ONNX MLIR應用示例（含源碼鏈接）
開放式神經網絡交換在MLIR中的實現 (http://onnx.ai/onnx-mlir/)。

Prebuilt Containers
開始使用ONNX-MLIR的一個簡單方法是使用預構建的docker映像。這些映像是在主干上成功合并生成的結果。最新的圖像代表主干的頂端。目前，Docker Hub中保存的amd64、ppc64le和s390x的發布和調試模式映像分別為onnxmlirczar/onnx-mlir和onnxmlirczar/onnx-mlir-dev。要使用其中一個映像，可以直接從Docker Hub中取出，啟動一個容器，運行一個交互式bash shell，或者用作dockerfile中的基礎映像。onnx mlir映像只包含構建的編譯器，可以立即使用編譯模型，無需任何安裝。提供了一個python便利腳本，在docker容器中運行ONNX-MLIR，就像直接在主機上運行ONNX-MLIR編譯器一樣。例如，

docker/onnx-mlir.py --EmitLib mnist/model.onnx

505a5a6fb7d0: Pulling fs layer
505a5a6fb7d0: Verifying Checksum
505a5a6fb7d0: Download complete
505a5a6fb7d0: Pull complete
Shared library model.so has been compiled.
如果onnx mlir映像在本地不可用，腳本將提取該映像，將包含model.onnx的目錄load到容器中，在同一目錄中編譯和生成model.so。
onnx mlir dev映像包含完整的構建樹，其中包括先決條件和源代碼的克隆?？梢栽谌萜髦行薷脑创a，重建onnx-mlir，因此可以用作開發環境。還可以將vscode附加到正在運行的容器。docs文件夾中，可以看到一個對開發和vscode配置文件有用的Dockerfile示例。如果運行Docker build的目錄中，不存在工作區目錄和vscode文件，應注釋掉或刪除引用的行。
本文參考鏈接：
https://github.com/clang-ykt/llvm-project
Dockerfile顯示在這里。
FROM onnxmlirczar/onnx-mlir-dev
WORKDIR /workdir
ENV HOME=/workdir

1) Install packages.

ENV PATH=$PATH:/workdir/bin
RUN apt-get update
RUN apt-get install -y python-numpy
RUN apt-get install -y python3-pip
RUN python -m pip install --upgrade pip
RUN apt-get install -y gdb
RUN apt-get install -y lldb
RUN apt-get install -y emacs
RUN apt-get install -y vim

2) Instal optional packages, uncomment/add as you see fit.

RUN apt-get install -y valgrind

RUN apt-get install -y libeigen3-dev

RUN apt-get install -y clang-format

RUN python -m pip install wheel

RUN python -m pip install numpy

RUN python -m pip install torch1.6.0+cpu torchvision0.7.0+cpu -f https://download.pytorch.org/whl/torch_stable.html

RUN git clone https://github.com/onnx/tutorials.git

Install clang-12.

RUN apt-get install -y lsb-release wget software-properties-common

RUN bash -c “$(wget -O - https://apt.llvm.org/llvm.sh)”

3) When using vscode, copy your .vscode in the Dockerfile dir and

uncomment the two lines below.

WORKDIR /workdir/.vscode

ADD .vscode /workdir/.vscode

4) When using a personal workspace folder, set your workspace sub-directory

in the Dockerfile dir and uncomment the two lines below.

WORKDIR /workdir/workspace

ADD workspace /workdir/workspace

5) Fix git by reattaching head and making git see other branches than main.

WORKDIR /workdir/onnx-mlir
RUN git checkout main
RUN git fetch --unshallow

6) Set the PATH environment vars for make/debug mode. Replace Debug

with Release in the PATH below when using Release mode.

WORKDIR /workdir
ENV MLIR_DIR=/workdir/llvm-project/build/lib/cmake/mlir
ENV NPROC=4
ENV PATH=$PATH:/workdir/onnx-mlir/build/Debug/bin/:/workdir/onnx-mlir/build/Debug/lib:/workdir/llvm-project/build/bin
Prerequisites
gcc >= 6.4
libprotoc >= 3.11.0
cmake >= 3.15.4
ninja >= 1.10.2
GCC可以在這里找到https://gcc.gnu.org/install/，或者如果有自制軟件https://docs.brew.sh/Installation，可以使用brew安裝GCC。要檢查已安裝的gcc版本，運行gcc–version。
安裝libprotoc，或者，如果有自制軟件，可以運行brew，安裝protobuf。要檢查已安裝的版本，運行protoc–version。
可以在這里https://cmake.org/download/找到Cmake。但是，要使用Cmake，您需要遵循“如何安裝以供命令行使用”教程，該教程可以在Cmake中的“工具”>“如何安裝以供命令行使用”下找到。要檢查您擁有的版本，您可以在桌面版本中的CMake>About下查看，或者運行CMake–version。
安裝忍者的說明可以在這里找到https://ninja-build.org/。或者，使用自制軟件，可以運行brew安裝。要檢查版本，運行ninja–version。
在任何時候，ONNX-MLIR都取決于LLVM項目的特定提交，該提交已證明與該項目一起工作。維護人員需要定期遷移到更新的LLVM級別。需要更新utils/clone-mlir.sh中的提交字符串。進行更改的結果是TravisCI構建將失敗，直到重建包含prereqs的Docker映像。有一個GitHub工作流，可以為amd64體系結構重建映像，目前必須手動重建ppc64le和s390x映像。要完成的Dockerfiles在repo中。
Installation on UNIX
MLIR
Firstly, install MLIR (as a part of LLVM-Project):
git clone https://github.com/llvm/llvm-project.git

Check out a specific branch that is known to work with ONNX MLIR.

cd llvm-project && git checkout 0bf230d4220660af8b2667506f8905df2f716bdf && cd …
mkdir llvm-project/build
cd llvm-project/build
cmake -G Ninja …/llvm
-DLLVM_ENABLE_PROJECTS=mlir
-DLLVM_TARGETS_TO_BUILD=“host”
-DCMAKE_BUILD_TYPE=Release
-DLLVM_ENABLE_ASSERTIONS=ON
-DLLVM_ENABLE_RTTI=ON

cmake --build . – ${MAKEFLAGS}
cmake --build . --target check-mlir
ONNX-MLIR (this project)
The following environment variables can be set before building onnx-mlir (or alternatively, they need to be passed as CMake variables):
? MLIR_DIR should point to the mlir cmake module inside an llvm-project build or install directory (e.g., llvm-project/build/lib/cmake/mlir).
This project uses lit (LLVM’s Integrated Tester) for unit tests. When running CMake, we can also specify the path to the lit tool from LLVM using the LLVM_EXTERNAL_LIT define but it is not required as long as MLIR_DIR points to a build directory of llvm-project. If MLIR_DIR points to an install directory of llvm-project, LLVM_EXTERNAL_LIT is required.
To build ONNX-MLIR, use the following commands:
git clone --recursive https://github.com/onnx/onnx-mlir.git

Export environment variables pointing to LLVM-Projects.

export MLIR_DIR=$(pwd)/llvm-project/build/lib/cmake/mlir

mkdir onnx-mlir/build && cd onnx-mlir/build
cmake -G Ninja …
cmake --build .

Run lit tests:

export LIT_OPTS=-v
cmake --build . --target check-onnx-lit
If you are running on OSX Big Sur, you need to add -DCMAKE_CXX_COMPILER=/usr/bin/c++ to the cmake … command due to changes in the compilers. After the above commands succeed, an onnx-mlir executable should appear in the bin directory.
LLVM and ONNX-MLIR CMake variables
The following CMake variables from LLVM and ONNX MLIR can be used when compiling ONNX MLIR.
MLIR_DIR:PATH Path to to the mlir cmake module inside an llvm-project build or install directory (e.g., c:/repos/llvm-project/build/lib/cmake/mlir). This is required if MLIR_DIR is not specified as an environment variable.
LLVM_EXTERNAL_LIT:PATH Path to the lit tool. Defaults to an empty string and LLVM will find the tool based on MLIR_DIR if possible. This is required when MLIR_DIR points to an install directory.
Installation on Windows
Building onnx-mlir on Windows requires building some additional prerequisites that are not available by default.
Note that the instructions in this file assume you are using Visual Studio 2019 Community Edition with ninja. It is recommended that you have the Desktop development with C++ and Linux development with C++ workloads installed. This ensures you have all toolchains and libraries needed to compile this project and its dependencies on Windows.
Run all the commands from a shell started from “Developer Command Prompt for VS 2019”.
Protobuf
Build protobuf as a static library.
git clone --recurse-submodules https://github.com/protocolbuffers/protobuf.git
REM Check out a specific branch that is known to work with ONNX MLIR.
REM This corresponds to the v3.11.4 tag
cd protobuf && git checkout d0bfd5221182da1a7cc280f3337b5e41a89539cf && cd …

set root_dir=%cd%
md protobuf_build
cd protobuf_build
call cmake %root_dir%\protobuf\cmake -G “Ninja” ^
-DCMAKE_INSTALL_PREFIX="%root_dir%\protobuf_install" ^
-DCMAKE_BUILD_TYPE=Release ^
-Dprotobuf_BUILD_EXAMPLES=OFF ^
-Dprotobuf_BUILD_SHARED_LIBS=OFF ^
-Dprotobuf_BUILD_TESTS=OFF ^
-Dprotobuf_MSVC_STATIC_RUNTIME=OFF ^
-Dprotobuf_WITH_ZLIB=OFF

call cmake --build . --config Release
call cmake --build . --config Release --target install
Before running CMake for onnx-mlir, ensure that the bin directory to this protobuf is before any others in your PATH:
set PATH=%root_dir%\protobuf_install\bin;%PATH%
MLIR
Install MLIR (as a part of LLVM-Project):
git clone https://github.com/llvm/llvm-project.git

Check out a specific branch that is known to work with ONNX MLIR.

cd llvm-project && git checkout 0bf230d4220660af8b2667506f8905df2f716bdf && cd …
set root_dir=%cd%
md llvm-project\build
cd llvm-project\build
call cmake %root_dir%\llvm-project\llvm -G “Ninja” ^
-DCMAKE_INSTALL_PREFIX="%root_dir%\llvm-project\build\install" ^
-DLLVM_ENABLE_PROJECTS=mlir ^
-DLLVM_TARGETS_TO_BUILD=“host” ^
-DCMAKE_BUILD_TYPE=Release ^
-DLLVM_ENABLE_ASSERTIONS=ON ^
-DLLVM_ENABLE_RTTI=ON ^
-DLLVM_ENABLE_ZLIB=OFF

call cmake --build . --config Release
call cmake --build . --config Release --target install
call cmake --build . --config Release --target check-mlir
ONNX-MLIR (this project)
The following environment variables can be set before building onnx-mlir (or alternatively, they need to be passed as CMake variables):
? MLIR_DIR should point to the mlir cmake module inside an llvm-project build or install directory (e.g., c:/repos/llvm-project/build/lib/cmake/mlir).
This project uses lit (LLVM’s Integrated Tester) for unit tests. When running CMake, we can also specify the path to the lit tool from LLVM using the LLVM_EXTERNAL_LIT define but it is not required as long as MLIR_DIR points to a build directory of llvm-project. If MLIR_DIR points to an install directory of llvm-project, LLVM_EXTERNAL_LIT is required.
To build ONNX MLIR, use the following commands:
git clone --recursive https://github.com/onnx/onnx-mlir.git

set root_dir=%cd%

md onnx-mlir\build
cd onnx-mlir\build
call cmake %root_dir%\onnx-mlir -G “Ninja” ^
-DCMAKE_BUILD_TYPE=Release ^
-DCMAKE_PREFIX_PATH=%root_dir%\protobuf_install ^
-DLLVM_LIT_ARGS=-v ^
-DMLIR_DIR=%root_dir%\llvm-project\build\lib\cmake\mlir

call cmake --build . --config Release --target onnx-mlir
To run the lit ONNX MLIR tests, use the following command:
call cmake --build . --config Release --target check-onnx-lit
To run the numerical ONNX MLIR tests, use the following command:
call cmake --build . --config Release --target check-onnx-numerical
To run the doc ONNX MLIR tests, use the following command after installing third_party ONNX:
call cmake --build . --config Release --target check-docs
After the above commands succeed, an onnx-mlir executable should appear in the bin directory.
LLVM and ONNX-MLIR CMake variables
The following CMake variables from LLVM and ONNX MLIR can be used when compiling ONNX MLIR.
MLIR_DIR:PATH Path to to the mlir cmake module inside an llvm-project build or install directory (e.g., c:/repos/llvm-project/build/lib/cmake/mlir). This is required if MLIR_DIR is not specified as an environment variable.
LLVM_EXTERNAL_LIT:PATH Path to the lit tool. Defaults to an empty string and LLVM will find the tool based on MLIR_DIR if possible. This is required when MLIR_DIR points to an install directory.
Using ONNX-MLIR
The usage of onnx-mlir is as such:
OVERVIEW: ONNX MLIR modular optimizer driver

USAGE: onnx-mlir [options]

OPTIONS:

Generic Options:

–help - Display available options (–help-hidden for more)
–help-list - Display list of available options (–help-list-hidden for more)
–version - Display the version of this program

ONNX MLIR Options:
These are frontend options.

Choose target to emit:
–EmitONNXBasic - Ingest ONNX and emit the basic ONNX operations without inferred shapes.
–EmitONNXIR - Ingest ONNX and emit corresponding ONNX dialect.
–EmitMLIR - Lower model to MLIR built-in transformation dialect.
–EmitLLVMIR - Lower model to LLVM IR (LLVM dialect).
–EmitLib - Lower model to LLVM IR, emit (to file) LLVM bitcode for model, compile and link it to a shared library.
Simple Example
For example, to lower an ONNX model (e.g., add.onnx) to ONNX dialect, use the following command:
./onnx-mlir --EmitONNXIR add.onnx
The output should look like:
module {
func @main_graph(%arg0: tensor<10x10x10xf32>, %arg1: tensor<10x10x10xf32>) -> tensor<10x10x10xf32> {
%0 = “onnx.Add”(%arg0, %arg1) : (tensor<10x10x10xf32>, tensor<10x10x10xf32>) -> tensor<10x10x10xf32>
return %0 : tensor<10x10x10xf32>
}
}
An example based on the add operation is found here, which build an ONNX model using a python script, and then provide a main program to load the model’s value, compute, and print the models output.
End to end example
An end to end example is provided here, which train, compile, and execute a simple MNINST example.
Troubleshooting
If the latest LLVM project fails to work due to the latest changes to the MLIR subproject please consider using a slightly older version of LLVM. One such version, which we use, can be found here.
Installing third_party ONNX for Backend Tests or Rebuilding ONNX Operations
Backend tests are triggered by make check-onnx-backend in the build directory and require a few preliminary steps to run successfully. Similarily, rebuilding the ONNX operations in ONNX-MLIR from their ONNX descriptions is triggered by make OMONNXOpsIncTranslation.
You will need to install python 3.x if its not default in your environment, and possibly set the cmake PYTHON_EXECUTABLE varialbe in your top cmake file.
You will also need pybind11 which may need to be installed (mac: brew install pybind11 for example) and you may need to indicate where to find the software (Mac, POWER, possibly other platforms: export pybind11_DIR=). Then install the third_party/onnx software (Mac: pip install -e third_party/onnx) typed in the top directory.
On Macs/POWER and possibly other platforms, there is currently an issue that arises when installing ONNX. If you get an error during the build, try a fix where you edit the top CMakefile as reported in this PR: https://github.com/onnx/onnx/pull/2482/files.
Slack channel
We have a slack channel established under the Linux Foundation AI and Data Workspace, named #onnx-mlir-discussion. This channel can be used for asking quick questions related to this project.

參考鏈接：
https://github.com/onnx/onnx-mlir

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