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