Struct ModuleList 

pub struct ModuleList<T: Float> { /* private fields */ }

An ordered list of modules, registered for parameter tracking.

Unlike Sequential, ModuleList does not define a forward pass. Users iterate over the list manually and call each module’s forward() as needed. This mirrors PyTorch’s nn.ModuleList.

Named parameters

Parameters are prefixed by list index: "0.weight", "1.weight", etc.

Examples

fn example(input: &Tensor<f32>) -> Result<Tensor<f32>, FerrotorchError> {
    let list = ModuleList::<f32>::new(vec![
        Box::new(Linear::<f32>::new(10, 10, true)?),
        Box::new(Linear::<f32>::new(10, 10, true)?),
    ]);

    let mut x = input.clone();
    for i in 0..list.len() {
        let module = list.get(i).ok_or_else(|| FerrotorchError::InvalidArgument {
            message: format!("ModuleList index {i} out of bounds"),
        })?;
        x = module.forward(&x)?;
    }
    Ok(x)
}

Implementations

impl<T: Float> ModuleList<T>

pub fn new(modules: Vec<Box<dyn Module<T>>>) -> Self

Create a new module list from a vector of modules.

pub fn empty() -> Self

Create an empty module list.

pub fn get(&self, index: usize) -> Option<&dyn Module<T>>

Get a reference to the module at the given index.

pub fn get_mut(&mut self, index: usize) -> Option<&mut dyn Module<T>>

Get a mutable reference to the module at the given index.

pub fn push(&mut self, module: Box<dyn Module<T>>)

Append a module to the end of the list.

pub fn len(&self) -> usize

Number of modules.

pub fn is_empty(&self) -> bool

Whether the list is empty.

Trait Implementations

impl<T: Float> Display for ModuleList<T>

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<T: Float> Module<T> for ModuleList<T>

fn forward(&self, _input: &Tensor<T>) -> FerrotorchResult<Tensor<T>>

ModuleList does not implement forward.

Users should iterate manually and call each module’s forward().

fn parameters(&self) -> Vec<&Parameter<T>>

Iterate over all learnable parameters.

fn parameters_mut(&mut self) -> Vec<&mut Parameter<T>>

Iterate over all learnable parameters mutably.

fn named_parameters(&self) -> Vec<(String, &Parameter<T>)>

Named parameters for state dict serialization.

fn train(&mut self)

Set training mode. Affects dropout, batchnorm, etc.

fn eval(&mut self)

Set evaluation mode.

fn is_training(&self) -> bool

Whether the module is in training mode.

fn to_device(&mut self, device: Device) -> FerrotorchResult<()>

Move all parameters and buffers to a device.

fn state_dict(&self) -> StateDict<T>

Export parameters and buffers as a state dict (torch parity).

fn buffers(&self) -> Vec<&Buffer<T>>

Iterate over all non-trainable buffers (e.g. running mean / variance in BatchNorm). Default returns empty — concrete modules with buffers override.

fn buffers_mut(&mut self) -> Vec<&mut Buffer<T>>

Mutable iteration over all buffers. Default returns empty.

fn named_buffers(&self) -> Vec<(String, &Buffer<T>)>

Named buffers (dot-separated paths for nested modules). Default returns empty.

fn as_any(&self) -> Option<&dyn Any>

Downcast hook for type-erased buffer-loader dispatch. (#984)

fn children(&self) -> Vec<&dyn Module<T>>

Direct child modules. Default returns empty (leaf module).

fn named_children(&self) -> Vec<(String, &dyn Module<T>)>

Direct child modules with their attribute names. Default returns empty.

fn modules(&self) -> Vec<&dyn Module<T>>

where Self: Sized,

All modules in this subtree, depth-first (self first, then each child’s descendants in order).

fn descendants_dyn(&self) -> Vec<&dyn Module<T>>

All strict descendants of self in depth-first order. Object-safe.

fn named_modules(&self) -> Vec<(String, &dyn Module<T>)>

where Self: Sized,

All modules in this subtree with dot-separated path names. The root is named ""; children paths are joined with ..

fn named_descendants_dyn(&self) -> Vec<(String, &dyn Module<T>)>

Strict descendants with dot-paths. Object-safe.

fn with_forward_hook( self, hook: ForwardHook<T>, ) -> (HookedModule<Self, T>, HookHandle)

where Self: Sized,

Wrap this module in a HookedModule and register a forward hook. Returns the wrapper paired with a HookHandle that can be used to remove the hook later. The wrapper implements Module<T> itself, so it slots into any place the original module did. Mirrors torch.nn.Module.register_forward_hook.

fn with_forward_pre_hook( self, hook: ForwardPreHook<T>, ) -> (HookedModule<Self, T>, HookHandle)

where Self: Sized,

Wrap this module in a HookedModule and register a forward pre-hook. See Self::with_forward_hook. Mirrors torch.nn.Module.register_forward_pre_hook.

fn with_backward_hook( self, hook: BackwardHook<T>, ) -> (HookedModule<Self, T>, HookHandle)

where Self: Sized,

Wrap this module in a HookedModule and register a backward hook. See Self::with_forward_hook. Mirrors torch.nn.Module.register_backward_hook.

fn zero_grad(&self) -> FerrotorchResult<()>

Set the gradient of every parameter to None.

fn requires_grad_(&mut self, requires_grad: bool)

Toggle requires_grad on every parameter (freeze / unfreeze the module). Mirrors torch.nn.Module.requires_grad_.

fn apply_to_parameters(&mut self, f: &mut dyn FnMut(&mut Parameter<T>))

Apply a function to every parameter in this module. Mirrors torch.nn.Module.apply for the parameter case (true apply recurses over all submodules; the recursive form requires &mut dyn Module which conflicts with this trait’s &mut self borrow).

fn load_state_dict( &mut self, state: &StateDict<T>, strict: bool, ) -> FerrotorchResult<()>

Load parameters from a state dict.