Add topk sampling. (#1923)

candle-transformers/src/generation/mod.rs

@@ -1,24 +1,35 @@
 use candle::{DType, Error, Result, Tensor};
 use rand::{distributions::Distribution, SeedableRng};
 
+#[derive(Clone, PartialEq, Debug)]
+pub enum Sampling {
+    ArgMax,
+    All { temperature: f64 },
+    TopK { k: usize, temperature: f64 },
+    TopP { p: f64, temperature: f64 },
+}
+
 pub struct LogitsProcessor {
     rng: rand::rngs::StdRng,
-    temperature: Option<f64>,
-    top_p: Option<f64>,
+    sampling: Sampling,
 }
 
 impl LogitsProcessor {
+    pub fn from_sampling(seed: u64, sampling: Sampling) -> Self {
+        let rng = rand::rngs::StdRng::seed_from_u64(seed);
+        Self { rng, sampling }
+    }
+
     pub fn new(seed: u64, temperature: Option<f64>, top_p: Option<f64>) -> Self {
-        let temperature = if temperature.map_or(true, |v| v < 1e-7) {
-            None
-        } else {
-            temperature
+        let temperature = temperature.and_then(|v| if v < 1e-7 { None } else { Some(v) });
+        let sampling = match temperature {
+            None => Sampling::ArgMax,
+            Some(temperature) => match top_p {
+                None => Sampling::All { temperature },
+                Some(p) => Sampling::TopP { p, temperature },
+            },
         };
-        Self {
-            rng: rand::rngs::StdRng::seed_from_u64(seed),
-            temperature,
-            top_p,
-        }
+        Self::from_sampling(seed, sampling)
     }
 
     fn sample_argmax(&mut self, logits: Tensor) -> Result<u32> {
@@ -38,14 +49,14 @@ impl LogitsProcessor {
         Ok(next_token)
     }
 
+    /// top-p sampling (or "nucleus sampling") samples from the smallest set of tokens that exceed
+    /// probability top_p. This way we never sample tokens that have very low probabilities and are
+    /// less likely to go "off the rails".
     fn sample_topp(&mut self, prs: &mut Vec<f32>, top_p: f32) -> Result<u32> {
-        // top-p sampling (or "nucleus sampling") samples from the smallest set of
-        // tokens that exceed probability top_p. This way we never sample tokens that
-        // have very low probabilities and are less likely to go "off the rails".
         let mut argsort_indices = (0..prs.len()).collect::<Vec<_>>();
 
         // Sort by descending probability.
-        argsort_indices.sort_by(|&i, &j| prs[j].partial_cmp(&prs[i]).unwrap());
+        argsort_indices.sort_by(|&i, &j| prs[j].total_cmp(&prs[i]));
 
         // Clamp smaller probabilities to zero.
         let mut cumsum = 0.;
@@ -60,23 +71,49 @@ impl LogitsProcessor {
         self.sample_multinomial(prs)
     }
 
+    // top-k sampling samples from the k tokens with the largest probabilities.
+    fn sample_topk(&mut self, prs: &mut Vec<f32>, top_k: usize) -> Result<u32> {
+        if top_k >= prs.len() {
+            self.sample_multinomial(prs)
+        } else {
+            let mut argsort_indices = (0..prs.len()).collect::<Vec<_>>();
+            // Sort by descending probability.
+            let (indices, _, _) =
+                argsort_indices.select_nth_unstable_by(top_k, |&i, &j| prs[j].total_cmp(&prs[i]));
+            let prs = indices.iter().map(|&i| prs[i]).collect::<Vec<_>>();
+            let index = self.sample_multinomial(&prs)?;
+            Ok(indices[index as usize] as u32)
+        }
+    }
+
     pub fn sample(&mut self, logits: &Tensor) -> Result<u32> {
         let logits = logits.to_dtype(DType::F32)?;
-        let next_token = match self.temperature {
-            None => self.sample_argmax(logits)?,
-            Some(temperature) => {
-                let logits = &(&logits / temperature)?;
-                let prs = candle_nn::ops::softmax_last_dim(logits)?;
-                let mut prs: Vec<f32> = prs.to_vec1()?;
-                let top_p = self.top_p.unwrap_or(1.);
-                if top_p <= 0.0 || top_p >= 1.0 {
+        let prs = |temperature: f64| -> Result<Vec<f32>> {
+            let logits = (&logits / temperature)?;
+            let prs = candle_nn::ops::softmax_last_dim(&logits)?;
+            prs.to_vec1()
+        };
+
+        let next_token = match &self.sampling {
+            Sampling::ArgMax => self.sample_argmax(logits)?,
+            Sampling::All { temperature } => {
+                let prs = prs(*temperature)?;
+                self.sample_multinomial(&prs)?
+            }
+            Sampling::TopP { p, temperature } => {
+                let mut prs = prs(*temperature)?;
+                if *p <= 0.0 || *p >= 1.0 {
                     // simply sample from the predicted probability distribution
                     self.sample_multinomial(&prs)?
                 } else {
                     // top-p (nucleus) sampling, clamping the least likely tokens to zero
-                    self.sample_topp(&mut prs, top_p as f32)?
+                    self.sample_topp(&mut prs, *p as f32)?
                 }
             }
+            Sampling::TopK { k, temperature } => {
+                let mut prs = prs(*temperature)?;
+                self.sample_topk(&mut prs, *k)?
+            }
         };
         Ok(next_token)
     }

candle-transformers/tests/generation_tests.rs

@@ -27,3 +27,30 @@ fn sample_with_top_p() -> Result<()> {
     assert_eq!(token, 2);
     Ok(())
 }
+
+#[test]
+fn sample_with_top_k() -> Result<()> {
+    let mut logits_process = LogitsProcessor::from_sampling(
+        42,
+        candle_transformers::generation::Sampling::TopK {
+            k: 1,
+            temperature: 1.0,
+        },
+    );
+    let logits = Tensor::new(&[0.1, 0.2, 0.3, 0.4], &Device::Cpu)?;
+    let token = logits_process.sample(&logits)?;
+    assert_eq!(token, 3);
+    let mut logits_process = LogitsProcessor::from_sampling(
+        42,
+        candle_transformers::generation::Sampling::TopK {
+            k: 2,
+            temperature: 1.0,
+        },
+    );
+    let logits = Tensor::new(&[0.1, 0.2, 0.3, 0.4], &Device::Cpu)?;
+    let token = logits_process.sample(&logits)?;
+    assert_eq!(token, 3);
+    let token = logits_process.sample(&logits)?;
+    assert_eq!(token, 2);
+    Ok(())
+}