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.typings/mlx/nn/layers/base.pyi
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"""
This type stub file was generated by pyright.
"""
from typing import Any, Callable, List, Optional, Tuple, Union
import mlx.core as mx
class Module(dict):
"""Base class for building neural networks with MLX.
All the layers provided in :mod:`layers` subclass this class and
your models should do the same.
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:`Module.parameters`.
In addition, the ``Module`` has the concept of trainable and non trainable
parameters (called "frozen"). When using :func:`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`.
.. code-block:: python
import 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())
"""
def __call__(self, *args: Any, **kwargs: Any) -> mx.array: ...
def __init__(self) -> None:
"""Should be called by the subclasses of ``Module``."""
def __getitem__(self, key: str) -> mx.array | Module: ...
def get(
self, key: str, default: mx.array | Module | None = ...
) -> mx.array | Module | None: ...
@property
def training(self): # -> bool:
"""Boolean indicating if the model is in training mode."""
@property
def state(self): # -> Self:
"""The module's state dictionary
The module's state dictionary contains any attribute set on the
module including parameters in :meth:`Module.parameters`
Unlike :meth:`Module.parameters`, the :attr:`Module.state` property is
a reference to the module's state. Updates to it will be reflected in
the original module.
"""
def __repr__(self): # -> str:
...
def __getattr__(self, key: str): # -> None:
...
def __setattr__(self, key: str, val: Any): # -> None:
...
def __delattr__(self, name): # -> None:
...
def load_weights(
self,
file_or_weights: Union[str, List[Tuple[str, mx.array]]],
strict: bool = ...,
) -> Module:
"""
Update the model's weights from a ``.npz``, a ``.safetensors`` file, or a list.
Args:
file_or_weights (str or list(tuple(str, mx.array))): The path to
the weights ``.npz`` file (``.npz`` or ``.safetensors``) or a list
of pairs of parameter names and arrays.
strict (bool, optional): If ``True`` then checks that the provided
weights exactly match the parameters of the model. Otherwise,
only the weights actually contained in the model are loaded and
shapes are not checked. Default: ``True``.
Returns:
The module instance after updating the weights.
Example:
.. code-block:: python
import mlx.core as mx
import mlx.nn as nn
model = nn.Linear(10, 10)
# Load from file
model.load_weights("weights.npz")
# Load from .safetensors file
model.load_weights("weights.safetensors")
# Load from list
weights = [
("weight", mx.random.uniform(shape=(10, 10))),
("bias", mx.zeros((10,))),
]
model.load_weights(weights)
# Missing weight
weights = [
("weight", mx.random.uniform(shape=(10, 10))),
]
# Raises a ValueError exception
model.load_weights(weights)
# Ok, only updates the weight but not the bias
model.load_weights(weights, strict=False)
"""
def save_weights(self, file: str): # -> None:
"""
Save the model's weights to a file. The saving method is determined by the file extension:
- ``.npz`` will use :func:`mx.savez`
- ``.safetensors`` will use :func:`mx.save_safetensors`
"""
@staticmethod
def is_module(value): # -> bool:
...
@staticmethod
def valid_child_filter(module, key, value): # -> bool:
...
@staticmethod
def valid_parameter_filter(module, key, value): # -> bool:
...
@staticmethod
def trainable_parameter_filter(module, key, value): # -> bool:
...
def filter_and_map(
self,
filter_fn: Callable[[Module, str, Any], bool],
map_fn: Optional[Callable] = ...,
is_leaf_fn: Optional[Callable[[Module, str, Any], bool]] = ...,
): # -> dict[Any, Any | dict[Any, Any | dict[Any, Any] | list[Any]] | dict[Any, Any] | list[Any]]:
"""Recursively filter the contents of the module using ``filter_fn``,
namely only select keys and values where ``filter_fn`` returns true.
This is used to implement :meth:`parameters` and :meth:`trainable_parameters`
but it can also be used to extract any subset of the module's parameters.
Args:
filter_fn (Callable): Given a value, the key in which it is found
and the containing module, decide whether to keep the value or
drop it.
map_fn (Callable, optional): Optionally transform the value before
returning it.
is_leaf_fn (Callable, optional): Given a value, the key in which it
is found and the containing module decide if it is a leaf.
Returns:
A dictionary containing the contents of the module recursively filtered
"""
def parameters(
self,
) -> mx.MX_ARRAY_TREE:
"""Recursively return all the :class:`mlx.core.array` members of this Module
as a dict of dicts and lists."""
def trainable_parameters(
self,
) -> mx.MX_ARRAY_TREE: # -> dict[Any, Any | dict[Any, Any | dict[Any, Any] | list[Any]] | dict[Any, Any] | list[Any]]:
"""Recursively return all the non frozen :class:`mlx.core.array` members of
this Module as a dict of dicts and lists."""
def children(
self,
) -> mx.MX_ARRAY_TREE: # -> dict[Any, Any | dict[Any, Any | dict[Any, Any] | list[Any]] | dict[Any, Any] | list[Any]]:
"""Return the direct descendants of this Module instance."""
def leaf_modules(
self,
) -> mx.MX_ARRAY_TREE: # -> dict[Any, Any | dict[Any, Any | dict[Any, Any] | list[Any]] | dict[Any, Any] | list[Any]]:
"""Return the submodules that do not contain other modules."""
def update(self, parameters: dict[str, Any], strict: bool = ...) -> Module:
"""Replace the parameters of this Module with the provided ones in the
dict of dicts and lists.
Commonly used by the optimizer to change the model to the updated
(optimized) parameters. Also used by the :meth:`value_and_grad` to set the
tracers in the model in order to compute gradients.
The passed in parameters dictionary need not be a full dictionary
similar to :meth:`parameters`. Only the provided locations will be
updated.
Args:
parameters (dict): A complete or partial dictionary of the modules
parameters.
strict (bool): If ``True`` checks that ``parameters`` is a
subset of the module's parameters. Default: ``True``.
Returns:
The module instance after updating the parameters.
"""
def apply(
self,
map_fn: Callable[[mx.array], mx.array],
filter_fn: Optional[Callable[[Module, str, Any], bool]] = ...,
) -> Module:
"""Map all the parameters using the provided ``map_fn`` and immediately
update the module with the mapped parameters.
For instance running ``model.apply(lambda x: x.astype(mx.float16))``
casts all parameters to 16 bit floats.
Args:
map_fn (Callable): Maps an array to another array
filter_fn (Callable, optional): Filter to select which arrays to
map (default: :meth:`Module.valid_parameter_filter`).
Returns:
The module instance after updating the parameters.
"""
def update_modules(self, modules: dict, strict: bool = ...) -> Module:
"""Replace the child modules of this :class:`Module` instance with the
provided ones in the dict of dicts and lists.
It is the equivalent of :meth:`Module.update` but for modules instead
of parameters and allows us to flexibly edit complex architectures by
programmatically swapping layers.
The passed in parameters dictionary need not be a full dictionary
similar to :meth:`modules`. Only the provided locations will be
updated.
Args:
modules (dict): A complete or partial dictionary of the module's
submodules.
strict (bool): If ``True`` checks that ``modules`` is a
subset of the child modules of this instance. Default: ``True``.
Returns:
The module instance after updating the submodules.
"""
def apply_to_modules(self, apply_fn: Callable[[str, Module], Any]) -> Module:
"""Apply a function to all the modules in this instance (including this
instance).
Args:
apply_fn (Callable): The function to apply to the modules.
Returns:
The module instance after updating submodules.
"""
def modules(self): # -> list[Any]:
"""Return a list with all the modules in this instance.
Returns:
A list of :class:`Module` instances.
"""
def named_modules(self): # -> list[Any]:
"""Return a list with all the modules in this instance and their name
with dot notation.
Returns:
A list of tuples (str, :class:`Module`).
"""
def freeze(
self,
*,
recurse: bool = ...,
keys: Optional[Union[str, List[str]]] = ...,
strict: bool = ...,
) -> Module:
"""Freeze the Module's parameters or some of them. Freezing a parameter means not
computing gradients for it.
This function is idempotent i.e. freezing a frozen model is a no-op.
Example:
For instance to only train the attention parameters from a Transformer:
.. code-block:: python
model = nn.Transformer()
model.freeze()
model.apply_to_modules(lambda k, v: v.unfreeze() if k.endswith("attention") else None)
Args:
recurse (bool, optional): If True then freeze the parameters of the
submodules as well. Default: ``True``.
keys (str or list[str], optional): If provided then only these
parameters will be frozen otherwise all the parameters of a
module. For instance freeze all biases by calling
``module.freeze(keys="bias")``.
strict (bool, optional): If set to ``True`` validate that the passed keys exist.
Default: ``False``.
Returns:
The module instance after freezing the parameters.
"""
def unfreeze(
self,
*,
recurse: bool = ...,
keys: Optional[Union[str, List[str]]] = ...,
strict: bool = ...,
) -> Module:
"""Unfreeze the Module's parameters or some of them.
This function is idempotent ie unfreezing a model that is not frozen is
a noop.
Example:
For instance to only train the biases of a Transformer one can do:
.. code-block:: python
model = nn.Transformer()
model.freeze()
model.unfreeze(keys="bias")
Args:
recurse (bool, optional): If True then unfreeze the parameters of the
submodules as well. Default: ``True``.
keys (str or list[str], optional): If provided then only these
parameters will be unfrozen otherwise all the parameters of a
module. For instance unfreeze all biases by calling
``module.unfreeze(keys="bias")``.
strict (bool, optional): If set to ``True`` validate that the passed keys exist.
Default: ``False``.
Returns:
The module instance after unfreezing the parameters.
"""
def train(self, mode: bool = ...) -> Module:
"""Set the model in or out of training mode.
Training mode only applies to certain layers. For example
:obj:`Dropout` applies a random mask in training mode, but is the
identity in evaluation mode.
Args:
mode (bool): Indicate if the model should be in training or
evaluation mode. Default: ``True``.
Returns:
The module instance after updating the training mode.
"""
def eval(self) -> Module:
"""Set the model to evaluation mode.
See :func:`train`.
"""
def set_dtype(
self, dtype: mx.Dtype, predicate: Optional[Callable[[mx.Dtype], bool]] = ...
): # -> None:
"""Set the dtype of the module's parameters.
Args:
dtype (Dtype): The new dtype.
predicate (typing.Callable, optional): A predicate to select
parameters to cast. By default, only parameters of type
:attr:`floating` will be updated to avoid casting integer
parameters to the new dtype.
"""