Metadata-Version: 2.1
Name: autoray
Version: 0.2.3
Summary: Write backend agnostic numeric code compatible with any numpy-ish array library.
Home-page: http://github.com/jcmgray/autoray
Author: Johnnie Gray
Author-email: johnniemcgray@gmail.com
License: Apache
Description: 
        A lightweight python AUTOmatic-arRAY library. Write numeric code that works for:
        
        * `numpy <https://github.com/numpy/numpy>`_
        * `cupy <https://github.com/cupy/cupy>`_
        * `dask <https://github.com/dask/dask>`_
        * `autograd <https://github.com/HIPS/autograd>`_
        * `jax <https://github.com/google/jax>`_
        * `mars <https://github.com/mars-project/mars>`_
        * `tensorflow <https://github.com/tensorflow/tensorflow>`_
        * `pytorch <https://pytorch.org/>`_
        * ... and indeed **any** library that provides a numpy-*ish* api.
        
        .. image:: https://travis-ci.org/jcmgray/autoray.svg?branch=master
          :target: https://travis-ci.org/jcmgray/autoray
          :alt: Travis-CI
        .. image:: https://codecov.io/gh/jcmgray/autoray/branch/master/graph/badge.svg
          :target: https://codecov.io/gh/jcmgray/autoray
          :alt: Code Coverage
        .. image:: https://img.shields.io/lgtm/grade/python/g/jcmgray/autoray.svg
          :target: https://lgtm.com/projects/g/jcmgray/autoray/
          :alt: Code Quality
        
        As an example consider this function that orthogonalizes a matrix using the modified Gram-Schmidt algorithm:
        
        .. code:: python3
        
            from autoray import do
        
            def modified_gram_schmidt(X):
        
                Q = []
                for j in range(0, X.shape[0]):
        
                    q = X[j, :]
                    for i in range(0, j):
                        rij = do('tensordot', do('conj', Q[i]), q, 1)
                        q = q - rij * Q[i]
        
                    rjj = do('linalg.norm', q, 2)
                    Q.append(q / rjj)
        
                return do('stack', Q, axis=0, like=X)
        
        Which is now compatible with **all** of the above mentioned libraries! (N.B. this particular example is also probably slow). If you don't like the explicit ``do`` syntax, then you can import the fake ``numpy`` object as a **drop-in replacement** instead:
        
        .. code:: python3
        
            from autoray import numpy as np
        
            x = np.random.uniform(size=(2, 3, 4), like='tensorflow')
            np.tensordot(x, x, [(2, 1), (2, 1)])
            # <tf.Tensor 'Tensordot:0' shape=(2, 2) dtype=float32>
        
            np.eye(3, like=x)  # many functions obviously can't dispatch without the `like` keyword
            # <tf.Tensor 'eye/MatrixDiag:0' shape=(3, 3) dtype=float32>
        
        Of course complete compatibility is not going to be possible for all functions, operations and libraries, but ``autoray`` hopefully makes the job much easier. Of the above, ``tensorflow`` has *quite* a different interface and ``pytorch`` probably the *most* different. Whilst for example not every function will work out-of-the-box for these two, ``autoray`` is also designed with the easy addition of new functions in mind (for example adding new translations is often a one-liner).
        
        **How does it work?**
        
        ``autoray`` works using essentially a single dispatch mechanism on the first  argument for ``do``, or the ``like`` keyword argument if specified, fetching functions from the whichever module defined that supplied array. Additionally, it caches a few custom translations and lookups so as to handle libraries like ``tensorflow`` that don't exactly replicate the ``numpy`` api (for example ``sum`` gets translated to ``tensorflow.reduce_sum``).
        
        Special Functions
        -----------------
        
        The main function is ``do``, but the following special (i.e. not in ``numpy``) functions are also implemented that may be useful:
        
        * ``autoray.infer_backend`` - check what library is being inferred for a given array
        * ``autoray.to_backend_dtype`` - convert a string specified dtype like ``'float32'`` to ``torch.float32`` for example
        * ``autoray.get_dtype_name`` - convert a backend dtype back into the equivalent string specifier like ``'complex64'``
        * ``autoray.astype`` - backend agnostic dtype conversion of arrays
        * ``autoray.to_numpy`` - convert any array to a ``numpy.ndarray``
        
        Here are all of those in action:
        
        .. code:: python3
        
            import autoray as ar
        
            backend = 'torch'
            dtype = ar.to_backend_dtype('float64', like=backend)
            dtype
            # torch.float64
        
            x = ar.do('random.normal', size=(4,), dtype=dtype, like=backend)
            x
            # tensor([ 0.0461,  0.3028,  0.1790, -0.1494], dtype=torch.float64)
        
            ar.infer_backend(x)
            # 'torch'
        
            ar.get_dtype_name(x)
            # 'float64'
        
            x32 = ar.astype(x, 'float32')
            ar.to_numpy(x32)
            # array([ 0.04605161,  0.30280888,  0.17903718, -0.14936243], dtype=float32)
        
        Registering Your Own functions
        ------------------------------
        
        If you want to directly provide a missing or alternative implementation of some function for a particular backend you can do so with ``autoray.register_function``:
        
        .. code:: python3
        
            def my_custom_torch_svd(x):
                import torch
        
                print('Hello SVD!')
                u, s, v = torch.svd(x)
        
                return u, s, v.T
        
            ar.register_function('torch', 'linalg.svd', my_custom_torch_svd)
        
            x = ar.do('random.uniform', size=(3, 4), like='torch')
        
            ar.do('linalg.svd', x)
            # Hello SVD!
            # (tensor([[-0.5832,  0.6188, -0.5262],
            #          [-0.5787, -0.7711, -0.2655],
            #          [-0.5701,  0.1497,  0.8078]]),
            #  tensor([2.0336, 0.8518, 0.4572]),
            #  tensor([[-0.4568, -0.3166, -0.6835, -0.4732],
            #          [-0.5477,  0.2825, -0.2756,  0.7377],
            #          [ 0.2468, -0.8423, -0.0993,  0.4687]]))
        
        If you want to make use of the existing function you can supply ``wrap=True`` in which case the custom function supplied should act like a decorator:
        
        .. code:: python3
        
            def my_custom_sum_wrapper(old_fn):
        
                def new_fn(*args, **kwargs):
                    print('Hello sum!')
                    return old_fn(*args **kwargs)
        
                return new_fn
        
            ar.register_function('torch', 'sum', my_custom_sum_wrapper, wrap=True)
        
            ar.do('sum', x)
            # Hello sum!
            # tensor(5.4099)
        
        Though be careful, if you call ``register_function`` again it will now wrap the *new* function!
        
        Deviations from `numpy`
        =======================
        
        `autoray` doesn't have an API as such, since it is essentially just a fancy single dispatch mechanism.
        On the other hand, where translations *are* in place, they generally use the numpy API. So
        ``autoray.do('stack', arrays=pytorch_tensors, axis=0)``
        gets automatically translated into
        ``torch.stack(tensors=pytorch_tensors, dims=0)``
        and so forth.
        
        Currently the one place this isn't true is ``autoray.do('linalg.svd', x)`` where instead ``full_matrices=False``
        is used as the default since this generally makes more sense and many libraries don't even implement the other case.
        Autoray also dispatches ``'linalg.expm'`` for ``numpy`` arrays to ``scipy``, and may well do with other scipy-only functions at some point.
        
        Installation
        ------------
        
        You can install ``autoray`` via `conda-forge <https://conda-forge.org/>`_ as well as with ``pip``. Alternatively, simply copy the monolithic ``autoray.py`` into your project internally (if dependencies aren't your thing).
        
        **Alternatives**
        
        * The ``__array_function__`` protocol has been `suggested <https://www.numpy.org/neps/nep-0018-array-function-protocol.html>`_ and now implemented in ``numpy``. Hopefully this will eventually negate the need for ``autoray``. On the other hand, third party libraries themselves need to implement the interface, which has not been done, for example, in ``tensorflow`` yet.
        * The `uarray <https://github.com/Quansight-Labs/uarray>`_ project aims to develop a generic array interface but comes with the warning *"This is experimental and very early research code. Don't use this."*.
        
        Contributing
        ------------
        
        Pull requests such as extra translations are very welcome!
        
Keywords: array agnostic numeric numpy cupy dask tensorflow jax autograd
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.5
Provides-Extra: tests
