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Awni Hannun
2024-02-08 12:44:23 -08:00
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@@ -5,9 +5,12 @@ Function Transforms
.. currentmodule:: mlx.core
MLX uses composable function transformations for automatic differentiation and
vectorization. The key idea behind composable function transformations is that
every transformation returns a function which can be further transformed.
MLX uses composable function transformations for automatic differentiation,
vectorization, and compute graph optimizations. To see the complete list of
function transformations check-out the :ref:`API documentation <transforms>`.
The key idea behind composable function transformations is that every
transformation returns a function which can be further transformed.
Here is a simple example:
@@ -36,10 +39,10 @@ Using :func:`grad` on the output of :func:`grad` is always ok. You keep
getting higher order derivatives.
Any of the MLX function transformations can be composed in any order to any
depth. To see the complete list of function transformations check-out the
:ref:`API documentation <transforms>`. See the following sections for more
information on :ref:`automatic differentiaion <auto diff>` and
:ref:`automatic vectorization <vmap>`.
depth. See the following sections for more information on :ref:`automatic
differentiaion <auto diff>` and :ref:`automatic vectorization <vmap>`.
For more information on :func:`compile` see the :ref:`compile documentation <compile>`.
Automatic Differentiation
-------------------------