Struct SparseEmbedding 

pub struct SparseEmbedding { /* private fields */ }

Sparse embedding layer

This layer implements an embedding lookup table using sparse matrices, which can significantly reduce memory usage for large vocabularies with sparse word distributions.

Implementations

impl SparseEmbedding

pub fn new( vocab_size: usize, embedding_dim: usize, sparsity: f32, ) -> TorshResult<Self>

Create a new sparse embedding layer

pub fn with_config( vocab_size: usize, embedding_dim: usize, sparsity: f32, config: SparseLayerConfig, ) -> TorshResult<Self>

Create sparse embedding layer with configuration

pub fn from_weight(weight: CsrTensor) -> TorshResult<Self>

Create from existing sparse weight matrix

pub fn forward(&self, indices: &[usize]) -> TorshResult<Tensor>

Forward pass - lookup embeddings for given indices

pub fn forward_tensor(&self, indices: &Tensor) -> TorshResult<Tensor>

Forward pass with tensor input

pub fn get_embedding(&self, index: usize) -> TorshResult<Tensor>

Get embedding for a single index

pub fn weight(&self) -> &CsrTensor

Get weight matrix

pub fn weight_mut(&mut self) -> &mut CsrTensor

Get mutable weight matrix

pub fn vocab_size(&self) -> usize

Get vocabulary size

pub fn embedding_dim(&self) -> usize

Get embedding dimension

pub fn sparsity(&self) -> f32

Get current sparsity level

pub fn num_parameters(&self) -> usize

Get number of parameters (non-zero weights)

pub fn prune_to_sparsity(&mut self, target_sparsity: f32) -> TorshResult<()>

Update sparsity level by pruning weights

pub fn config(&self) -> &SparseLayerConfig

Get configuration

pub fn output_dimensions(&self, input_dims: &[usize]) -> Vec<usize>

Calculate output dimensions for given input dimensions

impl SparseEmbedding

pub fn memory_stats(&self) -> SparseEmbeddingStats

Get memory usage statistics

Trait Implementations

impl SparseLayer for SparseEmbedding

fn forward( &self, input: &dyn SparseTensor, ) -> TorshResult<Box<dyn SparseTensor>>

Forward pass through the layer

fn parameters(&self) -> Vec<&CsrTensor>

Get layer parameters in sparse format

fn parameters_mut(&mut self) -> Vec<&mut CsrTensor>

Get mutable layer parameters

fn layer_type(&self) -> &'static str

Get layer name/type

fn dimensions(&self) -> (Vec<usize>, Vec<usize>)

Get input and output dimensions

fn sparsity_stats(&self) -> SparseStats

Get sparsity statistics

fn train(&mut self, training: bool)

Set training mode

fn training(&self) -> bool

Check if layer is in training mode