Contribute

This document is a summary of how to do various tasks one runs into as a developer of Cirq. Note that all commands assume a Debian environment, and all commands (except the initial repository cloning command) assume your current working directory is the cirq repo root.

Cloning the repository

The simplest way to get a local copy of cirq that you can edit is by cloning Cirq's github repository:

git clone git@github.com:quantumlib/cirq.git
cd Cirq

Recommended git setup

The following command will setup large refactoring revisions to be ignored, when using git blame.

git config blame.ignoreRevsFile .git-blame-ignore-revs

Note that if you are using PyCharm, you might have to Restart & Invalidate Caches to have the change being picked up.

Docker

You can build the stable and pre_release docker images with our Dockerfile.

    docker build -t cirq --target cirq_stable .
    docker run -it cirq python -c "import cirq_google; print(cirq_google.Sycamore23)"

    docker build -t cirq_pre --target cirq_pre_release .
    docker run -it cirq_pre python -c "import cirq_google; print(cirq_google.Sycamore23)"

If you want to contribute changes to Cirq, you will instead want to fork the repository and submit pull requests from your fork.

Forking the repository

Fork the Cirq repo (Fork button in upper right corner of repo page). Forking creates a new github repo at the location https://github.com/USERNAME/cirq where USERNAME is your github id.
Clone the fork you created to your local machine at the directory where you would like to store your local copy of the code, and cd into the newly created directory.
```
 git clone git@github.com:USERNAME/cirq.git
 cd Cirq
```
(Alternatively, you can clone the repository using the URL provided on your repo page under the green "Clone or Download" button)
Add a remote called upstream to git. This remote will represent the main git repo for cirq (as opposed to the clone, which you just created, which will be the origin remote). This remote can be used to merge changes from Cirq's main repository into your local development copy.
```
git remote add upstream https://github.com/quantumlib/cirq.git
```
To verify the remote, run git remote -v. You should see both the origin and upstream remotes.
Sync up your local git with the upstream remote:
```
git fetch upstream
```
You can check the branches that are on the upstream remote by running git ls-remote --heads upstream or git branch -r. Most importantly you should see upstream/main listed.
Merge the upstream main into your local main so that it is up to date.
```
 git checkout main
 git merge upstream/main
```

At this point your local git main should be synced with the main from the main cirq repo.

Setting up an environment

These instructions are primarily for linux-based environments that use the apt package manager.

First clone the repository, if you have not already done so. See the previous section for instructions.
Install system dependencies.

Make sure you have python 3.10 or greater. You can install most other dependencies via apt-get:
```
cat apt-system-requirements.txt dev_tools/conf/apt-list-dev-tools.txt | xargs sudo apt-get install --yes
```
This installs docker among other things. You may need to restart docker or configure permissions, see docker install instructions. Note that docker is necessary only for cirq_rigetti.

There are some extra steps if protocol buffers are changed; see the next section.
Prepare a virtual environment including the dev tools (such as mypy).

One of the system dependencies we installed was virtualenvwrapper, which makes it easy to create virtual environments. If you did not have virtualenvwrapper previously, you may need to re-open your terminal or run source ~/.bashrc before these commands will work:
```
mkvirtualenv cirq-py3 --python=/usr/bin/python3
workon cirq-py3
python -m pip install --upgrade pip    
python -m pip install -r dev_tools/requirements/dev.env.txt
```
(When you later open another terminal, you can activate the virtualenv with workon cirq-py3.)

Note: Some highly managed or customized devices have configurations that interfere with virtualenv. In that case, anaconda environments may be a better choice.
Check that the tests pass.
```
./check/pytest .
```
(OPTIONAL) include your development copy of cirq and its subpackages in your python path.
```
source dev_tools/pypath
```
or add it to the python path, but only in the virtualenv by first listing the modules
```
python dev_tools/modules.py list 
```
and then adding these to the virtualenv:
```
add2virtualenv <paste modules from last command>
```
(Typically add2virtualenv is not executable using xargs, so this two step process is necessary.)

Editable installs

If you want to pip install cirq in an editable fashion, you'll have to install it per module, e.g.:

pip install -e ./cirq-core -e ./cirq-google -e ./cirq-ionq -e ./cirq-aqt

Note that pip install -e . will install the cirq metapackage only, and your code changes won't get picked up!

Protocol buffers

Protocol buffers are used in Cirq for converting circuits, gates, and other objects into a standard form that can be written and read by other programs. Cirq's protobufs live at cirq-google/api/v2 and may need to be changed or extended from time to time.

If any protos are updated, their dependents can be rebuilt by calling the script dev_tools/build-protos.sh. This script uses grpcio-tools and protobuf version 3.8.0 to generate the python proto api.

Continuous integration and local testing

There are a few options for running continuous integration checks, varying from easy and fast to slow and reliable.

The simplest way to run checks is to invoke pytest, pylint, or mypy for yourself as follows:

pytest
pylint --rcfile=dev_tools/conf/.pylintrc cirq
mypy --config-file=dev_tools/conf/mypy.ini .

This can be a bit tedious, because you have to specify the configuration files each time. A more convenient way to run checks is to via the scripts in the check/ directory, which specify configuration arguments for you and cover more use cases:

Fast checks (complete in seconds or tens of seconds)
- Check or apply code formatting to changed lines:
```
./check/format-incremental [--apply] [BASE_REVISION]
```
- Run tests associated with changed files:
```
./check/pytest-changed-files [BASE_REVISION]
```
- Run tests embedded in docstrings:
```
./check/doctest
```
- Compute incremental coverage using only tests associated with changed files:
```
./check/pytest-changed-files-and-incremental-coverage [BASE_REVISION]
```
  Note: this check is stricter than the incremental coverage check we actually enforce, where lines may be covered by tests in unassociated files.
- Type checking:
```
./check/mypy [files-and-flags-for-mypy]
```
- Miscellaneous checks:
```
./check/misc
```
  (Currently just checks that nothing outside cirq.contrib references anything inside cirq.contrib.)
Slow checks (each takes a few minutes)
- Run all tests:
```
./check/pytest [files-and-flags-for-pytest]
```
- Check for lint:
```
./check/pylint [files-and-flags-for-pylint]
```
- Compute incremental coverage:
```
./check/pytest-and-incremental-coverage [BASE_REVISION]
```
- Run all continuous integration checks:
```
./check/all [BASE_REVISION] [--only-changed-files] [--apply-format-changes]
```
  If --only-changed-files is set, checks that can will focus down to just files that were changed (trading accuracy for speed).

In the above, [BASE_REVISION] controls what commit is being compared against for an incremental check (e.g. in order to determine which files changed.) If not specified, it defaults to the upstream/main branch if it exists, or else the origin/main branch if it exists, or else the main branch. The actual commit used for comparison is the git merge-base of the base revision and the working directory.

The above scripts may not exactly match the results computed by the continuous integration builds run on Travis. For example, you may be running an older version of pylint or numpy. If you need to test against the actual continuous integration check, open up a pull request. For this pull request you may want to mark it as [Testing] so that it is not reviewed.

Writing docstrings and generating documentation

Cirq uses Google style doc strings with a markdown flavor and support for latex. Here is an example docstring:

def some_method(a: int, b: str) -> float:
    r"""One line summary of method.

    Additional information about the method, perhaps with some sort of latex
    equation to make it clearer:

        $$
        M = \begin{bmatrix}
                0 & 1 \\
                1 & 0
            \end{bmatrix}
        $$

    Notice that this docstring is an r-string, since the latex has backslashes.
    We can also include example code:

        print(cirq_google.Sycamore) 

    You can also do inline latex like $y = x^2$ and inline code like
    `cirq.unitary(cirq.X)`.

    And of course there's the standard sections.

    Args:
        a: The first argument.
        b: Another argument.

    Returns:
        An important value.

    Raises:
        ValueError: The value of `a` wasn't quite right.
    """

Dependencies

Production dependencies

Cirq follows a modular design. Each module should specify their dependencies within their folder. See for example cirq-core/requirements.txt and cirq-google/requirements.txt. In general we should try to keep dependencies as minimal as possible and if we have to add them, keep them as relaxed as possible instead of pinning to exact versions. If exact versions or constraints are known, those should be documented in form of a comment.

Development dependencies

For local development:

For a development environment there is a single file that installs all the module dependencies and all of the dev tools as well: dev_tools/requirements/dev.env.txt. If this is too heavy weight for you, you can instead use dev_tools/requirements/deps/dev-tools.txt and the given module dependencies.

For continuous integration:

Each job might need different set of requirements and it would be inefficient to install a full blown dev env for every tiny job (e.g. mypy check). Instead in dev_tools/requirements create a separate .env.txt and include the necessary tools in there. Requirements files can include each other, which is heavily leveraged in our requirements files in order to remove duplication.

You can call the following utility to unroll the content of a file:

python dev_tools/requirements/reqs.py dev_tools/requirements/dev.env.txt

Producing a pypi package

Do a dry run with test pypi.

If you're making a release, you should have access to a test pypi account capable of uploading packages to cirq. Put its credentials into the environment variables TEST_TWINE_USERNAME and TEST_TWINE_PASSWORD then run
```
./dev_tools/packaging/publish-dev-package.sh EXPECTED_VERSION --test
```
You must specify the EXPECTED_VERSION argument to match the version in cirq/_version.py, and it must contain the string dev. This is to prevent accidentally uploading the wrong version.

The script will append the current date and time to the expected version number before uploading to test pypi. It will print out the full version that it uploaded. Take not of this value.

Once the package has uploaded, verify that it works
```
./dev_tools/packaging/verify-published-package.sh FULL_VERSION_REPORTED_BY_PUBLISH_SCRIPT --test
```
The script will create fresh virtual environments, install cirq and its dependencies, check that code importing cirq executes, and run the tests over the installed code. If everything goes smoothly, the script will finish by printing VERIFIED.
Do a dry run with prod pypi

This step is essentially identical to the test dry run, but with production pypi. You should have access to a production pypi account capable of uploading packages to cirq. Put its credentials into the environment variables PROD_TWINE_USERNAME and PROD_TWINE_PASSWORD then run
```
./dev_tools/packaging/publish-dev-package.sh EXPECTED_VERSION --prod
```
Once the package has uploaded, verify that it works
```
./dev_tools/packaging/verify-published-package.sh FULL_VERSION_REPORTED_BY_PUBLISH_SCRIPT --prod
```
If everything goes smoothly, the script will finish by printing VERIFIED.
Set the version number in cirq/_version.py.

Development versions end with .dev or .dev#. For example, 0.0.4.dev500 is a development version of the release version 0.0.4. For a release, create a pull request turning #.#.#.dev* into #.#.# and a follow up pull request turning #.#.# into (#+1).#.#.dev.
Run dev_tools/packaging/produce-package.sh to produce pypi artifacts.
```
./dev_tools/packaging/produce-package.sh dist
```
The output files will be placed in the directory dist/.
Create a github release.

Describe major changes (especially breaking changes) in the summary. Make sure you point the tag being created at the one and only revision with the non-dev version number. Attach the package files you produced to the release.

Upload to pypi.

You can use a tool such as twine for this. For example:

twine upload -u "${PROD_TWINE_USERNAME}" -p "${PROD_TWINE_PASSWORD}" dist/*

You should then run the verification script to check that the uploaded package works:

./dev_tools/packaging/verify-published-package.sh VERSION_YOU_UPLOADED --prod

And try it out for yourself:

python -m pip install cirq
python -c "import cirq; print(cirq_google.Sycamore)"
python -c "import cirq; print(cirq.__version__)"

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development.md

development.md

Contribute

Cloning the repository

Recommended git setup

Docker

Forking the repository

Setting up an environment

Editable installs

Protocol buffers

Continuous integration and local testing

Writing docstrings and generating documentation

Dependencies

Production dependencies

Development dependencies

Producing a pypi package

Files

development.md

Latest commit

History

development.md

File metadata and controls

Contribute

Cloning the repository

Recommended git setup

Docker

Forking the repository

Setting up an environment

Editable installs

Protocol buffers

Continuous integration and local testing

Writing docstrings and generating documentation

Dependencies

Production dependencies

Development dependencies

Producing a pypi package