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‎.github/workflows/ci-build.yml

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name: CI
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on:
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# Triggers the workflow on push or pull request events but only for the main branch
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push:
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branches: [ main ]
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pull_request:
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branches: [ main ]
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# Allows you to run this workflow manually from the Actions tab
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workflow_dispatch:
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jobs:
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build:
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runs-on: ubuntu-latest
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strategy:
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fail-fast: false
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matrix:
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python-version: ["3.7", "3.9"]
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steps:
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- name: Cancel previous
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uses: styfle/[email protected]
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with:
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access_token: ${{ github.token }}
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if: ${{github.ref != 'refs/head/main'}}
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- uses: actions/checkout@v2
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v2
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with:
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python-version: ${{ matrix.python-version }}
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- name: Get pip cache dir
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id: pip-cache
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run: |
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python -m pip install --upgrade pip wheel
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echo "::set-output name=dir::$(pip cache dir)"
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- name: pip cache
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uses: actions/cache@v2
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with:
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path: ${{ steps.pip-cache.outputs.dir }}
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key: ${{ runner.os }}-pip-${{ hashFiles('**/setup.py') }}
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- name: Install jax-tree-math
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run: |
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pip install -e .[tests]
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- name: Run unit tests
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run: |
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pytest tree_math

‎.gitignore

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*.egg-info
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.DS_Store
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build
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dist
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__pycache__

‎CONTRIBUTING.md

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# How to Contribute
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We'd love to accept your patches and contributions to this project. There are
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just a few small guidelines you need to follow.
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## Contributor License Agreement
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Contributions to this project must be accompanied by a Contributor License
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Agreement (CLA). You (or your employer) retain the copyright to your
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contribution; this simply gives us permission to use and redistribute your
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contributions as part of the project. Head over to
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<https://cla.developers.google.com/> to see your current agreements on file or
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to sign a new one.
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You generally only need to submit a CLA once, so if you've already submitted one
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again.
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## Code Reviews
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All submissions, including submissions by project members, require review. We
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use GitHub pull requests for this purpose. Consult
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[GitHub Help](https://help.github.com/articles/about-pull-requests/) for more
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## Community Guidelines
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This project follows
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[Google's Open Source Community Guidelines](https://opensource.google/conduct/).

‎LICENSE

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‎README.md

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# tree-math: mathematical operations for JAX pytrees
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tree-math makes it easy to implement numerical algorithms that work on
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[JAX pytrees](https://jax.readthedocs.io/en/latest/pytrees.html), such as
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iterative methods for optimization and equation solving. It does so by providing
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a wrapper class `tree_math.Vector` that defines array operations such as
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infix arithmetic and dot-products on pytrees.
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For example, here's how we could write the preconditioned conjugate gradient
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method. Notice how similar the implementation is to the [pseudocode from
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Wikipedia](https://en.wikipedia.org/wiki/Conjugate_gradient_method#The_preconditioned_conjugate_gradient_method),
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unlike the [implementation in JAX](https://github.com/google/jax/blob/b5aea7bc2da4fb5ef96c87a59bfd1486d8958dd7/jax/_src/scipy/sparse/linalg.py#L111-L121):
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```python
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import functools
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from jax import lax
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import tree_math
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import tree_math.numpy as tnp
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@functools.partial(tree_math.wrap, vector_argnames=['b', 'x0'])
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def cg(A, b, x0, M=lambda x: x, maxiter=5, tol=1e-5, atol=0.0):
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"""jax.scipy.sparse.linalg.cg, written with tree_math."""
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A = tree_math.unwrap(A)
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M = tree_math.unwrap(M)
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# tolerance handling uses the "non-legacy" behavior of scipy.sparse.linalg.cg
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bs = b @ b
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atol2 = tnp.maximum(tol**2 * bs, atol**2)
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def cond_fun(value):
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x, r, gamma, p, k = value
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rs = r @ r
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return (rs > atol2) & (k < maxiter)
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def body_fun(value):
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x, r, gamma, p, k = value
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Ap = A(p)
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alpha = gamma / (p.conj() @ Ap)
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x_ = x + alpha * p
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r_ = r - alpha * Ap
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z_ = M(r_)
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gamma_ = r_.conj() @ z_
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beta_ = gamma_ / gamma
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p_ = z_ + beta_ * p
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return x_, r_, gamma_, p_, k + 1
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r0 = b - A(x0)
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p0 = z0 = M(r0)
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gamma0 = r0 @ z0
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initial_value = (x0, r0, gamma0, p0, 0)
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x_final, *_ = lax.while_loop(cond_fun, body_fun, initial_value)
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return x_final
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```
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For most operations, we recommend working directly with `Vector` objects or the
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`wrap` and `unwrap` helper functions. You can also find a few functions defined
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on vectors in `tree_math.numpy`, which implements a very restricted subset
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of `jax.numpy`. (In the long term, this separate module might dissappear if we
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can support `Vector` objects directly inside `jax.numpy`).
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One important different between `tree_math` and `jax.numpy` is that dot
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products in `tree_math` default to full precision on all platforms, rather
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than defaulting to bfloat16 precision on TPUs. This is useful for writing most
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numerical algorithms, and will likely be JAX's default behavior
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[in the future](https://github.com/google/jax/pull/7859).
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TODO(shoyer): add a full tutorial!

‎conftest.py

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# Copyright 2021 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Configure FLAGS with default values for absltest."""
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from absl import app
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try:
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app.run(lambda argv: None)
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except SystemExit:
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pass

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