1use serde::{Deserialize, Serialize};
8
9#[derive(Debug, Clone)]
11pub struct SplitMix64 {
12 state: u64,
13}
14
15impl SplitMix64 {
16 pub fn new(seed: u64) -> Self {
17 Self { state: seed }
18 }
19
20 pub fn from_entropy() -> Self {
22 let t = std::time::SystemTime::now()
23 .duration_since(std::time::UNIX_EPOCH)
24 .unwrap_or_default();
25 let addr = Box::into_raw(Box::new(0u8)) as u64;
26 unsafe { drop(Box::from_raw(addr as *mut u8)) };
28 Self::new(t.as_nanos() as u64 ^ addr.rotate_left(17) ^ 0x9E3779B97F4A7C15)
29 }
30
31 #[inline]
32 pub fn next_u64(&mut self) -> u64 {
33 self.state = self.state.wrapping_add(0x9E3779B97F4A7C15);
34 let mut z = self.state;
35 z = (z ^ (z >> 30)).wrapping_mul(0xBF58476D1CE4E5B9);
36 z = (z ^ (z >> 27)).wrapping_mul(0x94D049BB133111EB);
37 z ^ (z >> 31)
38 }
39
40 #[inline]
42 pub fn next_f32(&mut self) -> f32 {
43 (self.next_u64() >> 40) as f32 / (1u64 << 24) as f32
44 }
45}
46
47#[derive(Debug, Clone, Serialize, Deserialize)]
49pub struct SamplerConfig {
50 pub temperature: f32,
51 pub top_p: f32,
52 pub top_k: u32,
53 pub repetition_penalty: f32,
54 pub min_p: f32,
55 #[serde(default)]
57 pub seed: Option<u64>,
58}
59
60impl Default for SamplerConfig {
61 fn default() -> Self {
62 Self {
63 temperature: 0.7,
64 top_p: 0.9,
65 top_k: 40,
66 repetition_penalty: 1.1,
67 min_p: 0.05,
68 seed: None,
69 }
70 }
71}
72
73pub fn sample(
76 logits: &[f32],
77 config: &SamplerConfig,
78 past_tokens: &[u32],
79 rng: &mut SplitMix64,
80) -> u32 {
81 let mut probs = logits.to_vec();
82
83 if config.repetition_penalty != 1.0 {
84 apply_repetition_penalty(&mut probs, past_tokens, config.repetition_penalty);
85 }
86
87 if config.temperature < 1e-6 {
88 return argmax(&probs); }
90 if config.temperature != 1.0 {
91 for p in probs.iter_mut() {
92 *p /= config.temperature;
93 }
94 }
95
96 softmax_inplace(&mut probs);
97
98 if config.min_p > 0.0 {
99 let max_prob = probs.iter().cloned().fold(0.0f32, f32::max);
100 let threshold = max_prob * config.min_p;
101 for p in probs.iter_mut() {
102 if *p < threshold {
103 *p = 0.0;
104 }
105 }
106 }
107
108 if config.top_k > 0 && (config.top_k as usize) < probs.len() {
109 apply_top_k(&mut probs, config.top_k as usize);
110 }
111
112 if config.top_p < 1.0 && config.top_p > 0.0 {
113 apply_top_p(&mut probs, config.top_p);
114 }
115
116 let sum: f32 = probs.iter().sum();
117 if sum > 0.0 {
118 for p in probs.iter_mut() {
119 *p /= sum;
120 }
121 } else {
122 return argmax(logits);
124 }
125
126 categorical_sample(&probs, rng.next_f32())
127}
128
129pub fn argmax(values: &[f32]) -> u32 {
131 values
132 .iter()
133 .enumerate()
134 .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
135 .map(|(i, _)| i as u32)
136 .unwrap_or(0)
137}
138
139fn softmax_inplace(logits: &mut [f32]) {
140 let max_val = logits.iter().cloned().fold(f32::NEG_INFINITY, f32::max);
141 let mut sum = 0.0f32;
142 for v in logits.iter_mut() {
143 *v = (*v - max_val).exp();
144 sum += *v;
145 }
146 if sum > 0.0 {
147 for v in logits.iter_mut() {
148 *v /= sum;
149 }
150 }
151}
152
153fn apply_repetition_penalty(logits: &mut [f32], past_tokens: &[u32], penalty: f32) {
154 for &tok in past_tokens {
155 let idx = tok as usize;
156 if idx < logits.len() {
157 if logits[idx] > 0.0 {
158 logits[idx] /= penalty;
159 } else {
160 logits[idx] *= penalty;
161 }
162 }
163 }
164}
165
166fn apply_top_k(probs: &mut [f32], k: usize) {
169 if k == 0 || k >= probs.len() {
170 return;
171 }
172 let mut sorted: Vec<f32> = probs.to_vec();
173 sorted.sort_by(|a, b| b.partial_cmp(a).unwrap_or(std::cmp::Ordering::Equal));
174 let threshold = sorted[k - 1];
175 for p in probs.iter_mut() {
176 if *p < threshold {
177 *p = 0.0;
178 }
179 }
180}
181
182fn apply_top_p(probs: &mut [f32], top_p: f32) {
185 let mut indexed: Vec<(usize, f32)> = probs.iter().copied().enumerate().collect();
186 indexed.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
187
188 let mut cumsum = 0.0f32;
189 let mut cutoff_idx = indexed.len();
190 for (i, &(_, prob)) in indexed.iter().enumerate() {
191 cumsum += prob;
192 if cumsum >= top_p {
193 cutoff_idx = i + 1;
194 break;
195 }
196 }
197
198 let kept: std::collections::HashSet<usize> =
199 indexed[..cutoff_idx].iter().map(|&(i, _)| i).collect();
200 for (i, p) in probs.iter_mut().enumerate() {
201 if !kept.contains(&i) {
202 *p = 0.0;
203 }
204 }
205}
206
207fn categorical_sample(probs: &[f32], r: f32) -> u32 {
209 let mut cumsum = 0.0f32;
210 for (i, &p) in probs.iter().enumerate() {
211 cumsum += p;
212 if r < cumsum {
213 return i as u32;
214 }
215 }
216 probs.iter().rposition(|&p| p > 0.0).unwrap_or(0) as u32
217}
218
219#[cfg(test)]
220mod tests {
221 use super::*;
222
223 #[test]
224 fn test_argmax() {
225 let logits = vec![0.1, 0.5, 0.3, 0.9, 0.2];
226 assert_eq!(argmax(&logits), 3);
227 }
228
229 #[test]
230 fn test_greedy_sampling() {
231 let logits = vec![1.0, 5.0, 2.0, 3.0];
232 let config = SamplerConfig {
233 temperature: 0.0,
234 ..Default::default()
235 };
236 let mut rng = SplitMix64::new(1);
237 assert_eq!(sample(&logits, &config, &[], &mut rng), 1);
238 }
239
240 #[test]
241 fn test_softmax() {
242 let mut logits = vec![1.0, 2.0, 3.0];
243 softmax_inplace(&mut logits);
244 let sum: f32 = logits.iter().sum();
245 assert!((sum - 1.0).abs() < 1e-5);
246 assert!(logits[2] > logits[1] && logits[1] > logits[0]);
247 }
248
249 #[test]
250 fn test_repetition_penalty() {
251 let mut logits = vec![1.0, 2.0, 3.0, 4.0];
252 apply_repetition_penalty(&mut logits, &[1, 3], 2.0);
253 assert_eq!(logits, vec![1.0, 1.0, 3.0, 2.0]);
254 }
255
256 #[test]
257 fn top_k_keeps_exactly_k() {
258 let mut probs = vec![0.1, 0.4, 0.05, 0.3, 0.15];
259 apply_top_k(&mut probs, 2);
260 let kept = probs.iter().filter(|&&p| p > 0.0).count();
261 assert_eq!(kept, 2, "top-k must keep exactly k (was k+1 in v1)");
262 assert!(probs[1] > 0.0 && probs[3] > 0.0);
263 }
264
265 #[test]
266 fn rng_reaches_full_cdf() {
267 let probs = vec![0.25f32; 4];
270 let mut rng = SplitMix64::new(42);
271 let mut hits = [0usize; 4];
272 for _ in 0..4000 {
273 let i = categorical_sample(&probs, rng.next_f32()) as usize;
274 hits[i] += 1;
275 }
276 for (i, &h) in hits.iter().enumerate() {
277 assert!(h > 700, "index {i} sampled only {h}/4000 — biased RNG");
278 }
279 }
280
281 #[test]
282 fn same_seed_same_sequence() {
283 let logits: Vec<f32> = (0..32).map(|i| (i as f32 * 0.37).sin()).collect();
284 let config = SamplerConfig {
285 temperature: 1.0,
286 seed: Some(7),
287 ..Default::default()
288 };
289 let run = |seed: u64| -> Vec<u32> {
290 let mut rng = SplitMix64::new(seed);
291 (0..16).map(|_| sample(&logits, &config, &[], &mut rng)).collect()
292 };
293 assert_eq!(run(7), run(7), "same seed must reproduce");
294 assert_ne!(run(7), run(8), "different seed must differ");
295 }
296}