_dict[key] = np.expand_dims(feed_dict[key], 0).repeat( 1, axis=0) # verify different input int_type if(self.feed_types_[key] == int64_type): int64_feed_names.append(key) if isinstance(feed_dict[key], np.ndarray): shape_lst.extend(list(feed_dict[key].shape)) int64_shape.append(shape_lst) self.has_numpy_input = True else: int64_shape.append(self.feed_shapes_[key]) self.all_numpy_input = False if "{}.lod".format(key) in feed_dict: int64_lod_slot_batch.append(feed_dict["{}.lod".format(key)]) else: int64_lod_slot_batch.append([]) int64_slot.append(np.ascontiguousarray(feed_dict[key])) else: int32_feed_names.append(key) if isinstance(feed_dict[key], np.ndarray): shape_lst.extend(list(feed_dict[key].shape)) int32_shape.append(shape_lst) self.has_numpy_input = True else: int32_shape.append(self.feed_shapes_[key]) self.all_numpy_input = False if "{}.lod".format(key) in feed_dict: int32_lod_slot_batch.append(feed_dict["{}.lod".format(key)]) else: int32_lod_slot_batch.append([]) int32_slot.append(np.ascontiguousarray(feed_dict[key])) elif self.feed_types_[key] in float_type: float_feed_names.append(key) shape_lst = [] if batch == False: feed_dict[key] = np.expand_dims(feed_dict[key], 0).repeat( 1, axis=0) if isinstance(feed_dict[key], np.ndarray): shape_lst.extend(list(feed_dict[key].shape)) float_shape.append(shape_lst) else: float_shape.append(self.feed_shapes_[key]) if "{}.lod".format(key) in feed_dict: float_lod_slot_batch.append(feed_dict["{}.lod".format(key)]) else: float_lod_slot_batch.append([]) if isinstance(feed_dict[key], np.ndarray): float_slot.append(np.ascontiguousarray(feed_dict[key])) self.has_numpy_input = True else: float_slot.append(np.ascontiguousarray(feed_dict[key])) self.all_numpy_input = False #if input is string, feed is not numpy. elif self.feed_types_[key] in string_type: string_feed_names.append(key) string_shape.append(self.feed_shapes_[key]) if "{}.lod".format(key) in feed_dict: string_lod_slot_batch.append(feed_dict["{}.lod".format( key)]) else: string_lod_slot_batch.append([]) if type(feed_dict[key]) is np.ndarray: string_slot.append(feed_dict[key].tostring()) else: string_slot.append(feed_dict[key]) self.has_numpy_input = True self.profile_.record('py_prepro_1') self.profile_.record('py_client_infer_0') result_batch_handle = self.predictorres_constructor() if self.all_numpy_input: res = self.client_handle_.numpy_predict( float_slot, float_feed_names, float_shape, float_lod_slot_batch, int32_slot, int32_feed_names, int32_shape, int32_lod_slot_batch, int64_slot, int64_feed_names, int64_shape, int64_lod_slot_batch, string_slot, string_feed_names, string_shape, string_lod_slot_batch, fetch_names, result_batch_handle, self.pid, log_id) elif self.has_numpy_input == False: raise ValueError( "Please make sure all of your inputs are numpy array") else: raise ValueError( "Please make sure the inputs are all in list type or all in numpy.array type" ) self.profile_.record('py_client_infer_1') self.profile_.record('py_postpro_0') if res == -1: return None multi_result_map = [] model_engine_names = result_batch_handle.get_engine_names() for mi, engine_name in enumerate(model_engine_names): result_map = {} # fetch 为空，则会取所有的输出结果 if len(fetch_names) == 0: fetch_names = result_batch_handle.get_tensor_alias_names(mi) # result map needs to be a numpy array for i, name in enumerate(fetch_names): if self.fetch_names_to_type_[name] == int64_type: # result_map[name] will be py::array(numpy array) result_map[name] = result_batch_handle.get_int64_by_name( mi, name) shape = result_batch_handle.get_shape(mi, name) if result_map[name].size == 0: raise ValueError( "Failed to fetch, maybe the type of [{}]" " is wrong, please check the model file".format( name)) result_map[name].shape = shape if name in self.lod_tensor_set: tmp_lod = result_batch_handle.get_lod(mi, name) if np.size(tmp_lod) > 0: result_map["{}.lod".format(name)] = tmp_lod elif self.fetch_names_to_type_[name] == float32_type: result_map[name] = result_batch_handle.get_float_by_name( mi, name) if result_map[name].size == 0: raise ValueError( "Failed to fetch, maybe the type of [{}]" " is wrong, please check the model file".format( name)) shape = result_batch_handle.get_shape(mi, name) result_map[name].shape = shape if name in self.lod_tensor_set: tmp_lod = result_batch_handle.get_lod(mi, name) if np.size(tmp_lod) > 0: result_map["{}.lod".format(name)] = tmp_lod elif self.fetch_names_to_type_[name] == int32_type: # result_map[name] will be py::array(numpy array) result_map[name] = result_batch_handle.get_int32_by_name( mi, name) if result_map[name].size == 0: raise ValueError( "Failed to fetch, maybe the type of [{}]" " is wrong, please check the model file".format( name)) shape = result_batch_handle.get_shape(mi, name) result_map[name].shape = shape if name in self.lod_tensor_set: tmp_lod = result_batch_handle.get_lod(mi, name) if np.size(tmp_lod) > 0: result_map["{}.lod".format(name)] = tmp_lod elif self.fetch_names_to_type_[name] == uint8_type: # result_map[name] will be py::array(numpy array) tmp_str = result_batch_handle.get_string_by_name( mi, name) result_map[name] = np.fromstring(tmp_str, dtype = np.uint8) if result_map[name].size == 0: raise ValueError( "Failed to fetch, maybe the type of [{}]" " is wrong, please check the model file".format( name)) shape = result_batch_handle.get_shape(mi, name) result_map[name].shape = shape if name in self.lod_tensor_set: tmp_lod = result_batch_handle.get_lod(mi, name) if np.size(tmp_lod) > 0: result_map["{}.lod".format(name)] = tmp_lod elif self.fetch_names_to_type_[name] == int8_type: # result_map[name] will be py::array(numpy array) tmp_str = result_batch_handle.get_string_by_name( mi, name) result_map[name] = np.fromstring(tmp_str, dtype = np.int8) if result_map[name].size == 0: raise ValueError( "Failed to fetch, maybe the type of [{}]" " is wrong, please check the model file".format( name)) shape = result_batch_handle.get_shape(mi, name) result_map[name].shape = shape if name in self.lod_tensor_set: tmp_lod = result_batch_handle.get_lod(mi, name) if np.size(tmp_lod) > 0: result_map["{}.lod".format(name)] = tmp_lod multi_result_map.append(result_map) ret = None if len(model_engine_names) == 1: # If only one model result is returned, the format of ret is result_map ret = multi_result_map[0] else: # If multiple model results are returned, the format of ret is {name: result_map} ret = { engine_name: multi_result_map[mi] for mi, engine_name in enumerate(model_engine_names) } self.profile_.record('py_postpro_1') self.profile_.print_profile() # When using the A/B test, the tag of variant needs to be returned return ret if not need_variant_tag else [ ret, result_batch_handle.variant_tag() ] def release(self): self.client_handle_.destroy_predictor() self.client_handle_ = None