Add repetition penalty, add colors to outputs based on probabilities, try to make softmax() more numerically stable.

README.md

@@ -56,6 +56,12 @@ cast to 32-bit floats.
 You can use `--temperature`, `--top-p` and `--top-k` to adjust token sampler
 settings.
 
+There is `--repetition-penalty` setting. 1.0 means no penalty. This value
+likely should be between 0 and 1. Values smaller than 1.0 give a penalty to
+tokens that appear in the context, by
+`x*(repetitition_penalty^num_occurrences)` before applying `softmax()` on the
+output probabilities. Or in other words, values smaller than 1.0 apply penalty.
+
 You can also use `--prompt-file` to read the prompt from a file instead from
 the command line.
 

src/rllama_main.rs

@@ -36,6 +36,8 @@ struct Cli {
     top_p: Option<f32>,
     #[arg(long)]
     top_k: Option<i32>,
+    #[arg(long)]
+    repetition_penalty: Option<f32>,
 
     #[cfg(feature = "opencl")]
     #[arg(long)]
@@ -185,7 +187,11 @@ pub fn main() -> Result<(), Box<dyn std::error::Error>> {
 
     let mut toks_id: Vec<TokenId> = tok.tokenize_to_ids(prompt.clone());
     let mut prev_pos = 0;
-    let mut token_sampler = TokenSampler::new().temperature(0.8).top_p(0.9).top_k(50);
+    let mut token_sampler = TokenSampler::new()
+        .temperature(0.8)
+        .top_p(0.9)
+        .top_k(50)
+        .repetition_penalty(0.8);
 
     if let Some(temperature) = cli.temperature {
         token_sampler = token_sampler.temperature(temperature);
@@ -196,6 +202,9 @@ pub fn main() -> Result<(), Box<dyn std::error::Error>> {
     if let Some(top_k) = cli.top_k {
         token_sampler = token_sampler.top_k(top_k as usize);
     }
+    if let Some(repetition_penalty) = cli.repetition_penalty {
+        token_sampler = token_sampler.repetition_penalty(repetition_penalty);
+    }
 
     pln!("---");
     pln!(" dim: {}", params.dim);
@@ -209,6 +218,10 @@ pub fn main() -> Result<(), Box<dyn std::error::Error>> {
     pln!("Temperature: {}", token_sampler.get_temperature());
     pln!("Top P: {}", token_sampler.get_top_p());
     pln!("Top K: {}", token_sampler.get_top_k());
+    pln!(
+        "Repetition penalty: {}",
+        token_sampler.get_repetition_penalty()
+    );
     pln!("---");
     pln!(
         "{}",
@@ -229,9 +242,9 @@ pub fn main() -> Result<(), Box<dyn std::error::Error>> {
     let mut stop_seen: bool = false;
     while toks_id.len() < max_seq_len {
         let now = std::time::Instant::now();
-
         let preds = tr.forward(&toks_id[prev_pos..], prev_pos, &mut caches);
-        let highest_pred_idx = token_sampler.sample(&preds);
+
+        let (highest_pred_idx, token_prob) = token_sampler.sample(&preds, &tok, &toks_id);
         toks_id.push(highest_pred_idx as TokenId);
 
         for (tok_idx, tok_id) in toks_id[prev_pos + 1..].iter().enumerate() {
@@ -252,7 +265,18 @@ pub fn main() -> Result<(), Box<dyn std::error::Error>> {
             if first && tok_idx < toks_id.len() - 2 {
                 // intentionally left empty
             } else {
-                print!("{}", tok_str.truecolor(128, 255, 128));
+                let redness: f32 = token_prob * 255.0;
+                let redness = if redness > 255.0 {
+                    255
+                } else if redness < 0.0 {
+                    0
+                } else {
+                    redness as u8
+                };
+                print!(
+                    "{}",
+                    tok_str.truecolor(128 + redness / 2, 255 - redness / 2, 128)
+                );
             }
         }
         if first {

src/token_sampler.rs

@@ -1,11 +1,13 @@
 use crate::tensor::Tensor;
-use crate::tokenizer::TokenId;
+use crate::tokenizer::{TokenId, Tokenizer};
 use rand::Rng;
+use std::collections::BTreeMap;
 
 pub struct TokenSampler {
     temperature: f32,
     top_p: f32,
     top_k: usize,
+    repetition_penalty: f32,
 }
 
 impl Default for TokenSampler {
@@ -17,9 +19,11 @@ impl Default for TokenSampler {
 impl TokenSampler {
     pub fn new() -> Self {
         Self {
-            temperature: 0.8,
+            temperature: 0.2,
             top_p: 1.0,
             top_k: 1, // same as argmax
+            repetition_penalty: 0.8, // 1.0 = no penalty. values above 1.0 make repetition
+                      // encouraged which can quickly devolve into repeating loop
         }
     }
 
@@ -35,6 +39,10 @@ impl TokenSampler {
         self.top_k
     }
 
+    pub fn get_repetition_penalty(&self) -> f32 {
+        self.repetition_penalty
+    }
+
     pub fn temperature(self, temperature: f32) -> Self {
         Self {
             temperature,
@@ -50,20 +58,77 @@ impl TokenSampler {
         Self { top_k, ..self }
     }
 
-    pub fn sample(&self, logits: &Tensor) -> TokenId {
+    pub fn repetition_penalty(self, repetition_penalty: f32) -> Self {
+        Self {
+            repetition_penalty,
+            ..self
+        }
+    }
+
+    pub fn sample(
+        &self,
+        logits: &Tensor,
+        tokenizer: &Tokenizer,
+        existing_tokens: &[TokenId],
+    ) -> (TokenId, f32) {
+        let mut times_used: BTreeMap<TokenId, usize> = BTreeMap::new();
+        for token in existing_tokens {
+            times_used
+                .entry(*token)
+                .and_modify(|e| *e += 1)
+                .or_insert(1);
+        }
+
         let nrows = logits.rows();
         assert!(logits.cols() == 1);
         let mut logits = logits.transpose();
         if self.temperature > 0.0 {
             logits = logits.scalar_multiply_f32(1.0 / self.temperature);
-            logits = logits.softmax();
         }
 
+        if self.repetition_penalty != 1.0 {
+            for token_idx in 0..logits.rows() {
+                if let Some(count) = times_used.get(&(token_idx as TokenId)) {
+                    let penalty = self.repetition_penalty.powf(*count as f32);
+                    logits.set_f32(0, token_idx, logits.get_f32(0, token_idx) * penalty);
+                }
+            }
+        }
+        let mut maxv: f32 = std::f32::NEG_INFINITY;
+        for token_idx in 0..logits.rows() {
+            let v = logits.get_f32(0, token_idx);
+            if v > maxv {
+                maxv = v;
+            }
+        }
+        // To numerically stabilize, remove maxv from all logits
+        // softmax(x + c) = softmax(x) where c is a constant, and we make use of htat
+        for token_idx in 0..logits.rows() {
+            logits.set_f32(0, token_idx, logits.get_f32(0, token_idx) - maxv);
+        }
+        logits = logits.softmax();
+
         let mut logitsf: Vec<(TokenId, f32)> = Vec::with_capacity(nrows as usize);
         for i in 0..nrows {
-            logitsf.push((i as TokenId, logits.get_f32(0, i)));
+            let score = logits.get_f32(0, i);
+            logitsf.push((i as TokenId, score));
+        }
+        logitsf.sort_unstable_by(|a, b| {
+            match b.1.partial_cmp(&a.1) {
+                Some(c) => c,
+                None => {
+                    // Sort NaNs to bottom
+                    if b.1.is_nan() {
+                        return std::cmp::Ordering::Less;
+                    } else if a.1.is_nan() {
+                        return std::cmp::Ordering::Greater;
+                    } else {
+                        return std::cmp::Ordering::Equal;
+                    }
                 }
-        logitsf.sort_unstable_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
+            }
+        });
+
         logitsf.truncate(self.top_k);
         let mut p_accum: f32 = 0.0;
         for (idx, v) in logitsf.iter().enumerate() {
@@ -78,14 +143,14 @@ impl TokenSampler {
             total_p += v.1;
         }
         let mut rng = rand::thread_rng();
-        let p: f32 = rng.gen_range(0.0..total_p);
+        let p: f32 = rng.gen_range(0.0..=total_p);
         p_accum = 0.0;
         for v in logitsf.into_iter() {
             p_accum += v.1;
             if p_accum >= p {
-                return v.0;
+                return (v.0, v.1 / total_p);
             }
         }
-        0
+        (0, 0.0)
     }
 }