ferrum_interfaces/

sampler.rs

//! Sampling and logits processing interfaces
//!
//! This module provides abstractions for sampling tokens from model outputs,
//! including various sampling strategies and logits processors. These are
//! completely separate from model execution to allow for flexible composition.

use ferrum_types::{Result, SamplingParams, TokenId};
use rand::{RngCore, SeedableRng};
use rand_chacha::ChaCha12Rng;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::fmt;

/// Stable request-local RNG used by every product sampling path.
///
/// `rand::rngs::StdRng` intentionally does not promise a portable algorithm.
/// Naming the generator and seed expansion here makes seeded request replay an
/// explicit Ferrum contract instead of an incidental dependency choice.
pub const SAMPLING_RNG_ALGORITHM_ID: &str = "chacha12-rand-core-pcg32-u64-v1";

#[derive(Clone, Debug)]
pub struct SamplingRng {
    inner: ChaCha12Rng,
}

impl SamplingRng {
    pub fn seeded(seed: u64) -> Self {
        Self {
            inner: ChaCha12Rng::seed_from_u64(seed),
        }
    }

    pub fn from_seed_bytes(seed: [u8; 32]) -> Self {
        Self {
            inner: ChaCha12Rng::from_seed(seed),
        }
    }

    pub fn from_entropy() -> Self {
        let mut entropy = rand::rng();
        Self {
            inner: ChaCha12Rng::from_rng(&mut entropy),
        }
    }

    pub const fn algorithm_id() -> &'static str {
        SAMPLING_RNG_ALGORITHM_ID
    }
}

impl RngCore for SamplingRng {
    fn next_u32(&mut self) -> u32 {
        self.inner.next_u32()
    }

    fn next_u64(&mut self) -> u64 {
        self.inner.next_u64()
    }

    fn fill_bytes(&mut self, dest: &mut [u8]) {
        self.inner.fill_bytes(dest);
    }
}

/// Sampling context passed to logits processors and samplers
#[derive(Debug)]
pub struct SamplingContext<'a> {
    /// Current generation step (0-based)
    pub step: usize,
    /// Request-specific sampling parameters
    pub sampling_params: &'a SamplingParams,
    /// Current logits (mutable for processing)
    pub logits: &'a mut [f32],
    /// Previous token IDs in sequence  
    pub previous_tokens: &'a [TokenId],
    /// Token frequencies for repetition penalty
    pub token_frequencies: &'a HashMap<TokenId, usize>,
    /// Vocabulary size
    pub vocab_size: usize,
    /// Additional metadata
    pub metadata: HashMap<String, f32>,
}

impl<'a> SamplingContext<'a> {
    /// Create new sampling context
    pub fn new(
        step: usize,
        sampling_params: &'a SamplingParams,
        logits: &'a mut [f32],
        previous_tokens: &'a [TokenId],
        token_frequencies: &'a HashMap<TokenId, usize>,
        vocab_size: usize,
    ) -> Self {
        Self {
            step,
            sampling_params,
            logits,
            previous_tokens,
            token_frequencies,
            vocab_size,
            metadata: HashMap::new(),
        }
    }

    /// Get logit value for specific token
    pub fn get_logit(&self, token_id: TokenId) -> Option<f32> {
        if usize::from(token_id) < self.logits.len() {
            Some(self.logits[usize::from(token_id)])
        } else {
            None
        }
    }

    /// Set logit value for specific token
    pub fn set_logit(&mut self, token_id: TokenId, value: f32) -> bool {
        if usize::from(token_id) < self.logits.len() {
            self.logits[usize::from(token_id)] = value;
            true
        } else {
            false
        }
    }

    /// Mask (set to negative infinity) specific tokens
    pub fn mask_tokens(&mut self, token_ids: &[TokenId]) {
        for &token_id in token_ids {
            if usize::from(token_id) < self.logits.len() {
                self.logits[usize::from(token_id)] = f32::NEG_INFINITY;
            }
        }
    }
}

/// Logits processor trait for modifying raw model outputs
pub trait LogitsProcessor: Send + Sync {
    /// Process logits in-place
    fn process(&self, ctx: &mut SamplingContext) -> Result<()>;

    /// Get processor name for debugging/logging
    fn name(&self) -> &str;

    /// Whether this processor should be applied before others
    fn priority(&self) -> ProcessorPriority {
        ProcessorPriority::Normal
    }
}

/// Priority levels for logits processors
#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord)]
pub enum ProcessorPriority {
    /// Applied first (e.g., hard constraints, token masking)
    High = 3,
    /// Normal processing order
    Normal = 2,
    /// Applied later (e.g., temperature scaling)
    Low = 1,
}

/// Token sampler trait for selecting next token from processed logits
pub trait Sampler: Send + Sync {
    /// Sample next token from logits
    fn sample(&self, logits: &[f32], rng: &mut dyn RngCore) -> Result<TokenId>;

    /// Sample with additional context (default implementation ignores context)
    fn sample_with_context(&self, ctx: &SamplingContext, rng: &mut dyn RngCore) -> Result<TokenId> {
        self.sample(ctx.logits, rng)
    }

    /// Get sampler name
    fn name(&self) -> &str;

    /// Whether this sampler is deterministic
    fn is_deterministic(&self) -> bool;
}

/// Multi-sample capability for beam search and parallel sampling
pub trait MultiSampler: Sampler {
    /// Sample multiple tokens at once
    fn sample_multiple(
        &self,
        logits: &[f32],
        num_samples: usize,
        rng: &mut dyn RngCore,
    ) -> Result<Vec<TokenId>>;

    /// Sample with probabilities for each token
    fn sample_with_probabilities(
        &self,
        logits: &[f32],
        rng: &mut dyn RngCore,
    ) -> Result<(TokenId, Vec<f32>)>;
}

/// Logits processor chain for composing multiple processors
pub struct LogitsProcessorChain {
    processors: Vec<Box<dyn LogitsProcessor>>,
}

impl LogitsProcessorChain {
    /// Create new processor chain
    pub fn new() -> Self {
        Self {
            processors: Vec::new(),
        }
    }

    /// Add processor to chain
    pub fn add_processor(mut self, processor: Box<dyn LogitsProcessor>) -> Self {
        self.processors.push(processor);
        // Sort by priority (high to low)
        self.processors
            .sort_by(|a, b| b.priority().cmp(&a.priority()));
        self
    }

    /// Process logits through entire chain
    pub fn process(&self, ctx: &mut SamplingContext) -> Result<()> {
        for processor in &self.processors {
            processor.process(ctx)?;
        }
        Ok(())
    }

    /// Get all processor names in order
    pub fn processor_names(&self) -> Vec<&str> {
        self.processors.iter().map(|p| p.name()).collect()
    }

    /// Whether raw logits can be sampled without bypassing a processor.
    pub fn is_empty(&self) -> bool {
        self.processors.is_empty()
    }
}

impl fmt::Debug for LogitsProcessorChain {
    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
        f.debug_list()
            .entries(self.processors.iter().map(|processor| processor.name()))
            .finish()
    }
}

impl Default for LogitsProcessorChain {
    fn default() -> Self {
        Self::new()
    }
}

/// Common logits processors

/// Temperature scaling processor
pub struct TemperatureProcessor {
    pub temperature: f32,
}

impl TemperatureProcessor {
    pub fn new(temperature: f32) -> Self {
        Self { temperature }
    }
}

impl LogitsProcessor for TemperatureProcessor {
    fn process(&self, ctx: &mut SamplingContext) -> Result<()> {
        if self.temperature > 0.0 && self.temperature != 1.0 {
            for logit in ctx.logits.iter_mut() {
                *logit /= self.temperature;
            }
        }
        Ok(())
    }

    fn name(&self) -> &str {
        "temperature"
    }

    fn priority(&self) -> ProcessorPriority {
        // Penalties change raw logits first. Temperature must run before
        // probability-relative filters such as min-p and top-p.
        ProcessorPriority::Normal
    }
}

/// Top-k filtering processor
pub struct TopKProcessor {
    pub k: usize,
}

impl TopKProcessor {
    pub fn new(k: usize) -> Self {
        Self { k }
    }
}

impl LogitsProcessor for TopKProcessor {
    fn process(&self, ctx: &mut SamplingContext) -> Result<()> {
        if self.k > 0 && self.k < ctx.logits.len() {
            // Find k-th largest logit
            let mut indices: Vec<usize> = (0..ctx.logits.len()).collect();
            indices.sort_by(|&a, &b| {
                ctx.logits[b]
                    .partial_cmp(&ctx.logits[a])
                    .unwrap_or(std::cmp::Ordering::Equal)
            });

            let threshold = ctx.logits[indices[self.k - 1]];

            // Mask tokens below threshold
            for logit in ctx.logits.iter_mut() {
                if *logit < threshold {
                    *logit = f32::NEG_INFINITY;
                }
            }
        }
        Ok(())
    }

    fn name(&self) -> &str {
        "top_k"
    }

    fn priority(&self) -> ProcessorPriority {
        ProcessorPriority::Low
    }
}

/// Top-p (nucleus) filtering processor
pub struct TopPProcessor {
    pub p: f32,
}

impl TopPProcessor {
    pub fn new(p: f32) -> Self {
        Self { p }
    }
}

impl LogitsProcessor for TopPProcessor {
    fn process(&self, ctx: &mut SamplingContext) -> Result<()> {
        if self.p < 1.0 && self.p > 0.0 {
            // A preceding top-k/min-p processor may already have reduced a
            // full vocabulary to a small finite set. Sorting only that set
            // keeps the common combined path proportional to its candidates.
            let mut candidates = ctx
                .logits
                .iter()
                .copied()
                .enumerate()
                .filter(|(_, logit)| logit.is_finite())
                .collect::<Vec<_>>();
            if candidates.is_empty() {
                return Ok(());
            }

            candidates.sort_by(|(left_idx, left), (right_idx, right)| {
                right.total_cmp(left).then_with(|| left_idx.cmp(right_idx))
            });

            let max_logit = candidates[0].1;
            let sum = candidates
                .iter()
                .map(|(_, logit)| (*logit - max_logit).exp())
                .sum::<f32>();
            let mut cum_prob = 0.0;
            let mut cutoff_idx = candidates.len();
            for (i, (_, logit)) in candidates.iter().enumerate() {
                cum_prob += (*logit - max_logit).exp() / sum;
                if cum_prob >= self.p {
                    cutoff_idx = i + 1;
                    break;
                }
            }

            for (idx, _) in candidates.into_iter().skip(cutoff_idx) {
                ctx.logits[idx] = f32::NEG_INFINITY;
            }
        }
        Ok(())
    }

    fn name(&self) -> &str {
        "top_p"
    }

    fn priority(&self) -> ProcessorPriority {
        ProcessorPriority::Low
    }
}

/// Minimum-probability filtering processor.
///
/// A token remains eligible when its probability is at least `min_p` times
/// the most likely token's probability. In logit space the equivalent
/// threshold is `max_logit + ln(min_p)`, so this needs no softmax allocation.
pub struct MinPProcessor {
    pub min_p: f32,
}

impl MinPProcessor {
    pub fn new(min_p: f32) -> Self {
        Self { min_p }
    }
}

impl LogitsProcessor for MinPProcessor {
    fn process(&self, ctx: &mut SamplingContext) -> Result<()> {
        if self.min_p > 0.0 && self.min_p <= 1.0 {
            let max_logit = ctx.logits.iter().copied().fold(f32::NEG_INFINITY, f32::max);
            let threshold = max_logit + self.min_p.ln();
            for logit in ctx.logits.iter_mut() {
                if *logit < threshold {
                    *logit = f32::NEG_INFINITY;
                }
            }
        }
        Ok(())
    }

    fn name(&self) -> &str {
        "min_p"
    }

    fn priority(&self) -> ProcessorPriority {
        ProcessorPriority::Low
    }
}

/// Repetition penalty processor
pub struct RepetitionPenaltyProcessor {
    pub penalty: f32,
}

impl RepetitionPenaltyProcessor {
    pub fn new(penalty: f32) -> Self {
        Self { penalty }
    }
}

impl LogitsProcessor for RepetitionPenaltyProcessor {
    fn process(&self, ctx: &mut SamplingContext) -> Result<()> {
        if self.penalty != 1.0 {
            for &token_id in ctx.token_frequencies.keys() {
                if usize::from(token_id) >= ctx.logits.len() {
                    continue;
                }
                let idx = usize::from(token_id);
                let current_logit = ctx.logits[idx];
                if current_logit > 0.0 {
                    ctx.logits[idx] = current_logit / self.penalty;
                } else {
                    ctx.logits[idx] = current_logit * self.penalty;
                }
            }
        }
        Ok(())
    }

    fn name(&self) -> &str {
        "repetition_penalty"
    }

    fn priority(&self) -> ProcessorPriority {
        ProcessorPriority::High // Apply penalties early
    }
}

/// OpenAI-compatible additive penalties over generated-token counts.
///
/// Repetition penalty is multiplicative and remains a separate processor.
/// Presence and frequency penalties are additive and are applied afterwards:
/// `logit -= presence * seen + frequency * count`.
pub struct PresenceFrequencyPenaltyProcessor {
    pub presence_penalty: f32,
    pub frequency_penalty: f32,
}

impl PresenceFrequencyPenaltyProcessor {
    pub fn new(presence_penalty: f32, frequency_penalty: f32) -> Self {
        Self {
            presence_penalty,
            frequency_penalty,
        }
    }
}

impl LogitsProcessor for PresenceFrequencyPenaltyProcessor {
    fn process(&self, ctx: &mut SamplingContext) -> Result<()> {
        if self.presence_penalty == 0.0 && self.frequency_penalty == 0.0 {
            return Ok(());
        }
        for (&token_id, &count) in ctx.token_frequencies {
            let idx = usize::from(token_id);
            if idx >= ctx.logits.len() || count == 0 {
                continue;
            }
            ctx.logits[idx] -= self.presence_penalty + self.frequency_penalty * count as f32;
        }
        Ok(())
    }

    fn name(&self) -> &str {
        "presence_frequency_penalty"
    }

    fn priority(&self) -> ProcessorPriority {
        ProcessorPriority::High
    }
}

/// Common samplers

/// Greedy sampler (always picks highest probability token)
pub struct GreedySampler;

impl Sampler for GreedySampler {
    fn sample(&self, logits: &[f32], _rng: &mut dyn RngCore) -> Result<TokenId> {
        let max_idx = logits
            .iter()
            .enumerate()
            .filter(|(_, logit)| logit.is_finite())
            .reduce(|best, candidate| match candidate.1.total_cmp(best.1) {
                std::cmp::Ordering::Greater => candidate,
                std::cmp::Ordering::Equal if candidate.0 < best.0 => candidate,
                _ => best,
            })
            .map(|(idx, _)| idx)
            .ok_or_else(|| {
                ferrum_types::FerrumError::backend("No finite logits available for sampling")
            })?;

        Ok(TokenId::new(max_idx as u32))
    }

    fn name(&self) -> &str {
        "greedy"
    }

    fn is_deterministic(&self) -> bool {
        true
    }
}

#[cfg(test)]
mod greedy_sampler_tests {
    use super::{GreedySampler, Sampler, SamplingRng};

    #[test]
    fn ties_choose_the_lowest_token_id() {
        let mut rng = SamplingRng::seeded(1);
        let token = GreedySampler
            .sample(&[-1.0, 4.0, 4.0, f32::NAN], &mut rng)
            .unwrap();
        assert_eq!(token.get(), 1);
    }

    #[test]
    fn non_finite_logits_are_never_selected() {
        let mut rng = SamplingRng::seeded(1);
        let token = GreedySampler
            .sample(&[f32::NAN, f32::INFINITY, -2.0], &mut rng)
            .unwrap();
        assert_eq!(token.get(), 2);
        assert!(GreedySampler
            .sample(&[f32::NAN, f32::INFINITY], &mut rng)
            .is_err());
    }
}

/// Multinomial sampler for probabilistic sampling
pub struct MultinomialSampler;

impl Sampler for MultinomialSampler {
    fn sample(&self, logits: &[f32], rng: &mut dyn RngCore) -> Result<TokenId> {
        // Convert logits to probabilities
        let max_logit = logits.iter().fold(f32::NEG_INFINITY, |a, &b| a.max(b));

        let mut probs: Vec<f32> = logits
            .iter()
            .map(|&logit| {
                if logit.is_finite() && logit > f32::NEG_INFINITY {
                    (logit - max_logit).exp()
                } else {
                    0.0
                }
            })
            .collect();

        let sum: f32 = probs.iter().sum();
        if sum <= 0.0 {
            return Err(ferrum_types::FerrumError::backend(
                "No valid tokens for sampling",
            ));
        }

        for prob in probs.iter_mut() {
            *prob /= sum;
        }

        // Sample from categorical distribution
        let threshold = rng.next_u32() as f32 / u32::MAX as f32;
        let mut cumulative = 0.0;

        for (idx, prob) in probs.iter().enumerate() {
            cumulative += prob;
            if cumulative >= threshold {
                return Ok(TokenId::new(idx as u32));
            }
        }

        // Fallback to last token (shouldn't happen with proper normalization)
        Ok(TokenId::new((probs.len() - 1) as u32))
    }

    fn name(&self) -> &str {
        "multinomial"
    }

    fn is_deterministic(&self) -> bool {
        false
    }
}

/// Sampling configuration builder
pub struct SamplingConfigBuilder {
    processors: Vec<Box<dyn LogitsProcessor>>,
    sampler: Option<Box<dyn Sampler>>,
}

impl SamplingConfigBuilder {
    /// Create new builder
    pub fn new() -> Self {
        Self {
            processors: Vec::new(),
            sampler: None,
        }
    }

    /// Add temperature scaling
    pub fn with_temperature(mut self, temperature: f32) -> Self {
        if temperature > 0.0 && temperature != 1.0 {
            self.processors
                .push(Box::new(TemperatureProcessor::new(temperature)));
        }
        self
    }

    /// Add top-k filtering
    pub fn with_top_k(mut self, k: usize) -> Self {
        if k > 0 {
            self.processors.push(Box::new(TopKProcessor::new(k)));
        }
        self
    }

    /// Add top-p filtering
    pub fn with_top_p(mut self, p: f32) -> Self {
        if p > 0.0 && p < 1.0 {
            self.processors.push(Box::new(TopPProcessor::new(p)));
        }
        self
    }

    /// Add minimum-probability filtering.
    pub fn with_min_p(mut self, min_p: f32) -> Self {
        if min_p > 0.0 && min_p <= 1.0 {
            self.processors.push(Box::new(MinPProcessor::new(min_p)));
        }
        self
    }

    /// Add repetition penalty
    pub fn with_repetition_penalty(mut self, penalty: f32) -> Self {
        if penalty != 1.0 {
            self.processors
                .push(Box::new(RepetitionPenaltyProcessor::new(penalty)));
        }
        self
    }

    /// Add OpenAI-compatible presence and frequency penalties.
    pub fn with_presence_frequency_penalty(
        mut self,
        presence_penalty: f32,
        frequency_penalty: f32,
    ) -> Self {
        if presence_penalty != 0.0 || frequency_penalty != 0.0 {
            self.processors
                .push(Box::new(PresenceFrequencyPenaltyProcessor::new(
                    presence_penalty,
                    frequency_penalty,
                )));
        }
        self
    }

    /// Set sampler (greedy vs multinomial)
    pub fn with_sampler(mut self, sampler: Box<dyn Sampler>) -> Self {
        self.sampler = Some(sampler);
        self
    }

    /// Build sampling configuration
    pub fn build(self) -> SamplingConfig {
        let mut chain = LogitsProcessorChain::new();
        for processor in self.processors {
            chain = chain.add_processor(processor);
        }

        let sampler = self.sampler.unwrap_or_else(|| Box::new(MultinomialSampler));

        SamplingConfig {
            processor_chain: chain,
            sampler,
        }
    }
}

impl Default for SamplingConfigBuilder {
    fn default() -> Self {
        Self::new()
    }
}

/// Complete sampling configuration
pub struct SamplingConfig {
    pub processor_chain: LogitsProcessorChain,
    pub sampler: Box<dyn Sampler>,
}

impl fmt::Debug for SamplingConfig {
    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
        f.debug_struct("SamplingConfig")
            .field("processor_chain", &self.processor_chain)
            .field("sampler", &self.sampler.name())
            .finish()
    }
}

impl SamplingConfig {
    /// Create from sampling parameters
    pub fn from_params(params: &SamplingParams) -> Self {
        let mut builder = SamplingConfigBuilder::new()
            .with_temperature(params.temperature)
            .with_repetition_penalty(params.repetition_penalty)
            .with_presence_frequency_penalty(params.presence_penalty, params.frequency_penalty);

        if let Some(min_p) = params.min_p {
            builder = builder.with_min_p(min_p);
        }

        if let Some(top_k) = params.top_k {
            builder = builder.with_top_k(top_k);
        }

        if params.top_p < 1.0 {
            builder = builder.with_top_p(params.top_p);
        }

        // Choose sampler based on temperature
        let sampler: Box<dyn Sampler> = if params.temperature == 0.0 {
            Box::new(GreedySampler)
        } else {
            Box::new(MultinomialSampler)
        };

        builder.with_sampler(sampler).build()
    }

    /// Whether the current plan is exactly raw greedy argmax.
    ///
    /// The legacy speculative runner only represents temperature and drafts
    /// with argmax. Any logits processor would otherwise be silently skipped.
    pub fn supports_raw_greedy_speculation(&self) -> bool {
        self.sampler.is_deterministic() && self.processor_chain.is_empty()
    }

    /// Process logits and sample token
    pub fn sample(&self, mut ctx: SamplingContext, rng: &mut dyn RngCore) -> Result<TokenId> {
        // Apply all logits processors
        self.processor_chain.process(&mut ctx)?;

        // Sample token
        self.sampler.sample_with_context(&ctx, rng)
    }
}

/// Sampling statistics for monitoring
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SamplingStats {
    /// Total sampling operations
    pub total_samples: u64,
    /// Average sampling time in microseconds
    pub avg_sample_time_us: f64,
    /// Distribution of sampled tokens
    pub token_distribution: HashMap<TokenId, u64>,
    /// Effective temperature (entropy-based measure)
    pub effective_temperature: f32,
    /// Processor execution times
    pub processor_times: HashMap<String, f64>,
}