[Other] Change all XPU to KunlunXin (#973)

* [FlyCV] Bump up FlyCV -> official release 1.0.0

* XPU to KunlunXin

* update

* update model link

* update doc

* update device

* update code

* useless code

Co-authored-by: DefTruth <qiustudent_r@163.com>
Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>

CMakeLists.txt

@@ -66,7 +66,7 @@ option(ENABLE_TEXT "Whether to enable text models usage." OFF)
 option(ENABLE_FLYCV "Whether to enable flycv to boost image preprocess." OFF)
 option(WITH_ASCEND "Whether to compile for Huawei Ascend deploy." OFF)
 option(WITH_TIMVX "Whether to compile for TIMVX deploy." OFF)
-option(WITH_XPU "Whether to compile for KunlunXin XPU deploy." OFF)
+option(WITH_KUNLUNXIN "Whether to compile for KunlunXin XPU deploy." OFF)
 option(WITH_TESTING "Whether to compile with unittest." OFF)
 ############################# Options for Android cross compiling #########################
 option(WITH_OPENCV_STATIC "Use OpenCV static lib for Android." OFF)
@@ -148,12 +148,12 @@ if (WITH_ASCEND)
   include(${PROJECT_SOURCE_DIR}/cmake/ascend.cmake)
 endif()
 
-if (WITH_XPU)
+if (WITH_KUNLUNXIN)
   if(NOT ENABLE_LITE_BACKEND)
       set(ENABLE_LITE_BACKEND ON)
   endif()
   if(NOT CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "x86_64")
-    message(FATAL_ERROR "XPU is only supported on Linux x64 platform")
+    message(FATAL_ERROR "KunlunXin XPU is only supported on Linux x64 platform")
   endif()
   if(NOT PADDLELITE_URL)
     set(PADDLELITE_URL "https://bj.bcebos.com/fastdeploy/third_libs/lite-linux-x64-xpu-20221215.tgz")

FastDeploy.cmake.in

@@ -27,7 +27,7 @@ set(OPENCV_DIRECTORY "@OPENCV_DIRECTORY@")
 set(ORT_DIRECTORY "@ORT_DIRECTORY@")
 set(OPENVINO_DIRECTORY "@OPENVINO_DIRECTORY@")
 set(RKNN2_TARGET_SOC "@RKNN2_TARGET_SOC@")
-set(WITH_XPU @WITH_XPU@)
+set(WITH_KUNLUNXIN @WITH_KUNLUNXIN@)
 
 set(FASTDEPLOY_LIBS "")
 set(FASTDEPLOY_INCS "")
@@ -246,7 +246,7 @@ if(ENABLE_PADDLE_FRONTEND)
   list(APPEND FASTDEPLOY_LIBS ${PADDLE2ONNX_LIB})
 endif()
 
-if(WITH_XPU)
+if(WITH_KUNLUNXIN)
   list(APPEND FASTDEPLOY_LIBS -lpthread -lrt -ldl)
 endif()
 

cmake/summary.cmake

@@ -39,7 +39,7 @@ function(fastdeploy_summary)
   message(STATUS "  ENABLE_OPENVINO_BACKEND   : ${ENABLE_OPENVINO_BACKEND}")
   message(STATUS "  WITH_ASCEND               : ${WITH_ASCEND}")
   message(STATUS "  WITH_TIMVX                : ${WITH_TIMVX}")
-  message(STATUS "  WITH_XPU                  : ${WITH_XPU}")
+  message(STATUS "  WITH_KUNLUNXIN            : ${WITH_KUNLUNXIN}")
   if(ENABLE_ORT_BACKEND)
     message(STATUS "  ONNXRuntime version       : ${ONNXRUNTIME_VERSION}")
   endif()

docs/README.md

@@ -8,7 +8,7 @@
 - [Build and Install FastDeploy Library on GPU Platform](en/build_and_install/gpu.md)
 - [Build and Install FastDeploy Library on CPU Platform](en/build_and_install/cpu.md)
 - [Build and Install FastDeploy Library on IPU Platform](en/build_and_install/ipu.md)
-- [Build and Install FastDeploy Library on KunlunXin XPU Platform](en/build_and_install/xpu.md)
+- [Build and Install FastDeploy Library on KunlunXin XPU Platform](en/build_and_install/kunlunxin.md)
 - [Build and Install on RV1126 Platform](en/build_and_install/rv1126.md)
 - [Build and Install on RK3588 and RK356X Platform](en/build_and_install/rknpu2.md)
 - [Build and Install on A311D Platform](en/build_and_install/a311d.md)

docs/README_CN.md

@@ -8,7 +8,7 @@
 - [GPU部署环境编译安装](cn/build_and_install/gpu.md)
 - [CPU部署环境编译安装](cn/build_and_install/cpu.md)
 - [IPU部署环境编译安装](cn/build_and_install/ipu.md)
-- [昆仑芯XPU部署环境编译安装](cn/build_and_install/xpu.md)
+- [昆仑芯XPU部署环境编译安装](cn/build_and_install/kunlunxin.md)
 - [瑞芯微RV1126部署环境编译安装](cn/build_and_install/rv1126.md)
 - [瑞芯微RK3588部署环境编译安装](cn/build_and_install/rknpu2.md)
 - [晶晨A311D部署环境编译安装](cn/build_and_install/a311d.md)

docs/README_EN.md

@@ -8,7 +8,7 @@
 - [Build and Install FastDeploy Library on GPU Platform](en/build_and_install/gpu.md)
 - [Build and Install FastDeploy Library on CPU Platform](en/build_and_install/cpu.md)
 - [Build and Install FastDeploy Library on IPU Platform](en/build_and_install/ipu.md)
-- [Build and Install FastDeploy Library on KunlunXin XPU Platform](en/build_and_install/xpu.md)
+- [Build and Install FastDeploy Library on KunlunXin XPU Platform](en/build_and_install/kunlunxin.md)
 - [Build and Install on RV1126 Platform](en/build_and_install/rv1126.md)
 - [Build and Install on RK3588 Platform](en/build_and_install/rknpu2.md)
 - [Build and Install on A311D Platform](en/build_and_install/a311d.md)

docs/cn/build_and_install/README.md

@@ -14,7 +14,7 @@
 - [瑞芯微RV1126部署环境](rv1126.md)
 - [瑞芯微RK3588部署环境](rknpu2.md)
 - [晶晨A311D部署环境](a311d.md)
-- [昆仑芯XPU部署环境](xpu.md)
+- [昆仑芯XPU部署环境](kunlunxin.md)
 - [华为昇腾部署环境](huawei_ascend.md)
 
 
@@ -27,7 +27,7 @@
 | ENABLE_LITE_BACKEND     | 默认OFF，是否编译集成Paddle Lite后端(编译Android库时需要设置为ON)                          |
 | ENABLE_RKNPU2_BACKEND   | 默认OFF，是否编译集成RKNPU2后端(RK3588/RK3568/RK3566上推荐打开)                           |
 | WITH_ASCEND             | 默认OFF，当在华为昇腾NPU上部署时, 需要设置为ON                                              |
-| WITH_XPU                | 默认OFF，当在昆仑芯XPU上部署时，需设置为ON                                                |
+| WITH_KUNLUNXIN          | 默认OFF，当在昆仑芯XPU上部署时，需设置为ON                                                |
 | WITH_TIMVX              | 默认OFF，需要在RV1126/RV1109/A311D上部署时，需设置为ON                                   |
 | ENABLE_TRT_BACKEND      | 默认OFF，是否编译集成TensorRT后端(GPU上推荐打开)                                          |
 | ENABLE_OPENVINO_BACKEND | 默认OFF，是否编译集成OpenVINO后端(CPU上推荐打开)                                          |

docs/cn/build_and_install/kunlunxin.md

@@ -1,4 +1,4 @@
-[English](../../en/build_and_install/xpu.md) | 简体中文
+[English](../../en/build_and_install/kunlunxin.md) | 简体中文
 
 # 昆仑芯 XPU 部署环境编译安装
 
@@ -10,7 +10,7 @@ FastDeploy 基于 Paddle Lite 后端支持在昆仑芯 XPU 上进行部署推理
 相关编译选项说明如下：  
 |编译选项|默认值|说明|备注|  
 |:---|:---|:---|:---|  
-| WITH_XPU| OFF | 需要在XPU上部署时需要设置为ON | - |
+| WITH_KUNLUNXIN| OFF | 需要在昆仑芯XPU上部署时需要设置为ON | - |
 | ENABLE_ORT_BACKEND | OFF | 是否编译集成ONNX Runtime后端 | - |
 | ENABLE_PADDLE_BACKEND | OFF | 是否编译集成Paddle Inference后端 | - |
 | ENABLE_OPENVINO_BACKEND | OFF | 是否编译集成OpenVINO后端 | - |
@@ -41,11 +41,11 @@ cd FastDeploy
 mkdir build && cd build
 
 # CMake configuration with KunlunXin xpu toolchain
-cmake -DWITH_XPU=ON  \
+cmake -DWITH_KUNLUNXIN=ON  \
       -DWITH_GPU=OFF  \ # 不编译 GPU
       -DENABLE_ORT_BACKEND=ON  \ # 可选择开启 ORT 后端
       -DENABLE_PADDLE_BACKEND=ON  \ # 可选择开启 Paddle 后端
-      -DCMAKE_INSTALL_PREFIX=fastdeploy-xpu \
+      -DCMAKE_INSTALL_PREFIX=fastdeploy-kunlunxin \
       -DENABLE_VISION=ON \ # 是否编译集成视觉模型的部署模块，可选择开启
       -DOPENCV_DIRECTORY=/usr/lib/x86_64-linux-gnu/cmake/opencv4 \
       ..
@@ -54,14 +54,14 @@ cmake -DWITH_XPU=ON  \
 make -j8
 make install
 ```  
-编译完成之后，会生成 fastdeploy-xpu 目录，表示基于 Paddle Lite 的 FastDeploy 库编译完成。
+编译完成之后，会生成 fastdeploy-kunlunxin 目录，表示基于 Paddle Lite 的 FastDeploy 库编译完成。
 
 ## Python 编译
 编译命令如下：
 ```bash
 git clone https://github.com/PaddlePaddle/FastDeploy.git
 cd FastDeploy/python
-export WITH_XPU=ON
+export WITH_KUNLUNXIN=ON
 export WITH_GPU=OFF
 export ENABLE_ORT_BACKEND=ON
 export ENABLE_PADDLE_BACKEND=ON

docs/en/build_and_install/README.md

@@ -15,7 +15,7 @@ English | [中文](../../cn/build_and_install/README.md)
 - [Build and Install on RV1126 Platform](rv1126.md)
 - [Build and Install on RK3588 Platform](rknpu2.md)
 - [Build and Install on A311D Platform](a311d.md)
-- [Build and Install on KunlunXin XPU Platform](xpu.md)
+- [Build and Install on KunlunXin XPU Platform](kunlunxin.md)
 
 
 ## Build options
@@ -29,7 +29,7 @@ English | [中文](../../cn/build_and_install/README.md)
 | ENABLE_VISION | Default OFF，whether to enable vision models deployment module |
 | ENABLE_TEXT | Default OFF，whether to enable text models deployment module |
 | WITH_GPU | Default OFF, if build on GPU, this needs to be ON |
-| WITH_XPU | Default OFF，if deploy on KunlunXin XPU，this needs to be ON |
+| WITH_KUNLUNXIN | Default OFF，if deploy on KunlunXin XPU，this needs to be ON |
 | WITH_TIMVX | Default OFF，if deploy on RV1126/RV1109/A311D，this needs to be ON |
 | WITH_ASCEND | Default OFF，if deploy on Huawei Ascend，this needs to be ON |
 | CUDA_DIRECTORY | Default /usr/local/cuda, if build on GPU, this defines the path of CUDA(>=11.2) |

docs/en/build_and_install/kunlunxin.md

@@ -1,4 +1,4 @@
-English | [中文](../../cn/build_and_install/xpu.md)
+English | [中文](../../cn/build_and_install/kunlunxin.md)
 
 # How to Build KunlunXin XPU Deployment Environment
 
@@ -10,7 +10,7 @@ The relevant compilation options are described as follows:
 |Compile Options|Default Values|Description|Remarks|  
 |:---|:---|:---|:---|  
 | ENABLE_LITE_BACKEND | OFF | It needs to be set to ON when compiling the RK library| - |  
-| WITH_XPU | OFF | It needs to be set to ON when compiling the KunlunXin XPU library| - |
+| WITH_KUNLUNXIN | OFF | It needs to be set to ON when compiling the KunlunXin XPU library| - |
 | ENABLE_ORT_BACKEND | OFF | whether to intergrate ONNX Runtime backend | - |
 | ENABLE_PADDLE_BACKEND | OFF | whether to intergrate Paddle Inference backend | - |
 | ENABLE_OPENVINO_BACKEND | OFF | whether to intergrate OpenVINO backend | - |
@@ -44,11 +44,11 @@ cd FastDeploy
 mkdir build && cd build
 
 # CMake configuration with KunlunXin xpu toolchain
-cmake -DWITH_XPU=ON  \
+cmake -DWITH_KUNLUNXIN=ON  \
       -DWITH_GPU=OFF  \
       -DENABLE_ORT_BACKEND=ON  \
       -DENABLE_PADDLE_BACKEND=ON  \
-      -DCMAKE_INSTALL_PREFIX=fastdeploy-xpu \
+      -DCMAKE_INSTALL_PREFIX=fastdeploy-kunlunxin \
       -DENABLE_VISION=ON \
       -DOPENCV_DIRECTORY=/usr/lib/x86_64-linux-gnu/cmake/opencv4 \
       ..
@@ -57,14 +57,14 @@ cmake -DWITH_XPU=ON  \
 make -j8
 make install
 ```  
-After the compilation is complete, the fastdeploy-xpu directory will be generated, indicating that the Padddle Lite based FastDeploy library has been compiled.
+After the compilation is complete, the fastdeploy-kunlunxin directory will be generated, indicating that the Padddle Lite based FastDeploy library has been compiled.
 
 ## Python compile
 The compilation command is as follows:
 ```bash
 git clone https://github.com/PaddlePaddle/FastDeploy.git
 cd FastDeploy/python
-export WITH_XPU=ON
+export WITH_KUNLUNXIN=ON
 export WITH_GPU=OFF
 export ENABLE_ORT_BACKEND=ON
 export ENABLE_PADDLE_BACKEND=ON

examples/multimodal/stable_diffusion/README.md

@@ -41,7 +41,7 @@ python infer.py --model_dir stable-diffusion-v1-4/ --scheduler "pndm" --backend
 python infer.py --model_dir stable-diffusion-v1-5/ --scheduler "euler_ancestral" --backend paddle
 
 # 在昆仑芯XPU上推理
-python infer.py --model_dir stable-diffusion-v1-5/ --scheduler "euler_ancestral" --backend paddle-xpu
+python infer.py --model_dir stable-diffusion-v1-5/ --scheduler "euler_ancestral" --backend paddle-kunlunxin
 ```
 
 #### 参数说明
@@ -52,7 +52,7 @@ python infer.py --model_dir stable-diffusion-v1-5/ --scheduler "euler_ancestral"
 |----------|--------------|
 | --model_dir | 导出后模型的目录。 |
 | --model_format | 模型格式。默认为`'paddle'`，可选列表：`['paddle', 'onnx']`。 |
-| --backend | 推理引擎后端。默认为`paddle`，可选列表：`['onnx_runtime', 'paddle', 'paddle-xpu']`，当模型格式为`onnx`时，可选列表为`['onnx_runtime']`。 |
+| --backend | 推理引擎后端。默认为`paddle`，可选列表：`['onnx_runtime', 'paddle', 'paddle-kunlunxin']`，当模型格式为`onnx`时，可选列表为`['onnx_runtime']`。 |
 | --scheduler | StableDiffusion 模型的scheduler。默认为`'pndm'`。可选列表：`['pndm', 'euler_ancestral']`，StableDiffusio模型对应的scheduler可参考[ppdiffuser模型列表](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/ppdiffusers/examples/textual_inversion)。|
 | --unet_model_prefix | UNet模型前缀。默认为`unet`。 |
 | --vae_model_prefix | VAE模型前缀。默认为`vae_decoder`。 |

examples/multimodal/stable_diffusion/infer.py

@@ -69,7 +69,7 @@ def parse_arguments():
         type=str,
         default='paddle',
         # Note(zhoushunjie): Will support 'tensorrt', 'paddle-tensorrt' soon.
-        choices=['onnx_runtime', 'paddle', 'paddle-xpu'],
+        choices=['onnx_runtime', 'paddle', 'paddle-kunlunxin'],
         help="The inference runtime backend of unet model and text encoder model."
     )
     parser.add_argument(
@@ -175,9 +175,9 @@ def create_trt_runtime(model_dir,
     return fd.Runtime(option)
 
 
-def create_xpu_runtime(model_dir, model_prefix, device_id=0):
+def create_kunlunxin_runtime(model_dir, model_prefix, device_id=0):
     option = fd.RuntimeOption()
-    option.use_xpu(
+    option.use_kunlunxin(
         device_id,
         l3_workspace_size=(64 * 1024 * 1024 - 4 * 1024),
         locked=False,
@@ -306,18 +306,18 @@ if __name__ == "__main__":
             dynamic_shape=unet_dynamic_shape,
             device_id=args.device_id)
         print(f"Spend {time.time() - start : .2f} s to load unet model.")
-    elif args.backend == "paddle-xpu":
+    elif args.backend == "paddle-kunlunxin":
         print("=== build text_encoder_runtime")
-        text_encoder_runtime = create_xpu_runtime(
+        text_encoder_runtime = create_kunlunxin_runtime(
             args.model_dir,
             args.text_encoder_model_prefix,
             device_id=args.device_id)
         print("=== build vae_decoder_runtime")
-        vae_decoder_runtime = create_xpu_runtime(
+        vae_decoder_runtime = create_kunlunxin_runtime(
             args.model_dir, args.vae_model_prefix, device_id=args.device_id)
         print("=== build unet_runtime")
         start = time.time()
-        unet_runtime = create_xpu_runtime(
+        unet_runtime = create_kunlunxin_runtime(
             args.model_dir, args.unet_model_prefix, device_id=args.device_id)
         print(f"Spend {time.time() - start : .2f} s to load unet model.")
     pipe = StableDiffusionFastDeployPipeline(

examples/text/ernie-3.0/cpp/README.md

@@ -35,8 +35,8 @@ tar xvfz ernie-3.0-medium-zh-afqmc.tgz
 # GPU Inference
 ./seq_cls_infer_demo --device gpu --model_dir ernie-3.0-medium-zh-afqmc
 
-# XPU 推理
+# KunlunXin XPU 推理
-./seq_cls_infer_demo --device xpu --model_dir ernie-3.0-medium-zh-afqmc
+./seq_cls_infer_demo --device kunlunxin --model_dir ernie-3.0-medium-zh-afqmc
 ```
 The result returned after running is as follows：
 ```bash

examples/text/ernie-3.0/cpp/seq_cls_infer.cc

@@ -32,7 +32,7 @@ const char sep = '/';
 DEFINE_string(model_dir, "", "Directory of the inference model.");
 DEFINE_string(vocab_path, "", "Path of the vocab file.");
 DEFINE_string(device, "cpu",
-              "Type of inference device, support 'cpu', 'xpu' or 'gpu'.");
+              "Type of inference device, support 'cpu', 'kunlunxin' or 'gpu'.");
 DEFINE_string(backend, "onnx_runtime",
               "The inference runtime backend, support: ['onnx_runtime', "
               "'paddle', 'openvino', 'tensorrt', 'paddle_tensorrt']");
@@ -61,8 +61,8 @@ bool CreateRuntimeOption(fastdeploy::RuntimeOption* option) {
                      << ", param_path = " << param_path << std::endl;
   option->SetModelPath(model_path, param_path);
 
-  if (FLAGS_device == "xpu") {
+  if (FLAGS_device == "kunlunxin") {
-    option->UseXpu();
+    option->UseKunlunXin();
     return true;
   } else if (FLAGS_device == "gpu") {
     option->UseGpu();

examples/text/ernie-3.0/python/README.md

@@ -40,8 +40,8 @@ python seq_cls_infer.py --device cpu --model_dir ernie-3.0-medium-zh-afqmc
 # GPU Inference
 python seq_cls_infer.py --device gpu --model_dir ernie-3.0-medium-zh-afqmc
 
-# XPU Inference
+# KunlunXin XPU Inference
-python seq_cls_infer.py --device xpu --model_dir ernie-3.0-medium-zh-afqmc
+python seq_cls_infer.py --device kunlunxin --model_dir ernie-3.0-medium-zh-afqmc
 
 ```
 The result returned after running is as follows:

examples/text/ernie-3.0/python/seq_cls_infer.py

@@ -35,8 +35,8 @@ def parse_arguments():
         "--device",
         type=str,
         default='cpu',
-        choices=['gpu', 'cpu', 'xpu'],
+        choices=['gpu', 'cpu', 'kunlunxin'],
-        help="Type of inference device, support 'cpu', 'xpu' or 'gpu'.")
+        help="Type of inference device, support 'cpu', 'kunlunxin' or 'gpu'.")
     parser.add_argument(
         "--backend",
         type=str,
@@ -94,8 +94,8 @@ class ErnieForSequenceClassificationPredictor(object):
         model_path = os.path.join(args.model_dir, "infer.pdmodel")
         params_path = os.path.join(args.model_dir, "infer.pdiparams")
         option.set_model_path(model_path, params_path)
-        if args.device == 'xpu':
+        if args.device == 'kunlunxin':
-            option.use_xpu()
+            option.use_kunlunxin()
             option.use_paddle_lite_backend()
             return fd.Runtime(option)
         if args.device == 'cpu':

examples/vision/classification/paddleclas/cpp/infer.cc

@@ -96,13 +96,13 @@ void IpuInfer(const std::string& model_dir, const std::string& image_file) {
   std::cout << res.Str() << std::endl;
 }
 
-void XpuInfer(const std::string& model_dir, const std::string& image_file) {
+void KunlunXinInfer(const std::string& model_dir, const std::string& image_file) {
   auto model_file = model_dir + sep + "inference.pdmodel";
   auto params_file = model_dir + sep + "inference.pdiparams";
   auto config_file = model_dir + sep + "inference_cls.yaml";
 
   auto option = fastdeploy::RuntimeOption();
-  option.UseXpu();
+  option.UseKunlunXin();
   auto model = fastdeploy::vision::classification::PaddleClasModel(
       model_file, params_file, config_file, option);
   if (!model.Initialized()) {
@@ -179,7 +179,7 @@ int main(int argc, char* argv[]) {
                  "e.g ./infer_demo ./ResNet50_vd ./test.jpeg 0"
               << std::endl;
     std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
-                 "with gpu; 2: run with gpu and use tensorrt backend; 3: run with ipu; 4: run with xpu."
+                 "with gpu; 2: run with gpu and use tensorrt backend; 3: run with ipu; 4: run with kunlunxin."
               << std::endl;
     return -1;
   }
@@ -193,7 +193,7 @@ int main(int argc, char* argv[]) {
   } else if (std::atoi(argv[3]) == 3) {
     IpuInfer(argv[1], argv[2]);
   } else if (std::atoi(argv[3]) == 4) {
-    XpuInfer(argv[1], argv[2]);
+    KunlunXinInfer(argv[1], argv[2]);
   } else if (std::atoi(argv[3]) == 5) {
     AscendInfer(argv[1], argv[2]);
   }

examples/vision/classification/paddleclas/python/README.md

@@ -25,8 +25,8 @@ python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg -
 python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --use_trt True --topk 1
 # IPU推理（注意：IPU推理首次运行会有序列化模型的操作，有一定耗时，需要耐心等待）
 python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device ipu --topk 1
-# XPU推理
+# 昆仑芯XPU推理
-python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device xpu --topk 1
+python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device kunlunxin --topk 1
 # 华为昇腾NPU推理
 python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device ascend --topk 1
 ```

examples/vision/classification/paddleclas/python/infer.py

@@ -17,7 +17,7 @@ def parse_arguments():
         "--device",
         type=str,
         default='cpu',
-        help="Type of inference device, support 'cpu' or 'gpu' or 'ipu' or 'xpu' or 'ascend' ."
+        help="Type of inference device, support 'cpu' or 'gpu' or 'ipu' or 'kunlunxin' or 'ascend' ."
     )
     parser.add_argument(
         "--use_trt",
@@ -36,8 +36,8 @@ def build_option(args):
     if args.device.lower() == "ipu":
         option.use_ipu()
 
-    if args.device.lower() == "xpu":
+    if args.device.lower() == "kunlunxin":
-        option.use_xpu()
+        option.use_kunlunxin()
 
     if args.device.lower() == "ascend":
         option.use_ascend()

examples/vision/detection/paddledetection/cpp/infer_faster_rcnn.cc

@@ -47,12 +47,12 @@ void CpuInfer(const std::string& model_dir, const std::string& image_file) {
   std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
 }
 
-void XpuInfer(const std::string& model_dir, const std::string& image_file) {
+void KunlunXinInfer(const std::string& model_dir, const std::string& image_file) {
   auto model_file = model_dir + sep + "model.pdmodel";
   auto params_file = model_dir + sep + "model.pdiparams";
   auto config_file = model_dir + sep + "infer_cfg.yml";
   auto option = fastdeploy::RuntimeOption();
-  option.UseXpu(0, 0, false, false);
+  option.UseKunlunXin(0, 0, false, false);
   auto model = fastdeploy::vision::detection::FasterRCNN(
       model_file, params_file, config_file, option);
   if (!model.Initialized()) {
@@ -109,7 +109,7 @@ int main(int argc, char* argv[]) {
            "e.g ./infer_model ./faster_rcnn_r50_vd_fpn_2x_coco ./test.jpeg 0"
         << std::endl;
     std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
-                 "with gpu; 2: run with xpu."
+                 "with gpu; 2: run with kunlunxin."
               << std::endl;
     return -1;
   }
@@ -119,7 +119,7 @@ int main(int argc, char* argv[]) {
   } else if (std::atoi(argv[3]) == 1) {
     GpuInfer(argv[1], argv[2]);
   } else if (std::atoi(argv[3]) == 2) {
-    XpuInfer(argv[1], argv[2]);
+    KunlunXinInfer(argv[1], argv[2]);
   }
   return 0;
 }

examples/vision/detection/paddledetection/cpp/infer_mask_rcnn.cc

@@ -47,12 +47,12 @@ void CpuInfer(const std::string& model_dir, const std::string& image_file) {
   std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
 }
 
-void XpuInfer(const std::string& model_dir, const std::string& image_file) {
+void KunlunXinInfer(const std::string& model_dir, const std::string& image_file) {
   auto model_file = model_dir + sep + "model.pdmodel";
   auto params_file = model_dir + sep + "model.pdiparams";
   auto config_file = model_dir + sep + "infer_cfg.yml";
   auto option = fastdeploy::RuntimeOption();
-  option.UseXpu(0, 0, false, false);
+  option.UseKunlunXin(0, 0, false, false);
   auto model = fastdeploy::vision::detection::MaskRCNN(model_file, params_file,
                                                        config_file, option);
   if (!model.Initialized()) {
@@ -109,7 +109,7 @@ int main(int argc, char* argv[]) {
            "e.g ./infer_model ./mask_rcnn_r50_1x_coco/ ./test.jpeg 0"
         << std::endl;
     std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
-                 "with gpu; 2: run with xpu."
+                 "with gpu; 2: run with kunlunxin."
               << std::endl;
     return -1;
   }
@@ -119,7 +119,7 @@ int main(int argc, char* argv[]) {
   } else if (std::atoi(argv[3]) == 1) {
     GpuInfer(argv[1], argv[2]);
   } else if (std::atoi(argv[3]) == 2) {
-    XpuInfer(argv[1], argv[2]);
+    KunlunXinInfer(argv[1], argv[2]);
   } else if (std::atoi(argv[3]) == 3) {
     std::cout
         << "Backend::TRT has not been supported yet, will skip this inference."

examples/vision/detection/paddledetection/cpp/infer_picodet.cc

@@ -47,12 +47,12 @@ void CpuInfer(const std::string& model_dir, const std::string& image_file) {
   std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
 }
 
-void XpuInfer(const std::string& model_dir, const std::string& image_file) {
+void KunlunXinInfer(const std::string& model_dir, const std::string& image_file) {
   auto model_file = model_dir + sep + "model.pdmodel";
   auto params_file = model_dir + sep + "model.pdiparams";
   auto config_file = model_dir + sep + "infer_cfg.yml";
   auto option = fastdeploy::RuntimeOption();
-  option.UseXpu();
+  option.UseKunlunXin();
   auto model = fastdeploy::vision::detection::PicoDet(model_file, params_file,
                                                       config_file, option);
   if (!model.Initialized()) {
@@ -138,7 +138,7 @@ int main(int argc, char* argv[]) {
            "e.g ./infer_model ./picodet_model_dir ./test.jpeg 0"
         << std::endl;
     std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
-                 "with gpu; 2: run with gpu and use tensorrt backend; 3: run with xpu."
+                 "with gpu; 2: run with gpu and use tensorrt backend; 3: run with kunlunxin."
               << std::endl;
     return -1;
   }
@@ -150,7 +150,7 @@ int main(int argc, char* argv[]) {
   } else if (std::atoi(argv[3]) == 2) {
     TrtInfer(argv[1], argv[2]);
   }  else if (std::atoi(argv[3]) == 3) {
-    XpuInfer(argv[1], argv[2]);
+    KunlunXinInfer(argv[1], argv[2]);
   }
   return 0;
 }

examples/vision/detection/paddledetection/cpp/infer_ppyolo.cc

@@ -47,12 +47,12 @@ void CpuInfer(const std::string& model_dir, const std::string& image_file) {
   std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
 }
 
-void XpuInfer(const std::string& model_dir, const std::string& image_file) {
+void KunlunXinInfer(const std::string& model_dir, const std::string& image_file) {
   auto model_file = model_dir + sep + "model.pdmodel";
   auto params_file = model_dir + sep + "model.pdiparams";
   auto config_file = model_dir + sep + "infer_cfg.yml";
   auto option = fastdeploy::RuntimeOption();
-  option.UseXpu();
+  option.UseKunlunXin();
   auto model = fastdeploy::vision::detection::PPYOLO(model_file, params_file,
                                                      config_file, option);
   if (!model.Initialized()) {
@@ -109,7 +109,7 @@ int main(int argc, char* argv[]) {
            "e.g ./infer_model ./ppyolo_dirname ./test.jpeg 0"
         << std::endl;
     std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
-                 "with gpu; 2: run with xpu."
+                 "with gpu; 2: run with kunlunxin."
               << std::endl;
     return -1;
   }
@@ -119,7 +119,7 @@ int main(int argc, char* argv[]) {
   } else if (std::atoi(argv[3]) == 1) {
     GpuInfer(argv[1], argv[2]);
   } else if (std::atoi(argv[3]) == 2) {
-    XpuInfer(argv[1], argv[2]);
+    KunlunXinInfer(argv[1], argv[2]);
   }
   return 0;
 }

examples/vision/detection/paddledetection/cpp/infer_ppyoloe.cc

@@ -47,12 +47,12 @@ void CpuInfer(const std::string& model_dir, const std::string& image_file) {
   std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
 }
 
-void XpuInfer(const std::string& model_dir, const std::string& image_file) {
+void KunlunXinInfer(const std::string& model_dir, const std::string& image_file) {
   auto model_file = model_dir + sep + "model.pdmodel";
   auto params_file = model_dir + sep + "model.pdiparams";
   auto config_file = model_dir + sep + "infer_cfg.yml";
   auto option = fastdeploy::RuntimeOption();
-  option.UseXpu();
+  option.UseKunlunXin();
   auto model = fastdeploy::vision::detection::PPYOLOE(model_file, params_file,
                                                       config_file, option);
   if (!model.Initialized()) {
@@ -138,7 +138,7 @@ int main(int argc, char* argv[]) {
            "e.g ./infer_model ./ppyoloe_model_dir ./test.jpeg 0"
         << std::endl;
     std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
-                 "with gpu; 2: run with gpu and use tensorrt backend; 3: run with xpu."
+                 "with gpu; 2: run with gpu and use tensorrt backend; 3: run with kunlunxin."
               << std::endl;
     return -1;
   }
@@ -150,7 +150,7 @@ int main(int argc, char* argv[]) {
   } else if (std::atoi(argv[3]) == 2) {
     TrtInfer(argv[1], argv[2]);
   } else if (std::atoi(argv[3]) == 3) {
-    XpuInfer(argv[1], argv[2]);
+    KunlunXinInfer(argv[1], argv[2]);
   }
   return 0;
 }

examples/vision/detection/paddledetection/cpp/infer_rtmdet.cc

@@ -48,12 +48,12 @@ void CpuInfer(const std::string& model_dir, const std::string& image_file) {
   std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
 }
 
-void XpuInfer(const std::string& model_dir, const std::string& image_file) {
+void KunlunXinInfer(const std::string& model_dir, const std::string& image_file) {
   auto model_file = model_dir + sep + "model.pdmodel";
   auto params_file = model_dir + sep + "model.pdiparams";
   auto config_file = model_dir + sep + "infer_cfg.yml";
   auto option = fastdeploy::RuntimeOption();
-  option.UseXpu();
+  option.UseKunlunXin();
   auto model = fastdeploy::vision::detection::RTMDet(model_file, params_file,
                                                      config_file, option);
   if (!model.Initialized()) {
@@ -141,7 +141,7 @@ int main(int argc, char* argv[]) {
            "e.g ./infer_model ./ppyolo_dirname ./test.jpeg 0"
         << std::endl;
     std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
-                 "with gpu, 2: run with gpu and use tensorrt backend; 3: run with xpu."
+                 "with gpu, 2: run with gpu and use tensorrt backend; 3: run with kunlunxin."
               << std::endl;
     return -1;
   }
@@ -153,7 +153,7 @@ int main(int argc, char* argv[]) {
   } else if(std::atoi(argv[3]) == 2) {
     TrtInfer(argv[1], argv[2]);
   } else if(std::atoi(argv[3]) == 3) {
-    XpuInfer(argv[1], argv[2]);
+    KunlunXinInfer(argv[1], argv[2]);
   }
   return 0;
 }

examples/vision/detection/paddledetection/cpp/infer_ssd.cc

@@ -48,12 +48,12 @@ void CpuInfer(const std::string& model_dir, const std::string& image_file) {
   std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
 }
 
-void XpuInfer(const std::string& model_dir, const std::string& image_file) {
+void KunlunXinInfer(const std::string& model_dir, const std::string& image_file) {
   auto model_file = model_dir + sep + "model.pdmodel";
   auto params_file = model_dir + sep + "model.pdiparams";
   auto config_file = model_dir + sep + "infer_cfg.yml";
   auto option = fastdeploy::RuntimeOption();
-  option.UseXpu();
+  option.UseKunlunXin();
   auto model = fastdeploy::vision::detection::SSD(model_file, params_file,
                                                      config_file, option);
   if (!model.Initialized()) {
@@ -111,7 +111,7 @@ int main(int argc, char* argv[]) {
            "e.g ./infer_model ./ssd_dirname ./test.jpeg 0"
         << std::endl;
     std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
-                 "with gpu; 2: run with xpu."
+                 "with gpu; 2: run with kunlunxin."
               << std::endl;
     return -1;
   }
@@ -121,7 +121,7 @@ int main(int argc, char* argv[]) {
   } else if (std::atoi(argv[3]) == 1) {
     GpuInfer(argv[1], argv[2]);
   } else if (std::atoi(argv[3]) == 2) {
-    XpuInfer(argv[1], argv[2]);
+    KunlunXinInfer(argv[1], argv[2]);
   }
   return 0;
 }

examples/vision/detection/paddledetection/cpp/infer_yolov3.cc

@@ -47,12 +47,12 @@ void CpuInfer(const std::string& model_dir, const std::string& image_file) {
   std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
 }
 
-void XpuInfer(const std::string& model_dir, const std::string& image_file) {
+void KunlunXinInfer(const std::string& model_dir, const std::string& image_file) {
   auto model_file = model_dir + sep + "model.pdmodel";
   auto params_file = model_dir + sep + "model.pdiparams";
   auto config_file = model_dir + sep + "infer_cfg.yml";
   auto option = fastdeploy::RuntimeOption();
-  option.UseXpu();
+  option.UseKunlunXin();
   auto model = fastdeploy::vision::detection::YOLOv3(model_file, params_file,
                                                      config_file, option);
   if (!model.Initialized()) {
@@ -109,7 +109,7 @@ int main(int argc, char* argv[]) {
            "e.g ./infer_model ./ppyolo_dirname ./test.jpeg 0"
         << std::endl;
     std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
-                 "with gpu; 2: run with xpu."
+                 "with gpu; 2: run with kunlunxin."
               << std::endl;
     return -1;
   }
@@ -119,7 +119,7 @@ int main(int argc, char* argv[]) {
   } else if (std::atoi(argv[3]) == 1) {
     GpuInfer(argv[1], argv[2]);
   } else if (std::atoi(argv[3]) == 2) {
-    XpuInfer(argv[1], argv[2]);
+    KunlunXinInfer(argv[1], argv[2]);
   }
   return 0;
 }

examples/vision/detection/paddledetection/cpp/infer_yolov5.cc

@@ -48,12 +48,12 @@ void CpuInfer(const std::string& model_dir, const std::string& image_file) {
   std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
 }
 
-void XpuInfer(const std::string& model_dir, const std::string& image_file) {
+void KunlunXinInfer(const std::string& model_dir, const std::string& image_file) {
   auto model_file = model_dir + sep + "model.pdmodel";
   auto params_file = model_dir + sep + "model.pdiparams";
   auto config_file = model_dir + sep + "infer_cfg.yml";
   auto option = fastdeploy::RuntimeOption();
-  option.UseXpu();
+  option.UseKunlunXin();
   auto model = fastdeploy::vision::detection::PaddleYOLOv5(model_file, params_file,
                                                      config_file, option);
   if (!model.Initialized()) {
@@ -142,7 +142,7 @@ int main(int argc, char* argv[]) {
            "e.g ./infer_model ./ppyolo_dirname ./test.jpeg 0"
         << std::endl;
     std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
-                 "with gpu; 2: run with gpu and use tensorrt backend; 3: run with xpu."
+                 "with gpu; 2: run with gpu and use tensorrt backend; 3: run with kunlunxin."
               << std::endl;
     return -1;
   }
@@ -154,7 +154,7 @@ int main(int argc, char* argv[]) {
   } else if(std::atoi(argv[3]) == 2){
     TrtInfer(argv[1], argv[2]);
   } else if(std::atoi(argv[3]) == 3){
-    XpuInfer(argv[1], argv[2]);
+    KunlunXinInfer(argv[1], argv[2]);
   }
   return 0;
 }

examples/vision/detection/paddledetection/cpp/infer_yolov6.cc

@@ -48,12 +48,12 @@ void CpuInfer(const std::string& model_dir, const std::string& image_file) {
   std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
 }
 
-void XpuInfer(const std::string& model_dir, const std::string& image_file) {
+void KunlunXinInfer(const std::string& model_dir, const std::string& image_file) {
   auto model_file = model_dir + sep + "model.pdmodel";
   auto params_file = model_dir + sep + "model.pdiparams";
   auto config_file = model_dir + sep + "infer_cfg.yml";
   auto option = fastdeploy::RuntimeOption();
-  option.UseXpu();
+  option.UseKunlunXin();
   auto model = fastdeploy::vision::detection::PaddleYOLOv6(model_file, params_file,
                                                      config_file, option);
   if (!model.Initialized()) {
@@ -141,7 +141,7 @@ int main(int argc, char* argv[]) {
            "e.g ./infer_model ./ppyolo_dirname ./test.jpeg 0"
         << std::endl;
     std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
-                 "with gpu; 2: run with gpu and use tensorrt backend; 3: run with xpu."
+                 "with gpu; 2: run with gpu and use tensorrt backend; 3: run with kunlunxin."
               << std::endl;
     return -1;
   }
@@ -153,7 +153,7 @@ int main(int argc, char* argv[]) {
   } else if(std::atoi(argv[3]) == 2){
     TrtInfer(argv[1], argv[2]);
   } else if(std::atoi(argv[3]) == 3){
-    XpuInfer(argv[1], argv[2]);
+    KunlunXinInfer(argv[1], argv[2]);
   }
   return 0;
 }

examples/vision/detection/paddledetection/cpp/infer_yolov7.cc

@@ -48,12 +48,12 @@ void CpuInfer(const std::string& model_dir, const std::string& image_file) {
   std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
 }
 
-void XpuInfer(const std::string& model_dir, const std::string& image_file) {
+void KunlunXinInfer(const std::string& model_dir, const std::string& image_file) {
   auto model_file = model_dir + sep + "model.pdmodel";
   auto params_file = model_dir + sep + "model.pdiparams";
   auto config_file = model_dir + sep + "infer_cfg.yml";
   auto option = fastdeploy::RuntimeOption();
-  option.UseXpu();
+  option.UseKunlunXin();
   auto model = fastdeploy::vision::detection::PaddleYOLOv7(model_file, params_file,
                                                      config_file, option);
   if (!model.Initialized()) {
@@ -140,7 +140,7 @@ int main(int argc, char* argv[]) {
            "e.g ./infer_model ./ppyolo_dirname ./test.jpeg 0"
         << std::endl;
     std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
-                 "with gpu; 2: run with gpu and use tensorrt backend; 3: run with xpu."
+                 "with gpu; 2: run with gpu and use tensorrt backend; 3: run with kunlunxin."
               << std::endl;
     return -1;
   }
@@ -152,7 +152,7 @@ int main(int argc, char* argv[]) {
   } else if(std::atoi(argv[3]) == 2){
     TrtInfer(argv[1], argv[2]);
   } else if(std::atoi(argv[3]) == 3){
-    XpuInfer(argv[1], argv[2]);
+    KunlunXinInfer(argv[1], argv[2]);
   }
   return 0;
 }

examples/vision/detection/paddledetection/cpp/infer_yolox.cc

@@ -47,12 +47,12 @@ void CpuInfer(const std::string& model_dir, const std::string& image_file) {
   std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
 }
 
-void XpuInfer(const std::string& model_dir, const std::string& image_file) {
+void KunlunXinInfer(const std::string& model_dir, const std::string& image_file) {
   auto model_file = model_dir + sep + "model.pdmodel";
   auto params_file = model_dir + sep + "model.pdiparams";
   auto config_file = model_dir + sep + "infer_cfg.yml";
   auto option = fastdeploy::RuntimeOption();
-  option.UseXpu();
+  option.UseKunlunXin();
   auto model = fastdeploy::vision::detection::PaddleYOLOX(
       model_file, params_file, config_file, option);
   if (!model.Initialized()) {
@@ -138,7 +138,7 @@ int main(int argc, char* argv[]) {
            "e.g ./infer_model ./paddle_yolox_dirname ./test.jpeg 0"
         << std::endl;
     std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
-                 "with gpu; 2: run with gpu by tensorrt; 3: run with xpu."
+                 "with gpu; 2: run with gpu by tensorrt; 3: run with kunlunxin."
               << std::endl;
     return -1;
   }
@@ -150,7 +150,7 @@ int main(int argc, char* argv[]) {
   } else if (std::atoi(argv[3]) == 2) {
     TrtInfer(argv[1], argv[2]);
   } else if (std::atoi(argv[3]) == 3) {
-    XpuInfer(argv[1], argv[2]);
+    KunlunXinInfer(argv[1], argv[2]);
   }
   return 0;
 }

examples/vision/detection/paddledetection/python/README.md

@@ -24,7 +24,7 @@ python infer_ppyoloe.py --model_dir ppyoloe_crn_l_300e_coco --image 000000014439
 # GPU上使用TensorRT推理 （注意：TensorRT推理第一次运行，有序列化模型的操作，有一定耗时，需要耐心等待）
 python infer_ppyoloe.py --model_dir ppyoloe_crn_l_300e_coco --image 000000014439.jpg --device gpu --use_trt True
 # 昆仑芯XPU推理
-python infer_ppyoloe.py --model_dir ppyoloe_crn_l_300e_coco --image 000000014439.jpg --device xpu
+python infer_ppyoloe.py --model_dir ppyoloe_crn_l_300e_coco --image 000000014439.jpg --device kunlunxin
 ```
 
 运行完成可视化结果如下图所示

examples/vision/detection/paddledetection/python/infer_faster_rcnn.py

@@ -17,7 +17,7 @@ def parse_arguments():
         "--device",
         type=str,
         default='cpu',
-        help="Type of inference device, support 'xpu', 'cpu' or 'gpu'.")
+        help="Type of inference device, support 'kunlunxin', 'cpu' or 'gpu'.")
     parser.add_argument(
         "--use_trt",
         type=ast.literal_eval,
@@ -29,8 +29,8 @@ def parse_arguments():
 def build_option(args):
     option = fd.RuntimeOption()
 
-    if args.device.lower() == "xpu":
+    if args.device.lower() == "kunlunxin":
-        option.use_xpu(autotune=False, l3_workspace_size=0)
+        option.use_kunlunxin(autotune=False, l3_workspace_size=0)
 
     if args.device.lower() == "gpu":
         option.use_gpu()

examples/vision/detection/paddledetection/python/infer_mask_rcnn.py

@@ -17,7 +17,7 @@ def parse_arguments():
         "--device",
         type=str,
         default='cpu',
-        help="Type of inference device, support 'xpu', 'cpu' or 'gpu'.")
+        help="Type of inference device, support 'kunlunxin', 'cpu' or 'gpu'.")
     parser.add_argument(
         "--use_trt",
         type=ast.literal_eval,
@@ -29,8 +29,8 @@ def parse_arguments():
 def build_option(args):
     option = fd.RuntimeOption()
 
-    if args.device.lower() == "xpu":
+    if args.device.lower() == "kunlunxin":
-        option.use_xpu(autotune=False, l3_workspace_size=0)
+        option.use_kunlunxin(autotune=False, l3_workspace_size=0)
 
     if args.device.lower() == "gpu":
         # option.use_gpu()

examples/vision/detection/paddledetection/python/infer_picodet.py

@@ -17,7 +17,7 @@ def parse_arguments():
         "--device",
         type=str,
         default='cpu',
-        help="Type of inference device, support 'xpu', 'cpu' or 'gpu'.")
+        help="Type of inference device, support 'kunlunxin', 'cpu' or 'gpu'.")
     parser.add_argument(
         "--use_trt",
         type=ast.literal_eval,
@@ -29,8 +29,8 @@ def parse_arguments():
 def build_option(args):
     option = fd.RuntimeOption()
 
-    if args.device.lower() == "xpu":
+    if args.device.lower() == "kunlunxin":
-        option.use_xpu()
+        option.use_kunlunxin()
 
     if args.device.lower() == "gpu":
         option.use_gpu()

examples/vision/detection/paddledetection/python/infer_ppyolo.py

@@ -17,7 +17,7 @@ def parse_arguments():
         "--device",
         type=str,
         default='cpu',
-        help="Type of inference device, support 'xpu', 'cpu' or 'gpu'.")
+        help="Type of inference device, support 'kunlunxin', 'cpu' or 'gpu'.")
     parser.add_argument(
         "--use_trt",
         type=ast.literal_eval,
@@ -29,8 +29,8 @@ def parse_arguments():
 def build_option(args):
     option = fd.RuntimeOption()
 
-    if args.device.lower() == "xpu":
+    if args.device.lower() == "kunlunxin":
-        option.use_xpu()
+        option.use_kunlunxin()
 
     if args.device.lower() == "gpu":
         option.use_gpu()

examples/vision/detection/paddledetection/python/infer_ppyoloe.py

@@ -18,7 +18,7 @@ def parse_arguments():
         "--device",
         type=str,
         default='cpu',
-        help="Type of inference device, support 'xpu', 'cpu' or 'gpu'.")
+        help="Type of inference device, support 'kunlunxin', 'cpu' or 'gpu'.")
     parser.add_argument(
         "--use_trt",
         type=ast.literal_eval,
@@ -30,8 +30,8 @@ def parse_arguments():
 def build_option(args):
     option = fd.RuntimeOption()
 
-    if args.device.lower() == "xpu":
+    if args.device.lower() == "kunlunxin":
-        option.use_xpu()
+        option.use_kunlunxin()
 
     if args.device.lower() == "gpu":
         option.use_gpu()

examples/vision/detection/paddledetection/python/infer_rtmdet.py

@@ -17,7 +17,7 @@ def parse_arguments():
         "--device",
         type=str,
         default='cpu',
-        help="Type of inference device, support 'xpu', 'cpu' or 'gpu'.")
+        help="Type of inference device, support 'kunlunxin', 'cpu' or 'gpu'.")
     parser.add_argument(
         "--use_trt",
         type=ast.literal_eval,
@@ -29,8 +29,8 @@ def parse_arguments():
 def build_option(args):
     option = fd.RuntimeOption()
 
-    if args.device.lower() == "xpu":
+    if args.device.lower() == "kunlunxin":
-        option.use_xpu()
+        option.use_kunlunxin()
 
     if args.device.lower() == "gpu":
         option.use_gpu()

examples/vision/detection/paddledetection/python/infer_ssd.py

@@ -17,14 +17,14 @@ def parse_arguments():
         "--device",
         type=str,
         default='cpu',
-        help="Type of inference device, support 'xpu', 'cpu' or 'gpu'.")
+        help="Type of inference device, support 'kunlunxin', 'cpu' or 'gpu'.")
     return parser.parse_args()
 
 
 def build_option(args):
     option = fd.RuntimeOption()
-    if args.device.lower() == "xpu":
+    if args.device.lower() == "kunlunxin":
-        option.use_xpu()
+        option.use_kunlunxin()
 
     if args.device.lower() == "gpu":
         option.use_gpu()

examples/vision/detection/paddledetection/python/infer_yolov3.py

@@ -17,7 +17,7 @@ def parse_arguments():
         "--device",
         type=str,
         default='cpu',
-        help="Type of inference device, support 'xpu', 'cpu' or 'gpu'.")
+        help="Type of inference device, support 'kunlunxin', 'cpu' or 'gpu'.")
     parser.add_argument(
         "--use_trt",
         type=ast.literal_eval,
@@ -29,8 +29,8 @@ def parse_arguments():
 def build_option(args):
     option = fd.RuntimeOption()
 
-    if args.device.lower() == "xpu":
+    if args.device.lower() == "kunlunxin":
-        option.use_xpu()
+        option.use_kunlunxin()
 
     if args.device.lower() == "gpu":
         option.use_gpu()

examples/vision/detection/paddledetection/python/infer_yolov5.py

@@ -17,7 +17,7 @@ def parse_arguments():
         "--device",
         type=str,
         default='cpu',
-        help="Type of inference device, support 'xpu', 'cpu' or 'gpu'.")
+        help="Type of inference device, support 'kunlunxin', 'cpu' or 'gpu'.")
     parser.add_argument(
         "--use_trt",
         type=ast.literal_eval,
@@ -29,8 +29,8 @@ def parse_arguments():
 def build_option(args):
     option = fd.RuntimeOption()
 
-    if args.device.lower() == "xpu":
+    if args.device.lower() == "kunlunxin":
-        option.use_xpu()
+        option.use_kunlunxin()
 
     if args.device.lower() == "gpu":
         option.use_gpu()

examples/vision/detection/paddledetection/python/infer_yolov6.py

@@ -17,7 +17,7 @@ def parse_arguments():
         "--device",
         type=str,
         default='cpu',
-        help="Type of inference device, support 'xpu', 'cpu' or 'gpu'.")
+        help="Type of inference device, support 'kunlunxin', 'cpu' or 'gpu'.")
     parser.add_argument(
         "--use_trt",
         type=ast.literal_eval,
@@ -29,8 +29,8 @@ def parse_arguments():
 def build_option(args):
     option = fd.RuntimeOption()
 
-    if args.device.lower() == "xpu":
+    if args.device.lower() == "kunlunxin":
-        option.use_xpu()
+        option.use_kunlunxin()
 
     if args.device.lower() == "gpu":
         option.use_gpu()

examples/vision/detection/paddledetection/python/infer_yolov7.py

@@ -17,7 +17,7 @@ def parse_arguments():
         "--device",
         type=str,
         default='cpu',
-        help="Type of inference device, support 'xpu', 'cpu' or 'gpu'.")
+        help="Type of inference device, support 'kunlunxin', 'cpu' or 'gpu'.")
     parser.add_argument(
         "--use_trt",
         type=ast.literal_eval,
@@ -29,8 +29,8 @@ def parse_arguments():
 def build_option(args):
     option = fd.RuntimeOption()
 
-    if args.device.lower() == "xpu":
+    if args.device.lower() == "kunlunxin":
-        option.use_xpu()
+        option.use_kunlunxin()
 
     if args.device.lower() == "gpu":
         option.use_gpu()

examples/vision/detection/paddledetection/python/infer_yolox.py

@@ -17,7 +17,7 @@ def parse_arguments():
         "--device",
         type=str,
         default='cpu',
-        help="Type of inference device, support 'xpu', 'cpu' or 'gpu'.")
+        help="Type of inference device, support 'kunlunxin', 'cpu' or 'gpu'.")
     parser.add_argument(
         "--use_trt",
         type=ast.literal_eval,
@@ -29,8 +29,8 @@ def parse_arguments():
 def build_option(args):
     option = fd.RuntimeOption()
 
-    if args.device.lower() == "xpu":
+    if args.device.lower() == "kunlunxin":
-        option.use_xpu()
+        option.use_kunlunxin()
 
     if args.device.lower() == "gpu":
         option.use_gpu()

examples/vision/detection/yolov5/cpp/README.md

@@ -29,7 +29,7 @@ wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/0000000
 ./infer_paddle_demo yolov5s_infer 000000014439.jpg 1
 # GPU上TensorRT推理
 ./infer_paddle_demo yolov5s_infer 000000014439.jpg 2
-# XPU推理
+# 昆仑芯XPU推理
 ./infer_paddle_demo yolov5s_infer 000000014439.jpg 3
 ```
 

examples/vision/detection/yolov5/cpp/infer_paddle_model.cc

@@ -102,11 +102,11 @@ void TrtInfer(const std::string& model_dir, const std::string& image_file) {
   std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
 }
 
-void XpuInfer(const std::string& model_dir, const std::string& image_file) {
+void KunlunXinInfer(const std::string& model_dir, const std::string& image_file) {
   auto model_file = model_dir + sep + "model.pdmodel";
   auto params_file = model_dir + sep + "model.pdiparams";
   fastdeploy::RuntimeOption option;
-  option.UseXpu();
+  option.UseKunlunXin();
   auto model = fastdeploy::vision::detection::YOLOv5(
       model_file, params_file, option, fastdeploy::ModelFormat::PADDLE);
 
@@ -148,7 +148,7 @@ int main(int argc, char* argv[]) {
   } else if (std::atoi(argv[3]) == 2) {
     TrtInfer(argv[1], argv[2]);
   } else if (std::atoi(argv[3]) == 3) {
-    XpuInfer(argv[1], argv[2]);
+    KunlunXinInfer(argv[1], argv[2]);
   }
   return 0;
 }

examples/vision/detection/yolov5/python/README.md

@@ -23,8 +23,8 @@ python infer.py --model yolov5s_infer --image 000000014439.jpg --device cpu
 python infer.py --model yolov5s_infer --image 000000014439.jpg --device gpu
 # GPU上使用TensorRT推理
 python infer.py --model yolov5s_infer --image 000000014439.jpg --device gpu --use_trt True
-# XPU推理
+# 昆仑芯XPU推理
-python infer.py --model yolov5s_infer --image 000000014439.jpg --device xpu
+python infer.py --model yolov5s_infer --image 000000014439.jpg --device kunlunxin
 ```
 
 运行完成可视化结果如下图所示

examples/vision/detection/yolov5/python/infer.py

@@ -14,7 +14,7 @@ def parse_arguments():
         "--device",
         type=str,
         default='cpu',
-        help="Type of inference device, support 'cpu' or 'gpu' or 'xpu'.")
+        help="Type of inference device, support 'cpu' or 'gpu' or 'kunlunxin'.")
     parser.add_argument(
         "--use_trt",
         type=ast.literal_eval,
@@ -25,8 +25,8 @@ def parse_arguments():
 
 def build_option(args):
     option = fd.RuntimeOption()
-    if args.device.lower() == "xpu":
+    if args.device.lower() == "kunlunxin":
-        option.use_xpu()
+        option.use_kunlunxin()
 
     if args.device.lower() == "gpu":
         option.use_gpu()

examples/vision/detection/yolov6/cpp/README.md

@@ -19,16 +19,16 @@ cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
 make -j
 
 #下载Paddle模型文件和测试图片
-https://bj.bcebos.com/paddlehub/fastdeploy/yolov6s_infer.tar
+wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov6s_infer.tar
 tar -xf yolov6s_infer.tar
 wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
 
 # CPU推理
-./infer_paddle_demo ./../yolov6s_infer 000000014439.jpg 0
+./infer_paddle_demo yolov6s_infer 000000014439.jpg 0
 # GPU推理
-./infer_paddle_demo ./../yolov6s_infer 000000014439.jpg 1
+./infer_paddle_demo yolov6s_infer 000000014439.jpg 1
-# XPU推理
+# 昆仑芯XPU推理
-./infer_paddle_demo ./../yolov6s_infer 000000014439.jpg 2
+./infer_paddle_demo yolov6s_infer 000000014439.jpg 2
 ```
 
 如果想要验证ONNX模型的推理，可以参考如下命令：

examples/vision/detection/yolov6/cpp/infer_paddle_model.cc

@@ -45,9 +45,9 @@ void CpuInfer(const std::string& model_dir, const std::string& image_file) {
   std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
 }
 
-void XpuInfer(const std::string& model_dir, const std::string& image_file) {
+void KunlunXinInfer(const std::string& model_dir, const std::string& image_file) {
   fastdeploy::RuntimeOption option;
-  option.UseXpu();
+  option.UseKunlunXin();
   auto model_file = model_dir + sep + "model.pdmodel";
   auto params_file = model_dir + sep + "model.pdiparams";
   auto model = fastdeploy::vision::detection::YOLOv6(model_file, params_file, option, fastdeploy::ModelFormat::PADDLE);
@@ -103,7 +103,7 @@ int main(int argc, char* argv[]) {
                  "e.g ./infer_model ./yolov6s_infer ./test.jpeg 0"
               << std::endl;
     std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
-                 "with gpu; 2: run with xpu."
+                 "with gpu; 2: run with kunlunxin."
               << std::endl;
     return -1;
   }
@@ -113,7 +113,7 @@ int main(int argc, char* argv[]) {
   } else if (std::atoi(argv[3]) == 1) {
     GpuInfer(argv[1], argv[2]);
   } else if (std::atoi(argv[3]) == 2) {
-    XpuInfer(argv[1], argv[2]);
+    KunlunXinInfer(argv[1], argv[2]);
   }
   return 0;
 }

examples/vision/detection/yolov6/python/README.md

@@ -12,7 +12,7 @@
 git clone https://github.com/PaddlePaddle/FastDeploy.git
 cd examples/vision/detection/yolov6/python/
 
-https://bj.bcebos.com/paddlehub/fastdeploy/yolov6s_infer.tar
+wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov6s_infer.tar
 tar -xf yolov6s_infer.tar
 wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
 
@@ -20,8 +20,8 @@ wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/0000000
 python infer_paddle_model.py --model yolov6s_infer --image 000000014439.jpg  --device cpu
 # GPU推理
 python infer_paddle_model.py --model yolov6s_infer --image 000000014439.jpg  --device gpu
-# XPU推理
+# 昆仑芯XPU推理
-python infer_paddle_model.py --model yolov6s_infer --image 000000014439.jpg  --device xpu
+python infer_paddle_model.py --model yolov6s_infer --image 000000014439.jpg  --device kunlunxin
 ```
 如果想要验证ONNX模型的推理，可以参考如下命令：
 ```bash

examples/vision/detection/yolov6/python/infer_paddle_model.py

@@ -16,7 +16,7 @@ def parse_arguments():
         "--device",
         type=str,
         default='cpu',
-        help="Type of inference device, support 'cpu', 'xpu' or 'gpu'.")
+        help="Type of inference device, support 'cpu', 'kunlunxin' or 'gpu'.")
     return parser.parse_args()
 
 
@@ -25,8 +25,8 @@ def build_option(args):
     if args.device.lower() == "gpu":
         option.use_gpu(0)
 
-    if args.device.lower() == "xpu":
+    if args.device.lower() == "kunlunxin":
-        option.use_xpu()
+        option.use_kunlunxin()
 
     return option
 

examples/vision/detection/yolov7/cpp/README.md

@@ -26,7 +26,7 @@ wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/0000000
 ./infer_paddle_model_demo yolov7_infer 000000014439.jpg 0
 # GPU推理
 ./infer_paddle_model_demo yolov7_infer 000000014439.jpg 1
-# XPU推理
+# 昆仑芯XPU推理
 ./infer_paddle_model_demo yolov7_infer 000000014439.jpg 2
 ```
 如果想要验证ONNX模型的推理，可以参考如下命令：

examples/vision/detection/yolov7/cpp/infer_paddle_model.cc

@@ -52,7 +52,7 @@ int main(int argc, char* argv[]) {
               << std::endl;
     std::cout << "The data type of run_option is int, 0: run on cpu with ORT "
                  "backend; 1: run "
-                 "on gpu with TensorRT backend ; 2: run with xpu. "
+                 "on gpu with TensorRT backend ; 2: run with kunlunxin. "
               << std::endl;
     return -1;
   }
@@ -67,7 +67,7 @@ int main(int argc, char* argv[]) {
     option.UseGpu();
     option.UseTrtBackend();
   } else if (flag == 2) {
-    option.UseXpu();
+    option.UseKunlunXin();
     }
 
   std::string model_dir = argv[1];

examples/vision/detection/yolov7/python/README.md

@@ -22,8 +22,8 @@ wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/0000000
 python infer_paddle_model.py --model yolov7_infer --image 000000014439.jpg --device cpu
 # GPU推理
 python infer_paddle_model.py --model yolov7_infer --image 000000014439.jpg --device gpu
-# XPU推理
+# 昆仑芯XPU推理
-python infer_paddle_model.py --model yolov7_infer --image 000000014439.jpg --device xpu
+python infer_paddle_model.py --model yolov7_infer --image 000000014439.jpg --device kunlunxin
 ```
 如果想要验证ONNX模型的推理，可以参考如下命令：
 ```bash

examples/vision/detection/yolov7/python/README_EN.md

@@ -22,8 +22,8 @@ wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/0000000
 python infer_paddle_model.py --model yolov7_infer --image 000000014439.jpg --device cpu
 # GPU
 python infer_paddle_model.py --model yolov7_infer --image 000000014439.jpg --device gpu
-# XPU
+# KunlunXin XPU
-python infer_paddle_model.py --model yolov7_infer --image 000000014439.jpg --device xpu
+python infer_paddle_model.py --model yolov7_infer --image 000000014439.jpg --device kunlunxin
 ```
 If you want to test ONNX model:
 ```bash

examples/vision/detection/yolov7/python/infer_paddle_model.py

@@ -16,7 +16,7 @@ def parse_arguments():
         "--device",
         type=str,
         default='cpu',
-        help="Type of inference device, support 'cpu', 'xpu' or 'gpu'.")
+        help="Type of inference device, support 'cpu', 'kunlunxin' or 'gpu'.")
     return parser.parse_args()
 
 
@@ -25,8 +25,8 @@ def build_option(args):
     if args.device.lower() == "gpu":
         option.use_gpu(0)
 
-    if args.device.lower() == "xpu":
+    if args.device.lower() == "kunlunxin":
-        option.use_xpu()
+        option.use_kunlunxin()
 
     return option
 

examples/vision/faceid/adaface/cpp/README.md

@@ -41,7 +41,7 @@ tar zxvf mobilefacenet_adaface.tgz -C ./
               mobilefacenet_adaface/mobilefacenet_adaface.pdiparams \
               face_0.jpg face_1.jpg face_2.jpg 2
 
-# XPU推理
+# 昆仑芯XPU推理
 ./infer_demo mobilefacenet_adaface/mobilefacenet_adaface.pdmodel \
               mobilefacenet_adaface/mobilefacenet_adaface.pdiparams \
               face_0.jpg face_1.jpg face_2.jpg 3

examples/vision/faceid/adaface/cpp/infer.cc

@@ -52,10 +52,10 @@ void CpuInfer(const std::string &model_file, const std::string &params_file,
             << ", Cosine 02:" << cosine02 << std::endl;
 }
 
-void XpuInfer(const std::string &model_file, const std::string &params_file,
+void KunlunXinInfer(const std::string &model_file, const std::string &params_file,
               const std::vector<std::string> &image_file) {
   auto option = fastdeploy::RuntimeOption();
-  option.UseXpu();
+  option.UseKunlunXin();
   auto model = fastdeploy::vision::faceid::AdaFace(model_file, params_file);
   if (!model.Initialized()) {
     std::cerr << "Failed to initialize." << std::endl;
@@ -182,7 +182,7 @@ int main(int argc, char *argv[]) {
                  "test_lite_focal_AdaFace_2.JPG 0"
               << std::endl;
     std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
-                 "with gpu; 2: run with gpu and use tensorrt backend; 3: run with xpu."
+                 "with gpu; 2: run with gpu and use tensorrt backend; 3: run with kunlunxin."
               << std::endl;
     return -1;
   }
@@ -196,7 +196,7 @@ int main(int argc, char *argv[]) {
   } else if (std::atoi(argv[6]) == 2) {
     TrtInfer(argv[1], argv[2], image_files);
   } else if (std::atoi(argv[6]) == 3) {
-    CpuInfer(argv[1], argv[2], image_files);
+    KunlunXinInfer(argv[1], argv[2], image_files);
   }
   return 0;
 }

examples/vision/faceid/adaface/python/README.md

@@ -45,13 +45,13 @@ python infer.py --model mobilefacenet_adaface/mobilefacenet_adaface.pdmodel \
                 --device gpu \
                 --use_trt True
 
-# XPU推理
+# 昆仑芯XPU推理
 python infer.py --model mobilefacenet_adaface/mobilefacenet_adaface.pdmodel \
                 --params_file mobilefacenet_adaface/mobilefacenet_adaface.pdiparams \
                 --face test_lite_focal_arcface_0.JPG \
                 --face_positive test_lite_focal_arcface_1.JPG \
                 --face_negative test_lite_focal_arcface_2.JPG \
-                 --device xpu
+                 --device kunlunxin
 ```
 
 运行完成可视化结果如下图所示

examples/vision/faceid/adaface/python/infer.py

@@ -39,7 +39,7 @@ def parse_arguments():
         "--device",
         type=str,
         default='cpu',
-        help="Type of inference device, support 'cpu' , 'xpu' or 'gpu'.")
+        help="Type of inference device, support 'cpu' , 'kunlunxin' or 'gpu'.")
     parser.add_argument(
         "--use_trt",
         type=ast.literal_eval,
@@ -54,8 +54,8 @@ def build_option(args):
     if args.device.lower() == "gpu":
         option.use_gpu()
 
-    if args.device.lower() == "xpu":
+    if args.device.lower() == "kunlunxin":
-        option.use_xpu()
+        option.use_kunlunxin()
 
     if args.use_trt:
         option.use_trt_backend()

examples/vision/keypointdetection/det_keypoint_unite/cpp/README.md

@@ -33,7 +33,7 @@ wget https://bj.bcebos.com/paddlehub/fastdeploy/000000018491.jpg
 ./infer_demo PP_PicoDet_V2_S_Pedestrian_320x320_infer PP_TinyPose_256x192_infer 000000018491.jpg 1
 # GPU上TensorRT推理
 ./infer_demo PP_PicoDet_V2_S_Pedestrian_320x320_infer PP_TinyPose_256x192_infer 000000018491.jpg 2
-# XPU推理
+# 昆仑芯XPU推理
 ./infer_demo PP_PicoDet_V2_S_Pedestrian_320x320_infer PP_TinyPose_256x192_infer 000000018491.jpg 3
 ```
 

examples/vision/keypointdetection/det_keypoint_unite/cpp/det_keypoint_unite_infer.cc

@@ -66,11 +66,11 @@ void CpuInfer(const std::string& det_model_dir,
             << std::endl;
 }
 
-void XpuInfer(const std::string& det_model_dir,
+void KunlunXinInfer(const std::string& det_model_dir,
               const std::string& tinypose_model_dir,
               const std::string& image_file) {
   auto option = fastdeploy::RuntimeOption();
-  option.UseXpu();
+  option.UseKunlunXin();
   auto det_model_file = det_model_dir + sep + "model.pdmodel";
   auto det_params_file = det_model_dir + sep + "model.pdiparams";
   auto det_config_file = det_model_dir + sep + "infer_cfg.yml";
@@ -229,7 +229,7 @@ int main(int argc, char* argv[]) {
                  "./test.jpeg 0"
               << std::endl;
     std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
-                 "with gpu; 2: run with gpu and use tensorrt backend; 3: run with xpu."
+                 "with gpu; 2: run with gpu and use tensorrt backend; 3: run with kunlunxin."
               << std::endl;
     return -1;
   }
@@ -241,7 +241,7 @@ int main(int argc, char* argv[]) {
   } else if (std::atoi(argv[4]) == 2) {
     TrtInfer(argv[1], argv[2], argv[3]);
   } else if (std::atoi(argv[4]) == 3) {
-    XpuInfer(argv[1], argv[2], argv[3]);
+    KunlunXinInfer(argv[1], argv[2], argv[3]);
   }
   return 0;
 }

examples/vision/keypointdetection/det_keypoint_unite/python/README.md

@@ -25,8 +25,8 @@ python det_keypoint_unite_infer.py --tinypose_model_dir PP_TinyPose_256x192_infe
 python det_keypoint_unite_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --det_model_dir PP_PicoDet_V2_S_Pedestrian_320x320_infer --image 000000018491.jpg --device gpu
 # GPU上使用TensorRT推理 （注意：TensorRT推理第一次运行，有序列化模型的操作，有一定耗时，需要耐心等待）
 python det_keypoint_unite_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --det_model_dir PP_PicoDet_V2_S_Pedestrian_320x320_infer --image 000000018491.jpg --device gpu --use_trt True
-# XPU推理
+# 昆仑芯XPU推理
-python det_keypoint_unite_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --det_model_dir PP_PicoDet_V2_S_Pedestrian_320x320_infer --image 000000018491.jpg --device xpu
+python det_keypoint_unite_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --det_model_dir PP_PicoDet_V2_S_Pedestrian_320x320_infer --image 000000018491.jpg --device kunlunxin
 ```
 
 运行完成可视化结果如下图所示

examples/vision/keypointdetection/det_keypoint_unite/python/det_keypoint_unite_infer.py

@@ -19,7 +19,7 @@ def parse_arguments():
         "--device",
         type=str,
         default='cpu',
-        help="type of inference device, support 'cpu', 'xpu' or 'gpu'.")
+        help="type of inference device, support 'cpu', 'kunlunxin' or 'gpu'.")
     parser.add_argument(
         "--use_trt",
         type=ast.literal_eval,
@@ -47,8 +47,8 @@ def build_tinypose_option(args):
     if args.device.lower() == "gpu":
         option.use_gpu()
 
-    if args.device.lower() == "xpu":
+    if args.device.lower() == "kunlunxin":
-        option.use_xpu()
+        option.use_kunlunxin()
 
     if args.use_trt:
         option.use_trt_backend()

examples/vision/keypointdetection/tiny_pose/cpp/README.md

@@ -32,7 +32,7 @@ wget https://bj.bcebos.com/paddlehub/fastdeploy/hrnet_demo.jpg
 ./infer_tinypose_demo PP_TinyPose_256x192_infer hrnet_demo.jpg 1
 # GPU上TensorRT推理
 ./infer_tinypose_demo PP_TinyPose_256x192_infer hrnet_demo.jpg 2
-# XPU推理
+# 昆仑芯XPU推理
 ./infer_tinypose_demo PP_TinyPose_256x192_infer hrnet_demo.jpg 3
 ```
 

examples/vision/keypointdetection/tiny_pose/cpp/pptinypose_infer.cc

@@ -53,13 +53,13 @@ void CpuInfer(const std::string& tinypose_model_dir,
             << std::endl;
 }
 
-void XpuInfer(const std::string& tinypose_model_dir,
+void KunlunXinInfer(const std::string& tinypose_model_dir,
               const std::string& image_file) {
   auto tinypose_model_file = tinypose_model_dir + sep + "model.pdmodel";
   auto tinypose_params_file = tinypose_model_dir + sep + "model.pdiparams";
   auto tinypose_config_file = tinypose_model_dir + sep + "infer_cfg.yml";
   auto option = fastdeploy::RuntimeOption();
-  option.UseXpu();
+  option.UseKunlunXin();
   auto tinypose_model = fastdeploy::vision::keypointdetection::PPTinyPose(
       tinypose_model_file, tinypose_params_file, tinypose_config_file, option);
   if (!tinypose_model.Initialized()) {
@@ -163,7 +163,7 @@ int main(int argc, char* argv[]) {
                  "e.g ./infer_model ./pptinypose_model_dir ./test.jpeg 0"
               << std::endl;
     std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
-                 "with gpu; 2: run with gpu and use tensorrt backend; 3: run with xpu."
+                 "with gpu; 2: run with gpu and use tensorrt backend; 3: run with kunlunxin."
               << std::endl;
     return -1;
   }
@@ -175,7 +175,7 @@ int main(int argc, char* argv[]) {
   } else if (std::atoi(argv[3]) == 2) {
     TrtInfer(argv[1], argv[2]);
   } else if (std::atoi(argv[3]) == 3) {
-    TrtInfer(argv[1], argv[2]);
+    KunlunXinInfer(argv[1], argv[2]);
   }
   return 0;
 }

examples/vision/keypointdetection/tiny_pose/python/README.md

@@ -25,8 +25,8 @@ python pptinypose_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --imag
 python pptinypose_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --image hrnet_demo.jpg --device gpu
 # GPU上使用TensorRT推理 （注意：TensorRT推理第一次运行，有序列化模型的操作，有一定耗时，需要耐心等待）
 python pptinypose_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --image hrnet_demo.jpg --device gpu --use_trt True
-# XPU推理
+# 昆仑芯XPU推理
-python pptinypose_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --image hrnet_demo.jpg --device xpu
+python pptinypose_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --image hrnet_demo.jpg --device kunlunxin
 ```
 
 运行完成可视化结果如下图所示

examples/vision/keypointdetection/tiny_pose/python/pptinypose_infer.py

@@ -17,7 +17,7 @@ def parse_arguments():
         "--device",
         type=str,
         default='cpu',
-        help="type of inference device, support 'cpu', 'xpu' or 'gpu'.")
+        help="type of inference device, support 'cpu', 'kunlunxin' or 'gpu'.")
     parser.add_argument(
         "--use_trt",
         type=ast.literal_eval,
@@ -32,8 +32,8 @@ def build_tinypose_option(args):
     if args.device.lower() == "gpu":
         option.use_gpu()
 
-    if args.device.lower() == "xpu":
+    if args.device.lower() == "kunlunxin":
-        option.use_xpu()
+        option.use_kunlunxin()
 
     if args.use_trt:
         option.use_trt_backend()

examples/vision/matting/ppmatting/cpp/README.md

@@ -31,7 +31,7 @@ wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_bgr.jpg
 ./infer_demo PP-Matting-512 matting_input.jpg matting_bgr.jpg 1
 # GPU上TensorRT推理
 ./infer_demo PP-Matting-512 matting_input.jpg matting_bgr.jpg 2
-# XPU推理
+# 昆仑芯XPU推理
 ./infer_demo PP-Matting-512 matting_input.jpg matting_bgr.jpg 3
 ```
 

examples/vision/matting/ppmatting/cpp/infer.cc

@@ -51,13 +51,13 @@ void CpuInfer(const std::string& model_dir, const std::string& image_file,
             << std::endl;
 }
 
-void XpuInfer(const std::string& model_dir, const std::string& image_file,
+void KunlunXinInfer(const std::string& model_dir, const std::string& image_file,
               const std::string& background_file) {
   auto model_file = model_dir + sep + "model.pdmodel";
   auto params_file = model_dir + sep + "model.pdiparams";
   auto config_file = model_dir + sep + "deploy.yaml";
   auto option = fastdeploy::RuntimeOption();
-  option.UseXpu();
+  option.UseKunlunXin();
   auto model = fastdeploy::vision::matting::PPMatting(model_file, params_file,
                                                       config_file, option);
   if (!model.Initialized()) {
@@ -156,7 +156,7 @@ int main(int argc, char* argv[]) {
            "e.g ./infer_model ./PP-Matting-512 ./test.jpg ./test_bg.jpg 0"
         << std::endl;
     std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
-                 "with gpu; 2: run with gpu and use tensorrt backend, 3: run with xpu."
+                 "with gpu; 2: run with gpu and use tensorrt backend, 3: run with kunlunxin."
               << std::endl;
     return -1;
   }
@@ -167,7 +167,7 @@ int main(int argc, char* argv[]) {
   } else if (std::atoi(argv[4]) == 2) {
     TrtInfer(argv[1], argv[2], argv[3]);
   } else if (std::atoi(argv[4]) == 3) {
-    XpuInfer(argv[1], argv[2], argv[3]);
+    KunlunXinInfer(argv[1], argv[2], argv[3]);
   }
   return 0;
 }

examples/vision/matting/ppmatting/python/README.md

@@ -23,8 +23,8 @@ python infer.py --model PP-Matting-512 --image matting_input.jpg --bg matting_bg
 python infer.py --model PP-Matting-512 --image matting_input.jpg --bg matting_bgr.jpg --device gpu
 # GPU上使用TensorRT推理 （注意：TensorRT推理第一次运行，有序列化模型的操作，有一定耗时，需要耐心等待）
 python infer.py --model PP-Matting-512 --image matting_input.jpg --bg matting_bgr.jpg --device gpu --use_trt True
-# XPU推理
+# 昆仑芯XPU推理
-python infer.py --model PP-Matting-512 --image matting_input.jpg --bg matting_bgr.jpg --device xpu
+python infer.py --model PP-Matting-512 --image matting_input.jpg --bg matting_bgr.jpg --device kunlunxin
 ```
 
 运行完成可视化结果如下图所示

examples/vision/matting/ppmatting/python/infer.py

@@ -21,7 +21,7 @@ def parse_arguments():
         "--device",
         type=str,
         default='cpu',
-        help="Type of inference device, support 'cpu', 'xpu' or 'gpu'.")
+        help="Type of inference device, support 'cpu', 'kunlunxin' or 'gpu'.")
     parser.add_argument(
         "--use_trt",
         type=ast.literal_eval,
@@ -40,8 +40,8 @@ def build_option(args):
         option.use_trt_backend()
         option.set_trt_input_shape("img", [1, 3, 512, 512])
 
-    if args.device.lower() == "xpu":
+    if args.device.lower() == "kunlunxin":
-        option.use_xpu()
+        option.use_kunlunxin()
     return option
 
 

examples/vision/ocr/PP-OCRv2/cpp/README.md

@@ -41,7 +41,7 @@ wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_
 ./infer_demo ./ch_PP-OCRv2_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv2_rec_infer ./ppocr_keys_v1.txt ./12.jpg 2
 # GPU上Paddle-TRT推理
 ./infer_demo ./ch_PP-OCRv2_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv2_rec_infer ./ppocr_keys_v1.txt ./12.jpg 3
-# XPU推理
+# 昆仑芯XPU推理
 ./infer_demo ./ch_PP-OCRv2_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv2_rec_infer ./ppocr_keys_v1.txt ./12.jpg 4
 ```
 

examples/vision/ocr/PP-OCRv2/cpp/infer.cc

@@ -100,7 +100,7 @@ int main(int argc, char* argv[]) {
                  "./ppocr_keys_v1.txt ./12.jpg 0"
               << std::endl;
     std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
-                 "with gpu; 2: run with gpu and use tensorrt backend; 3: run with gpu and use Paddle-TRT; 4: run with xpu."
+                 "with gpu; 2: run with gpu and use tensorrt backend; 3: run with gpu and use Paddle-TRT; 4: run with kunlunxin."
               << std::endl;
     return -1;
   }
@@ -121,7 +121,7 @@ int main(int argc, char* argv[]) {
     option.EnablePaddleTrtCollectShape();
     option.EnablePaddleToTrt();
   } else if (flag == 4) {
-    option.UseXpu();
+    option.UseKunlunXin();
   }
 
   std::string det_model_dir = argv[1];

examples/vision/ocr/PP-OCRv2/python/README.md

@@ -34,8 +34,8 @@ python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2
 python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu
 # GPU上使用TensorRT推理
 python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu --backend trt
-# XPU推理
+# 昆仑芯XPU推理
-python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device xpu
+python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device kunlunxin
 ```
 
 运行完成可视化结果如下图所示

examples/vision/ocr/PP-OCRv2/python/infer.py

@@ -41,7 +41,7 @@ def parse_arguments():
         "--device",
         type=str,
         default='cpu',
-        help="Type of inference device, support 'cpu', 'xpu' or 'gpu'.")
+        help="Type of inference device, support 'cpu', 'kunlunxin' or 'gpu'.")
     parser.add_argument(
         "--backend",
         type=str,
@@ -68,8 +68,8 @@ def build_option(args):
 
     option.set_cpu_thread_num(args.cpu_thread_num)
 
-    if args.device.lower() == "xpu":
+    if args.device.lower() == "kunlunxin":
-        option.use_xpu()
+        option.use_kunlunxin()
         return option
 
     if args.backend.lower() == "trt":

examples/vision/ocr/PP-OCRv3/cpp/README.md

@@ -41,7 +41,7 @@ wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_
 ./infer_demo ./ch_PP-OCRv3_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv3_rec_infer ./ppocr_keys_v1.txt ./12.jpg 2
 # GPU上Paddle-TRT推理
 ./infer_demo ./ch_PP-OCRv3_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv3_rec_infer ./ppocr_keys_v1.txt ./12.jpg 3
-# XPU推理
+# 昆仑芯XPU推理
 ./infer_demo ./ch_PP-OCRv3_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv3_rec_infer ./ppocr_keys_v1.txt ./12.jpg 4
 ```
 

examples/vision/ocr/PP-OCRv3/cpp/infer.cc

@@ -101,7 +101,7 @@ int main(int argc, char* argv[]) {
                  "./ppocr_keys_v1.txt ./12.jpg 0"
               << std::endl;
     std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
-                 "with gpu; 2: run with gpu and use tensorrt backend; 3: run with gpu and use Paddle-TRT; 4: run with xpu."
+                 "with gpu; 2: run with gpu and use tensorrt backend; 3: run with gpu and use Paddle-TRT; 4: run with kunlunxin."
               << std::endl;
     return -1;
   }
@@ -122,7 +122,7 @@ int main(int argc, char* argv[]) {
     option.EnablePaddleTrtCollectShape();
     option.EnablePaddleToTrt();
   } else if (flag == 4) {
-    option.UseXpu();
+    option.UseKunlunXin();
   }
 
   std::string det_model_dir = argv[1];

examples/vision/ocr/PP-OCRv3/python/README.md

@@ -33,8 +33,8 @@ python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2
 python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu
 # GPU上使用TensorRT推理
 python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu --backend trt
-# XPU推理
+# 昆仑芯XPU推理
-python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device xpu
+python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device kunlunxin
 ```
 
 运行完成可视化结果如下图所示

examples/vision/ocr/PP-OCRv3/python/infer.py

@@ -41,7 +41,7 @@ def parse_arguments():
         "--device",
         type=str,
         default='cpu',
-        help="Type of inference device, support 'cpu', 'xpu' or 'gpu'.")
+        help="Type of inference device, support 'cpu', 'kunlunxin' or 'gpu'.")
     parser.add_argument(
         "--backend",
         type=str,
@@ -68,8 +68,8 @@ def build_option(args):
 
     option.set_cpu_thread_num(args.cpu_thread_num)
 
-    if args.device.lower() == "xpu":
+    if args.device.lower() == "kunlunxin":
-        option.use_xpu()
+        option.use_kunlunxin()
         return option
 
     if args.backend.lower() == "trt":

examples/vision/segmentation/paddleseg/cpp/README.md

@@ -32,7 +32,7 @@ wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
 ./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 1
 # GPU上TensorRT推理
 ./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 2
-# XPU推理
+# 昆仑芯XPU推理
 ./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 3
 ```
 

examples/vision/segmentation/paddleseg/cpp/infer.cc

@@ -48,12 +48,12 @@ void CpuInfer(const std::string& model_dir, const std::string& image_file) {
   std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
 }
 
-void XpuInfer(const std::string& model_dir, const std::string& image_file) {
+void KunlunXinInfer(const std::string& model_dir, const std::string& image_file) {
   auto model_file = model_dir + sep + "model.pdmodel";
   auto params_file = model_dir + sep + "model.pdiparams";
   auto config_file = model_dir + sep + "deploy.yaml";
   auto option = fastdeploy::RuntimeOption();
-  option.UseXpu();
+  option.UseKunlunXin();
   auto model = fastdeploy::vision::segmentation::PaddleSegModel(
       model_file, params_file, config_file, option);
 
@@ -142,7 +142,7 @@ int main(int argc, char* argv[]) {
            "e.g ./infer_model ./ppseg_model_dir ./test.jpeg 0"
         << std::endl;
     std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
-                 "with gpu; 2: run with gpu and use tensorrt backend; 3: run with xpu."
+                 "with gpu; 2: run with gpu and use tensorrt backend; 3: run with kunlunxin."
               << std::endl;
     return -1;
   }
@@ -154,7 +154,7 @@ int main(int argc, char* argv[]) {
   } else if (std::atoi(argv[3]) == 2) {
     TrtInfer(argv[1], argv[2]);
   } else if (std::atoi(argv[3]) == 3) {
-    XpuInfer(argv[1], argv[2]);
+    KunlunXinInfer(argv[1], argv[2]);
   }
   return 0;
 }

examples/vision/segmentation/paddleseg/python/README.md

@@ -25,8 +25,8 @@ python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_
 python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device gpu
 # GPU上使用TensorRT推理 （注意：TensorRT推理第一次运行，有序列化模型的操作，有一定耗时，需要耐心等待）
 python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device gpu --use_trt True
-# XPU推理
+# 昆仑芯XPU推理
-python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device xpu
+python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device kunlunxin
 ```
 
 运行完成可视化结果如下图所示

examples/vision/segmentation/paddleseg/python/infer.py

@@ -15,7 +15,7 @@ def parse_arguments():
         "--device",
         type=str,
         default='cpu',
-        help="Type of inference device, support 'xpu', 'cpu' or 'gpu'.")
+        help="Type of inference device, support 'kunlunxin', 'cpu' or 'gpu'.")
     parser.add_argument(
         "--use_trt",
         type=ast.literal_eval,
@@ -30,8 +30,8 @@ def build_option(args):
     if args.device.lower() == "gpu":
         option.use_gpu()
 
-    if args.device.lower() == "xpu":
+    if args.device.lower() == "kunlunxin":
-        option.use_xpu()
+        option.use_kunlunxin()
 
     if args.use_trt:
         option.use_trt_backend()

fastdeploy/backends/lite/lite_backend.cc

@@ -43,7 +43,7 @@ void LiteBackend::BuildOption(const LiteBackendOption& option) {
   option_ = option;
   std::vector<paddle::lite_api::Place> valid_places;
   if (option_.enable_int8) {
-    if(option_.enable_xpu) {
+    if(option_.enable_kunlunxin) {
       valid_places.push_back(
           paddle::lite_api::Place{TARGET(kXPU), PRECISION(kInt8)});
     } else {
@@ -54,7 +54,7 @@ void LiteBackend::BuildOption(const LiteBackendOption& option) {
            << "inference with int8 precision!" << std::endl;    
   }
   if (option_.enable_fp16) {
-    if(option_.enable_xpu){
+    if(option_.enable_kunlunxin){
       valid_places.push_back(
           paddle::lite_api::Place{TARGET(kXPU), PRECISION(kFP16)});
     } else {
@@ -127,17 +127,17 @@ void LiteBackend::BuildOption(const LiteBackendOption& option) {
         paddle::lite_api::Place{TARGET(kARM), PRECISION(kInt8)}); 
   }
   
-  if(option_.enable_xpu){
+  if(option_.enable_kunlunxin){
     valid_places.push_back(
       paddle::lite_api::Place{TARGET(kXPU), PRECISION(kFloat)});
     valid_places.push_back(
       paddle::lite_api::Place{TARGET(kX86), PRECISION(kFloat)});
     config_.set_xpu_dev_per_thread(option_.device_id);
-    config_.set_xpu_workspace_l3_size_per_thread(option_.xpu_l3_workspace_size);
+    config_.set_xpu_workspace_l3_size_per_thread(option_.kunlunxin_l3_workspace_size);
-    config_.set_xpu_l3_cache_method(option_.xpu_l3_workspace_size, option_.xpu_locked);
+    config_.set_xpu_l3_cache_method(option_.kunlunxin_l3_workspace_size, option_.kunlunxin_locked);
-    config_.set_xpu_conv_autotune(option_.xpu_autotune, option_.xpu_autotune_file);
+    config_.set_xpu_conv_autotune(option_.kunlunxin_autotune, option_.kunlunxin_autotune_file);
-    config_.set_xpu_multi_encoder_method(option_.xpu_precision, option_.xpu_adaptive_seqlen);
+    config_.set_xpu_multi_encoder_method(option_.kunlunxin_precision, option_.kunlunxin_adaptive_seqlen);
-    if (option_.xpu_enable_multi_stream) {
+    if (option_.kunlunxin_enable_multi_stream) {
       config_.enable_xpu_multi_stream();
     }
   } else {
@@ -221,7 +221,7 @@ bool LiteBackend::InitFromPaddle(const std::string& model_file,
     auto shape = tensor->shape();
     info.shape.assign(shape.begin(), shape.end());
     info.name = output_names[i];
-    if(!option_.enable_xpu){
+    if(!option_.enable_kunlunxin){
       info.dtype = LiteDataTypeToFD(tensor->precision());
     }
     outputs_desc_.emplace_back(info);

fastdeploy/backends/lite/lite_backend.h

@@ -53,15 +53,15 @@ struct LiteBackendOption {
   std::vector<std::string> nnadapter_device_names = {};
   bool enable_timvx = false;
   bool enable_ascend = false;
-  bool enable_xpu = false;
+  bool enable_kunlunxin = false;
   int device_id = 0;
-  int xpu_l3_workspace_size = 0xfffc00;
+  int kunlunxin_l3_workspace_size = 0xfffc00;
-  bool xpu_locked = false;
+  bool kunlunxin_locked = false;
-  bool xpu_autotune = true;
+  bool kunlunxin_autotune = true;
-  std::string xpu_autotune_file = "";
+  std::string kunlunxin_autotune_file = "";
-  std::string xpu_precision = "int16";
+  std::string kunlunxin_precision = "int16";
-  bool xpu_adaptive_seqlen = false;
+  bool kunlunxin_adaptive_seqlen = false;
-  bool xpu_enable_multi_stream = false;
+  bool kunlunxin_enable_multi_stream = false;
 };
 
 // Convert data type from paddle lite to fastdeploy

fastdeploy/core/fd_type.cc

@@ -65,8 +65,8 @@ std::string Str(const Device& d) {
     case Device::ASCEND:
       out = "Device::ASCEND";
       break;
-    case Device::XPU:
+    case Device::KUNLUNXIN:
-      out = "Device::XPU";
+      out = "Device::KUNLUNXIN";
       break;
     default:
       out = "Device::UNKOWN";
@@ -88,8 +88,8 @@ std::ostream& operator<<(std::ostream& out,const Device& d){
   case Device::TIMVX:
     out << "Device::TIMVX";
     break;
-  case Device::XPU:
+  case Device::KUNLUNXIN:
-    out << "Device::XPU";
+    out << "Device::KUNLUNXIN";
     break;
   case Device::ASCEND:
     out << "Device::ASCEND";

fastdeploy/core/fd_type.h

@@ -22,7 +22,7 @@
 
 namespace fastdeploy {
 
-enum FASTDEPLOY_DECL Device { CPU, GPU, RKNPU, IPU, TIMVX, XPU, ASCEND};
+enum FASTDEPLOY_DECL Device { CPU, GPU, RKNPU, IPU, TIMVX, KUNLUNXIN, ASCEND};
 
 FASTDEPLOY_DECL std::string Str(const Device& d);
 

fastdeploy/fastdeploy_model.cc

@@ -52,7 +52,7 @@ bool FastDeployModel::InitRuntimeWithSpecifiedBackend() {
   bool use_rknpu = (runtime_option.device == Device::RKNPU);
   bool use_timvx = (runtime_option.device == Device::TIMVX);
   bool use_ascend = (runtime_option.device == Device::ASCEND); 
-  bool use_xpu = (runtime_option.device == Device::XPU);
+  bool use_kunlunxin = (runtime_option.device == Device::KUNLUNXIN);
 
   if (use_gpu) {
     if (!IsSupported(valid_gpu_backends, runtime_option.backend)) {
@@ -74,9 +74,9 @@ bool FastDeployModel::InitRuntimeWithSpecifiedBackend() {
       FDERROR << "The valid ascend backends of model " << ModelName() << " are " << Str(valid_ascend_backends) << ", " << runtime_option.backend << " is not supported." << std::endl;
       return false;
     }
-  } else if (use_xpu) {
+  } else if (use_kunlunxin) {
-    if (!IsSupported(valid_xpu_backends, runtime_option.backend)) {
+    if (!IsSupported(valid_kunlunxin_backends, runtime_option.backend)) {
-      FDERROR << "The valid xpu backends of model " << ModelName() << " are " << Str(valid_xpu_backends) << ", " << runtime_option.backend << " is not supported." << std::endl;
+      FDERROR << "The valid kunlunxin backends of model " << ModelName() << " are " << Str(valid_kunlunxin_backends) << ", " << runtime_option.backend << " is not supported." << std::endl;
       return false;
     }
   } else if(use_ipu) {
@@ -116,8 +116,8 @@ bool FastDeployModel::InitRuntimeWithSpecifiedDevice() {
     return CreateTimVXBackend();
   } else if (runtime_option.device == Device::ASCEND) {
     return CreateASCENDBackend();
-  } else if (runtime_option.device == Device::XPU) {
+  } else if (runtime_option.device == Device::KUNLUNXIN) {
-    return CreateXPUBackend();
+    return CreateKunlunXinBackend();
   } else if (runtime_option.device == Device::IPU) {
 #ifdef WITH_IPU
     return CreateIpuBackend();
@@ -127,7 +127,7 @@ bool FastDeployModel::InitRuntimeWithSpecifiedDevice() {
     return false;
 #endif
   }
-  FDERROR << "Only support CPU/GPU/IPU/RKNPU/TIMVX/XPU/ASCEND now." << std::endl;
+  FDERROR << "Only support CPU/GPU/IPU/RKNPU/TIMVX/KunlunXin/ASCEND now." << std::endl;
   return false;
 }
 
@@ -241,18 +241,18 @@ bool FastDeployModel::CreateTimVXBackend() {
   return false;
 }
 
-bool FastDeployModel::CreateXPUBackend() {
+bool FastDeployModel::CreateKunlunXinBackend() {
-  if (valid_xpu_backends.size() == 0) {
+  if (valid_kunlunxin_backends.size() == 0) {
-    FDERROR << "There's no valid xpu backends for model: " << ModelName()
+    FDERROR << "There's no valid KunlunXin backends for model: " << ModelName()
             << std::endl;
     return false;
   }
 
-  for (size_t i = 0; i < valid_xpu_backends.size(); ++i) {
+  for (size_t i = 0; i < valid_kunlunxin_backends.size(); ++i) {
-    if (!IsBackendAvailable(valid_xpu_backends[i])) {
+    if (!IsBackendAvailable(valid_kunlunxin_backends[i])) {
       continue;
     }
-    runtime_option.backend = valid_xpu_backends[i];
+    runtime_option.backend = valid_kunlunxin_backends[i];
     runtime_ = std::unique_ptr<Runtime>(new Runtime());
     if (!runtime_->Init(runtime_option)) {
       return false;

fastdeploy/fastdeploy_model.h

@@ -50,7 +50,7 @@ class FASTDEPLOY_DECL FastDeployModel {
   std::vector<Backend> valid_ascend_backends = {};
   /** Model's valid KunlunXin xpu backends. This member defined all the KunlunXin xpu backends have successfully tested for the model
    */
-  std::vector<Backend> valid_xpu_backends = {};
+  std::vector<Backend> valid_kunlunxin_backends = {};
   /** Model's valid hardware backends. This member defined all the gpu backends have successfully tested for the model
    */
   std::vector<Backend> valid_rknpu_backends = {};
@@ -149,7 +149,7 @@ class FASTDEPLOY_DECL FastDeployModel {
   bool CreateIpuBackend();
   bool CreateRKNPUBackend();
   bool CreateTimVXBackend();
-  bool CreateXPUBackend();
+  bool CreateKunlunXinBackend();
   bool CreateASCENDBackend();
 
   std::shared_ptr<Runtime> runtime_;

fastdeploy/pybind/runtime.cc

@@ -25,7 +25,7 @@ void BindRuntime(pybind11::module& m) {
       .def("use_cpu", &RuntimeOption::UseCpu)
       .def("use_rknpu2", &RuntimeOption::UseRKNPU2)
       .def("use_ascend", &RuntimeOption::UseAscend)
-      .def("use_xpu", &RuntimeOption::UseXpu)
+      .def("use_kunlunxin", &RuntimeOption::UseKunlunXin)
       .def("set_external_stream", &RuntimeOption::SetExternalStream)
       .def("set_cpu_thread_num", &RuntimeOption::SetCpuThreadNum)
       .def("use_paddle_backend", &RuntimeOption::UsePaddleBackend)
@@ -114,20 +114,20 @@ void BindRuntime(pybind11::module& m) {
                      &RuntimeOption::ipu_available_memory_proportion)
       .def_readwrite("ipu_enable_half_partial",
                      &RuntimeOption::ipu_enable_half_partial)
-      .def_readwrite("xpu_l3_workspace_size",
+      .def_readwrite("kunlunxin_l3_workspace_size",
-                     &RuntimeOption::xpu_l3_workspace_size)
+                     &RuntimeOption::kunlunxin_l3_workspace_size)
-      .def_readwrite("xpu_locked",
+      .def_readwrite("kunlunxin_locked",
-                     &RuntimeOption::xpu_locked)
+                     &RuntimeOption::kunlunxin_locked)
-      .def_readwrite("xpu_autotune",
+      .def_readwrite("kunlunxin_autotune",
-                     &RuntimeOption::xpu_autotune)
+                     &RuntimeOption::kunlunxin_autotune)
-      .def_readwrite("xpu_autotune_file",
+      .def_readwrite("kunlunxin_autotune_file",
-                     &RuntimeOption::xpu_autotune_file)
+                     &RuntimeOption::kunlunxin_autotune_file)
-      .def_readwrite("xpu_precision",
+      .def_readwrite("kunlunxin_precision",
-                     &RuntimeOption::xpu_precision)
+                     &RuntimeOption::kunlunxin_precision)
-      .def_readwrite("xpu_adaptive_seqlen",
+      .def_readwrite("kunlunxin_adaptive_seqlen",
-                     &RuntimeOption::xpu_adaptive_seqlen)
+                     &RuntimeOption::kunlunxin_adaptive_seqlen)
-      .def_readwrite("xpu_enable_multi_stream",
+      .def_readwrite("kunlunxin_enable_multi_stream",
-                     &RuntimeOption::xpu_enable_multi_stream);                              
+                     &RuntimeOption::kunlunxin_enable_multi_stream);                              
 
   pybind11::class_<TensorInfo>(m, "TensorInfo")
       .def_readwrite("name", &TensorInfo::name)

fastdeploy/runtime.cc

@@ -263,7 +263,7 @@ void RuntimeOption::UseTimVX() {
   device = Device::TIMVX;
 }
 
-void RuntimeOption::UseXpu(int xpu_id, 
+void RuntimeOption::UseKunlunXin(int kunlunxin_id, 
                           int l3_workspace_size,
                           bool locked,
                           bool autotune,
@@ -271,16 +271,16 @@ void RuntimeOption::UseXpu(int xpu_id,
                           const std::string &precision,
                           bool adaptive_seqlen,
                           bool enable_multi_stream) {
-  enable_xpu = true;
+  enable_kunlunxin = true;
-  device_id = xpu_id;
+  device_id = kunlunxin_id;
-  xpu_l3_workspace_size = l3_workspace_size;
+  kunlunxin_l3_workspace_size = l3_workspace_size;
-  xpu_locked=locked;
+  kunlunxin_locked=locked;
-  xpu_autotune=autotune;
+  kunlunxin_autotune=autotune;
-  xpu_autotune_file=autotune_file;
+  kunlunxin_autotune_file=autotune_file;
-  xpu_precision = precision;
+  kunlunxin_precision = precision;
-  xpu_adaptive_seqlen=adaptive_seqlen;
+  kunlunxin_adaptive_seqlen=adaptive_seqlen;
-  xpu_enable_multi_stream=enable_multi_stream;
+  kunlunxin_enable_multi_stream=enable_multi_stream;
-  device = Device::XPU;
+  device = Device::KUNLUNXIN;
 }
 
 void RuntimeOption::UseAscend(){
@@ -611,8 +611,8 @@ bool Runtime::Init(const RuntimeOption& _option) {
     FDINFO << "Runtime initialized with Backend::OPENVINO in "
            << Str(option.device) << "." << std::endl;
   } else if (option.backend == Backend::LITE) {
-    FDASSERT(option.device == Device::CPU || option.device == Device::TIMVX || option.device == Device::XPU || option.device == Device::ASCEND,
+    FDASSERT(option.device == Device::CPU || option.device == Device::TIMVX || option.device == Device::KUNLUNXIN || option.device == Device::ASCEND,
-             "Backend::LITE only supports Device::CPU/Device::TIMVX/Device::XPU.");
+             "Backend::LITE only supports Device::CPU/Device::TIMVX/Device::KUNLUNXIN.");
     CreateLiteBackend();
     FDINFO << "Runtime initialized with Backend::LITE in " << Str(option.device)
            << "." << std::endl;
@@ -882,15 +882,15 @@ void Runtime::CreateLiteBackend() {
   lite_option.nnadapter_mixed_precision_quantization_config_path = option.lite_nnadapter_mixed_precision_quantization_config_path;
   lite_option.enable_timvx = option.enable_timvx;
   lite_option.enable_ascend = option.enable_ascend;
-  lite_option.enable_xpu = option.enable_xpu;
+  lite_option.enable_kunlunxin = option.enable_kunlunxin;
   lite_option.device_id  = option.device_id;
-  lite_option.xpu_l3_workspace_size  = option.xpu_l3_workspace_size;
+  lite_option.kunlunxin_l3_workspace_size  = option.kunlunxin_l3_workspace_size;
-  lite_option.xpu_locked = option.xpu_locked;
+  lite_option.kunlunxin_locked = option.kunlunxin_locked;
-  lite_option.xpu_autotune = option.xpu_autotune;
+  lite_option.kunlunxin_autotune = option.kunlunxin_autotune;
-  lite_option.xpu_autotune_file = option.xpu_autotune_file;
+  lite_option.kunlunxin_autotune_file = option.kunlunxin_autotune_file;
-  lite_option.xpu_precision  = option.xpu_precision;
+  lite_option.kunlunxin_precision  = option.kunlunxin_precision;
-  lite_option.xpu_adaptive_seqlen = option.xpu_adaptive_seqlen;
+  lite_option.kunlunxin_adaptive_seqlen = option.kunlunxin_adaptive_seqlen;
-  lite_option.xpu_enable_multi_stream = option.xpu_enable_multi_stream;
+  lite_option.kunlunxin_enable_multi_stream = option.kunlunxin_enable_multi_stream;
 
   FDASSERT(option.model_format == ModelFormat::PADDLE,
            "LiteBackend only support model format of ModelFormat::PADDLE");

fastdeploy/runtime.h

@@ -120,9 +120,9 @@ struct FASTDEPLOY_DECL RuntimeOption {
   void UseAscend();
 
   ///
-  /// \brief Turn on XPU.
+  /// \brief Turn on KunlunXin XPU.
   ///
-  /// \param xpu_id the XPU card to use (default is 0).
+  /// \param kunlunxin_id the KunlunXin XPU card to use (default is 0).
   /// \param l3_workspace_size The size of the video memory allocated by the l3
   ///         cache, the maximum is 16M.
   /// \param locked Whether the allocated L3 cache can be locked. If false,
@@ -139,9 +139,10 @@ struct FASTDEPLOY_DECL RuntimeOption {
   ///       file will be used and autotune will not be performed again.
   /// \param precision Calculation accuracy of multi_encoder
   /// \param adaptive_seqlen Is the input of multi_encoder variable length
-  /// \param enable_multi_stream Whether to enable the multi stream of xpu.
+  /// \param enable_multi_stream Whether to enable the multi stream of
+  ///        KunlunXin XPU.
   ///
-  void UseXpu(int xpu_id = 0,
+  void UseKunlunXin(int kunlunxin_id = 0,
               int l3_workspace_size = 0xfffc00,
               bool locked = false,
               bool autotune = true,
@@ -449,7 +450,7 @@ struct FASTDEPLOY_DECL RuntimeOption {
 
   bool enable_timvx = false;
   bool enable_ascend = false;
-  bool enable_xpu = false;
+  bool enable_kunlunxin = false;
 
   // ======Only for Trt Backend=======
   std::map<std::string, std::vector<int32_t>> trt_max_shape;
@@ -482,14 +483,14 @@ struct FASTDEPLOY_DECL RuntimeOption {
   fastdeploy::rknpu2::CoreMask rknpu2_core_mask_ =
       fastdeploy::rknpu2::CoreMask::RKNN_NPU_CORE_AUTO;
 
-  // ======Only for XPU Backend=======
+  // ======Only for KunlunXin XPU Backend=======
-  int xpu_l3_workspace_size = 0xfffc00;
+  int kunlunxin_l3_workspace_size = 0xfffc00;
-  bool xpu_locked = false;
+  bool kunlunxin_locked = false;
-  bool xpu_autotune = true;
+  bool kunlunxin_autotune = true;
-  std::string xpu_autotune_file = "";
+  std::string kunlunxin_autotune_file = "";
-  std::string xpu_precision = "int16";
+  std::string kunlunxin_precision = "int16";
-  bool xpu_adaptive_seqlen = false;
+  bool kunlunxin_adaptive_seqlen = false;
-  bool xpu_enable_multi_stream = false;
+  bool kunlunxin_enable_multi_stream = false;
 
   std::string model_file = "";   // Path of model file
   std::string params_file = "";  // Path of parameters file, can be empty

fastdeploy/vision/classification/ppcls/model.cc

@@ -30,7 +30,7 @@ PaddleClasModel::PaddleClasModel(const std::string& model_file,
     valid_gpu_backends = {Backend::ORT, Backend::PDINFER, Backend::TRT};
     valid_timvx_backends = {Backend::LITE};
     valid_ascend_backends = {Backend::LITE};
-    valid_xpu_backends = {Backend::LITE};
+    valid_kunlunxin_backends = {Backend::LITE};
     valid_ipu_backends = {Backend::PDINFER};
   } else if (model_format == ModelFormat::ONNX) {
     valid_cpu_backends = {Backend::ORT, Backend::OPENVINO};

fastdeploy/vision/detection/contrib/yolov5/yolov5.cc

@@ -27,7 +27,7 @@ YOLOv5::YOLOv5(const std::string& model_file, const std::string& params_file,
   } else {
     valid_cpu_backends = {Backend::PDINFER, Backend::ORT, Backend::LITE};
     valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
-    valid_xpu_backends = {Backend::LITE};
+    valid_kunlunxin_backends = {Backend::LITE};
     valid_timvx_backends = {Backend::LITE};
     valid_ascend_backends = {Backend::LITE};
   }

fastdeploy/vision/detection/contrib/yolov6.cc

@@ -72,7 +72,7 @@ YOLOv6::YOLOv6(const std::string& model_file, const std::string& params_file,
   } else {
     valid_cpu_backends = {Backend::PDINFER, Backend::ORT, Backend::LITE};
     valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
-    valid_xpu_backends = {Backend::LITE};
+    valid_kunlunxin_backends = {Backend::LITE};
     valid_ascend_backends = {Backend::LITE};
   }
   runtime_option = custom_option;

fastdeploy/vision/detection/contrib/yolov7/yolov7.cc

@@ -27,7 +27,7 @@ YOLOv7::YOLOv7(const std::string& model_file, const std::string& params_file,
   } else {
     valid_cpu_backends = {Backend::PDINFER, Backend::ORT, Backend::LITE};
     valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
-    valid_xpu_backends = {Backend::LITE};
+    valid_kunlunxin_backends = {Backend::LITE};
     valid_ascend_backends = {Backend::LITE};
   }
   runtime_option = custom_option;