Jingxu10/release 26000 (#3476)

jingxu10 · ishaghosh27 · Harryalways317 · web-flow · commit 88a794feecab · 2025-01-23T17:58:12.000+09:00
* Update README.md (#3472) Co-authored-by: ishaghosh27 <94150575+ishaghosh27@users.noreply.github.com> * added import os for exit() to fix NameError (#3475) Name error coming up on code if reached to that line, Co-authored-by: Harish Vadaparty <harishvadaparty@gmail.com> * [Doc] Update Custom OPs broken references (#739) Signed-off-by: Emmanuel Ferdman <emmanuelferdman@gmail.com> Small PR - Commit f63ed97 moved a bunch of files. This PR adjusts the source for changes. * Add options to build with system LIBXSMM/oneDNN/sleef/MKL/ideep/gtest Signed-off-by: Sv. Lockal <lockalsash@gmail.com> --------- Signed-off-by: Sv. Lockal <lockalsash@gmail.com> Co-authored-by: ishaghosh27 <94150575+ishaghosh27@users.noreply.github.com> Co-authored-by: Harish Vadaparty <harishvadaparty@gmail.com> Co-authored-by: Emmanuel Ferdman <emmanuelferdman@gmail.com> Co-authored-by: Sv. Lockal <lockalsash@gmail.com>
diff --git a/cmake/Modules/FindoneMKL.cmake b/cmake/Modules/FindoneMKL.cmake
@@ -72,6 +72,10 @@ endfunction()
 # IPEX CPU lib always download and install mkl-static lib and use static linker for mkl-static lib.
 # IPEX CPU lib can manual config to use the dynamic link for oneMKL lib.
 if(BUILD_MODULE_TYPE STREQUAL "GPU")
+  set(USE_SYSTEM_MKL ON)
+endif()
+
+if(USE_SYSTEM_MKL)
   get_mkl_from_env_var()
 else()
   if(BUILD_WITH_XPU)
diff --git a/cmake/cpu/Options.cmake b/cmake/cpu/Options.cmake
@@ -7,6 +7,13 @@ set(Options_CPU_cmake_included true)
 # The options to build cpu
 include(CMakeDependentOption)
 
+option(USE_SYSTEM_LIBXSMM "Use system LIBXSMM library" OFF)
+option(USE_SYSTEM_ONEDNN "Use system oneDNN library" OFF)
+option(USE_SYSTEM_SLEEF "Use system SLEEF library" OFF)
+option(USE_SYSTEM_MKL "Use system MKL library" OFF)
+option(USE_SYSTEM_IDEEP "Use system ideep library" OFF)
+option(USE_SYSTEM_GTEST "Use system GoogleTest library" OFF)
+
 option(BUILD_LIBXSMM_VIA_CMAKE "Build LIBXSMM via CMake" ON)
 option(USE_LIBXSMM "Enable LIBXSMM" ON)
 option(USE_DNNL_GRAPH_COMPILER  "Build with DNNL Graph Compiler" ON)
diff --git a/csrc/cpu/CMakeLists.txt b/csrc/cpu/CMakeLists.txt
@@ -37,8 +37,20 @@ if((DEFINED ENV{DNNL_GRAPH_BUILD_COMPILER_BACKEND}) AND USE_DNNL_GRAPH_COMPILER)
 endif()
 
 set(THIRD_PARTY_BUILD_PATH_NAME "cpu_third_party")
-add_subdirectory(${IPEX_CPU_CPP_THIRD_PARTY_ROOT}/ideep/mkl-dnn ${THIRD_PARTY_BUILD_PATH_NAME}/ideep/mkl-dnn EXCLUDE_FROM_ALL)
-# add_subdirectory(${IPEX_CPU_CPP_THIRD_PARTY_ROOT}/mkl-dnn cpu_third_party/mkl-dnn)
+if(USE_SYSTEM_ONEDNN)
+  find_package(dnnl 3.4.1 CONFIG REQUIRED)
+  get_target_property(ONEDNN_INCLUDE_DIR DNNL::dnnl INTERFACE_INCLUDE_DIRECTORIES)
+  set(ONEDNN_LIBRARY DNNL::dnnl)
+  set(ONEDNN_GENERATED_INCLUDE ${ONEDNN_INCLUDE_DIR})
+else()
+  add_subdirectory(${IPEX_CPU_CPP_THIRD_PARTY_ROOT}/ideep/mkl-dnn ${THIRD_PARTY_BUILD_PATH_NAME}/ideep/mkl-dnn EXCLUDE_FROM_ALL)
+  set(ONEDNN_INCLUDE_DIR ${IPEX_CPU_CPP_THIRD_PARTY_ROOT}/ideep/mkl-dnn/include)
+  set(ONEDNN_LIBRARY dnnl)
+
+  # path of oneDNN .h.in generated file
+  file(RELATIVE_PATH CUR_DIR_REL_PATH "${IPEX_ROOT_DIR}" "${CMAKE_CURRENT_SOURCE_DIR}")
+  set(ONEDNN_GENERATED_INCLUDE "${CMAKE_BINARY_DIR}/${CUR_DIR_REL_PATH}/${THIRD_PARTY_BUILD_PATH_NAME}/ideep/mkl-dnn/include")
+endif()
 
 IF(IPEX_DISP_OP)
   set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DIPEX_DISP_OP")
@@ -120,7 +132,7 @@ add_library(${PLUGIN_NAME_CPU} SHARED ${IPEX_CPU_CPP_SRCS})
 # For IPEX_API macro
 target_compile_definitions(${PLUGIN_NAME_CPU} PUBLIC "BUILD_IPEX_MAIN_LIB")
 
-set_target_properties(${PLUGIN_NAME_CPU} PROPERTIES ONEDNN_INCLUDE_DIR "${IPEX_CPU_CPP_THIRD_PARTY_ROOT}/ideep/mkl-dnn/include")
+set_target_properties(${PLUGIN_NAME_CPU} PROPERTIES ONEDNN_INCLUDE_DIR ${ONEDNN_INCLUDE_DIR})
 
 # includes
 target_include_directories(${PLUGIN_NAME_CPU} PUBLIC ${IPEX_ROOT_DIR})
@@ -133,19 +145,21 @@ target_include_directories(${PLUGIN_NAME_CPU} PUBLIC ${IPEX_CPU_ROOT_DIR}/jit)
 target_include_directories(${PLUGIN_NAME_CPU} PUBLIC ${IPEX_JIT_CPP_ROOT})
 target_include_directories(${PLUGIN_NAME_CPU} PUBLIC ${IPEX_UTLIS_CPP_ROOT})
 
-target_include_directories(${PLUGIN_NAME_CPU} PUBLIC ${IPEX_CPU_CPP_THIRD_PARTY_ROOT}/ideep/mkl-dnn/include)
+target_include_directories(${PLUGIN_NAME_CPU} PUBLIC ${ONEDNN_INCLUDE_DIR})
 
 if(USE_LIBXSMM)
   target_include_directories(${PLUGIN_NAME_CPU} PUBLIC ${IPEX_CPU_ROOT_DIR}/tpp)
-  target_include_directories(${PLUGIN_NAME_CPU} PUBLIC ${IPEX_CPU_CPP_THIRD_PARTY_ROOT}/libxsmm/include)
+  target_include_directories(${PLUGIN_NAME_CPU} PUBLIC ${LIBXSMM_INCLUDE_DIRS})
 endif(USE_LIBXSMM)
 
-# path of oneDNN .h.in generated file
-file(RELATIVE_PATH CUR_DIR_REL_PATH "${IPEX_ROOT_DIR}" "${CMAKE_CURRENT_SOURCE_DIR}")
-set(ONEDNN_GENERATED_INCLUDE "${CMAKE_BINARY_DIR}/${CUR_DIR_REL_PATH}/${THIRD_PARTY_BUILD_PATH_NAME}/ideep/mkl-dnn/include")
 target_include_directories(${PLUGIN_NAME_CPU} PUBLIC ${ONEDNN_GENERATED_INCLUDE})
 
-target_include_directories(${PLUGIN_NAME_CPU} PUBLIC ${IPEX_CPU_CPP_THIRD_PARTY_ROOT}/ideep/include)
+if(USE_SYSTEM_IDEEP)
+  find_path(IDEEP_INCLUDE_DIR ideep.hpp REQUIRED)
+else()
+  set(IDEEP_INCLUDE_DIR ${IPEX_CPU_CPP_THIRD_PARTY_ROOT}/ideep/include)
+endif()
+target_include_directories(${PLUGIN_NAME_CPU} PUBLIC ${IDEEP_INCLUDE_DIR})
 target_include_directories(${PLUGIN_NAME_CPU} PUBLIC ${PYTHON_INCLUDE_DIR})
 
 if(BUILD_CPU_WITH_ONECCL)
@@ -165,12 +179,17 @@ if(CLANG_FORMAT)
 endif()
 
 if(USE_LIBXSMM)
-  if(BUILD_LIBXSMM_VIA_CMAKE)
+  if(USE_SYSTEM_LIBXSMM)
+    find_package(PkgConfig REQUIRED)
+    pkg_check_modules(LIBXSMM REQUIRED libxsmm)
+    target_include_directories(${PLUGIN_NAME_CPU} PUBLIC ${LIBXSMM_INCLUDE_DIRS})
+    target_link_libraries(${PLUGIN_NAME_CPU} PRIVATE ${LIBXSMM_LIBRARIES})
+  elseif(BUILD_LIBXSMM_VIA_CMAKE)
     add_subdirectory(${IPEX_CPU_CPP_THIRD_PARTY_ROOT}/libxsmm cpu_third_party/libxsmm EXCLUDE_FROM_ALL)
     add_definitions(-DLIBXSMM_DEFAULT_CONFIG)
-    target_include_directories(${PLUGIN_NAME_CPU} PUBLIC ${IPEX_CPU_CPP_THIRD_PARTY_ROOT}/libxsmm/include)
+    set(LIBXSMM_INCLUDE_DIRS ${IPEX_CPU_CPP_THIRD_PARTY_ROOT}/libxsmm/include)
     target_link_libraries(${PLUGIN_NAME_CPU} PRIVATE xsmm)
-  else(BUILD_LIBXSMM_VIA_CMAKE)
+  else()
     include(${CMAKE_ROOT}/Modules/ExternalProject.cmake)
     set(args
     CC=${CMAKE_C_COMPILER}
@@ -188,20 +207,31 @@ if(USE_LIBXSMM)
         ${args}
       INSTALL_COMMAND ""
       )
+    set(LIBXSMM_INCLUDE_DIRS ${IPEX_CPU_CPP_THIRD_PARTY_ROOT}/libxsmm/include)
     target_link_libraries(${PLUGIN_NAME_CPU} PRIVATE ${IPEX_CPU_CPP_THIRD_PARTY_ROOT}/libxsmm/lib/libxsmm.a)
   endif(BUILD_LIBXSMM_VIA_CMAKE)
 endif(USE_LIBXSMM)
 
-# setup sleef options:
-set(SLEEF_BUILD_SHARED_LIBS OFF CACHE BOOL "Build sleef as static library" FORCE)
-set(SLEEF_BUILD_DFT OFF CACHE BOOL "Don't build sleef DFT lib" FORCE)
-set(SLEEF_BUILD_GNUABI_LIBS OFF CACHE BOOL "Don't build sleef gnuabi libs" FORCE)
-set(SLEEF_BUILD_TESTS OFF CACHE BOOL "Don't build sleef tests" FORCE)
-set(SLEEF_BUILD_SCALAR_LIB OFF CACHE BOOL "libsleefscalar will be built." FORCE)
-add_subdirectory(${IPEX_CPU_CPP_THIRD_PARTY_ROOT}/sleef ${THIRD_PARTY_BUILD_PATH_NAME}/sleef EXCLUDE_FROM_ALL)
-target_link_libraries(${PLUGIN_NAME_CPU} PRIVATE sleef)
+if(USE_SYSTEM_SLEEF)
+  find_package(PkgConfig REQUIRED)
+  pkg_check_modules(SLEEF REQUIRED sleef)
+  target_include_directories(${PLUGIN_NAME_CPU} PUBLIC ${SLEEF_INCLUDE_DIRS})
+  target_link_libraries(${PLUGIN_NAME_CPU} PRIVATE ${SLEEF_LIBRARIES})
+else()
+  # setup sleef options:
+  set(SLEEF_BUILD_SHARED_LIBS OFF CACHE BOOL "Build sleef as static library" FORCE)
+  set(SLEEF_BUILD_DFT OFF CACHE BOOL "Don't build sleef DFT lib" FORCE)
+  set(SLEEF_BUILD_GNUABI_LIBS OFF CACHE BOOL "Don't build sleef gnuabi libs" FORCE)
+  set(SLEEF_BUILD_TESTS OFF CACHE BOOL "Don't build sleef tests" FORCE)
+  set(SLEEF_BUILD_SCALAR_LIB OFF CACHE BOOL "libsleefscalar will be built." FORCE)
+  add_subdirectory(${IPEX_CPU_CPP_THIRD_PARTY_ROOT}/sleef ${THIRD_PARTY_BUILD_PATH_NAME}/sleef EXCLUDE_FROM_ALL)
+  target_link_libraries(${PLUGIN_NAME_CPU} PRIVATE sleef)
+endif()
+
+if(NOT USE_SYSTEM_ONEDNN)
+  add_dependencies(${PLUGIN_NAME_CPU} dnnl)
+endif()
 
-add_dependencies(${PLUGIN_NAME_CPU} dnnl)
 # If Graph Compiler is built, then it should link to its LLVM dependencies,
 # and not the LLVM symbols exposed by PyTorch.
 if ((DEFINED ENV{DNNL_GRAPH_BUILD_COMPILER_BACKEND}) AND USE_DNNL_GRAPH_COMPILER)
@@ -213,7 +243,7 @@ if ((DEFINED ENV{DNNL_GRAPH_BUILD_COMPILER_BACKEND}) AND USE_DNNL_GRAPH_COMPILER
     set(CMAKE_SHARED_LINKER_FLAGS "${CMAKE_SHARED_LINKER_FLAGS} -Wl,--exclude-libs=${DNNL_GRAPHCOMPILER_LLVM_LIB_EXCLUDE}")
   endif()
 else()
-  target_link_libraries(${PLUGIN_NAME_CPU} PUBLIC dnnl)
+  target_link_libraries(${PLUGIN_NAME_CPU} PUBLIC ${ONEDNN_LIBRARY})
 endif()
 find_package(oneMKL QUIET)
 if (ONEMKL_FOUND)
diff --git a/examples/cpu/inference/python/jupyter-notebooks/README.md b/examples/cpu/inference/python/jupyter-notebooks/README.md
@@ -2,15 +2,17 @@
 
 Intel® Extension for PyTorch (IPEX) extends PyTorch* with optimizations for extra performance boost on Intel® hardware. While most of the optimizations will be upstreamed in future PyTorch* releases, the extension delivers up-to-date features and optimizations for PyTorch workloads on Intel® hardware. The optimization approaches generally include operator optimization, graph optimization and runtime optimization.
 
+Before selecting a sample, please make sure to (1) Check [Prerequisites](#prerequisites), (2) complete [Environment Setup](#environment-setup), and (3) see instructions to [Run the Sample](#run-the-sample).
+
 ## Jupyter Notebooks Overview
 
 | Sample name | Description | Time to Complete             | Category   | Validated for AI Tools Selector |
 |---|---|---|---|---|
 [Getting Started with Intel® Extension for PyTorch* (IPEX)](https://github.com/intel/intel-extension-for-pytorch/tree/main/examples/cpu/inference/python/jupyter-notebooks/IPEX_Getting_Started.ipynb ) | This code sample demonstrates how to begin using the Intel® Extension for PyTorch* (IPEX). It will guide users how to run a PyTorch inference workload on CPU by using oneAPI AI Analytics Toolkit and also analyze the CPU usage via oneDNN verbose logs.| 15 minutes| Getting Started | Y | 
-[PyTorch Inference Optimizations with Intel® Advanced Matrix Extensions (Intel® AMX) Bfloat16 Integer8](https://github.com/intel-innersource/frameworks.ai.pytorch.ipex-cpu/blob/cpu-device/examples/cpu/inference/python/jupyter-notebooks/IntelPyTorch_InferenceOptimizations_AMX_BF16_INT8.ipynb) | This code sample demonstrates how to perform inference using the ResNet50 and BERT models using the Intel® Extension for PyTorch* (IPEX). IPEX allows you to speed up inference on Intel® Xeon Scalable processors with lower precision data formats and specialized computer instructions. The bfloat16 (BF16) data format uses half the bit width of floating-point-32 (FP32), which lessens the amount of memory needed and execution time to process. Likewise, the integer8 (INT8) data format uses half the bit width of BF16. | 5 minutes     | Code Optimization | Y|
-[Interactive Chat Based on DialoGPT Model Using Intel® Extension for PyTorch* Quantization](https://github.com/intel-innersource/frameworks.ai.pytorch.ipex-cpu/blob/cpu-device/examples/cpu/inference/python/jupyter-notebooks/IntelPytorch_Interactive_Chat_Quantization.ipynb)| This code sample demonstrates how to create interactive chat based on pre-trained DialoGPT model and add the Intel® Extension for PyTorch* (IPEX) quantization to it. The sample shows how to create interactive chat based on the pre-trained DialoGPT model from HuggingFace and how to add INT8 dynamic quantization to it. The Intel® Extension for PyTorch* (IPEX) gives users the ability to speed up operations on processors with INT8 data format and specialized computer instructions.| 10 minutes | Concepts and Functionality| Y|
-[Optimize PyTorch Models using Intel® Extension for PyTorch* (IPEX) Quantization](https://github.com/intel-innersource/frameworks.ai.pytorch.ipex-cpu/blob/cpu-device/examples/cpu/inference/python/jupyter-notebooks/IntelPytorch_Quantization.ipynb)|This code sample demonstrates how to quantize a ResNet50 model that is calibrated by the CIFAR10 dataset using the Intel® Extension for PyTorch* (IPEX). IPEX gives users the ability to speed up inference on Intel® Xeon Scalable processors with INT8 data format and specialized computer instructions. The INT8 data format uses quarter the bit width of floating-point-32 (FP32), lowering the amount of memory needed and execution time to process.| 5 minutes| Concepts and Functionality| Y|
-[Optimize PyTorch Models using Intel® Extension for PyTorch* (IPEX)](https://github.com/intel-innersource/frameworks.ai.pytorch.ipex-cpu/blob/cpu-device/examples/cpu/inference/python/jupyter-notebooks/optimize_pytorch_models_with_ipex.ipynb)| This sample notebook shows how to get started with Intel® Extension for PyTorch* (IPEX) for sample Computer Vision and NLP workloads. The sample starts by loading two models from the PyTorch hub: Faster-RCNN (Faster R-CNN) and distilbert (DistilBERT). After loading the models, the sample applies sequential optimizations from Intel® Extension for PyTorch* (IPEX) and examines performance gains for each incremental change.| 30 minutes | Code Optimization |Y|
+[PyTorch Inference Optimizations with Intel® Advanced Matrix Extensions (Intel® AMX) Bfloat16 Integer8](https://github.com/intel/intel-extension-for-pytorch/blob/main/examples/cpu/inference/python/jupyter-notebooks/IntelPyTorch_InferenceOptimizations_AMX_BF16_INT8.ipynb) | This code sample demonstrates how to perform inference using the ResNet50 and BERT models using the Intel® Extension for PyTorch* (IPEX). IPEX allows you to speed up inference on Intel® Xeon Scalable processors with lower precision data formats and specialized computer instructions. The bfloat16 (BF16) data format uses half the bit width of floating-point-32 (FP32), which lessens the amount of memory needed and execution time to process. Likewise, the integer8 (INT8) data format uses half the bit width of BF16. | 5 minutes     | Code Optimization | Y|
+[Interactive Chat Based on DialoGPT Model Using Intel® Extension for PyTorch* Quantization](https://github.com/intel/intel-extension-for-pytorch/blob/main/examples/cpu/inference/python/jupyter-notebooks/IntelPytorch_Interactive_Chat_Quantization.ipynb)| This code sample demonstrates how to create interactive chat based on pre-trained DialoGPT model and add the Intel® Extension for PyTorch* (IPEX) quantization to it. The sample shows how to create interactive chat based on the pre-trained DialoGPT model from HuggingFace and how to add INT8 dynamic quantization to it. The Intel® Extension for PyTorch* (IPEX) gives users the ability to speed up operations on processors with INT8 data format and specialized computer instructions.| 10 minutes | Concepts and Functionality| Y|
+[Optimize PyTorch Models using Intel® Extension for PyTorch (IPEX) Quantization](https://github.com/intel/intel-extension-for-pytorch/blob/main/examples/cpu/inference/python/jupyter-notebooks/IntelPytorch_Quantization.ipynb)|This code sample demonstrates how to quantize a ResNet50 model that is calibrated by the CIFAR10 dataset using the Intel® Extension for PyTorch* (IPEX). IPEX gives users the ability to speed up inference on Intel® Xeon Scalable processors with INT8 data format and specialized computer instructions. The INT8 data format uses quarter the bit width of floating-point-32 (FP32), lowering the amount of memory needed and execution time to process.| 5 minutes| Concepts and Functionality| Y|
+[Optimize PyTorch Models using Intel® Extension for PyTorch* (IPEX)](https://github.com/intel/intel-extension-for-pytorch/blob/main/examples/cpu/inference/python/jupyter-notebooks/optimize_pytorch_models_with_ipex.ipynb)| This sample notebook shows how to get started with Intel® Extension for PyTorch* (IPEX) for sample Computer Vision and NLP workloads. The sample starts by loading two models from the PyTorch hub: Faster-RCNN (Faster R-CNN) and distilbert (DistilBERT). After loading the models, the sample applies sequential optimizations from Intel® Extension for PyTorch* (IPEX) and examines performance gains for each incremental change.| 30 minutes | Code Optimization |Y|
 
 >**Note**: For Key Implementation Details, please refer to the .ipynb file of a sample.
 
diff --git a/intel_extension_for_pytorch/__init__.py b/intel_extension_for_pytorch/__init__.py
@@ -1,6 +1,6 @@
 # This Python file uses the following encoding: utf-8
 import re
-
+import os
 import torch
 
 
@@ -33,7 +33,7 @@
         + f"{ipex_version}.*, but PyTorch {torch.__version__} is found. "
         + "Please switch to the matching version and run again."
     )
-    exit(127)
+    os.exit(127)
 
 
 import os
diff --git a/tests/cpu/bench/custom_op_bench/README.md b/tests/cpu/bench/custom_op_bench/README.md
@@ -1,14 +1,14 @@
 # Running benchmarks for Intel Extension for PyTorch Custom OPs
-Evaluate performance for custom operator with [launcher](../../../../tutorials/intro_launch.md).
+Evaluate performance for custom operator with [launcher](../../../../docs/tutorials/performance_tuning/launch_script.md).
 ## Prepare envrioment
-Follow [performance_tuning_guide](../../../../tutorials/Performance_Tuning.md) to install Memory_Allocator(you can choose Tcmalloc or Jemalloc).
+Follow [performance_tuning_guide](../../../../docs/tutorials/performance_tuning/tuning_guide.md) to install Memory_Allocator(you can choose Tcmalloc or Jemalloc).
 Install intel-openmp:
 
 ```
 pip install intel-openmp=2024.1.2
 ```
 
-## Evaluate [Interaction](../../../../intel_extension_for_pytorch/nn/functional/interaction.py)
+## Evaluate [Interaction](../../../../intel_extension_for_pytorch/cpu/nn/interaction.py)
 
 1.Inference: 1 instance per core in real world scenario
 
@@ -34,7 +34,7 @@ python -m intel_extension_for_pytorch.cpu.launch --node-id 0 optimizer.py --opti
 python -m intel_extension_for_pytorch.cpu.launch --node-id 0 optimizer.py --optimizer adam # for adam
 ```
 
-## Evaluate IPEX [MergedEmbeddingBag](../../../../intel_extension_for_pytorch/nn/module/merged_embeddingbag.py)
+## Evaluate IPEX [MergedEmbeddingBag](../../../../intel_extension_for_pytorch/nn/modules/merged_embeddingbag.py)
 ```
 export CORES=`lscpu | grep Core | awk '{print $4}'`
 export BATCHSIZE=$((128*CORES))
diff --git a/tests/cpu/cpp/CMakeLists.txt b/tests/cpu/cpp/CMakeLists.txt
@@ -32,24 +32,33 @@ set(CMAKE_SHARED_LINKER_FLAGS "${CMAKE_CXX_FLAGS} -Wl,-Bsymbolic-functions")
 set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_CXX_FLAGS} -Wl,-Bsymbolic-functions")
 
 # Set the include dir
-include_directories(${PYTORCH_INSTALL_DIR}/include)
-include_directories(${PYTORCH_INSTALL_DIR}/include/torch/csrc/api/include/)
-include_directories(${THIRD_PARTY_ROOT}/googletest/googletest/include)
+include_directories(${TORCH_INCLUDE_DIRS})
 include_directories(${IPEX_PROJECT_TOP_DIR})
 include_directories(${IPEX_PROJECT_TOP_DIR}/csrc/include)
 
 link_directories(${PYTORCH_INSTALL_DIR}/lib)
 # search the lib directory for gtest
 link_directories(${CPP_TEST_BUILD_DIR}/lib)
 
-# add gtest cmake path
-add_subdirectory(${THIRD_PARTY_ROOT}/googletest ${CPP_TEST_BUILD_DIR}/third_party/googletest EXCLUDE_FROM_ALL)
-
 # Add the Test Files
 set(IPEX_CPP_TEST_SOURCES test_runtime_api.cpp test_dyndisp_and_isa_api.cpp)
 
 add_executable(${CPU_CPP_TEST_NAME} ${IPEX_CPP_TEST_SOURCES})
 
+if(USE_SYSTEM_GTEST)
+  find_package(GTest REQUIRED)
+  target_link_libraries(${CPU_CPP_TEST_NAME} PUBLIC GTest::gtest GTest::gtest_main)
+else()
+  # add gtest cmake path
+  add_subdirectory(${THIRD_PARTY_ROOT}/googletest ${CPP_TEST_BUILD_DIR}/third_party/googletest EXCLUDE_FROM_ALL)
+
+  # Link GTest
+  target_link_libraries(${CPU_CPP_TEST_NAME} PUBLIC gtest_main)
+  target_link_libraries(${CPU_CPP_TEST_NAME} PUBLIC gtest)
+
+  target_include_directories(${CPU_CPP_TEST_NAME} PRIVATE ${THIRD_PARTY_ROOT}/googletest/googletest/include)
+endif()
+
 set(BUILD_STATIC_ONEMKL ON)
 find_package(oneMKL QUIET)
 if (ONEMKL_FOUND)
@@ -58,14 +67,8 @@ endif()
 
 target_link_directories(${CPU_CPP_TEST_NAME} PRIVATE ${CMAKE_INSTALL_PREFIX}/${CMAKE_INSTALL_LIBDIR}/)
 
-# Link GTest
-target_link_libraries(${CPU_CPP_TEST_NAME} PUBLIC gtest_main)
-target_link_libraries(${CPU_CPP_TEST_NAME} PUBLIC gtest)
-
 # Link Pytorch
-target_link_directories(${CPU_CPP_TEST_NAME} PRIVATE ${CMAKE_INSTALL_PREFIX}/${CMAKE_INSTALL_LIBDIR})
-target_link_libraries(${CPU_CPP_TEST_NAME} PUBLIC torch_cpu)
-target_link_libraries(${CPU_CPP_TEST_NAME} PUBLIC c10)
+target_link_libraries(${CPU_CPP_TEST_NAME} PUBLIC ${TORCH_LIBRARIES})
 
 # Link IPEX
 target_link_libraries(${CPU_CPP_TEST_NAME} PUBLIC intel-ext-pt-cpu)
