autoray
¶
Subpackages¶
Submodules¶
Package Contents¶
Functions¶
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Do function named |
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Return the universally set backend, if any. |
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Set a default global backend. The argument |
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Context manager for setting a default backend. The argument |
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Get the name of the library that defined the class of |
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Infer which backend should be used for a function that takes multiple |
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Cached retrieval of correct function for backend, all the logic for |
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Get the shape of an array as a tuple of int. This should be preferred |
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Get the number of dimensions of an array. This should be preferred to |
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Get the size, or number of elements, of an array. This should be |
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Array conjugate. |
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Array transpose. |
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Array Hermitian transpose. |
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Array real part. |
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Array imaginary part. |
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Array reshaped. |
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Turn string specifier |
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Cast array as type |
Find string specifier |
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Compute the minimal dtype sufficient for |
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Get a numpy version of array |
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Register the name (and by default the module or submodule) of a custom |
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Directly provide your own function. |
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An alternative leaf tester for addressing only arrays within trees. |
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Map |
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Iterate over all leaves in |
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Apply |
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Flatten |
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Unflatten |
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Take a function consisting of multiple |
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Just-in-time compile an |
Attributes¶
- autoray.do(fn, *args, like=None, **kwargs)[source]¶
Do function named
fn
on(*args, **kwargs)
, peforming single dispatch to retrievefn
based on whichever library defines the class of theargs[0]
, or thelike
keyword argument if specified.Examples
Works on numpy arrays:
>>> import numpy as np >>> x_np = np.random.uniform(size=[5]) >>> y_np = do('sqrt', x_np) >>> y_np array([0.32464973, 0.90379787, 0.85037325, 0.88729814, 0.46768083])
>>> type(y_np) numpy.ndarray
Works on cupy arrays:
>>> import cupy as cp >>> x_cp = cp.random.uniform(size=[5]) >>> y_cp = do('sqrt', x_cp) >>> y_cp array([0.44541656, 0.88713113, 0.92626237, 0.64080557, 0.69620767])
>>> type(y_cp) cupy.core.core.ndarray
Works on tensorflow arrays:
>>> import tensorflow as tf >>> x_tf = tf.random.uniform(shape=[5]) >>> y_tf = do('sqrt', x_tf) >>> y_tf <tf.Tensor 'Sqrt_1:0' shape=(5,) dtype=float32>
>>> type(y_tf) tensorflow.python.framework.ops.Tensor
You get the idea.
For functions that don’t dispatch on the first argument you can use the
like
keyword:>>> do('eye', 3, like=x_tf) <tf.Tensor: id=91, shape=(3, 3), dtype=float32>
- autoray.get_backend(get_globally='auto')[source]¶
Return the universally set backend, if any.
- Parameters:
get_globally ({"auto", False, True}, optional) –
Which backend to return:
True: return the globally set backend, if any.
False: return the backend set for the current thread, if any.
”auto”: return the globally set backend, if this thread is the thread that imported autoray. Otherwise return the backend set for the current thread, if any.
- Returns:
backend – The name of the backend, or None if no backend is set.
- Return type:
str or None
- autoray.set_backend(like, set_globally='auto')[source]¶
Set a default global backend. The argument
like
can be an explicit backend name or anarray
.- Parameters:
like (str or array) – The backend to set. If an array, the backend of the array’s class will be set.
set_globally ({"auto", False, True}, optional) –
Whether to set the backend globally or for the current thread:
True: set the backend globally.
False: set the backend for the current thread.
”auto”: set the backend globally if this thread is the thread that imported autoray. Otherwise set the backend for the current thread.
Only one thread should ever call this function with
set_globally=True
, (by default this is importing thread).
- autoray.backend_like(like, set_globally='auto')[source]¶
Context manager for setting a default backend. The argument
like
can be an explicit backend name or anarray
to infer it from.- Parameters:
like (str or array) – The backend to set. If an array, the backend of the array’s class will be set.
set_globally ({"auto", False, True}, optional) –
Whether to set the backend globally or for the current thread:
True: set the backend globally.
False: set the backend for the current thread.
”auto”: set the backend globally if this thread is the thread that imported autoray. Otherwise set the backend for the current thread.
Only one thread should ever call this function with
set_globally=True
, (by default this is importing thread).
- autoray.infer_backend(array)[source]¶
Get the name of the library that defined the class of
array
- unlessarray
is directly a subclass ofnumpy.ndarray
, in which case assumenumpy
is the desired backend.
- autoray.infer_backend_multi(*arrays)[source]¶
Infer which backend should be used for a function that takes multiple arguments. This assigns a priority to each backend, and returns the backend with the highest priority. By default, the priority is:
builtins
: -2numpy
: -1other backends: 0
autoray.lazy
: 1
I.e. when mixing with
numpy
, other array libraries are preferred, when mixing withautoray.lazy
,autoray.lazy
is preferred. This has quite low overhead due to caching.
- autoray.get_lib_fn(backend, fn)[source]¶
Cached retrieval of correct function for backend, all the logic for finding the correct funtion only runs the first time.
- Parameters:
backend (str) – The module defining the array class to dispatch on.
fn (str) – The function to retrieve.
- Return type:
callable
- autoray.shape(x)[source]¶
Get the shape of an array as a tuple of int. This should be preferred to calling x.shape directly, as it:
Allows customization (e.g. for torch and aesara which return different types for shape - use @shape.register(backend) to customize the behavior from this default implementation).
Can be used on nested lists and tuples, without calling numpy.
- Parameters:
x (array_like) – The array to get the shape of. It can be an arbitrary nested list or tuple of arrays and scalars, but is assumed not to be ragged.
- Returns:
shape – The size of each dimension of the array.
- Return type:
tuple of int
- autoray.ndim(x)[source]¶
Get the number of dimensions of an array. This should be preferred to calling x.ndim, since not all backends implement that, and it can also be called on nested lists and tuples.
- Parameters:
x (array_like) – The array to get the number of dimensions of. It can be an arbitrary nested list or tuple of arrays and scalars.
- Returns:
ndim
- Return type:
int
- autoray.size(x)[source]¶
Get the size, or number of elements, of an array. This should be preferred to calling x.size, since not all backends implement that, and it can also be called on nested lists and tuples.
- Parameters:
x (array_like) – The array to get the size of. It can be an arbitrary nested list or tuple of arrays and scalars.
- Returns:
size
- Return type:
int
- autoray.to_backend_dtype(dtype_name, like)[source]¶
Turn string specifier
dtype_name
into dtype of backendlike
.
- autoray.astype(x, dtype_name, **kwargs)[source]¶
Cast array as type
dtype_name
- triesx.astype
first.
- autoray.register_backend(cls, name)[source]¶
Register the name (and by default the module or submodule) of a custom array class.
- Parameters:
cls (type) – The array class itself.
name (str) – The name of the backend that should be used for this class. By default this wil be assumed to be the location of the relevant functions for this class, but this can be overridden.
- autoray.register_function(backend, name, fn, wrap=False)[source]¶
Directly provide your own function.
- Parameters:
backend (str) – The name of the backend to register the function for.
name (str) – Name of the function, e.g. ‘sum’ or ‘linalg.svd’.
fn (callable) – The function to register.
wrap (bool, optional) – Whether to wrap the old function like
fn(old_fn)
rather than directly supply the entire new function.
- autoray.tree_map(f, tree, is_leaf=is_not_container)[source]¶
Map
f
over all leaves intree
, returning a new pytree.- Parameters:
f (callable) – A function to apply to all leaves in
tree
.tree (pytree) – A nested sequence of tuples, lists, dicts and other objects.
is_leaf (callable) – A function to determine if an object is a leaf,
f
is only applied to objects for whichis_leaf(x)
returnsTrue
.
- Return type:
pytree
- autoray.tree_iter(tree, is_leaf=is_not_container)[source]¶
Iterate over all leaves in
tree
.- Parameters:
f (callable) – A function to apply to all leaves in
tree
.tree (pytree) – A nested sequence of tuples, lists, dicts and other objects.
is_leaf (callable) – A function to determine if an object is a leaf,
f
is only applied to objects for whichis_leaf(x)
returnsTrue
.
- autoray.tree_apply(f, tree, is_leaf=is_not_container)[source]¶
Apply
f
to all leaves intree
, no new pytree is built.- Parameters:
f (callable) – A function to apply to all leaves in
tree
.tree (pytree) – A nested sequence of tuples, lists, dicts and other objects.
is_leaf (callable) – A function to determine if an object is a leaf,
f
is only applied to objects for whichis_leaf(x)
returnsTrue
.
- autoray.tree_flatten(tree, is_leaf=is_not_container, get_ref=False)[source]¶
Flatten
tree
into a list of leaves.- Parameters:
tree (pytree) – A nested sequence of tuples, lists, dicts and other objects.
is_leaf (callable) – A function to determine if an object is a leaf, only objects for which
is_leaf(x)
returnsTrue
are returned in the flattened list.get_ref (bool) – If
True
, a reference tree is also returned which can be used to reconstruct the original tree from a flattened list.
- Returns:
objs (list) – The flattened list of leaf objects.
(ref_tree) (pytree) – If
get_ref
isTrue
, a reference tree, with leaves ofLeaf
, is returned which can be used to reconstruct the original tree.
- autoray.tree_unflatten(objs, tree, is_leaf=is_leaf_placeholder)[source]¶
Unflatten
objs
into a pytree of the same structure astree
.- Parameters:
objs (sequence) – A sequence of objects to be unflattened into a pytree.
tree (pytree) – A nested sequence of tuples, lists, dicts and other objects, the objs will be inserted into a new pytree of the same structure.
is_leaf (callable) – A function to determine if an object is a leaf, only objects for which
is_leaf(x)
returnsTrue
will have the next item fromobjs
inserted. By default checks for theLeaf
object inserted bytree_flatten(..., get_ref=True)
.
- Return type:
pytree
- autoray.compose(fn, *, name=None)[source]¶
Take a function consisting of multiple
autoray.do
calls and compose it into a new, single, named function, registered withautoray.do
.This creates a default implementation of this function for each new backend encountered without explicitly having to write each out, but also allows for specific implementations to be overridden for specific backends.
If the function takes a
backend
argument, it will be supplied with the backend name, to save having to re-choose the backend.Specific implementations can be provided by calling the
register
method of the composed function, or it can itself be used like a decorator:@compose def foo(x): ... @foo.register("numpy") @numba.njit def foo_numba(x): ...
- Parameters:
fn (callable) – The funtion to compose, and its default implementation.
name (str, optional) – The name of the composed function. If not provided, the name of the function will be used.
- autoray.numpy¶
- autoray.autojit(fn=None, *, backend=None, compiler_opts=None)[source]¶
Just-in-time compile an
autoray
function, automatically choosing the backend based on the input arrays, or via keyword argument.The backend used to do the compilation can be set in three ways:
Automatically based on the arrays the function is called with, i.e.
cfn(*torch_arrays)
will usetorch.jit.trace
.In this wrapper,
@autojit(backend='jax')
, to provide a specific default instead.When you call the function
cfn(*arrays, backend='torch')
to override on a per-call basis.
If the arrays supplied are of a different backend type to the compiler, then the returned array will also be converted back, i.e.
cfn(*numpy_arrays, backend='tensorflow')
will return anumpy
array.The
'python'
backend simply extracts and unravels all thedo
calls into a code object usingcompile
which is then run withexec
. This makes use of shared intermediates and constant folding, strips away any python scaffoliding, and is compatible with any library, but the resulting function is not ‘low-level’ in the same way as the other backends.- Parameters:
fn (callable) – The autoray function to compile.
backend ({None, 'python', 'jax', 'torch', 'tensorflow'}, optional) – If set, use this as the default backend.
compiler_opts (dict[dict], optional) – Dict of dicts when you can supply options for each compiler backend separately, e.g.:
@autojit(compiler_opts={'tensorflow': {'jit_compile': True}})
.
- Returns:
cfn – The function with auto compilation.
- Return type:
callable