mlx/docs/src/python/nn.rst

.. _nn:

.. currentmodule:: mlx.nn

Neural Networks
===============

Writing arbitrarily complex neural networks in MLX can be done using only
:class:`mlx.core.array` and :meth:`mlx.core.value_and_grad`. However, this requires the
user to write again and again the same simple neural network operations as well
as handle all the parameter state and initialization manually and explicitly.

The module :mod:`mlx.nn` solves this problem by providing an intuitive way of
composing neural network layers, initializing their parameters, freezing them
for finetuning and more.

Quick Start with Neural Networks
---------------------------------

.. code-block:: python

    import mlx.core as mx
    import mlx.nn as nn

    class MLP(nn.Module):
        def __init__(self, in_dims: int, out_dims: int):
            super().__init__()

            self.layers = [
                nn.Linear(in_dims, 128),
                nn.Linear(128, 128),
                nn.Linear(128, out_dims),
            ]

        def __call__(self, x):
            for i, l in enumerate(self.layers):
                x = mx.maximum(x, 0) if i > 0 else x
                x = l(x)
            return x

    # The model is created with all its parameters but nothing is initialized
    # yet because MLX is lazily evaluated
    mlp = MLP(2, 10)

    # We can access its parameters by calling mlp.parameters()
    params = mlp.parameters()
    print(params["layers"][0]["weight"].shape)

    # Printing a parameter will cause it to be evaluated and thus initialized
    print(params["layers"][0])

    # We can also force evaluate all parameters to initialize the model
    mx.eval(mlp.parameters())

    # A simple loss function.
    # NOTE: It doesn't matter how it uses the mlp model. It currently captures
    #       it from the local scope. It could be a positional argument or a
    #       keyword argument.
    def l2_loss(x, y):
        y_hat = mlp(x)
        return (y_hat - y).square().mean()

    # Calling `nn.value_and_grad` instead of `mx.value_and_grad` returns the
    # gradient with respect to `mlp.trainable_parameters()`
    loss_and_grad = nn.value_and_grad(mlp, l2_loss)


.. _module_class:

The Module Class
----------------

The workhorse of any neural network library is the :class:`Module` class. In
MLX the :class:`Module` class is a container of :class:`mlx.core.array` or
:class:`Module` instances. Its main function is to provide a way to
recursively **access** and **update** its parameters and those of its
submodules.

Parameters
^^^^^^^^^^

A parameter of a module is any public member of type :class:`mlx.core.array` (its
name should not start with ``_``). It can be arbitrarily nested in other
:class:`Module` instances or lists and dictionaries.

:meth:`Module.parameters` can be used to extract a nested dictionary with all
the parameters of a module and its submodules.

A :class:`Module` can also keep track of "frozen" parameters.
:meth:`Module.trainable_parameters` returns only the subset of
:meth:`Module.parameters` that is not frozen. When using
:meth:`mlx.nn.value_and_grad` the gradients returned will be with respect to these
trainable parameters.

Updating the parameters
^^^^^^^^^^^^^^^^^^^^^^^

MLX modules allow accessing and updating individual parameters. However, most
times we need to update large subsets of a module's parameters. This action is
performed by :meth:`Module.update`. 

Value and grad
--------------

Using a :class:`Module` does not preclude using MLX's high order function
transformations (:meth:`mlx.core.value_and_grad`, :meth:`mlx.core.grad`, etc.). However,
these function transformations assume pure functions, namely the parameters
should be passed as an argument to the function being transformed.

There is an easy pattern to achieve that with MLX modules

.. code-block:: python

    model = ...

    def f(params, other_inputs):
        model.update(params)  # <---- Necessary to make the model use the passed parameters
        return model(other_inputs)

    f(model.trainable_parameters(), mx.zeros((10,)))

However, :meth:`mlx.nn.value_and_grad` provides precisely this pattern and only
computes the gradients with respect to the trainable parameters of the model.

In detail:

- it wraps the passed function with a function that calls :meth:`Module.update`
  to make sure the model is using the provided parameters.
- it calls :meth:`mlx.core.value_and_grad` to transform the function into a function
  that also computes the gradients with respect to the passed parameters.
- it wraps the returned function with a function that passes the trainable
  parameters as the first argument to the function returned by
  :meth:`mlx.core.value_and_grad`

.. autosummary::
   :toctree: _autosummary

   value_and_grad

Neural Network Layers
---------------------

.. autosummary::
   :toctree: _autosummary
   :template: nn-module-template.rst

   Embedding
   ReLU
   GELU
   SiLU
   Linear
   Conv1d
   Conv2d
   LayerNorm
   RMSNorm
   GroupNorm
   RoPE
   MultiHeadAttention
   Sequential

Layers without parameters (e.g. activation functions) are also provided as
simple functions.

.. autosummary::
   :toctree: _autosummary_functions
   :template: nn-module-template.rst

   gelu
   gelu_approx
   gelu_fast_approx
   relu
   silu

Loss Functions
--------------

.. autosummary::
   :toctree: _autosummary_functions
   :template: nn-module-template.rst

   losses.cross_entropy
   losses.binary_cross_entropy
   losses.l1_loss
   losses.mse_loss
   losses.nll_loss
   losses.kl_div_loss