Docs updates (#198)

Reorganize NN docs + a few other tidbits.

docs/.gitignore

@@ -1 +1,2 @@
 src/python/_autosummary*/
+src/python/nn/_autosummary*/

docs/src/examples/mlp.rst

@@ -61,7 +61,10 @@ set:
   def eval_fn(model, X, y):
       return mx.mean(mx.argmax(model(X), axis=1) == y)
 
-Next, setup the problem parameters and load the data:
+Next, setup the problem parameters and load the data. To load the data, you need our
+`mnist data loader
+<https://github.com/ml-explore/mlx-examples/blob/main/mnist/mnist.py>`_, which
+we will import as `mnist`.
 
 .. code-block:: python
 

docs/src/install.rst

@@ -35,8 +35,7 @@ Probably you are using a non-native Python. The output of
 
 should be ``arm``. If it is ``i386`` (and you have M series machine) then you
 are using a non-native Python. Switch your Python to a native Python. A good
-way to do this is with
-`Conda <https://stackoverflow.com/questions/65415996/how-to-specify-the-architecture-or-platform-for-a-new-conda-environment-apple>`_.
+way to do this is with `Conda <https://stackoverflow.com/q/65415996>`_.
 
 
 Build from source
@@ -166,3 +165,27 @@ should point to the path to the built metal library.
     .. code-block:: shell
 
       xcrun -sdk macosx --show-sdk-version
+
+Troubleshooting
+^^^^^^^^^^^^^^^
+
+Metal not found
+~~~~~~~~~~~~~~~
+
+You see the following error when you try to build:
+
+.. code-block:: shell
+
+  error: unable to find utility "metal", not a developer tool or in PATH
+
+To fix this, first make sure you have Xcode installed:
+
+.. code-block:: shell
+
+  xcode-select --install
+
+Then set the active developer directory:
+
+.. code-block:: shell
+
+  sudo xcode-select --switch /Applications/Xcode.app/Contents/Developer

docs/src/python/nn.rst

@@ -64,7 +64,6 @@ Quick Start with Neural Networks
     # gradient with respect to `mlp.trainable_parameters()`
     loss_and_grad = nn.value_and_grad(mlp, l2_loss)
 
-
 .. _module_class:
 
 The Module Class
@@ -86,20 +85,58 @@ name should not start with ``_``). It can be arbitrarily nested in other
 :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.
+A :class:`Module` can also keep track of "frozen" parameters. See the
+:meth:`Module.freeze` method for more details. :meth:`mlx.nn.value_and_grad`
+the gradients returned will be with respect to these trainable parameters.
 
-Updating the 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
+
+Inspecting Modules
+^^^^^^^^^^^^^^^^^^
+
+The simplest way to see the model architecture is to print it. Following along with
+the above example, you can print the ``MLP`` with:
+
+.. code-block:: python
+
+  print(mlp)
+
+This will display:
+
+.. code-block:: shell
+
+  MLP(
+    (layers.0): Linear(input_dims=2, output_dims=128, bias=True)
+    (layers.1): Linear(input_dims=128, output_dims=128, bias=True)
+    (layers.2): Linear(input_dims=128, output_dims=10, bias=True)
+  )
+
+To get more detailed information on the arrays in a :class:`Module` you can use
+:func:`mlx.utils.tree_map` on the parameters. For example, to see the shapes of
+all the parameters in a :class:`Module` do:
+
+.. code-block:: python
+    
+   from mlx.utils import tree_map
+   shapes = tree_map(lambda p: p.shape, mlp.parameters())
+
+As another example, you can count the number of parameters in a :class:`Module`
+with:
+
+.. code-block:: python
+    
+   from mlx.utils import tree_flatten
+   num_params = sum(v.size for _, v in tree_flatten(mlp.parameters()))
+
+
+Value and Grad
 --------------
 
 Using a :class:`Module` does not preclude using MLX's high order function
@@ -133,62 +170,14 @@ In detail:
   :meth:`mlx.core.value_and_grad`
 
 .. autosummary::
+   :recursive:
    :toctree: _autosummary
 
    value_and_grad
+   Module
 
-Neural Network Layers
----------------------
+.. toctree::
 
-.. autosummary::
-   :toctree: _autosummary
-   :template: nn-module-template.rst
-
-   Embedding
-   ReLU
-   PReLU
-   GELU
-   SiLU
-   Step
-   SELU
-   Mish
-   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
-   prelu
-   silu
-   step
-   selu
-   mish
-
-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
+   nn/layers
+   nn/functions
+   nn/losses

docs/src/python/nn/functions.rst

@@ -0,0 +1,23 @@
+.. _nn_functions:
+
+.. currentmodule:: mlx.nn
+
+Functions
+---------
+
+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
+   prelu
+   silu
+   step
+   selu
+   mish

docs/src/python/nn/layers.rst

@@ -0,0 +1,28 @@
+.. _layers:
+
+.. currentmodule:: mlx.nn
+
+Layers
+------
+
+.. autosummary::
+   :toctree: _autosummary
+   :template: nn-module-template.rst
+
+   Embedding
+   ReLU
+   PReLU
+   GELU
+   SiLU
+   Step
+   SELU
+   Mish
+   Linear
+   Conv1d
+   Conv2d
+   LayerNorm
+   RMSNorm
+   GroupNorm
+   RoPE
+   MultiHeadAttention
+   Sequential

docs/src/python/nn/losses.rst

@@ -0,0 +1,17 @@
+.. _losses:
+
+.. currentmodule:: mlx.nn.losses
+
+Loss Functions
+--------------
+
+.. autosummary::
+   :toctree: _autosummary_functions
+   :template: nn-module-template.rst
+
+   cross_entropy
+   binary_cross_entropy
+   l1_loss
+   mse_loss
+   nll_loss
+   kl_div_loss

docs/src/python/nn/module.rst

@@ -1,7 +0,0 @@
-mlx.nn.Module
-=============
-
-.. currentmodule:: mlx.nn
-
-.. autoclass:: Module
-   :members:

python/mlx/nn/layers/linear.py

@@ -7,12 +7,21 @@ from mlx.nn.layers.base import Module
 
 
 class Linear(Module):
-    """Applies an affine transformation to the input.
+    r"""Applies an affine transformation to the input.
+
+    Concretely:
+
+    .. math::
+
+        y = W^\top x + b
+
+    where :math:`W` has shape ``[output_dims, input_dims]``.
 
     Args:
         input_dims (int): The dimensionality of the input features
         output_dims (int): The dimensionality of the output features
-        bias (bool): If set to False then the layer will not use a bias
+        bias (bool, optional): If set to ``False`` then the layer will
+          not use a bias. Default ``True``.
     """
 
     def __init__(self, input_dims: int, output_dims: int, bias: bool = True):