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Getting Started with the CIRCT Project

Overview

Welcome to the CIRCT project!

"CIRCT" stands for "Circuit IR Compilers and Tools". The CIRCT project is an (experimental!) effort looking to apply MLIR and the LLVM development methodology to the domain of hardware design tools.

Take a look at the following diagram, which gives a brief overview of the current dialects and how they interact:

Setting this up

These commands can be used to setup CIRCT project:

  1. Install Dependencies of LLVM/MLIR according to the instructions, including cmake and ninja.

Note: CIRCT is known to build with at least GCC 9.4 and Clang 13.0.1, but older versions may not be supported. It is recommended to use the same C++ toolchain to compile both LLVM and CIRCT to avoid potential issues.

If you plan to use the Python bindings, you should start by reading the instructions for building the MLIR Python bindings, which describe extra dependencies, CMake variables, and helpful Python development practices. Note the extra CMake variables, which you will need to specify in step 3) below.

  1. Check out LLVM and CIRCT repos. CIRCT contains LLVM as a git submodule. The LLVM repo here includes staged changes to MLIR which may be necessary to support CIRCT. It also represents the version of LLVM that has been tested. MLIR is still changing relatively rapidly, so feel free to use the current version of LLVM, but APIs may have changed.
$ git clone git@github.com:circt/circt.git
$ cd circt
$ git submodule init
$ git submodule update

Note: The repository is set up so that git submodule update performs a shallow clone, meaning it downloads just enough of the LLVM repository to check out the currently specified commit. If you wish to work with the full history of the LLVM repository, you can manually "unshallow" the submodule:

$ cd llvm
$ git fetch --unshallow
  1. Build and test LLVM/MLIR:
$ cd circt
$ mkdir llvm/build
$ cd llvm/build
$ cmake -G Ninja ../llvm \
    -DLLVM_ENABLE_PROJECTS="mlir" \
    -DLLVM_TARGETS_TO_BUILD="X86;RISCV" \
    -DLLVM_ENABLE_ASSERTIONS=ON \
    -DCMAKE_BUILD_TYPE=DEBUG
$ ninja
$ ninja check-mlir
  1. Build and test CIRCT:
$ cd circt
$ mkdir build
$ cd build
$ cmake -G Ninja .. \
    -DMLIR_DIR=$PWD/../llvm/build/lib/cmake/mlir \
    -DLLVM_DIR=$PWD/../llvm/build/lib/cmake/llvm \
    -DLLVM_ENABLE_ASSERTIONS=ON \
    -DCMAKE_BUILD_TYPE=DEBUG
$ ninja
$ ninja check-circt
$ ninja check-circt-integration # Run the integration tests.

The -DCMAKE_BUILD_TYPE=DEBUG flag enables debug information, which makes the whole tree compile slower, but allows you to step through code into the LLVM and MLIR frameworks.

To get something that runs fast, use -DCMAKE_BUILD_TYPE=Release or -DCMAKE_BUILD_TYPE=RelWithDebInfo if you want to go fast and optionally if you want debug info to go with it. Release mode makes a very large difference in performance.

If you plan to use the Python bindings, you should also specify -DCIRCT_BINDINGS_PYTHON_ENABLED=ON.

  1. Optionally configure your environment:

It is useful to add the .../circt/build/bin and .../circt/llvm/build/bin directories to the end of your PATH, allowing you to use the tools like circt-opt in a natural way on the command line. Similarly, you need to be in the build directory to invoke ninja, which is super annoying. You might find a bash/zsh alias like this to be useful:

build() {
  (cd $HOME/Projects/circt/build/; ninja $1 $2 $3)
}

This allows you to invoke build check-circt from any directory and have it do the right thing.

  1. Run the Verilator tests: (optional)

Verilator can be used to check SystemVerilog code. To run the tests, build or install a recent version of Verilator (at least v4.034, ideally v4.110 or later to avoid a known bug). (Some Linux distributions have ancient versions.) If Verilator is in your PATH, build check-circt should run the tests which require Verilator.

We provide a script utils/get-verilator.sh to automate the download and compilation of Verilator into a known location. The testing script will check this location first. This script assumes that all the Verilator package dependencies are installed on your system. They are:

  • make
  • autoconf
  • g++
  • flex
  • bison
  • libfl2 # Ubuntu only (ignore if gives error)
  • libfl-dev # Ubuntu only (ignore if gives error)
  1. Install Cap'nProto (optional, affects ESI dialect only)

Some of the ESI dialect code requires libcapnp, 0.9.1 or newer. (Specifically, the cosimulation component.) Most of the ESI cosim integration tests also require the python bindings: pycapnp. The utils/get-capnp.sh script downloads, compiles, and installs a known good version to a directory within the circt source code. It optionally installs pycapnp via 'pip3'. The capnp compile requires libtool. Alternatively, you can use a docker image we provide via utils/run-docker.sh.

  1. Install OR-Tools (optional, enables additional schedulers)

OR-Tools is an open source software suite for (mathematical) optimization. It provides a uniform interface to several open-source and commercial solvers, e.g. for linear programs and satisfiability problems. Here, it is optionally used in the static scheduling infrastructure. Binary distributions often do not include the required CMake build info. The utils/get-or-tools.sh script downloads, compiles, and installs a known good version to a directory within the CIRCT source code,

Setting up VS Code Workspace

We've provided an example VS Code file in .vscode/Unified.code-workspace.jsonc that can be used with the VS Code editor. To use the file, first copy to into a workspace file:

cp .vscode/Unified.code-workspace.jsonc .vscode/circt.code-workspace

Next, open the workspace file in VS code using the command palette (Ctrl + Shift + P) and selecting "Open workspace from file" and selecting the .vscode/circt.code-workspace file.

Alternatively, open the file using:

code .vscode/circt.code-workspace

and select "open workspace" on the bottom right.

Once the workspace is loaded, install the recommended tools and select "CMake: Build" from the command palette to start the unified build process. This will build the LLVM dependencies and CIRCT together.

where it is then picked up automatically by the build.

Windows: notes on setting up with Ninja

Building on Windows using MSVC + Ninja + Python support is straight forward, though full of landmines. Here are some notes:

  • Ninja and cmake must be run in a VS Developer Command shell. If you use Powershell and don't want to start the VS GUI, you can run:
> $vsPath = &(Join-Path ${env:ProgramFiles(x86)} "\Microsoft Visual Studio\Installer\vswhere.exe") -property installationpath
> Import-Module (Get-ChildItem $vsPath -Recurse -File -Filter Microsoft.VisualStudio.DevShell.dll).FullName
> Enter-VsDevShell -VsInstallPath $vsPath -SkipAutomaticLocation
  • VSCode's cmake configure does not operate properly with Python support. The symptom is that the build will complete, but importing circt or mlir crashes Python. Doing everything from the command line is the only way CIRCT compiles have been made to work.

Cheat sheet for powershell:

# Install cmake, ninja, and Visual Studio
> python -m pip install psutil pyyaml numpy pybind11

> $vsPath = &(Join-Path ${env:ProgramFiles(x86)} "\Microsoft Visual Studio\Installer\vswhere.exe") -property installationpath
> Import-Module (Get-ChildItem $vsPath -Recurse -File -Filter Microsoft.VisualStudio.DevShell.dll).FullName
> Enter-VsDevShell -VsInstallPath $vsPath -SkipAutomaticLocation

> cd <circt clone>
> cmake -B<build_dir> llvm/llvm `
    -GNinja `
    -DLLVM_ENABLE_PROJECTS=mlir `
    -DCMAKE_BUILD_TYPE=Debug `
    -DLLVM_TARGETS_TO_BUILD=X86 `
    -DLLVM_ENABLE_ASSERTIONS=ON `
    -DMLIR_ENABLE_BINDINGS_PYTHON=ON `
    -DLLVM_EXTERNAL_PROJECTS=circt `
    -DLLVM_EXTERNAL_CIRCT_SOURCE_DIR="$(PWD)" `
    -DCIRCT_BINDINGS_PYTHON_ENABLED=ON `
    -DCMAKE_EXPORT_COMPILE_COMMANDS=ON
> ninja -C<build_dir> check-circt

Submitting changes to CIRCT

The project is small so there are few formal process yet. We generally follow the LLVM and MLIR community practices, but we currently use pull requests and GitHub issues. Here are some high-level guidelines:

  • Please use clang-format in the LLVM style. There are good plugins for common editors like VSCode, Atom, etc, or you can run it manually. This makes code easier to read and understand.

  • Beyond mechanical formatting issues, please follow the LLVM Coding Standards.

  • Please practice "incremental development", preferring to send a small series of incremental patches rather than large patches. There are other policies in the LLVM Developer Policy document that are worth skimming.

  • Please use "Squash and Merge" in PRs when they are approved - we don't need the intra-change history in the repository history.

  • Please create a PR to get a code review. For reviewers, it is good to look at the primary author of the code you are touching to make sure they are at least CC'd on the PR.

Submitting changes to LLVM / MLIR

This project depends on MLIR and LLVM, and it is occasionally useful to improve them. To get set up for this:

  1. Follow the "How to Contribute" instructions, and install the right tools, e.g. clang-format.
  2. Optional: Ask for LLVM commit access, the barrier is low. Alternatively, you can ask one of the reviewers on the GitHub pull-request to merge for you.

Submitting a patch

Patches are submitted to LLVM/MLIR via GitHub pull-requests, the basic flow is as follows:

  1. Check out the LLVM mono repo (as described above) or your fork of the same.
  2. Make changes to your codebase in a dedicated branch for your patch.
  3. Stage your changes with git add.
  4. Run clang-format to tidy up the patch with git clang-format origin/main.
  5. Run tests with ninja check-mlir (or whatever other target makes sense).
  6. Publish the branch on your fork of the repository and create a GitHub pull-request.

When your review converges and your patch is approved, it can be merged directly on GitHub. If you have commit access, you can do this yourself, otherwise a reviewer can do it for you.