LeiWang1999 commented on PR #16425: URL: https://github.com/apache/tvm/pull/16425#issuecomment-2420346718
```cmake # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. #-------------------------------------------------------------------- # Template custom cmake configuration for compiling # # This file is used to override the build options in build. # If you want to change the configuration, please use the following # steps. Assume you are on the root directory. First copy the this # file so that any local changes will be ignored by git # # $ mkdir build # $ cp cmake/config.cmake build # # Next modify the according entries, and then compile by # # $ cd build # $ cmake .. # # Then build in parallel with 8 threads # # $ make -j8 #-------------------------------------------------------------------- #--------------------------------------------- # Backend runtimes. #--------------------------------------------- # Whether enable CUDA during compile, # # Possible values: # - ON: enable CUDA with cmake's auto search # - OFF: disable CUDA # - /path/to/cuda: use specific path to cuda toolkit set(USE_CUDA OFF) # Whether to enable NCCL support: # - ON: enable NCCL with cmake's auto search # - OFF: disable NCCL # - /path/to/nccl: use specific path to nccl set(USE_NCCL OFF) # Whether enable ROCM runtime # # Possible values: # - ON: enable ROCM with cmake's auto search # - OFF: disable ROCM # - /path/to/rocm: use specific path to rocm set(USE_ROCM OFF) # Whether to enable RCCL support: # - ON: enable RCCL with cmake's auto search # - OFF: disable RCCL # - /path/to/rccl: use specific path to rccl set(USE_RCCL OFF) # Whether enable SDAccel runtime set(USE_SDACCEL OFF) # Whether enable Intel FPGA SDK for OpenCL (AOCL) runtime set(USE_AOCL OFF) # Whether enable OpenCL runtime # # Possible values: # - ON: enable OpenCL with OpenCL wrapper to remove dependency during build # time and trigger dynamic search and loading of OpenCL in runtime # - OFF: disable OpenCL # - /path/to/opencl-sdk: use specific path to opencl-sdk set(USE_OPENCL OFF) # Wheather to allow OPENCL cl_mem access to host # cl_mem will be allocated with CL_MEM_ALLOC_HOST_PTR # OpenCLWorkspace->GetHostPtr API returns the host accessible pointer set(USE_OPENCL_ENABLE_HOST_PTR OFF) # Whether enable Metal runtime set(USE_METAL OFF) # Whether enable Vulkan runtime # # Possible values: # - ON: enable Vulkan with cmake's auto search # - OFF: disable vulkan # - /path/to/vulkan-sdk: use specific path to vulkan-sdk set(USE_VULKAN OFF) # Whether to use spirv-tools.and SPIRV-Headers from Khronos github or gitlab. # # Possible values: # - OFF: not to use # - /path/to/install: path to your khronis spirv-tools and SPIRV-Headers installation directory # set(USE_KHRONOS_SPIRV OFF) # whether enable SPIRV_KHR_DOT_PRODUCT set(USE_SPIRV_KHR_INTEGER_DOT_PRODUCT OFF) # Whether enable OpenGL runtime set(USE_OPENGL OFF) # Whether enable MicroTVM runtime set(USE_MICRO OFF) # Whether enable RPC runtime set(USE_RPC ON) # Whether to build the C++ RPC server binary set(USE_CPP_RPC OFF) # Whether to build the C++ native runtime tool binary set(USE_CPP_RTVM OFF) # Whether to build the iOS RPC server application set(USE_IOS_RPC OFF) # Whether embed stackvm into the runtime set(USE_STACKVM_RUNTIME OFF) # Whether enable tiny embedded graph executor. set(USE_GRAPH_EXECUTOR ON) # Whether enable tiny graph executor with CUDA Graph set(USE_GRAPH_EXECUTOR_CUDA_GRAPH OFF) # Whether enable pipeline executor. set(USE_PIPELINE_EXECUTOR OFF) # Whether to enable the profiler for the graph executor and vm set(USE_PROFILER ON) # Whether enable microTVM standalone runtime set(USE_MICRO_STANDALONE_RUNTIME OFF) # Whether build with LLVM support # Requires LLVM version >= 4.0 # # Possible values: # - ON: enable llvm with cmake's find search # - OFF: disable llvm, note this will disable CPU codegen # which is needed for most cases # - /path/to/llvm-config: enable specific LLVM when multiple llvm-dev is available. set(USE_LLVM "/home/msra/cy/clang+llvm-13.0.0-x86_64-linux-gnu-ubuntu-20.04/bin/llvm-config --link-static") set(HIDE_PRIVATE_SYMBOLS ON) # Whether use MLIR to help analyze, requires USE_LLVM is enabled # Possible values: ON/OFF set(USE_MLIR OFF) #--------------------------------------------- # Contrib libraries #--------------------------------------------- # Whether to build with BYODT software emulated posit custom datatype # # Possible values: # - ON: enable BYODT posit, requires setting UNIVERSAL_PATH # - OFF: disable BYODT posit # # set(UNIVERSAL_PATH /path/to/stillwater-universal) for ON set(USE_BYODT_POSIT OFF) # Whether use BLAS, choices: openblas, atlas, apple set(USE_BLAS none) # Whether to use MKL # Possible values: # - ON: Enable MKL # - /path/to/mkl: mkl root path # - OFF: Disable MKL # set(USE_MKL /opt/intel/mkl) for UNIX # set(USE_MKL ../IntelSWTools/compilers_and_libraries_2018/windows/mkl) for WIN32 # set(USE_MKL <path to venv or site-packages directory>) if using `pip install mkl` set(USE_MKL OFF) # Whether use DNNL library, aka Intel OneDNN: https://oneapi-src.github.io/oneDNN # # Now matmul/dense/conv2d supported by -libs=dnnl, # and more OP patterns supported in DNNL codegen(json runtime) # # choices: # - ON: Enable DNNL in BYOC and -libs=dnnl, by default using json runtime in DNNL codegen # - JSON: same as above. # - C_SRC: use c source runtime in DNNL codegen # - path/to/oneDNN：oneDNN root path # - OFF: Disable DNNL set(USE_DNNL OFF) # Whether use Intel AMX instructions. set(USE_AMX OFF) # Whether use OpenMP thread pool, choices: gnu, intel # Note: "gnu" uses gomp library, "intel" uses iomp5 library set(USE_OPENMP none) # Whether use contrib.random in runtime set(USE_RANDOM ON) # Whether use NNPack set(USE_NNPACK OFF) # Possible values: # - ON: enable tflite with cmake's find search # - OFF: disable tflite # - /path/to/libtensorflow-lite.a: use specific path to tensorflow lite library set(USE_TFLITE OFF) # /path/to/tensorflow: tensorflow root path when use tflite library set(USE_TENSORFLOW_PATH none) # Required for full builds with TFLite. Not needed for runtime with TFLite. # /path/to/flatbuffers: flatbuffers root path when using tflite library set(USE_FLATBUFFERS_PATH none) # Possible values: # - OFF: disable tflite support for edgetpu # - /path/to/edgetpu: use specific path to edgetpu library set(USE_EDGETPU OFF) # Possible values: # - ON: enable cuDNN with cmake's auto search in CUDA directory # - OFF: disable cuDNN # - /path/to/cudnn: use specific path to cuDNN path set(USE_CUDNN OFF) # Whether use cuBLAS set(USE_CUBLAS OFF) # Whether use MIOpen set(USE_MIOPEN OFF) # Whether use MPS set(USE_MPS OFF) # Whether use rocBlas set(USE_ROCBLAS OFF) # Whether use contrib sort set(USE_SORT ON) # Whether to use Arm Compute Library (ACL) codegen # We provide 2 separate flags since we cannot build the ACL runtime on x86. # This is useful for cases where you want to cross-compile a relay graph # on x86 then run on AArch. # # An example of how to use this can be found here: docs/deploy/arm_compute_lib.rst. # # USE_ARM_COMPUTE_LIB - Support for compiling a relay graph offloading supported # operators to Arm Compute Library. OFF/ON # USE_ARM_COMPUTE_LIB_GRAPH_EXECUTOR - Run Arm Compute Library annotated functions via the ACL # runtime. OFF/ON/"path/to/ACL" set(USE_ARM_COMPUTE_LIB OFF) set(USE_ARM_COMPUTE_LIB_GRAPH_EXECUTOR OFF) # Whether to build with Arm Ethos-N support # Possible values: # - OFF: disable Arm Ethos-N support # - path/to/arm-ethos-N-stack: use a specific version of the # Ethos-N driver stack set(USE_ETHOSN OFF) # If USE_ETHOSN is enabled, use ETHOSN_HW (ON) if Ethos-N hardware is available on this machine # otherwise use ETHOSN_HW (OFF) to use the software test infrastructure set(USE_ETHOSN_HW OFF) # Whether to build with Arm(R) Ethos(TM)-U NPU codegen support set(USE_ETHOSU OFF) # Whether to build with CMSIS-NN external library support. # See https://github.com/ARM-software/CMSIS_5 set(USE_CMSISNN OFF) # Whether to build with TensorRT codegen or runtime # Examples are available here: docs/deploy/tensorrt.rst. # # USE_TENSORRT_CODEGEN - Support for compiling a relay graph where supported operators are # offloaded to TensorRT. OFF/ON # USE_TENSORRT_RUNTIME - Support for running TensorRT compiled modules, requires presense of # TensorRT library. OFF/ON/"path/to/TensorRT" set(USE_TENSORRT_CODEGEN OFF) set(USE_TENSORRT_RUNTIME OFF) # Whether use VITIS-AI codegen set(USE_VITIS_AI OFF) # Build Verilator codegen and runtime set(USE_VERILATOR OFF) #Whether to use CLML codegen set(USE_CLML OFF) # USE_CLML_GRAPH_EXECUTOR - CLML SDK PATH or ON or OFF set(USE_CLML_GRAPH_EXECUTOR OFF) # Build ANTLR parser for Relay text format # Possible values: # - ON: enable ANTLR by searching default locations (cmake find_program for antlr4 and /usr/local for jar) # - OFF: disable ANTLR # - /path/to/antlr-*-complete.jar: path to specific ANTLR jar file set(USE_ANTLR OFF) # Whether use Relay debug mode set(USE_RELAY_DEBUG OFF) # Whether to build fast VTA simulator driver set(USE_VTA_FSIM OFF) # Whether to build cycle-accurate VTA simulator driver set(USE_VTA_TSIM OFF) # Whether to build VTA FPGA driver (device side only) set(USE_VTA_FPGA OFF) # Whether use Thrust set(USE_THRUST OFF) # Whether use cuRAND set(USE_CURAND OFF) # Whether to build the TensorFlow TVMDSOOp module set(USE_TF_TVMDSOOP OFF) # Whether to build the PyTorch custom class module set(USE_PT_TVMDSOOP OFF) # Whether to use STL's std::unordered_map or TVM's POD compatible Map set(USE_FALLBACK_STL_MAP OFF) # Whether to enable Hexagon support set(USE_HEXAGON OFF) set(USE_HEXAGON_SDK /path/to/sdk) # Whether to build the minimal support android rpc server for Hexagon set(USE_HEXAGON_RPC OFF) # Hexagon architecture to target when compiling TVM itself (not the target for # compiling _by_ TVM). This applies to components like the TVM runtime, but is # also used to select correct include/library paths from the Hexagon SDK when # building runtime for Android. # Valid values are v65, v66, v68, v69, v73. set(USE_HEXAGON_ARCH "v68") # Whether to use QHL library set(USE_HEXAGON_QHL OFF) # Whether to use ONNX codegen set(USE_TARGET_ONNX OFF) # Whether enable BNNS runtime set(USE_BNNS OFF) # Whether to build static libtvm_runtime.a, the default is to build the dynamic # version: libtvm_runtime.so. # # The static runtime library needs to be linked into executables with the linker # option --whole-archive (or its equivalent). The reason is that the TVM registry # mechanism relies on global constructors being executed at program startup. # Global constructors alone are not sufficient for the linker to consider a # library member to be used, and some of such library members (object files) may # not be included in the final executable. This would make the corresponding # runtime functions to be unavailable to the program. set(BUILD_STATIC_RUNTIME OFF) # Caches the build so that building is faster when switching between branches. # If you switch branches, build and then encounter a linking error, you may # need to regenerate the build tree through "make .." (the cache will # still provide significant speedups). # Possible values: # - AUTO: search for path to ccache, disable if not found. # - ON: enable ccache by searching for the path to ccache, report an error if not found # - OFF: disable ccache # - /path/to/ccache: use specific path to ccache set(USE_CCACHE AUTO) # Whether to use libbacktrace to supply linenumbers on stack traces. # Possible values: # - ON: Find libbacktrace from system paths. Report an error if not found. # - OFF: Don't use libbacktrace. # - /path/to/libbacktrace: Looking for the libbacktrace header and static lib from a user-provided path. Report error if not found. # - COMPILE: Build and link to libbacktrace from 3rdparty/libbacktrace. # - AUTO: # - Find libbacktrace from system paths. # - If not found, fallback to COMPILE on Linux or MacOS, fallback to OFF on Windows or other platforms. set(USE_LIBBACKTRACE AUTO) # Whether to install a signal handler to print a backtrace on segfault. # Need to have USE_LIBBACKTRACE enabled. set(BACKTRACE_ON_SEGFAULT OFF) # Whether to enable PAPI support in profiling. PAPI provides access to hardware # counters while profiling. # Possible values: # - ON: enable PAPI support. Will search PKG_CONFIG_PATH for a papi.pc # - OFF: disable PAPI support. # - /path/to/folder/containing/: Path to folder containing papi.pc. set(USE_PAPI OFF) # Whether to use GoogleTest for C++ unit tests. When enabled, the generated # build file (e.g. Makefile) will have a target "cpptest". # Possible values: # - ON: enable GoogleTest. The package `GTest` will be required for cmake # to succeed. # - OFF: disable GoogleTest. # - AUTO: cmake will attempt to find the GTest package, if found GTest will # be enabled, otherwise it will be disabled. # Note that cmake will use `find_package` to find GTest. Please use cmake's # predefined variables to specify the path to the GTest package if needed. set(USE_GTEST AUTO) # Enable using CUTLASS as a BYOC backend # Need to have USE_CUDA=ON set(USE_CUTLASS OFF) # Enable to show a summary of TVM options set(SUMMARIZE OFF) # Whether to use LibTorch as backend # To enable pass the path to the root libtorch (or PyTorch) directory # OFF or /path/to/torch/ set(USE_LIBTORCH OFF) # Whether to use the Universal Modular Accelerator Interface set(USE_UMA OFF) # Set custom Alloc Alignment for device allocated memory ndarray points to set(USE_KALLOC_ALIGNMENT 64) # set(USE_LLVM /root/clang+llvm-10.0.1-x86_64-linux-gnu-ubuntu-18.04/bin/llvm-config) set(USE_LLVM /root/clang+llvm-16.0.0-x86_64-linux-gnu-ubuntu-18.04/bin/llvm-config) set(USE_CUDA /usr/local/cuda) ``` my config is quite simple, just enabled CUDA and LLVM. It's weird as I can both reproduce this issue on my nvidia-4090 and amd-mi250. my reproduce script is: ```python git checkout 726a1416497eeca7bfb7dcdbd799d00b33c39f79 git submodule update --init --recursive cd build cp ../cmake/config.cmake echo "set(USE_LLVM /root/clang+llvm-16.0.0-x86_64-linux-gnu-ubuntu-18.04/bin/llvm-config)" >> config.cmake echo "set(USE_CUDA /usr/local/cuda)" >> config.cmake cmake .. make -j cd .. python -c "import tvm" ``` -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
