Struct LoRAOptimizer 

pub struct LoRAOptimizer { /* private fields */ }

LoRA-specific optimizer that only updates adapter parameters.

Implementations

impl LoRAOptimizer

pub fn new( base_optimizer: Box<dyn OptimizerState>, config: LoRAConfig, learning_rate: f32, ) -> Self

Creates a new LoRA optimizer.

pub fn add_adapter( &mut self, module_name: &str, input_dim: usize, output_dim: usize, ) -> Result<()>

Add a LoRA adapter for a specific module.

pub fn remove_adapter(&mut self, module_name: &str) -> Option<LoRAAdapter>

Remove a LoRA adapter.

pub fn get_adapter(&self, module_name: &str) -> Option<&LoRAAdapter>

Get a reference to an adapter.

pub fn get_adapter_mut(&mut self, module_name: &str) -> Option<&mut LoRAAdapter>

Get a mutable reference to an adapter.

pub fn set_adapter_active( &mut self, module_name: &str, active: bool, ) -> Result<()>

Enable or disable an adapter.

pub fn set_all_adapters_active(&mut self, active: bool)

Enable or disable all adapters.

pub fn num_trainable_parameters(&self) -> usize

Get the total number of trainable parameters.

pub fn freeze_base_parameters(&mut self, parameters: HashMap<String, Tensor>)

Freeze base model parameters.

pub fn merge_adapters_into_base(&mut self) -> Result<()>

Merge all active adapters into their respective base weights.

pub fn save_adapters(&self) -> HashMap<String, (Tensor, Tensor, f32)>

Save adapter weights (for efficient storage/sharing).

pub fn load_adapters( &mut self, adapters: HashMap<String, (Tensor, Tensor, f32)>, ) -> Result<()>

Load adapter weights.

pub fn get_config(&self) -> &LoRAConfig

Get configuration.

Trait Implementations

impl Debug for LoRAOptimizer

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

Formats the value using the given formatter.

impl OptimizerState for LoRAOptimizer

fn zero_grad(&mut self) -> AnyhowResult<()>

Zero out gradients

fn step(&mut self, _parameters: &mut [Tensor]) -> AnyhowResult<()>

Perform optimization step

fn get_lr(&self) -> f32

Get current learning rate

fn set_lr(&mut self, lr: f32)

Set learning rate

fn state_dict(&self) -> AnyhowResult<HashMap<String, Tensor>>

Save optimizer state to dictionary

fn load_state_dict( &mut self, state: HashMap<String, Tensor>, ) -> AnyhowResult<()>

Load optimizer state from dictionary