Streaming mode for reporting the generated tokens

candle-examples/Cargo.toml

@@ -25,6 +25,7 @@ rayon = { workspace = true }
 safetensors = { workspace = true }
 serde = { workspace = true }
 serde_json = { workspace = true }
+tokenizers = { workspace = true, features = ["onig"] }
 
 [dev-dependencies]
 anyhow = { workspace = true }
@@ -35,7 +36,6 @@ imageproc = { workspace = true }
 memmap2 = { workspace = true }
 rand = { workspace = true }
 rusttype = { workspace = true }
-tokenizers = { workspace = true, features = ["onig"] }
 tracing = { workspace = true }
 tracing-chrome = { workspace = true }
 tracing-subscriber = { workspace = true }

candle-examples/examples/mistral/main.rs

@@ -10,6 +10,7 @@ use clap::Parser;
 use candle_transformers::models::mistral::{Config, Model};
 
 use candle::{DType, Device, Tensor};
+use candle_examples::token_output_stream::TokenOutputStream;
 use candle_nn::VarBuilder;
 use candle_transformers::generation::LogitsProcessor;
 use hf_hub::{api::sync::Api, Repo, RepoType};
@@ -18,7 +19,7 @@ use tokenizers::Tokenizer;
 struct TextGeneration {
     model: Model,
     device: Device,
-    tokenizer: Tokenizer,
+    tokenizer: TokenOutputStream,
     logits_processor: LogitsProcessor,
     repeat_penalty: f32,
     repeat_last_n: usize,
@@ -39,7 +40,7 @@ impl TextGeneration {
         let logits_processor = LogitsProcessor::new(seed, temp, top_p);
         Self {
             model,
-            tokenizer,
+            tokenizer: TokenOutputStream::new(tokenizer),
             logits_processor,
             repeat_penalty,
             repeat_last_n,
@@ -49,18 +50,24 @@ impl TextGeneration {
 
     fn run(&mut self, prompt: &str, sample_len: usize) -> Result<()> {
         use std::io::Write;
-        println!("starting the inference loop");
-        std::io::stdout().flush()?;
+        self.tokenizer.clear();
         let mut tokens = self
             .tokenizer
+            .tokenizer()
             .encode(prompt, true)
             .map_err(E::msg)?
             .get_ids()
             .to_vec();
+        for &t in tokens.iter() {
+            if let Some(t) = self.tokenizer.next_token(t)? {
+                print!("{t}")
+            }
+        }
+        std::io::stdout().flush()?;
 
         let mut generated_tokens = 0usize;
-        let eos_token = match self.tokenizer.get_vocab(true).get("</s>") {
-            Some(token) => *token,
+        let eos_token = match self.tokenizer.get_token("</s>") {
+            Some(token) => token,
             None => anyhow::bail!("cannot find the </s> token"),
         };
         let start_gen = std::time::Instant::now();
@@ -88,12 +95,15 @@ impl TextGeneration {
             if next_token == eos_token {
                 break;
             }
-            // TODO: print the generated tokens in a streaming way. Using `self.tokenizer.decode`
-            // on each token seems to swallow the whitespaces.
+            if let Some(t) = self.tokenizer.next_token(next_token)? {
+                print!("{t}");
+                std::io::stdout().flush()?;
+            }
         }
         let dt = start_gen.elapsed();
-        let generated_text = self.tokenizer.decode(&tokens, true).map_err(E::msg)?;
-        println!("Generated text:\n{generated_text}");
+        let rest = self.tokenizer.decode_rest().map_err(E::msg)?;
+        print!("{rest}");
+        std::io::stdout().flush()?;
         println!(
             "\n{generated_tokens} tokens generated ({:.2} token/s)",
             generated_tokens as f64 / dt.as_secs_f64(),

candle-examples/src/lib.rs

@@ -1,5 +1,6 @@
 pub mod coco_classes;
 pub mod imagenet;
+pub mod token_output_stream;
 
 use candle::{Device, Result, Tensor};
 

candle-examples/src/token_output_stream.rs

@@ -0,0 +1,74 @@
+use candle::Result;
+
+/// This is a wrapper around a tokenizer to ensure that tokens can be returned to the user in a
+/// streaming way rather than having to wait for the full decoding.
+pub struct TokenOutputStream {
+    tokenizer: tokenizers::Tokenizer,
+    tokens: Vec<u32>,
+    prev_index: usize,
+    current_index: usize,
+}
+
+impl TokenOutputStream {
+    pub fn new(tokenizer: tokenizers::Tokenizer) -> Self {
+        Self {
+            tokenizer,
+            tokens: Vec::new(),
+            prev_index: 0,
+            current_index: 0,
+        }
+    }
+
+    pub fn into_inner(self) -> tokenizers::Tokenizer {
+        self.tokenizer
+    }
+
+    fn decode(&self, tokens: &[u32]) -> Result<String> {
+        match self.tokenizer.decode(tokens, true) {
+            Ok(str) => Ok(str),
+            Err(err) => candle::bail!("cannot decode: {err}"),
+        }
+    }
+
+    // https://github.com/huggingface/text-generation-inference/blob/5ba53d44a18983a4de32d122f4cb46f4a17d9ef6/server/text_generation_server/models/model.py#L68
+    pub fn next_token(&mut self, token: u32) -> Result<Option<String>> {
+        let prev_text = if self.tokens.is_empty() {
+            String::new()
+        } else {
+            let tokens = &self.tokens[self.prev_index..self.current_index];
+            self.decode(tokens)?
+        };
+        self.tokens.push(token);
+        let text = self.decode(&self.tokens[self.prev_index..])?;
+        if text.len() > prev_text.len() && text.chars().last().unwrap().is_ascii() {
+            let text = text.split_at(prev_text.len());
+            self.prev_index = self.current_index;
+            self.current_index = self.tokens.len();
+            Ok(Some(text.1.to_string()))
+        } else {
+            Ok(None)
+        }
+    }
+
+    pub fn decode_rest(&self) -> Result<String> {
+        self.decode(&self.tokens[self.prev_index..])
+    }
+
+    pub fn decode_all(&self) -> Result<String> {
+        self.decode(&self.tokens)
+    }
+
+    pub fn get_token(&self, token_s: &str) -> Option<u32> {
+        self.tokenizer.get_vocab(true).get(token_s).copied()
+    }
+
+    pub fn tokenizer(&self) -> &tokenizers::Tokenizer {
+        &self.tokenizer
+    }
+
+    pub fn clear(&mut self) {
+        self.tokens.clear();
+        self.prev_index = 0;
+        self.current_index = 0;
+    }
+}