Struct Param 

pub struct Param<T: Parameter> {
    pub id: ParamId,
    /* private fields */
}

Parameters are the fundamental building blocks of modules where they serve as containers for tensors that can be updated during training, and loaded during inference. If you don’t want to save the tensors and/or don’t want to update it during training, you don’t need this type to wrap your tensor.

Core Lazy Initialization Architecture

Param<T> has a dual-state design using OnceCell<T>:

State Management

Two possible states:

1. Initialized: state: OnceCell<T> contains value, initialization: None
2. Uninitialized (Lazy): state is empty, initialization: Some(RwLock<Option<Uninitialized<T>>>)

Fields

id: ParamId

The unique ID of this parameter. This is used by eg. optimizers to associate a gradient with a specific parameter.

Implementations

impl<T: Parameter> Param<T>

pub fn initialized(id: ParamId, value: T) -> Self

Create a new parameter that is already initialized.

pub fn uninitialized<F>( id: ParamId, init: F, device: T::Device, is_require_grad: bool, shape: Shape, ) -> Self

where F: FnOnce(&T::Device, bool) -> T + Send + 'static,

Create a new parameter that is not already initialized.

pub fn val(&self) -> T

Gets the parameter value, initializing it lazily if needed.

For initialized parameters, this returns a clone of the cached value. For uninitialized parameters, this triggers initialization:

pub fn is_initialized(&self) -> bool

Check if the parameter has been initialized.

Returns true if the parameter’s value has been computed and cached, false if it’s still lazy and will be initialized on first access.

pub fn into_value(self) -> T

Gets the parameter’s value while consuming the parameter.

pub fn consume(self) -> (ParamId, T, ParamMapper<T>)

Gets the parameter id and value while consuming the parameter.

pub fn map<F: FnOnce(T) -> T>(self, func: F) -> Self

Execute the given function on the inner value.

pub fn from_mapped_value( id: ParamId, value: T, param_mapper: ParamMapper<T>, ) -> Self

Create an initialized parameter with the given id, value, and param mapper.

This is a helper method for creating parameters while preserving the param mapper, typically used in ModuleMapper implementations.

pub fn load_mapper<F: Fn(T) -> T + Send + Sync + 'static>(self, func: F) -> Self

Runs a transformation on the parameter when loading.

pub fn save_mapper<F: Fn(T) -> T + Send + Sync + 'static>(self, func: F) -> Self

Runs a transformation on the parameter when saving.

pub fn init_mapper<F: FnOnce(T) -> T + Send + 'static>(self, func: F) -> Self

where T: 'static,

Execute the given function on the inner value.

pub fn lazy_device(&self) -> T::Device

The device on which the parameter is or will be initialized, without triggering initialization.

This is critical for the load optimization: when loading tensors into an uninitialized parameter, we need to know the target device to move the loaded tensor appropriately, but we don’t want to trigger the initialization function (which would allocate an unnecessary tensor).

Use this instead of crate::tensor::Tensor::device when you need the device but want to preserve lazy initialization.

pub fn set_require_grad(self, require_grad: bool) -> Self

Override the gradient requirement for the current parameter.

impl<B: Backend, const D: usize> Param<Tensor<B, D>>

pub fn from_tensor(value: Tensor<B, D>) -> Self

Create a new parameter from a float tensor.

Warnings

We strongly recommend using Param::uninitialized if you are using this method to initialize parameters inside a module, since the tensor initialization will be lazy, making the loading of weights more performant.

pub fn lazy_shape(&self) -> Shape

The shape of the parameter, without triggering initialization.

This is critical for shape validation during loading: when applying tensors to an uninitialized parameter, we need to validate the shape without triggering the initialization function (which would allocate an unnecessary tensor).

Use this instead of crate::tensor::Tensor::shape when you need the shape but want to preserve lazy initialization.

pub fn from_data<T>(data: T, device: &B::Device) -> Self

where T: Into<TensorData>,

Create a new parameter from data.

pub fn transform_for_load(self, tensor: Tensor<B, D>, param_id: ParamId) -> Self

Transform a parameter for loading by applying load transformations.

This method is used to restore a parameter from a tensor (typically during deserialization). It ensures the tensor is moved to the expected device, applies the param mapper’s on_load transformation, and preserves the autodiff settings (require_grad).

pub fn transform_for_save(&self) -> Self

Transform a parameter for saving by applying save transformations.

This method is used to prepare a parameter for saving (typically during serialization). It applies the param mapper’s on_save transformation, which can be used to modify the tensor before serialization (e.g., quantization, precision conversion).

impl<B: Backend, const D: usize> Param<Tensor<B, D, Int>>

pub fn lazy_shape(&self) -> Shape

The shape of the parameter, without triggering initialization.

This is critical for shape validation during loading: when applying tensors to an uninitialized parameter, we need to validate the shape without triggering the initialization function (which would allocate an unnecessary tensor).

Use this instead of crate::tensor::Tensor::shape when you need the shape but want to preserve lazy initialization.

pub fn transform_for_load( self, tensor: Tensor<B, D, Int>, param_id: ParamId, ) -> Self

Transform a parameter for loading by applying load transformations.

This method is used to restore a parameter from a tensor (typically during deserialization). It ensures the tensor is moved to the expected device and applies the param mapper’s on_load transformation.

pub fn transform_for_save(&self) -> Self

Transform a parameter for saving by applying save transformations.

This method is used to prepare a parameter for saving (typically during serialization). It applies the param mapper’s on_save transformation, which can be used to modify the tensor before serialization (e.g., quantization, precision conversion).

impl<B: Backend, const D: usize> Param<Tensor<B, D, Bool>>

pub fn lazy_shape(&self) -> Shape

The shape of the parameter, without triggering initialization.

This is critical for shape validation during loading: when applying tensors to an uninitialized parameter, we need to validate the shape without triggering the initialization function (which would allocate an unnecessary tensor).

Returns:

• For uninitialized params: the shape from the Uninitialized struct
• For initialized params: the actual shape from the tensor

Use this instead of crate::tensor::Tensor::shape when you need the shape but want to preserve lazy initialization.

pub fn transform_for_load( self, tensor: Tensor<B, D, Bool>, param_id: ParamId, ) -> Self

Transform a parameter for loading by applying load transformations.

This method is used to restore a parameter from a tensor (typically during deserialization). It ensures the tensor is moved to the expected device and applies the param mapper’s on_load transformation.

pub fn transform_for_save(&self) -> Self

Transform a parameter for saving by applying save transformations.

This method is used to prepare a parameter for saving (typically during serialization). It applies the param mapper’s on_save transformation, which can be used to modify the tensor before serialization (e.g., quantization, precision conversion).

Trait Implementations

impl<const D: usize, B: AutodiffBackend> AutodiffModule<B> for Param<Tensor<B, D>>

type InnerModule = Param<Tensor<<B as AutodiffBackend>::InnerBackend, D>>

Inner module without auto-differentiation.

fn valid(&self) -> Self::InnerModule

Get the same module, but on the inner backend without auto-differentiation.

impl<const D: usize, B: AutodiffBackend> AutodiffModule<B> for Param<Tensor<B, D, Bool>>

type InnerModule = Param<Tensor<<B as AutodiffBackend>::InnerBackend, D, Bool>>

Inner module without auto-differentiation.

fn valid(&self) -> Self::InnerModule

Get the same module, but on the inner backend without auto-differentiation.

impl<const D: usize, B: AutodiffBackend> AutodiffModule<B> for Param<Tensor<B, D, Int>>

type InnerModule = Param<Tensor<<B as AutodiffBackend>::InnerBackend, D, Int>>

Inner module without auto-differentiation.

fn valid(&self) -> Self::InnerModule

Get the same module, but on the inner backend without auto-differentiation.

impl<T: Parameter> Clone for Param<T>

fn clone(&self) -> Self

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<T: Parameter> Debug for Param<T>

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

Formats the value using the given formatter.

impl<T: Parameter> Deref for Param<T>

type Target = T

The resulting type after dereferencing.

fn deref(&self) -> &Self::Target

Dereferences the value.

impl<T: Parameter> Display for Param<T>

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

Formats the value using the given formatter.

impl<const D: usize, B: Backend> Module<B> for Param<Tensor<B, D>>

type Record = Param<Tensor<B, D>>

Type to save and load the module.

fn visit<V: ModuleVisitor<B>>(&self, visitor: &mut V)

Visit each tensor parameter in the module with a visitor.

fn map<M: ModuleMapper<B>>(self, mapper: &mut M) -> Self

Map each tensor parameter in the module with a mapper.

fn into_record(self) -> Self::Record

Convert the module into a record containing the state.

fn load_record(self, record: Self::Record) -> Self

Load the module state from a record.

fn to_device(self, device: &Device<B>) -> Self

Move the module and all of its sub-modules to the given device.

fn fork(self, device: &Device<B>) -> Self

Fork the module and all of its sub-modules to the given device.

fn collect_devices(&self, devices: Vec<Device<B>>) -> Vec<Device<B>>

Return all the devices found in the underneath module tree added to the given vector without duplicates.

fn devices(&self) -> Devices<B>

Return all the devices found in the underneath module tree without duplicates.

fn no_grad(self) -> Self

Each tensor in the module tree will not require grad.

fn num_params(&self) -> usize

Get the number of parameters the module has, including all of its sub-modules.

fn save_file<FR, PB>( self, file_path: PB, recorder: &FR, ) -> Result<(), RecorderError>

where FR: FileRecorder<B>, PB: Into<PathBuf>,

Available on crate feature std only.

Save the module to a file using the provided file recorder.

fn load_file<FR, PB>( self, file_path: PB, recorder: &FR, device: &B::Device, ) -> Result<Self, RecorderError>

where FR: FileRecorder<B>, PB: Into<PathBuf>,

Available on crate feature std only.

Load the module from a file using the provided file recorder.

fn quantize_weights(self, quantizer: &mut Quantizer) -> Self

Quantize the weights of the module.

impl<const D: usize, B: Backend> Module<B> for Param<Tensor<B, D, Bool>>

type Record = Param<Tensor<B, D, Bool>>

Type to save and load the module.

fn visit<V: ModuleVisitor<B>>(&self, visitor: &mut V)

Visit each tensor parameter in the module with a visitor.

fn map<M: ModuleMapper<B>>(self, mapper: &mut M) -> Self

Map each tensor parameter in the module with a mapper.

fn into_record(self) -> Self::Record

Convert the module into a record containing the state.

fn load_record(self, record: Self::Record) -> Self

Load the module state from a record.

fn to_device(self, device: &Device<B>) -> Self

Move the module and all of its sub-modules to the given device.

fn fork(self, device: &Device<B>) -> Self

Fork the module and all of its sub-modules to the given device.

fn collect_devices(&self, devices: Vec<Device<B>>) -> Vec<Device<B>>

Return all the devices found in the underneath module tree added to the given vector without duplicates.

fn devices(&self) -> Devices<B>

Return all the devices found in the underneath module tree without duplicates.

fn no_grad(self) -> Self

Each tensor in the module tree will not require grad.

fn num_params(&self) -> usize

Get the number of parameters the module has, including all of its sub-modules.

fn save_file<FR, PB>( self, file_path: PB, recorder: &FR, ) -> Result<(), RecorderError>

where FR: FileRecorder<B>, PB: Into<PathBuf>,

Available on crate feature std only.

Save the module to a file using the provided file recorder.

fn load_file<FR, PB>( self, file_path: PB, recorder: &FR, device: &B::Device, ) -> Result<Self, RecorderError>

where FR: FileRecorder<B>, PB: Into<PathBuf>,

Available on crate feature std only.

Load the module from a file using the provided file recorder.

fn quantize_weights(self, quantizer: &mut Quantizer) -> Self

Quantize the weights of the module.

impl<const D: usize, B: Backend> Module<B> for Param<Tensor<B, D, Int>>

type Record = Param<Tensor<B, D, Int>>

Type to save and load the module.

fn visit<V: ModuleVisitor<B>>(&self, visitor: &mut V)

Visit each tensor parameter in the module with a visitor.

fn map<M: ModuleMapper<B>>(self, mapper: &mut M) -> Self

Map each tensor parameter in the module with a mapper.

fn into_record(self) -> Self::Record

Convert the module into a record containing the state.

fn load_record(self, record: Self::Record) -> Self

Load the module state from a record.

fn to_device(self, device: &Device<B>) -> Self

Move the module and all of its sub-modules to the given device.

fn fork(self, device: &Device<B>) -> Self

Fork the module and all of its sub-modules to the given device.

fn collect_devices(&self, devices: Vec<Device<B>>) -> Vec<Device<B>>

Return all the devices found in the underneath module tree added to the given vector without duplicates.

fn devices(&self) -> Devices<B>

Return all the devices found in the underneath module tree without duplicates.

fn no_grad(self) -> Self

Each tensor in the module tree will not require grad.

fn num_params(&self) -> usize

Get the number of parameters the module has, including all of its sub-modules.

fn save_file<FR, PB>( self, file_path: PB, recorder: &FR, ) -> Result<(), RecorderError>

where FR: FileRecorder<B>, PB: Into<PathBuf>,

Available on crate feature std only.

Save the module to a file using the provided file recorder.

fn load_file<FR, PB>( self, file_path: PB, recorder: &FR, device: &B::Device, ) -> Result<Self, RecorderError>

where FR: FileRecorder<B>, PB: Into<PathBuf>,

Available on crate feature std only.

Load the module from a file using the provided file recorder.

fn quantize_weights(self, quantizer: &mut Quantizer) -> Self

Quantize the weights of the module.

impl<const D: usize, B: Backend> ModuleDisplay for Param<Tensor<B, D>>

fn format(&self, passed_settings: DisplaySettings) -> String

Formats the module with provided display settings.

fn custom_settings(&self) -> Option<DisplaySettings>

Custom display settings for the module.

fn custom_content(&self, _content: Content) -> Option<Content>

Custom attributes for the module.

impl<const D: usize, B: Backend> ModuleDisplay for Param<Tensor<B, D, Bool>>

fn format(&self, passed_settings: DisplaySettings) -> String

Formats the module with provided display settings.

fn custom_settings(&self) -> Option<DisplaySettings>

Custom display settings for the module.

fn custom_content(&self, _content: Content) -> Option<Content>

Custom attributes for the module.

impl<const D: usize, B: Backend> ModuleDisplay for Param<Tensor<B, D, Int>>

fn format(&self, passed_settings: DisplaySettings) -> String

Formats the module with provided display settings.

fn custom_settings(&self) -> Option<DisplaySettings>

Custom display settings for the module.

fn custom_content(&self, _content: Content) -> Option<Content>

Custom attributes for the module.

impl<const D: usize, B: Backend> ModuleDisplayDefault for Param<Tensor<B, D>>

fn content(&self, content: Content) -> Option<Content>

Attributes of the module used for display purposes.

fn num_params(&self) -> usize

Gets the number of the parameters of the module.

impl<const D: usize, B: Backend> ModuleDisplayDefault for Param<Tensor<B, D, Bool>>

fn content(&self, content: Content) -> Option<Content>

Attributes of the module used for display purposes.

fn num_params(&self) -> usize

Gets the number of the parameters of the module.

impl<const D: usize, B: Backend> ModuleDisplayDefault for Param<Tensor<B, D, Int>>

fn content(&self, content: Content) -> Option<Content>

Attributes of the module used for display purposes.

fn num_params(&self) -> usize

Gets the number of the parameters of the module.

impl<B, const D: usize> Record<B> for Param<Tensor<B, D>>

where B: Backend,

type Item<S: PrecisionSettings> = ParamSerde<FloatTensorSerde<S>>

Type of the item that can be serialized and deserialized.

fn into_item<S: PrecisionSettings>(self) -> Self::Item<S>

Convert the current record into the corresponding item that follows the given settings.

fn from_item<S: PrecisionSettings>( item: Self::Item<S>, device: &B::Device, ) -> Self

Convert the given item into a record.

impl<B, const D: usize> Record<B> for Param<Tensor<B, D, Bool>>

where B: Backend,

type Item<S: PrecisionSettings> = ParamSerde<BoolTensorSerde>

Type of the item that can be serialized and deserialized.

fn into_item<S: PrecisionSettings>(self) -> Self::Item<S>

Convert the current record into the corresponding item that follows the given settings.

fn from_item<S: PrecisionSettings>( item: Self::Item<S>, device: &B::Device, ) -> Self

Convert the given item into a record.

impl<B, const D: usize> Record<B> for Param<Tensor<B, D, Int>>

where B: Backend,

type Item<S: PrecisionSettings> = ParamSerde<IntTensorSerde<S>>

Type of the item that can be serialized and deserialized.

fn into_item<S: PrecisionSettings>(self) -> Self::Item<S>

Convert the current record into the corresponding item that follows the given settings.

fn from_item<S: PrecisionSettings>( item: Self::Item<S>, device: &B::Device, ) -> Self

Convert the given item into a record.