# Welcome to autoray's documentation!

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[`autoray`](autoray) is a lightweight python AUTOmatic-arRAY library for
abstracting your tensor operations. Primarily it provides an
[*automatic* dispatch mechanism](automatic_dispatch) that means you can
write backend agnostic code that works for:

* [numpy](https://github.com/numpy/numpy)
* [pytorch](https://pytorch.org/)
* [jax](https://github.com/google/jax)
* [cupy](https://github.com/cupy/cupy)
* [mlx](https://github.com/ml-explore/mlx)
* [dask](https://github.com/dask/dask)
* [autograd](https://github.com/HIPS/autograd)
* [tensorflow](https://github.com/tensorflow/tensorflow)
* [sparse](https://sparse.pydata.org/)
* [paddle](https://github.com/paddlepaddle/paddle)
* ... and indeed **any** library that provides a numpy-*ish* api, even if it
  knows nothing about `autoray`.

Beyond that, abstracting the array interface allows you to:

* *swap [custom versions of functions](automatic_dispatch.md#functions)
  for specific backends*
* *generate [random numbers](random.md) with a single interface to every
  backend's generators and state*
* *trace through computations [lazily](lazy_computation) without actually
  running them*
* *automatically [share intermediates and fold constants](lazy_computation)
  in computations*
* *compile functions with a [unified interface](compilation) for different
  backends*


## Basic usage

The main function of `autoray` is [`do`](autoray.do), which takes a function
name followed by `*args` and `**kwargs`, and automatically looks up (and
caches) the correct function to match the equivalent numpy call:

```python
import autoray as ar


def noised_svd(x):
    # automatic dispatch based on supplied array
    U, s, VH = ar.do("linalg.svd", x)

    # automatic dispatch based on different array
    sn = s + 0.1 * ar.do("random.normal", size=ar.shape(s), like=s)

    # automatic dispatch for multiple arrays for certain functions
    return ar.do("einsum", "ij,j,jk->ik", U, sn, VH)


# explicit backend given by string
x = ar.do("random.uniform", size=(100, 100), like="torch")

# this function now works for any backend
y = noised_svd(x)

# explicit inference of backend from array
ar.infer_backend(y)
# 'torch'
```

If you don't like the explicit [`do`](autoray.do) syntax, or simply want a
drop-in replacement for existing code, you can also import the `autoray.numpy`
module:

```{code-block} python
from autoray import numpy as np

# set a temporary default backend
with ar.backend_like('cupy'):
    z = np.ones((3, 4), dtype='float32')

np.exp(z)
# array([[2.7182817, 2.7182817, 2.7182817, 2.7182817],
#        [2.7182817, 2.7182817, 2.7182817, 2.7182817],
#        [2.7182817, 2.7182817, 2.7182817, 2.7182817]], dtype=float32)
```

Alternatively you can use [`autoray.get_namespace`](autoray.get_namespace)
to get a backend specific (with optional default device and dtype) namespace
object, (c.f. the [Python Array Api](https://data-apis.org/array-api/latest/)):

```python
xp = ar.get_namespace(z)
xp.einsum("ii->i", z)
```

Custom backends and functions can be dynamically registered with:

* [`register_backend`](autoray.register_backend)
* [`register_function`](autoray.register_function)

---

## Advanced details

```{toctree}
:caption: Guides
:maxdepth: 2

installation.md
automatic_dispatch.md
random.md
lazy_computation.ipynb
compilation.ipynb
```

```{toctree}
:caption: Development

develop.md
changelog.md
```

```{toctree}
:hidden:

GitHub Repository <https://github.com/jcmgray/autoray>
```