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