mirror of
https://github.com/ml-explore/mlx.git
synced 2025-06-25 01:41:17 +08:00
68 lines
1.8 KiB
ReStructuredText
68 lines
1.8 KiB
ReStructuredText
Quick Start Guide
|
|
=================
|
|
|
|
|
|
Basics
|
|
------
|
|
|
|
.. currentmodule:: mlx.core
|
|
|
|
Import ``mlx.core`` and make an :class:`array`:
|
|
|
|
.. code-block:: python
|
|
|
|
>> import mlx.core as mx
|
|
>> a = mx.array([1, 2, 3, 4])
|
|
>> a.shape
|
|
[4]
|
|
>> a.dtype
|
|
int32
|
|
>> b = mx.array([1.0, 2.0, 3.0, 4.0])
|
|
>> b.dtype
|
|
float32
|
|
|
|
Operations in MLX are lazy. The outputs of MLX operations are not computed
|
|
until they are needed. To force an array to be evaluated use
|
|
:func:`eval`. Arrays will automatically be evaluated in a few cases. For
|
|
example, inspecting a scalar with :meth:`array.item`, printing an array,
|
|
or converting an array from :class:`array` to :class:`numpy.ndarray` all
|
|
automatically evaluate the array.
|
|
|
|
.. code-block:: python
|
|
|
|
>> c = a + b # c not yet evaluated
|
|
>> mx.eval(c) # evaluates c
|
|
>> c = a + b
|
|
>> print(c) # Also evaluates c
|
|
array([2, 4, 6, 8], dtype=float32)
|
|
>> c = a + b
|
|
>> import numpy as np
|
|
>> np.array(c) # Also evaluates c
|
|
array([2., 4., 6., 8.], dtype=float32)
|
|
|
|
|
|
See the page on :ref:`Lazy Evaluation <lazy eval>` for more details.
|
|
|
|
Function and Graph Transformations
|
|
----------------------------------
|
|
|
|
MLX has standard function transformations like :func:`grad` and :func:`vmap`.
|
|
Transformations can be composed arbitrarily. For example
|
|
``grad(vmap(grad(fn)))`` (or any other composition) is allowed.
|
|
|
|
.. code-block:: python
|
|
|
|
>> x = mx.array(0.0)
|
|
>> mx.sin(x)
|
|
array(0, dtype=float32)
|
|
>> mx.grad(mx.sin)(x)
|
|
array(1, dtype=float32)
|
|
>> mx.grad(mx.grad(mx.sin))(x)
|
|
array(-0, dtype=float32)
|
|
|
|
Other gradient transformations include :func:`vjp` for vector-Jacobian products
|
|
and :func:`jvp` for Jacobian-vector products.
|
|
|
|
Use :func:`value_and_grad` to efficiently compute both a function's output and
|
|
gradient with respect to the function's input.
|