Struct Prodigy 

pub struct Prodigy { /* private fields */ }

Prodigy optimizer with adaptive learning rate estimation.

Implementations

impl Prodigy

pub fn new() -> Self

Create a new Prodigy optimizer with default configuration.

pub fn with_config(config: ProdigyConfig) -> Self

Create Prodigy optimizer with custom configuration.

pub fn for_language_models() -> Self

Create Prodigy optimizer optimized for language models.

pub fn for_vision() -> Self

Create Prodigy optimizer optimized for computer vision.

pub fn for_fast_training() -> Self

Create Prodigy optimizer for fast training.

pub fn for_stable_training() -> Self

Create Prodigy optimizer for stable training.

pub fn get_lr(&self) -> f64

Get current global learning rate estimate.

pub fn set_lr(&mut self, distance: f64)

Set global distance estimate (equivalent to learning rate).

pub fn reset(&mut self)

Reset optimizer state.

pub fn memory_usage(&self) -> ProdigyMemoryStats

Get memory usage statistics.

pub fn update_parameter( &mut self, param_name: &str, param: &mut Tensor, grad: &Tensor, ) -> Result<()>

Updates a named parameter with its gradient.

Trait Implementations

impl Default for Prodigy

fn default() -> Self

Returns the “default value” for a type.

impl Optimizer for Prodigy

fn step(&mut self)

Performs a single optimization step.

fn zero_grad(&mut self)

Clears all accumulated gradients.

fn update(&mut self, param: &mut Tensor, grad: &Tensor) -> Result<()>

Updates a parameter based on its gradient.

fn get_lr(&self) -> f32

Gets the current learning rate.

fn set_lr(&mut self, lr: f32)

Sets a new learning rate.

fn accumulate_grad( &mut self, parameter: &mut Tensor, grad: &Tensor, ) -> Result<(), TrustformersError>

Accumulates gradients for gradient accumulation.

fn apply_accumulated_grads( &mut self, accumulation_steps: usize, ) -> Result<(), TrustformersError>

Applies accumulated gradients after gradient accumulation.

impl StatefulOptimizer for Prodigy

type Config = ProdigyConfig

The configuration type for this optimizer.

type State = ProdigyOptimizerState

The state type used by this optimizer.

fn config(&self) -> &Self::Config

Gets a reference to the optimizer’s configuration.

fn state(&self) -> &Self::State

Gets a reference to the optimizer’s internal state.

fn state_mut(&mut self) -> &mut Self::State

Gets a mutable reference to the optimizer’s internal state.

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

Saves the optimizer state to a dictionary for checkpointing.

fn load_state_dict(&mut self, state_dict: HashMap<String, Tensor>) -> Result<()>

Loads optimizer state from a dictionary during checkpoint restoration.

fn memory_usage(&self) -> StateMemoryStats

Gets memory usage statistics for this optimizer.

fn reset_state(&mut self)

Resets the optimizer state (useful for training restarts).

fn num_parameters(&self) -> usize

Returns the number of parameters being optimized.