Struct MicroAdam 

pub struct MicroAdam { /* private fields */ }

MicroAdam optimizer implementation

Provides memory-efficient Adam optimization through compressed gradient storage while maintaining convergence guarantees through careful state management.

Implementations

impl MicroAdam

pub fn new() -> Self

Create a new MicroAdam optimizer with default configuration

pub fn new_with_lr(learning_rate: f32) -> Self

Create MicroAdam with custom learning rate

pub fn for_large_models() -> Self

Create MicroAdam for large language models with optimized compression

pub fn for_memory_constrained() -> Self

Create MicroAdam for memory-constrained environments

pub fn with_config(config: MicroAdamConfig) -> Self

Create MicroAdam with custom configuration

pub fn memory_savings_ratio(&self) -> f32

Get memory savings compared to standard Adam

pub fn compression_statistics(&self) -> String

Get compression statistics

Trait Implementations

impl Debug for MicroAdam

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

Formats the value using the given formatter.

impl Default for MicroAdam

fn default() -> Self

Returns the “default value” for a type.

impl Optimizer for MicroAdam

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

Updates a parameter based on its gradient.

fn zero_grad(&mut self)

Clears all accumulated gradients.

fn step(&mut self)

Performs a single optimization step.

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 MicroAdam

type Config = MicroAdamConfig

The configuration type for this optimizer.

type State = OptimizerState

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.