Merge branch 'develop' into fix_encryption_getdata.sh

cmake/paddlepaddle.cmake

@@ -136,8 +136,8 @@ if (WITH_TRT)
 endif()
 
 if (WITH_LITE)
-    ADD_LIBRARY(paddle_api_full_bundled STATIC IMPORTED GLOBAL)
-    SET_PROPERTY(TARGET paddle_api_full_bundled PROPERTY IMPORTED_LOCATION ${PADDLE_INSTALL_DIR}/third_party/install/lite/cxx/lib/libpaddle_api_full_bundled.a)
+    ADD_LIBRARY(paddle_full_api_shared STATIC IMPORTED GLOBAL)
+    SET_PROPERTY(TARGET paddle_full_api_shared PROPERTY IMPORTED_LOCATION ${PADDLE_INSTALL_DIR}/third_party/install/lite/cxx/lib/libpaddle_full_api_shared.so)
     
     if (WITH_XPU)
         ADD_LIBRARY(xpuapi SHARED IMPORTED GLOBAL)
@@ -157,7 +157,7 @@ LIST(APPEND paddle_depend_libs
     xxhash)
 
 if(WITH_LITE)
-    LIST(APPEND paddle_depend_libs paddle_api_full_bundled)
+    LIST(APPEND paddle_depend_libs paddle_full_api_shared)
     if(WITH_XPU)
         LIST(APPEND paddle_depend_libs xpuapi xpurt)
     endif()

doc/FAQ.md

@@ -34,6 +34,42 @@
 
 **A:** http rpc 
 
+## 安装问题
+
+#### Q: pip install安装whl包过程，报错信息如下：
+```
+Collecting opencv-python
+  Using cached opencv-python-4.3.0.38.tar.gz (88.0 MB)
+  Installing build dependencies ... done
+  Getting requirements to build wheel ... error
+  ERROR: Command errored out with exit status 1:
+   command: /home/work/Python-2.7.17/build/bin/python /home/work/Python-2.7.17/build/lib/python2.7/site-packages/pip/_vendor/pep517/_in_process.py get_requires_for_build_wheel /tmp/tmpLiweA9
+       cwd: /tmp/pip-install-_w6AUI/opencv-python
+  Complete output (22 lines):
+  Traceback (most recent call last):
+    File "/home/work/Python-2.7.17/build/lib/python2.7/site-packages/pip/_vendor/pep517/_in_process.py", line 280, in <module>
+      main()
+    File "/home/work/Python-2.7.17/build/lib/python2.7/site-packages/pip/_vendor/pep517/_in_process.py", line 263, in main
+      json_out['return_val'] = hook(**hook_input['kwargs'])
+    File "/home/work/Python-2.7.17/build/lib/python2.7/site-packages/pip/_vendor/pep517/_in_process.py", line 114, in get_requires_for_build_wheel
+      return hook(config_settings)
+    File "/tmp/pip-build-env-AUCbP4/overlay/lib/python2.7/site-packages/setuptools/build_meta.py", line 146, in get_requires_for_build_wheel
+      return self._get_build_requires(config_settings, requirements=['wheel'])
+    File "/tmp/pip-build-env-AUCbP4/overlay/lib/python2.7/site-packages/setuptools/build_meta.py", line 127, in _get_build_requires
+      self.run_setup()
+    File "/tmp/pip-build-env-AUCbP4/overlay/lib/python2.7/site-packages/setuptools/build_meta.py", line 243, in run_setup
+      self).run_setup(setup_script=setup_script)
+    File "/tmp/pip-build-env-AUCbP4/overlay/lib/python2.7/site-packages/setuptools/build_meta.py", line 142, in run_setup
+      exec(compile(code, __file__, 'exec'), locals())
+    File "setup.py", line 448, in <module>
+      main()
+    File "setup.py", line 99, in main
+      % {"ext": re.escape(sysconfig.get_config_var("EXT_SUFFIX"))}
+    File "/home/work/Python-2.7.17/build/lib/python2.7/re.py", line 210, in escape
+      s = list(pattern)
+  TypeError: 'NoneType' object is not iterable
+```
+**A:** 指定opencv-python版本安装，pip install opencv-python==4.2.0.32，再安装whl包
 
 ## 编译问题
 

java/examples/src/main/java/PipelineClientExample.java

@@ -62,7 +62,7 @@ public class PipelineClientExample {
                 return false;
             }
         }
-        PipelineFuture future = StaticPipelineClient.client.asyn_pr::qedict(feed_data, fetch,false,0);
+        PipelineFuture future = StaticPipelineClient.client.asyn_predict(feed_data, fetch,false,0);
         HashMap<String,String> result = future.get();
         if (result == null) {
             return false;

java/examples/src/main/java/StaticPipelineClient.java

@@ -37,7 +37,7 @@ public class StaticPipelineClient {
             System.out.println("already connect.");
             return true;
         }
-        succ = clieint.connect(target);
+        succ = client.connect(target);
         if (succ != true) {
             System.out.println("connect failed.");
             return false;

paddle_inference/inferencer-fluid-arm/include/fluid_arm_engine.h

@@ -128,20 +128,22 @@ class FluidArmAnalysisCore : public FluidFamilyCore {
     config.DisableGpu();
     config.SetCpuMathLibraryNumThreads(1);
 
-    if (params.enable_memory_optimization()) {
-      config.EnableMemoryOptim();
+    if (params.use_lite()) {
+      config.EnableLiteEngine(PrecisionType::kFloat32, true);
     }
 
-    if (params.enable_memory_optimization()) {
-      config.EnableMemoryOptim();
+    if (params.use_xpu()) {
+      config.EnableXpu(2 * 1024 * 1024);
     }
 
-    if (params.use_lite()) {
-      config.EnableLiteEngine(PrecisionType::kFloat32, true);
+    if (params.enable_memory_optimization()) {
+      config.EnableMemoryOptim();
     }
 
-    if (params.use_xpu()) {
-      config.EnableXpu(100);
+    if (params.enable_ir_optimization()) {
+      config.SwitchIrOptim(true);
+    } else {
+      config.SwitchIrOptim(false);
     }
 
     config.SwitchSpecifyInputNames(true);
@@ -173,6 +175,14 @@ class FluidArmAnalysisDirCore : public FluidFamilyCore {
     config.SwitchSpecifyInputNames(true);
     config.SetCpuMathLibraryNumThreads(1);
 
+    if (params.use_lite()) {
+      config.EnableLiteEngine(PrecisionType::kFloat32, true);
+    }
+
+    if (params.use_xpu()) {
+      config.EnableXpu(2 * 1024 * 1024);
+    }
+
     if (params.enable_memory_optimization()) {
       config.EnableMemoryOptim();
     }
@@ -183,14 +193,6 @@ class FluidArmAnalysisDirCore : public FluidFamilyCore {
       config.SwitchIrOptim(false);
     }
 
-    if (params.use_lite()) {
-      config.EnableLiteEngine(PrecisionType::kFloat32, true);
-    }
-
-    if (params.use_xpu()) {
-      config.EnableXpu(100);
-    }
-
     AutoLock lock(GlobalPaddleCreateMutex::instance());
     _core = CreatePredictor(config);
     if (NULL == _core.get()) {

python/CMakeLists.txt

@@ -99,15 +99,27 @@ if (SERVER)
             DEPENDS ${SERVING_SERVER_CORE} server_config_py_proto ${PY_FILES})
         add_custom_target(paddle_python ALL DEPENDS ${PADDLE_SERVING_BINARY_DIR}/.timestamp)
     elseif(WITH_LITE)
-        add_custom_command(
-            OUTPUT ${PADDLE_SERVING_BINARY_DIR}/.timestamp
-            COMMAND cp -r
-            ${CMAKE_CURRENT_SOURCE_DIR}/paddle_serving_server_gpu/ ${PADDLE_SERVING_BINARY_DIR}/python/
-            COMMAND env ${py_env} ${PYTHON_EXECUTABLE} gen_version.py
-            "server_gpu" arm 
-            COMMAND env ${py_env} ${PYTHON_EXECUTABLE} setup.py bdist_wheel
-            DEPENDS ${SERVING_SERVER_CORE} server_config_py_proto ${PY_FILES})
-        add_custom_target(paddle_python ALL DEPENDS ${PADDLE_SERVING_BINARY_DIR}/.timestamp)
+        if(WITH_XPU)
+            add_custom_command(
+                OUTPUT ${PADDLE_SERVING_BINARY_DIR}/.timestamp
+                COMMAND cp -r
+                ${CMAKE_CURRENT_SOURCE_DIR}/paddle_serving_server_gpu/ ${PADDLE_SERVING_BINARY_DIR}/python/
+                COMMAND env ${py_env} ${PYTHON_EXECUTABLE} gen_version.py
+                "server_gpu" arm-xpu 
+                COMMAND env ${py_env} ${PYTHON_EXECUTABLE} setup.py bdist_wheel
+                DEPENDS ${SERVING_SERVER_CORE} server_config_py_proto ${PY_FILES})
+            add_custom_target(paddle_python ALL DEPENDS ${PADDLE_SERVING_BINARY_DIR}/.timestamp)
+        else()
+            add_custom_command(
+                OUTPUT ${PADDLE_SERVING_BINARY_DIR}/.timestamp
+                COMMAND cp -r
+                ${CMAKE_CURRENT_SOURCE_DIR}/paddle_serving_server_gpu/ ${PADDLE_SERVING_BINARY_DIR}/python/
+                COMMAND env ${py_env} ${PYTHON_EXECUTABLE} gen_version.py
+                "server_gpu" arm 
+                COMMAND env ${py_env} ${PYTHON_EXECUTABLE} setup.py bdist_wheel
+                DEPENDS ${SERVING_SERVER_CORE} server_config_py_proto ${PY_FILES})
+            add_custom_target(paddle_python ALL DEPENDS ${PADDLE_SERVING_BINARY_DIR}/.timestamp)
+        endif()
     else()
         add_custom_command(
             OUTPUT ${PADDLE_SERVING_BINARY_DIR}/.timestamp

python/examples/bert/README.md

@@ -3,9 +3,10 @@
 ([简体中文](./README_CN.md)|English)
 
 In the example, a BERT model is used for semantic understanding prediction, and the text is represented as a vector, which can be used for further analysis and prediction.
+If your python version is 3.X, replace the 'pip' field in the following command with 'pip3',replace 'python' with 'python3'.
 
 ### Getting Model
-
+method 1:
 This example use model [BERT Chinese Model](https://www.paddlepaddle.org.cn/hubdetail?name=bert_chinese_L-12_H-768_A-12&en_category=SemanticModel) from [Paddlehub](https://github.com/PaddlePaddle/PaddleHub).
 
 Install paddlehub first
@@ -22,11 +23,13 @@ the 128 in the command above means max_seq_len in BERT model, which is the lengt
 the config file and model file for server side are saved in the folder bert_seq128_model.
 the config file generated for client side is saved in the folder bert_seq128_client.
 
+method 2:
 You can also download the above model from BOS(max_seq_len=128). After decompression, the config file and model file for server side are stored in the bert_chinese_L-12_H-768_A-12_model folder, and the config file generated for client side is stored in the bert_chinese_L-12_H-768_A-12_client folder:
 ```shell
 wget https://paddle-serving.bj.bcebos.com/paddle_hub_models/text/SemanticModel/bert_chinese_L-12_H-768_A-12.tar.gz
 tar -xzf bert_chinese_L-12_H-768_A-12.tar.gz
 ```
+if your model is bert_chinese_L-12_H-768_A-12_model, replace the 'bert_seq128_model' field in the following command with 'bert_chinese_L-12_H-768_A-12_model',replace 'bert_seq128_client' with 'bert_chinese_L-12_H-768_A-12_client'.
 
 ### Getting Dict and Sample Dataset
 
@@ -36,11 +39,11 @@ sh get_data.sh
 this script will download Chinese Dictionary File vocab.txt and Chinese Sample Data data-c.txt
 
 ### RPC Inference Service
-Run
+start cpu inference service,Run
 ```
 python -m paddle_serving_server.serve --model bert_seq128_model/ --port 9292  #cpu inference service
 ```
-Or
+Or,start gpu inference service,Run
 ```
 python -m paddle_serving_server_gpu.serve --model bert_seq128_model/ --port 9292 --gpu_ids 0 #launch gpu inference service at GPU 0
 ```
@@ -59,12 +62,18 @@ head data-c.txt | python bert_client.py --model bert_seq128_client/serving_clien
 the client reads data from data-c.txt and send prediction request, the prediction is given by word vector. (Due to massive data in the word vector, we do not print it).
 
 ### HTTP Inference Service
+start cpu HTTP inference service,Run
+```
+ python bert_web_service.py bert_seq128_model/ 9292 #launch gpu inference service
+```
+
+Or,start gpu HTTP inference service,Run
 ```
  export CUDA_VISIBLE_DEVICES=0,1
 ```
 set environmental variable to specify which gpus are used, the command above means gpu 0 and gpu 1 is used.
 ```
- python bert_web_service.py bert_seq128_model/ 9292 #launch gpu inference service
+ python bert_web_service_gpu.py bert_seq128_model/ 9292 #launch gpu inference service
 ```
 ### HTTP Inference 
 

python/examples/bert/README_CN.md

@@ -4,8 +4,9 @@
 
 示例中采用BERT模型进行语义理解预测，将文本表示为向量的形式，可以用来做进一步的分析和预测。
 
+若使用python的版本为3.X, 将以下命令中的pip 替换为pip3, python替换为python3.
 ### 获取模型
-
+方法1：
 示例中采用[Paddlehub](https://github.com/PaddlePaddle/PaddleHub)中的[BERT中文模型](https://www.paddlepaddle.org.cn/hubdetail?name=bert_chinese_L-12_H-768_A-12&en_category=SemanticModel)。
 请先安装paddlehub
 ```
@@ -19,11 +20,15 @@ python prepare_model.py 128
 生成server端配置文件与模型文件，存放在bert_seq128_model文件夹。
 生成client端配置文件，存放在bert_seq128_client文件夹。
 
+方法2：
 您也可以从bos上直接下载上述模型（max_seq_len=128），解压后server端配置文件与模型文件存放在bert_chinese_L-12_H-768_A-12_model文件夹，client端配置文件存放在bert_chinese_L-12_H-768_A-12_client文件夹：
 ```shell
 wget https://paddle-serving.bj.bcebos.com/paddle_hub_models/text/SemanticModel/bert_chinese_L-12_H-768_A-12.tar.gz
 tar -xzf bert_chinese_L-12_H-768_A-12.tar.gz
 ```
+若使用bert_chinese_L-12_H-768_A-12_model模型，将下面命令中的bert_seq128_model字段替换为bert_chinese_L-12_H-768_A-12_model，bert_seq128_client字段替换为bert_chinese_L-12_H-768_A-12_client.
+
+
 
 ### 获取词典和样例数据
 
@@ -33,13 +38,15 @@ sh get_data.sh
 脚本将下载中文词典vocab.txt和中文样例数据data-c.txt
 
 ### 启动RPC预测服务
-执行
+启动cpu预测服务，执行
 ```
 python -m paddle_serving_server.serve --model bert_seq128_model/ --port 9292  #启动cpu预测服务
+
 ```
-或者
+或者，启动gpu预测服务，执行
 ```
 python -m paddle_serving_server_gpu.serve --model bert_seq128_model/ --port 9292 --gpu_ids 0 #在gpu 0上启动gpu预测服务
+
 ```
 
 ### 执行预测
@@ -51,17 +58,28 @@ pip install paddle_serving_app
 执行
 ```
 head data-c.txt | python bert_client.py --model bert_seq128_client/serving_client_conf.prototxt
+
 ```
 启动client读取data-c.txt中的数据进行预测，预测结果为文本的向量表示（由于数据较多，脚本中没有将输出进行打印），server端的地址在脚本中修改。
 
+
+
 ### 启动HTTP预测服务
+启动cpu HTTP预测服务，执行
+```
+python bert_web_service.py bert_seq128_model/ 9292 #启动gpu预测服务
+
+```
+
+或者，启动gpu HTTP预测服务，执行
 ```
  export CUDA_VISIBLE_DEVICES=0,1
 ```
 通过环境变量指定gpu预测服务使用的gpu，示例中指定索引为0和1的两块gpu
 ```
- python bert_web_service.py bert_seq128_model/ 9292 #启动gpu预测服务
+python bert_web_service_gpu.py bert_seq128_model/ 9292 #启动gpu预测服务
 ```
+
 ### 执行预测
 
 ```

python/examples/bert/bert_web_service_gpu.py

+# coding=utf-8
+# 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
+from paddle_serving_server_gpu.web_service import WebService
+from paddle_serving_app.reader import ChineseBertReader
+import sys
+import os
+import numpy as np
+
+
+class BertService(WebService):
+    def load(self):
+        self.reader = ChineseBertReader({
+            "vocab_file": "vocab.txt",
+            "max_seq_len": 128
+        })
+
+    def preprocess(self, feed=[], fetch=[]):
+        feed_res = []
+        is_batch = False
+        for ins in feed:
+            feed_dict = self.reader.process(ins["words"].encode("utf-8"))
+            for key in feed_dict.keys():
+                feed_dict[key] = np.array(feed_dict[key]).reshape(
+                    (len(feed_dict[key]), 1))
+            feed_res.append(feed_dict)
+        return feed_res, fetch, is_batch
+
+
+bert_service = BertService(name="bert")
+bert_service.load()
+bert_service.load_model_config(sys.argv[1])
+bert_service.prepare_server(
+    workdir="workdir", port=int(sys.argv[2]), device="gpu")
+bert_service.run_rpc_service()
+bert_service.run_web_service()

python/paddle_serving_app/local_predict.py

@@ -132,6 +132,7 @@ class LocalPredictor(object):
                 ops_filter=[])
 
         if use_xpu:
+            # 2MB l3 cache
             config.enable_xpu(8 * 1024 * 1024)
 
         self.predictor = create_paddle_predictor(config)

python/paddle_serving_server/web_service.py

@@ -20,7 +20,7 @@ from paddle_serving_server import OpMaker, OpSeqMaker, Server
 from paddle_serving_client import Client
 from contextlib import closing
 import socket
-
+import numpy as np
 from paddle_serving_server import pipeline
 from paddle_serving_server.pipeline import Op
 
@@ -64,8 +64,8 @@ class WebService(object):
         f = open(client_config, 'r')
         model_conf = google.protobuf.text_format.Merge(
             str(f.read()), model_conf)
-        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_vars = {var.name: var for var in model_conf.feed_var}
+        self.fetch_vars = {var.name: var for var in model_conf.fetch_var}
 
     def _launch_rpc_service(self):
         op_maker = OpMaker()
@@ -201,6 +201,15 @@ class WebService(object):
     def preprocess(self, feed=[], fetch=[]):
         print("This API will be deprecated later. Please do not use it")
         is_batch = True
+        feed_dict = {}
+        for var_name in self.feed_vars.keys():
+            feed_dict[var_name] = []
+        for feed_ins in feed:
+            for key in feed_ins:
+                feed_dict[key].append(np.array(feed_ins[key]).reshape(list(self.feed_vars[key].shape))[np.newaxis,:])
+        feed = {}
+        for key in feed_dict:
+            feed[key] = np.concatenate(feed_dict[key], axis=0) 
         return feed, fetch, is_batch
 
     def postprocess(self, feed=[], fetch=[], fetch_map=None):

python/paddle_serving_server_gpu/__init__.py

@@ -212,6 +212,7 @@ class Server(object):
         self.module_path = os.path.dirname(paddle_serving_server.__file__)
         self.cur_path = os.getcwd()
         self.use_local_bin = False
+        self.device = "cpu"
         self.gpuid = 0
         self.use_trt = False
         self.use_lite = False
@@ -279,6 +280,9 @@ class Server(object):
                 "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
 
@@ -311,18 +315,19 @@ class Server(object):
             engine.static_optimization = False
             engine.force_update_static_cache = False
             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)):
+                suffix = ""
+            else:
+                suffix = "_DIR" 
+            if device == "arm":
+                engine.use_lite = self.use_lite
+                engine.use_xpu = self.use_xpu
             if device == "cpu":
-                engine.type = "FLUID_CPU_ANALYSIS_DIR"
+                engine.type = "FLUID_CPU_ANALYSIS" + suffix
             elif device == "gpu":
-                engine.type = "FLUID_GPU_ANALYSIS_DIR"
+                engine.type = "FLUID_GPU_ANALYSIS" + suffix
             elif device == "arm":
-                engine.type = "FLUID_ARM_ANALYSIS_DIR"
-
+                engine.type = "FLUID_ARM_ANALYSIS" + suffix
             self.model_toolkit_conf.engines.extend([engine])
 
     def _prepare_infer_service(self, port):
@@ -425,7 +430,7 @@ class Server(object):
                 cuda_version = line.split("\"")[1]
                 if cuda_version == "101" or cuda_version == "102" or cuda_version == "110":
                     device_version = "serving-gpu-" + cuda_version + "-"
-                elif cuda_version == "arm":
+                elif cuda_version == "arm" or cuda_version == "arm-xpu":
                     device_version = "serving-" + cuda_version + "-"
                 else:
                     device_version = "serving-gpu-cuda" + cuda_version + "-"
@@ -528,7 +533,8 @@ class Server(object):
         else:
             print("Use local bin : {}".format(self.bin_path))
         #self.check_cuda()
-        if self.use_lite:
+        # 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 {} " \

python/paddle_serving_server_gpu/serve.py

@@ -73,6 +73,7 @@ def start_gpu_card_model(index, gpuid, args):  # pylint: disable=doc-string-miss
         server.set_lite()
         device = "arm"
 
+    server.set_device(device)
     if args.use_xpu:
         server.set_xpu()
 

python/paddle_serving_server_gpu/web_service.py

@@ -70,8 +70,8 @@ class WebService(object):
         f = open(client_config, 'r')
         model_conf = google.protobuf.text_format.Merge(
             str(f.read()), model_conf)
-        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_vars = {var.name: var for var in model_conf.feed_var}
+        self.fetch_vars = {var.name: var for var in model_conf.fetch_var}
 
     def set_gpus(self, gpus):
         print("This API will be deprecated later. Please do not use it")
@@ -107,6 +107,7 @@ class WebService(object):
         server.set_num_threads(thread_num)
         server.set_memory_optimize(mem_optim)
         server.set_ir_optimize(ir_optim)
+        server.set_device(device)
 
         if use_lite:
             server.set_lite()
@@ -278,6 +279,15 @@ class WebService(object):
     def preprocess(self, feed=[], fetch=[]):
         print("This API will be deprecated later. Please do not use it")
         is_batch = True
+        feed_dict = {}
+        for var_name in self.feed_vars.keys():
+            feed_dict[var_name] = []
+        for feed_ins in feed:
+            for key in feed_ins:
+                feed_dict[key].append(np.array(feed_ins[key]).reshape(list(self.feed_vars[key].shape))[np.newaxis,:])
+        feed = {}
+        for key in feed_dict:
+            feed[key] = np.concatenate(feed_dict[key], axis=0)
         return feed, fetch, is_batch
 
     def postprocess(self, feed=[], fetch=[], fetch_map=None):

python/pipeline/local_service_handler.py

@@ -249,6 +249,8 @@ class LocalServiceHandler(object):
             server = Server()
             if gpuid >= 0:
                 server.set_gpuid(gpuid)
+            # TODO: support arm or arm + xpu later
+            server.set_device(self._device_name)
 
         server.set_op_sequence(op_seq_maker.get_op_sequence())
         server.set_num_threads(thread_num)