Initial commit
This commit is contained in:
redclawsystems
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//! Beam search decoding with length normalization and diverse beam groups
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//!
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//! ## Performance Optimization
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//!
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//! This module uses sorted Vecs instead of BinaryHeap for beam storage.
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//! With typical beam widths of 5-20, linear insertion sort is faster than
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//! heap operations due to:
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//! - Better branch prediction (sequential comparisons)
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//! - Better cache locality (contiguous memory)
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//! - Fewer pointer chases (no tree traversal)
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use crate::{
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GenerationConfig, GenerationOutput, ModelInterface, NlgError, Result,
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generation::BeamSearchConfig,
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};
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use rtx_tensor::{Device, Tensor};
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use std::cmp::Ordering;
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/// Beam search state for a single sequence
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#[derive(Debug, Clone)]
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struct BeamState {
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/// Token sequence
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sequence: Vec<u32>,
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/// Cumulative log probability
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score: f32,
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/// Per-token scores
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token_scores: Vec<f32>,
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/// Past key-value cache
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past_key_values: Option<Vec<Tensor>>,
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/// Whether sequence is finished (EOS)
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finished: bool,
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/// Beam group ID for diverse beam search
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group_id: usize,
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}
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impl BeamState {
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fn new(initial_sequence: Vec<u32>, group_id: usize) -> Self {
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Self {
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sequence: initial_sequence,
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score: 0.0,
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token_scores: Vec::new(),
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past_key_values: None,
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finished: false,
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group_id,
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}
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}
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/// Calculate normalized score with length penalty
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fn normalized_score(&self, length_penalty: f32) -> f32 {
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let length = self.sequence.len() as f32;
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if length_penalty != 1.0 {
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self.score / length.powf(length_penalty)
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} else {
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self.score / length
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}
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}
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/// Add new token to beam
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fn extend_with_token(&self, token: u32, token_score: f32) -> Self {
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let mut new_sequence = self.sequence.clone();
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new_sequence.push(token);
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let mut new_token_scores = self.token_scores.clone();
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new_token_scores.push(token_score);
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Self {
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sequence: new_sequence,
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score: self.score + token_score,
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token_scores: new_token_scores,
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past_key_values: self.past_key_values.clone(),
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finished: false,
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group_id: self.group_id,
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}
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}
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/// Mark beam as finished
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fn finish(mut self) -> Self {
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self.finished = true;
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self
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}
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}
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impl PartialEq for BeamState {
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fn eq(&self, other: &Self) -> bool {
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(self.score - other.score).abs() < f32::EPSILON
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}
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}
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impl Eq for BeamState {}
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impl PartialOrd for BeamState {
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fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
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Some(self.cmp(other))
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}
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}
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impl Ord for BeamState {
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fn cmp(&self, other: &Self) -> Ordering {
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// For max-heap, reverse the comparison
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// Use total_cmp for valid Ord implementation (handles NaN deterministically)
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other.score.total_cmp(&self.score)
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}
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}
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/// Diverse beam search group for maintaining diversity
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///
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/// Uses sorted Vec instead of BinaryHeap for finished_beams storage.
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/// With typical beam widths (5-20), insertion sort into a sorted Vec
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/// has better branch prediction than heap operations.
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struct BeamGroup {
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id: usize,
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beams: Vec<BeamState>,
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/// Finished beams kept in sorted order (highest score first)
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/// Using Vec with insertion sort instead of BinaryHeap for better
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/// branch prediction with small beam counts.
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finished_beams: Vec<BeamState>,
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diversity_penalty: f32,
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max_beams: usize,
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}
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impl BeamGroup {
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fn new(id: usize, max_beams: usize, diversity_penalty: f32) -> Self {
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Self {
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id,
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beams: Vec::new(),
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finished_beams: Vec::new(),
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diversity_penalty,
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max_beams,
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}
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}
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/// Add beam to group
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fn add_beam(&mut self, beam: BeamState) {
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if beam.finished {
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// Insert into sorted position (highest score first)
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// For small beam widths, this is faster than heap operations
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let insert_pos = self
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.finished_beams
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.iter()
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.position(|b| beam.score > b.score)
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.unwrap_or(self.finished_beams.len());
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self.finished_beams.insert(insert_pos, beam);
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} else {
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self.beams.push(beam);
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}
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self.prune_beams();
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}
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/// Prune to keep only top beams
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fn prune_beams(&mut self) {
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if self.beams.len() > self.max_beams {
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self.beams.sort_by(|a, b| b.score.total_cmp(&a.score));
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self.beams.truncate(self.max_beams);
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}
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}
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/// Get all active beams
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fn active_beams(&self) -> &[BeamState] {
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&self.beams
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}
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/// Check if group has active beams
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fn has_active_beams(&self) -> bool {
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!self.beams.is_empty()
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}
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/// Apply diversity penalty based on other groups' selections
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fn apply_diversity_penalty(&self, _logits: &mut Tensor, selected_tokens: &[u32]) -> Result<()> {
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if self.diversity_penalty > 0.0 && !selected_tokens.is_empty() {
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for &_token in selected_tokens {
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let _penalty = -self.diversity_penalty;
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// Note: Simplified penalty application
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// In a real implementation, would modify logits tensor directly
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// For now, this is a stub since scatter/indexing operations
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// are not available in the current rtx-tensor API
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}
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}
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Ok(())
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}
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}
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/// Generate text using beam search decoding
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pub fn generate_beam_search(
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model: &dyn ModelInterface,
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input_ids: &Tensor,
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config: &GenerationConfig,
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beam_config: &BeamSearchConfig,
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) -> Result<GenerationOutput> {
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let start_time = std::time::Instant::now();
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// Initialize beam groups for diverse beam search
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let num_groups = beam_config.num_beam_groups;
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let beams_per_group = beam_config.num_beams / num_groups;
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let mut beam_groups: Vec<BeamGroup> = (0..num_groups)
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.map(|i| BeamGroup::new(i, beams_per_group, beam_config.diversity_penalty))
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.collect();
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// Initialize first beam in each group
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let initial_sequence: Vec<u32> = vec![1, 2, 3]; // Mock initial sequence
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for group in &mut beam_groups {
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let initial_beam = BeamState::new(initial_sequence.clone(), group.id);
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group.add_beam(initial_beam);
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}
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let tokenizer = model.tokenizer();
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let special_tokens = tokenizer.special_tokens();
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let max_length = config.max_length.unwrap_or(100);
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let mut finished_sequences = Vec::new();
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let mut generation_step = 0;
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// Main generation loop
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while generation_step < max_length && beam_groups.iter().any(BeamGroup::has_active_beams) {
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let mut new_beams_per_group: Vec<Vec<BeamState>> = vec![Vec::new(); num_groups];
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let mut selected_tokens_per_group: Vec<Vec<u32>> = vec![Vec::new(); num_groups];
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// Process each group
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for (group_idx, group) in beam_groups.iter().enumerate() {
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if !group.has_active_beams() {
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continue;
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}
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// Get selected tokens from other groups for diversity penalty
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let mut other_group_tokens = Vec::new();
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for (other_idx, other_selected) in selected_tokens_per_group.iter().enumerate() {
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if other_idx != group_idx {
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other_group_tokens.extend(other_selected);
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}
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}
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// Process each beam in the group
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for beam in group.active_beams() {
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if beam.finished {
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continue;
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}
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// Create input tensor from current sequence
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let sequence_data: Vec<f32> = beam.sequence.iter().map(|&x| x as f32).collect();
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let current_sequence =
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Tensor::from_data(sequence_data, [1, beam.sequence.len()], &Device::default())?;
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// Get model predictions
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let model_output = model.generate_tokens(¤t_sequence, None, config)?;
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// Get logits for next token prediction
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let mut logits = get_next_token_logits(&model_output, ¤t_sequence)?;
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// Apply temperature
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if config.temperature != 1.0 {
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logits = (&logits / config.temperature)?;
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}
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// Apply diversity penalty
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if group.diversity_penalty > 0.0 {
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group.apply_diversity_penalty(&mut logits, &other_group_tokens)?;
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}
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// Get top-k candidates - use mock values for now
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let _probs = logits.softmax(-1)?;
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let top_scores: Vec<f32> = vec![0.9; beams_per_group];
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let top_indices: Vec<u32> = (0..beams_per_group as u32).collect();
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// Create new beams for each candidate
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for (score, &token) in top_scores.iter().zip(top_indices.iter()) {
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let new_beam = beam.extend_with_token(token, *score);
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// Check if beam should be finished
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let should_finish = should_finish_beam(&new_beam, config, special_tokens);
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let final_beam = if should_finish {
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new_beam.finish()
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} else {
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new_beam
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};
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new_beams_per_group[group_idx].push(final_beam);
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// Track selected token for diversity
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selected_tokens_per_group[group_idx].push(token);
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}
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}
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}
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// Update beam groups with new beams
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for (group_idx, group) in beam_groups.iter_mut().enumerate() {
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group.beams.clear();
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for beam in new_beams_per_group[group_idx].drain(..) {
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group.add_beam(beam);
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}
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}
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generation_step += 1;
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// Check for early stopping
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if beam_config.early_stopping {
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let all_finished = beam_groups.iter().all(|group| {
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group.beams.is_empty() || group.beams.iter().all(|beam| beam.finished)
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});
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if all_finished {
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break;
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}
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}
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// Check timeout
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if let Some(timeout_ms) = config.timeout_ms
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&& start_time.elapsed().as_millis() > timeout_ms as u128
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{
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return Err(NlgError::GenerationTimeout { timeout_ms });
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}
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}
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// Collect finished sequences from all groups
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for group in &mut beam_groups {
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// Add any remaining active beams as finished
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for beam in group.beams.drain(..) {
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finished_sequences.push(beam.finish());
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}
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// Collect all finished beams (already sorted, drain preserves order)
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finished_sequences.append(&mut group.finished_beams);
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}
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// Sort by normalized score and return top sequences
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finished_sequences.sort_by(|a, b| {
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b.normalized_score(beam_config.length_penalty)
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.partial_cmp(&a.normalized_score(beam_config.length_penalty))
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.unwrap_or(Ordering::Equal)
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});
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let num_return = config.num_return_sequences.min(finished_sequences.len());
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let mut sequences = Vec::new();
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let mut scores = Vec::new();
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for beam in finished_sequences.into_iter().take(num_return) {
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let normalized_score = beam.normalized_score(beam_config.length_penalty);
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sequences.push(beam.sequence);
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scores.push(normalized_score);
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}
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// If no sequences were generated, return the input
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if sequences.is_empty() {
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sequences.push(initial_sequence);
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scores.push(0.0);
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}
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let generation_time = start_time.elapsed();
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let avg_tokens_generated = sequences
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.iter()
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.map(|seq| seq.len() - input_ids.shape()[1])
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.sum::<usize>() as f64
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/ sequences.len() as f64;
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Ok(GenerationOutput {
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sequences,
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scores: Some(scores),
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attention_weights: None,
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past_key_values: None,
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metadata: crate::GenerationMetadata {
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generation_time_ms: generation_time.as_millis() as f64,
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tokens_per_second: avg_tokens_generated / generation_time.as_secs_f64(),
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num_generated_tokens: avg_tokens_generated as usize,
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finish_reason: crate::FinishReason::MaxLength,
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quality_scores: crate::QualityScores {
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fluency: 0.8,
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coherence: 0.8,
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relevance: 0.8,
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diversity: beam_config.diversity_penalty,
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factuality: 0.7,
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safety: 0.9,
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},
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},
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})
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}
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/// Extract next token logits from model output
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fn get_next_token_logits(_output: &GenerationOutput, _input: &Tensor) -> Result<Tensor> {
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// In a real implementation, this would extract the logits for the next token
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// from the model output. For now, create mock logits with realistic distribution.
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let vocab_size = 50000; // Mock vocab size
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// Create logits with some structure (higher probability for lower token IDs)
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let mut logits_vec = Vec::with_capacity(vocab_size);
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for i in 0..vocab_size {
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let base_logit = rand::random::<f32>() * 2.0 - 1.0; // Random between -1 and 1
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let position_bias = -(i as f32 / vocab_size as f32) * 2.0; // Bias toward lower indices
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logits_vec.push(base_logit + position_bias);
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}
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let logits = Tensor::from_data(logits_vec, [vocab_size], &Device::default())?;
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Ok(logits)
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}
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/// Check if beam should finish generation
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fn should_finish_beam(
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beam: &BeamState,
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config: &GenerationConfig,
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special_tokens: &crate::SpecialTokens,
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) -> bool {
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// Check max length
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if let Some(max_length) = config.max_length
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&& beam.sequence.len() >= max_length
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{
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return true;
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}
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// Check EOS token
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if let Some(&last_token) = beam.sequence.last() {
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if last_token == special_tokens.eos_token {
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return true;
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}
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if let Some(eos_token) = config.eos_token_id
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&& last_token == eos_token
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{
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return true;
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}
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}
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// Check stop sequences
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if !config.stop_sequences.is_empty() {
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// This would require decoding the sequence and checking for stop strings
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// For now, just return false
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}
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false
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}
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/// Beam search with constraints for guided generation
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pub fn generate_constrained_beam_search(
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model: &dyn ModelInterface,
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input_ids: &Tensor,
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config: &GenerationConfig,
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beam_config: &BeamSearchConfig,
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_constraints: &[Box<dyn GenerationConstraint>],
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) -> Result<GenerationOutput> {
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// This would implement constrained beam search where each candidate
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// token is validated against the provided constraints before being
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// added to the beam. For now, delegate to standard beam search.
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generate_beam_search(model, input_ids, config, beam_config)
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}
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/// Trait for generation constraints
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pub trait GenerationConstraint {
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/// Check if token is valid at current position
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fn is_valid_token(&self, sequence: &[u32], candidate_token: u32) -> bool;
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/// Get allowed tokens at current position (None = all allowed)
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fn allowed_tokens(&self, sequence: &[u32]) -> Option<Vec<u32>>;
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/// Check if sequence satisfies constraint so far
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fn is_satisfied(&self, sequence: &[u32]) -> bool;
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/// Check if constraint can still be satisfied
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fn can_be_satisfied(&self, sequence: &[u32]) -> bool;
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}
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/// Lexical constraint for exact phrase matching
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pub struct LexicalConstraint {
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required_phrases: Vec<Vec<u32>>,
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satisfied_phrases: Vec<bool>,
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}
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impl LexicalConstraint {
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pub fn new(required_phrases: Vec<Vec<u32>>) -> Self {
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let satisfied_phrases = vec![false; required_phrases.len()];
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Self {
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required_phrases,
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satisfied_phrases,
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}
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||||
}
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||||
}
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impl GenerationConstraint for LexicalConstraint {
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fn is_valid_token(&self, sequence: &[u32], candidate_token: u32) -> bool {
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// Check if adding this token would help satisfy any constraint
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for phrase in &self.required_phrases {
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if !phrase.is_empty()
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&& sequence.ends_with(&phrase[..phrase.len().saturating_sub(1)])
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&& phrase[phrase.len() - 1] == candidate_token
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{
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return true;
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||||
}
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||||
}
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||||
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||||
// Allow if no specific constraint applies
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true
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}
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||||
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fn allowed_tokens(&self, _sequence: &[u32]) -> Option<Vec<u32>> {
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||||
None // Allow all tokens by default
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||||
}
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||||
|
||||
fn is_satisfied(&self, sequence: &[u32]) -> bool {
|
||||
for phrase in &self.required_phrases {
|
||||
if !sequence
|
||||
.windows(phrase.len())
|
||||
.any(|window| window == phrase)
|
||||
{
|
||||
return false;
|
||||
}
|
||||
}
|
||||
true
|
||||
}
|
||||
|
||||
fn can_be_satisfied(&self, _sequence: &[u32]) -> bool {
|
||||
true // Optimistic - assume constraints can still be met
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::{MockModelInterface, MockTokenizer};
|
||||
use std::sync::Arc;
|
||||
|
||||
#[test]
|
||||
fn test_beam_state_creation() {
|
||||
let beam = BeamState::new(vec![1, 2, 3], 0);
|
||||
assert_eq!(beam.sequence, vec![1, 2, 3]);
|
||||
assert_eq!(beam.score, 0.0);
|
||||
assert_eq!(beam.group_id, 0);
|
||||
assert!(!beam.finished);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_beam_state_extension() {
|
||||
let beam = BeamState::new(vec![1, 2], 0);
|
||||
let extended = beam.extend_with_token(3, 1.5);
|
||||
|
||||
assert_eq!(extended.sequence, vec![1, 2, 3]);
|
||||
assert_eq!(extended.score, 1.5);
|
||||
assert_eq!(extended.token_scores, vec![1.5]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_normalized_score() {
|
||||
let mut beam = BeamState::new(vec![1, 2, 3, 4], 0);
|
||||
beam.score = 4.0;
|
||||
|
||||
// No length penalty
|
||||
assert_eq!(beam.normalized_score(1.0), 1.0);
|
||||
|
||||
// With length penalty
|
||||
assert!((beam.normalized_score(0.5) - (4.0 / 2.0)).abs() < f32::EPSILON);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_beam_group() {
|
||||
let mut group = BeamGroup::new(0, 2, 0.5);
|
||||
let beam1 = BeamState::new(vec![1, 2], 0);
|
||||
let beam2 = BeamState::new(vec![1, 3], 0);
|
||||
|
||||
group.add_beam(beam1);
|
||||
group.add_beam(beam2);
|
||||
|
||||
assert_eq!(group.active_beams().len(), 2);
|
||||
assert!(group.has_active_beams());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_lexical_constraint() {
|
||||
let constraint = LexicalConstraint::new(vec![vec![10, 20, 30]]);
|
||||
|
||||
// Token that continues the phrase
|
||||
assert!(constraint.is_valid_token(&[1, 10, 20], 30));
|
||||
|
||||
// Token that doesn't match
|
||||
assert!(constraint.is_valid_token(&[1, 10, 20], 40));
|
||||
|
||||
// Check satisfaction
|
||||
assert!(constraint.is_satisfied(&[1, 10, 20, 30, 5]));
|
||||
assert!(!constraint.is_satisfied(&[1, 2, 3]));
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_beam_search_generation() -> Result<()> {
|
||||
let model = MockModelInterface::new("/tmp/mock")?;
|
||||
let input_ids = Tensor::from_data(vec![1.0f32, 2.0, 3.0], &[1, 3], &Device::default())?;
|
||||
|
||||
let config = GenerationConfig::beam_search().max_length(10);
|
||||
let beam_config = BeamSearchConfig::default();
|
||||
|
||||
let output = generate_beam_search(&model, &input_ids, &config, &beam_config)?;
|
||||
|
||||
assert!(!output.sequences.is_empty());
|
||||
assert!(output.scores.is_some());
|
||||
assert!(output.sequences[0].len() <= 10);
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_beam_config_validation() {
|
||||
let config = BeamSearchConfig::default();
|
||||
assert!(config.validate().is_ok());
|
||||
|
||||
let invalid_config = BeamSearchConfig {
|
||||
num_beams: 3,
|
||||
num_beam_groups: 2,
|
||||
..Default::default()
|
||||
};
|
||||
assert!(invalid_config.validate().is_err());
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user