mlx/docs/build/doctrees/python/nn/module.doctree

<EFBFBD><05><><EFBFBD><00>sphinx.addnodes<65><73>document<6E><74><EFBFBD>)<29><>}<7D>(<28>	rawsource<63><65><00><>children<65>]<5D><>docutils.nodes<65><73>section<6F><6E><EFBFBD>)<29><>}<7D>(hhh]<5D>(h	<09>title<6C><65><EFBFBD>)<29><>}<7D>(h<05>Module<6C>h]<5D>h	<09>Text<78><74><EFBFBD><EFBFBD>Module<6C><65><EFBFBD><EFBFBD><EFBFBD>}<7D>(<28>parent<6E>h<11>	_document<6E>h<03>source<63>N<EFBFBD>line<6E>Nuba<62>
attributes<EFBFBD>}<7D>(<28>ids<64>]<5D><>classes<65>]<5D><>names<65>]<5D><>dupnames<65>]<5D><>backrefs<66>]<5D>u<EFBFBD>tagname<6D>hhhhhh<1D>9/Users/awnihannun/repos/mlx/docs/src/python/nn/module.rst<73>hKubh<00>index<65><78><EFBFBD>)<29><>}<7D>(hhh]<5D>h}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D><>entries<65>]<5D>(<28>single<6C><65>Module (class in mlx.nn)<29><>
mlx.nn.Module<6C>hNt<4E>auh+h-hhhhhNhNubh<00>desc<73><63><EFBFBD>)<29><>}<7D>(hhh]<5D>(h<00>desc_signature<72><65><EFBFBD>)<29><>}<7D>(h<05>Module()<29>h]<5D>(h<00>desc_annotation<6F><6E><EFBFBD>)<29><>}<7D>(h<05>2[<#text: 'class'>, <desc_sig_space: <#text: ' '>>]<5D>h]<5D>(h<16>class<73><73><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhKhhhNhNubh<00>desc_sig_space<63><65><EFBFBD>)<29><>}<7D>(h<05>
 <20>h]<5D>h<16>
 <20><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhUhhhNhNubah}<7D>(h!]<5D>h#]<5D><>
w<>ah%]<5D>h']<5D>h)]<5D>uh+hShhKubeh}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D><>	xml:space<63><65>preserve<76>uh+hIhhEhhh<1D>_/Users/awnihannun/repos/mlx/python/mlx/nn/layers/base.py:docstring of mlx.nn.layers.base.Module<6C>hNubh<00>desc_addname<6D><65><EFBFBD>)<29><>}<7D>(h<05>mlx.nn.<2E>h]<5D>h<16>mlx.nn.<2E><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhohhhNhNubah}<7D>(h!]<5D>h#]<5D>(<28>sig-prename<6D><65>
descclassname<6D>eh%]<5D>h']<5D>h)]<5D>hjhkuh+hmhhEhhhhlhNubh<00>	desc_name<6D><65><EFBFBD>)<29><>}<7D>(h<05>Module<6C>h]<5D>h<16>Module<6C><65><EFBFBD><EFBFBD><EFBFBD>}<7D>(hh<>hhhNhNubah}<7D>(h!]<5D>h#]<5D>(<28>sig-name<6D><65>descname<6D>eh%]<5D>h']<5D>h)]<5D>hjhkuh+hhhEhhhhlhNubeh}<7D>(h!]<5D>h<ah#]<5D>(<28>sig<69><67>
sig-object<63>eh%]<5D>h']<5D>h)]<5D><>module<6C><65>mlx.nn<6E><6E>class<73>h<06>fullname<6D>h<EFBFBD>uh+hCh<1D>_/Users/awnihannun/repos/mlx/python/mlx/nn/layers/base.py:docstring of mlx.nn.layers.base.Module<6C>hK
hh@hhubh<00>desc_content<6E><74><EFBFBD>)<29><>}<7D>(hhh]<5D>(h	<09>	paragraph<70><68><EFBFBD>)<29><>}<7D>(h<05>1Base class for building neural networks with MLX.<2E>h]<5D>h<16>1Base class for building neural networks with MLX.<2E><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hh<>hhhNhNubah}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D>uh+h<>h<1D>_/Users/awnihannun/repos/mlx/python/mlx/nn/layers/base.py:docstring of mlx.nn.layers.base.Module<6C>hK
hh<>hhubh<62>)<29><>}<7D>(h<05>gAll the layers provided in :mod:`mlx.nn.layers` subclass this class and
your models should do the same.<2E>h]<5D>(h<16>All the layers provided in <20><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hh<>hhhNhNubh<00>pending_xref<65><66><EFBFBD>)<29><>}<7D>(h<05>:mod:`mlx.nn.layers`<60>h]<5D>h	<09>literal<61><6C><EFBFBD>)<29><>}<7D>(hh<>h]<5D>h<16>
mlx.nn.layers<72><73><EFBFBD><EFBFBD><EFBFBD>}<7D>(hh<>hhhNhNubah}<7D>(h!]<5D>h#]<5D>(<28>xref<65><66>py<70><79>py-mod<6F>eh%]<5D>h']<5D>h)]<5D>uh+h<>hh<>ubah}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D><>refdoc<6F><63>python/nn/module<6C><65>	refdomain<69>hόreftype<70><65>mod<6F><64>refexplicit<69><74><EFBFBD>refwarn<72><6E><EFBFBD>	py:module<6C>h<EFBFBD><68>py:class<73>h<EFBFBD><68>	reftarget<65><74>
mlx.nn.layers<72>uh+h<>hh<>hKhh<>ubh<16>8 subclass this class and
your models should do the same.<2E><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hh<>hhhNhNubeh}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D>uh+h<>hh<>hKhh<>hhubh<62>)<29><>}<7D>(hX
A ``Module`` can contain other ``Module`` instances or :class:`mlx.core.array`
instances in arbitrary nesting of python lists or dicts. The ``Module``
then allows recursively extracting all the :class:`mlx.core.array` instances
using :meth:`mlx.nn.Module.parameters`.<2E>h]<5D>(h<16>A <20><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hh<>hhhNhNubh<62>)<29><>}<7D>(h<05>
``Module``<60>h]<5D>h<16>Module<6C><65><EFBFBD><EFBFBD><EFBFBD>}<7D>(hh<>hhhNhNubah}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D>uh+h<>hh<>ubh<16> can contain other <20><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hh<>hhhNhNubh<62>)<29><>}<7D>(h<05>
``Module``<60>h]<5D>h<16>Module<6C><65><EFBFBD><EFBFBD><EFBFBD>}<7D>(hj	
hhhNhNubah}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D>uh+h<>hh<>ubh<16> instances or <20><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hh<>hhhNhNubh<62>)<29><>}<7D>(h<05>:class:`mlx.core.array`<60>h]<5D>h<EFBFBD>)<29><>}<7D>(hj
h]<5D>h<16>mlx.core.array<61><79><EFBFBD><EFBFBD><EFBFBD>}<7D>(hj
hhhNhNubah}<7D>(h!]<5D>h#]<5D>(hΌpy<70><79>py-class<73>eh%]<5D>h']<5D>h)]<5D>uh+h<>hj
ubah}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D><>refdoc<6F>hی	refdomain<69>j)
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instances in arbitrary nesting of python lists or dicts. The <20><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hh<>hhhNhNubh<62>)<29><>}<7D>(h<05>
``Module``<60>h]<5D>h<16>Module<6C><65><EFBFBD><EFBFBD><EFBFBD>}<7D>(hj?
hhhNhNubah}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D>uh+h<>hh<>ubh<16>,
then allows recursively extracting all the <20><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hh<>hhhNhNubh<62>)<29><>}<7D>(h<05>:class:`mlx.core.array`<60>h]<5D>h<EFBFBD>)<29><>}<7D>(hjS
h]<5D>h<16>mlx.core.array<61><79><EFBFBD><EFBFBD><EFBFBD>}<7D>(hjU
hhhNhNubah}<7D>(h!]<5D>h#]<5D>(hΌpy<70><79>py-class<73>eh%]<5D>h']<5D>h)]<5D>uh+h<>hjQ
ubah}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D><>refdoc<6F>hی	refdomain<69>j_
<00>reftype<70><65>class<73><73>refexplicit<69><74><EFBFBD>refwarn<72><6E>h<EFBFBD>h<EFBFBD>h<EFBFBD>h<EFBFBD>h<EFBFBD><68>mlx.core.array<61>uh+h<>hh<>hKhh<>ubh<16> instances
using <20><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hh<>hhhNhNubh<62>)<29><>}<7D>(h<05> :meth:`mlx.nn.Module.parameters`<60>h]<5D>h<EFBFBD>)<29><>}<7D>(hjw
h]<5D>h<16>mlx.nn.Module.parameters()<29><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hjy
hhhNhNubah}<7D>(h!]<5D>h#]<5D>(hΌpy<70><79>py-meth<74>eh%]<5D>h']<5D>h)]<5D>uh+h<>hju
ubah}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D><>refdoc<6F>hی	refdomain<69>j<EFBFBD>
<00>reftype<70><65>meth<74><68>refexplicit<69><74><EFBFBD>refwarn<72><6E>h<EFBFBD>h<EFBFBD>h<EFBFBD>h<EFBFBD>h<EFBFBD><68>mlx.nn.Module.parameters<72>uh+h<>hh<>hKhh<>ubh<16>
.<2E><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hh<>hhhNhNubeh}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D>uh+h<>hh<>hKhh<>hhubh<62>)<29><>}<7D>(hXE
In addition, the ``Module`` has the concept of trainable and non trainable
parameters (called "frozen"). When using :func:`mlx.nn.value_and_grad`
the gradients are returned only with respect to the trainable parameters.
All arrays in a module are trainable unless they are added in the "frozen"
set by calling :meth:`freeze`.<2E>h]<5D>(h<16>In addition, the <20><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hj<>
hhhNhNubh<62>)<29><>}<7D>(h<05>
``Module``<60>h]<5D>h<16>Module<6C><65><EFBFBD><EFBFBD><EFBFBD>}<7D>(hj<>
hhhNhNubah}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D>uh+h<>hj<>
ubh<16>] has the concept of trainable and non trainable
parameters (called “frozen”). When using <20><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hj<>
hhhNhNubh<62>)<29><>}<7D>(h<05>:func:`mlx.nn.value_and_grad`<60>h]<5D>h<EFBFBD>)<29><>}<7D>(hj<>
h]<5D>h<16>mlx.nn.value_and_grad()<29><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hj<>
hhhNhNubah}<7D>(h!]<5D>h#]<5D>(hΌpy<70><79>py-func<6E>eh%]<5D>h']<5D>h)]<5D>uh+h<>hj<>
ubah}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D><>refdoc<6F>hی	refdomain<69>j<EFBFBD>
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ubh<16><>
the gradients are returned only with respect to the trainable parameters.
All arrays in a module are trainable unless they are added in the “frozen”
set by calling <20><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hj<>
hhhNhNubh<62>)<29><>}<7D>(h<05>:meth:`freeze`<60>h]<5D>h<EFBFBD>)<29><>}<7D>(hj<>
h]<5D>h<16>freeze()<29><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hj<>
hhhNhNubah}<7D>(h!]<5D>h#]<5D>(hΌpy<70><79>py-meth<74>eh%]<5D>h']<5D>h)]<5D>uh+h<>hj<>
ubah}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D><>refdoc<6F>hی	refdomain<69>j<EFBFBD>
<00>reftype<70><65>meth<74><68>refexplicit<69><74><EFBFBD>refwarn<72><6E>h<EFBFBD>h<EFBFBD>h<EFBFBD>h<EFBFBD>h<EFBFBD><68>freeze<7A>uh+h<>hh<>hKhj<>
ubh<16>
.<2E><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hj<>
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literal_block<63><6B><EFBFBD>)<29><>}<7D>(hX4import mlx.core as mx
import mlx.nn as nn

class MyMLP(nn.Module):
    def __init__(self, in_dims: int, out_dims: int, hidden_dims: int = 16):
        super().__init__()

        self.in_proj = nn.Linear(in_dims, hidden_dims)
        self.out_proj = nn.Linear(hidden_dims, out_dims)

    def __call__(self, x):
        x = self.in_proj(x)
        x = mx.maximum(x, 0)
        return self.out_proj(x)

model = MyMLP(2, 1)

# All the model parameters are created but since MLX is lazy by
# default, they are not evaluated yet. Calling `mx.eval` actually
# allocates memory and initializes the parameters.
mx.eval(model.parameters())

# Setting a parameter to a new value is as simply as accessing that
# parameter and assigning a new array to it.
model.in_proj.weight = model.in_proj.weight * 2
mx.eval(model.parameters())<29>h]<5D>hX4import mlx.core as mx
import mlx.nn as nn

class MyMLP(nn.Module):
    def __init__(self, in_dims: int, out_dims: int, hidden_dims: int = 16):
        super().__init__()

        self.in_proj = nn.Linear(in_dims, hidden_dims)
        self.out_proj = nn.Linear(hidden_dims, out_dims)

    def __call__(self, x):
        x = self.in_proj(x)
        x = mx.maximum(x, 0)
        return self.out_proj(x)

model = MyMLP(2, 1)

# All the model parameters are created but since MLX is lazy by
# default, they are not evaluated yet. Calling `mx.eval` actually
# allocates memory and initializes the parameters.
mx.eval(model.parameters())

# Setting a parameter to a new value is as simply as accessing that
# parameter and assigning a new array to it.
model.in_proj.weight = model.in_proj.weight * 2
mx.eval(model.parameters())<29><><EFBFBD><EFBFBD><EFBFBD>}<7D>hj	sbah}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D>hjhk<68>force<63><65><EFBFBD>language<67><65>python<6F><6E>highlight_args<67>}<7D>uh+jhh<>hKhh<>hhubh	<09>rubric<69><63><EFBFBD>)<29><>}<7D>(h<05>
Attributes<EFBFBD>h]<5D>h<16>
Attributes<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>}<7D>(hjhhhNhNubah}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D>uh+jhh<>hhhh,hK	ubh<00>tabular_col_spec<65><63><EFBFBD>)<29><>}<7D>(hhh]<5D>h}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D><>spec<65><63>\X{1}{2}\X{1}{2}<7D>uh+j,hh<>hhh<1D>J/Users/awnihannun/repos/mlx/docs/src/python/nn/module.rst:14:<autosummary><3E>hNub<75>sphinx.ext.autosummary<72><79>autosummary_table<6C><65><EFBFBD>)<29><>}<7D>(h<05>I



Module.training

Boolean indicating if the model is in training mode.<2E>h]<5D>h	<09>table<6C><65><EFBFBD>)<29><>}<7D>(hhh]<5D>h	<09>tgroup<75><70><EFBFBD>)<29><>}<7D>(hhh]<5D>(h	<09>colspec<65><63><EFBFBD>)<29><>}<7D>(hhh]<5D>h}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D><>colwidth<74>K
uh+jKhjHubjL)<29><>}<7D>(hhh]<5D>h}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D><>colwidth<74>KZuh+jKhjHubh	<09>tbody<64><79><EFBFBD>)<29><>}<7D>(hhh]<5D>h	<09>row<6F><77><EFBFBD>)<29><>}<7D>(hhh]<5D>(h	<09>entry<72><79><EFBFBD>)<29><>}<7D>(hhh]<5D>h<EFBFBD>)<29><>}<7D>(h<05>3:py:obj:`Module.training <mlx.nn.Module.training>`\<5C>h]<5D>(h<>)<29><>}<7D>(h<05>2:py:obj:`Module.training <mlx.nn.Module.training>`<60>h]<5D>h<EFBFBD>)<29><>}<7D>(hjvh]<5D>h<16>Module.training<6E><67><EFBFBD><EFBFBD><EFBFBD>}<7D>(hjxhhhNhNubah}<7D>(h!]<5D>h#]<5D>(hΌpy<70><79>py-obj<62>eh%]<5D>h']<5D>h)]<5D>uh+h<>hjtubah}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D><>refdoc<6F>hی	refdomain<69>j<EFBFBD><00>reftype<70><65>obj<62><6A>refexplicit<69><74><EFBFBD>refwarn<72><6E>h<EFBFBD>h<EFBFBD>h<EFBFBD>h<EFBFBD>h<EFBFBD><68>mlx.nn.Module.training<6E>uh+h<>h<1D>J/Users/awnihannun/repos/mlx/docs/src/python/nn/module.rst:14:<autosummary><3E>hK
hjpubhh<06><><EFBFBD><EFBFBD>}<7D>(hjphhhNhNubeh}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D>uh+h<>hj<>hK
hjmubah}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D>uh+jkhjhubjl)<29><>}<7D>(hhh]<5D>h<EFBFBD>)<29><>}<7D>(h<05>4Boolean indicating if the model is in training mode.<2E>h]<5D>h<16>4Boolean indicating if the model is in training mode.<2E><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hj<>hhhNhNubah}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D>uh+h<>hj9hK
hj<>ubah}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D>uh+jkhjhubeh}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D>uh+jfhjcubah}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D>uh+jahjHubeh}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D><>cols<6C>Kuh+jFhjCubah}<7D>(h!]<5D>h#]<5D><>autosummary longtable<6C>ah%]<5D>h']<5D>h)]<5D>uh+jAhj=ubah}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D>hjhkuh+j;hh<>hhhj9hNubj:<00>autosummary_toc<6F><63><EFBFBD>)<29><>}<7D>(hhh]<5D>h<00>toctree<65><65><EFBFBD>)<29><>}<7D>(hhh]<5D>h}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D><>includefiles<65>]<5D><>-python/nn/_autosummary/mlx.nn.Module.training<6E>a<EFBFBD>entries<65>]<5D>Nj<4E><00><>a<EFBFBD>maxdepth<74>J<EFBFBD><4A><EFBFBD><EFBFBD><EFBFBD>glob<6F>N<EFBFBD>caption<6F>N<EFBFBD>
rawentries<EFBFBD>]<5D>uh+j<>hj<>ubah}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D>hjhkuh+j<>hh<>hhhj9hNubj)<29><>}<7D>(h<05>Methods<64>h]<5D>h<16>Methods<64><73><EFBFBD><EFBFBD><EFBFBD>}<7D>(hj<>hhhNhNubah}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D>uh+jhh<>hhhh,hKubj-)<29><>}<7D>(hhh]<5D>h}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D>j7j8uh+j,hh<>hhh<1D>J/Users/awnihannun/repos/mlx/docs/src/python/nn/module.rst:36:<autosummary><3E>hNubj<)<29><>}<7D>(hX<>



Module.apply(map_fn[, filter_fn])

Map all the parameters using the provided map_fn and immediately update the module with the mapped parameters.

Module.apply_to_modules(apply_fn)

Apply a function to all the modules in this instance (including this instance).

Module.children()

Return the direct descendants of this Module instance.

Module.eval()

Set the model to evaluation mode.

Module.filter_and_map(filter_fn[, map_fn, ...])

Recursively filter the contents of the module using filter_fn, namely only select keys and values where filter_fn returns true.

Module.freeze(*[, recurse, keys, strict])

Freeze the Module's parameters or some of them.

Module.leaf_modules()

Return the submodules that do not contain other modules.

Module.load_weights(file_or_weights[, strict])

Update the model's weights from a .npz or a list.

Module.modules()

Return a list with all the modules in this instance.

Module.named_modules()

Return a list with all the modules in this instance and their name with dot notation.

Module.parameters()

Recursively return all the mlx.core.array members of this Module as a dict of dicts and lists.

Module.save_weights(file)

Save the model's weights to a .npz file.

Module.train([mode])

Set the model in or out of training mode.

Module.trainable_parameters()

Recursively return all the non frozen mlx.core.array members of this Module as a dict of dicts and lists.

Module.unfreeze(*[, recurse, keys, strict])

Unfreeze the Module's parameters or some of them.

Module.update(parameters)

Replace the parameters of this Module with the provided ones in the dict of dicts and lists.

Module.update_modules(modules)

Replace the child modules of this Module instance with the provided ones in the dict of dicts and lists.<2E>h]<5D>jB)<29><>}<7D>(hhh]<5D>jG)<29><>}<7D>(hhh]<5D>(jL)<29><>}<7D>(hhh]<5D>h}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D><>colwidth<74>K
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