Add StableLM-2, StableLM Code and Zephyr variants (#1650)

* Add StableLM Code and Zephyr variants

* Add V2 models

* Update README

candle-examples/examples/stable-lm/README.md

@@ -8,6 +8,11 @@ Card](https://huggingface.co/stabilityai/stablelm-3b-4e1t).
 Note that this model is gated so you will have to request access on the Hub in
 order to be able to use it.
 
+Other available models are Stable-Code-3B, StableLM-2 and Zephyr variants.
+
+StableLM-2 uses a Tiktoken based GPT-3.5/GPT-4 tokenizer not supported by Candle, so to run it you can download a somewhat compatible [tokenizer.json](https://huggingface.co/Xenova/gpt-4/resolve/main/tokenizer.json?download=true)
+and pass it via the --tokenizer-file argument.
+
 ## Running some example
 
 ```bash

candle-examples/examples/stable-lm/main.rs

@@ -5,7 +5,7 @@ extern crate intel_mkl_src;
 extern crate accelerate_src;
 
 use anyhow::{Error as E, Result};
-use clap::Parser;
+use clap::{Parser, ValueEnum};
 
 use candle_transformers::models::quantized_stable_lm::Model as QStableLM;
 use candle_transformers::models::stable_lm::{Config, Model as StableLM};
@@ -122,6 +122,16 @@ impl TextGeneration {
     }
 }
 
+#[derive(Clone, Copy, Debug, ValueEnum, PartialEq, Eq)]
+enum Which {
+    V1Orig,
+    V1,
+    V1Zephyr,
+    V2,
+    V2Zephyr,
+    Code,
+}
+
 #[derive(Parser, Debug)]
 #[command(author, version, about, long_about = None)]
 struct Args {
@@ -155,12 +165,15 @@ struct Args {
     #[arg(long, short = 'n', default_value_t = 100)]
     sample_len: usize,
 
-    #[arg(long, default_value = "lmz/candle-stablelm-3b-4e1t")]
+    #[arg(long)]
-    model_id: String,
+    model_id: Option<String>,
 
     #[arg(long, default_value = "main")]
     revision: String,
 
+    #[arg(long, default_value = "v1-orig")]
+    which: Which,
+
     #[arg(long)]
     tokenizer_file: Option<String>,
 
@@ -207,8 +220,20 @@ fn main() -> Result<()> {
 
     let start = std::time::Instant::now();
     let api = Api::new()?;
+    let model_id = match args.model_id {
+        Some(model_id) => model_id,
+        None => match args.which {
+            Which::V1Orig => "lmz/candle-stablelm-3b-4e1t".to_string(),
+            Which::V1 => "stabilityai/stablelm-3b-4e1t".to_string(),
+            Which::V1Zephyr => "stabilityai/stablelm-zephyr-3b".to_string(),
+            Which::Code => "stabilityai/stable-code-3b".to_string(),
+            Which::V2 => "stabilityai/stablelm-2-1_6b".to_string(),
+            Which::V2Zephyr => "stabilityai/stablelm-2-zephyr-1_6b".to_string(),
+        },
+    };
+
     let repo = api.repo(Repo::with_revision(
-        args.model_id,
+        model_id,
         RepoType::Model,
         args.revision,
     ));
@@ -221,19 +246,35 @@ fn main() -> Result<()> {
             .split(',')
             .map(std::path::PathBuf::from)
             .collect::<Vec<_>>(),
-        None => {
+        None => match (args.which, args.quantized) {
-            if args.quantized {
+            (Which::V1Orig, true) => vec![repo.get("model-q4k.gguf")?],
-                vec![repo.get("model-q4k.gguf")?]
+            (Which::V1 | Which::V1Zephyr | Which::V2 | Which::V2Zephyr | Which::Code, true) => {
-            } else {
+                anyhow::bail!("Quantized {:?} variant not supported.", args.which)
+            }
+            (Which::V1Orig | Which::V1 | Which::V1Zephyr | Which::V2 | Which::V2Zephyr, false) => {
                 vec![repo.get("model.safetensors")?]
             }
-        }
+            (Which::Code, false) => {
+                candle_examples::hub_load_safetensors(&repo, "model.safetensors.index.json")?
+            }
+        },
     };
+
     println!("retrieved the files in {:?}", start.elapsed());
     let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
 
     let start = std::time::Instant::now();
-    let config = Config::stablelm_3b_4e1t(args.use_flash_attn);
+    let config = match args.which {
+        Which::V1Orig => Config::stablelm_3b_4e1t(args.use_flash_attn),
+        Which::V1 | Which::V1Zephyr | Which::V2 | Which::V2Zephyr | Which::Code => {
+            let config_filename = repo.get("config.json")?;
+            let config = std::fs::read_to_string(config_filename)?;
+            let mut config: Config = serde_json::from_str(&config)?;
+            config.set_use_flash_attn(args.use_flash_attn);
+            config
+        }
+    };
+
     let device = candle_examples::device(args.cpu)?;
     let (model, device) = if args.quantized {
         let filename = &filenames[0];

candle-transformers/src/models/stable_lm.rs

@@ -1,10 +1,11 @@
-use crate::models::with_tracing::{linear_no_bias, Linear};
+use crate::models::with_tracing::{linear, linear_no_bias, Linear};
 use candle::{DType, Device, Module, Result, Tensor, D};
 use candle_nn::{Activation, LayerNorm, VarBuilder};
+use serde::Deserialize;
 use std::sync::Arc;
 
 // https://huggingface.co/stabilityai/stablelm-3b-4e1t/blob/main/configuration_stablelm_epoch.py
-#[derive(Debug, Clone, PartialEq)]
+#[derive(Debug, Clone, PartialEq, Deserialize)]
 pub struct Config {
     pub(crate) vocab_size: usize,
     pub(crate) intermediate_size: usize,
@@ -18,7 +19,10 @@ pub struct Config {
     pub(crate) max_position_embeddings: usize,
     pub(crate) norm_eps: f64,
     pub(crate) use_cache: bool,
-    pub(crate) use_flash_attn: bool,
+    #[serde(default)]
+    pub(crate) use_qkv_bias: bool, // Used in StableLM-2
+    #[serde(default)]
+    pub(crate) use_flash_attn: bool, // Not in config.json
 }
 
 impl Config {
@@ -35,6 +39,7 @@ impl Config {
             rope_theta: 10_000.,
             max_position_embeddings: 4096,
             norm_eps: 1e-5,
+            use_qkv_bias: false,
             use_cache: true,
             use_flash_attn,
         }
@@ -51,6 +56,10 @@ impl Config {
     pub fn num_kv_groups(&self) -> usize {
         self.num_attention_heads / self.num_key_value_heads
     }
+
+    pub fn set_use_flash_attn(&mut self, use_flash_attn: bool) {
+        self.use_flash_attn = use_flash_attn
+    }
 }
 
 #[derive(Debug)]
@@ -179,9 +188,15 @@ impl Attention {
         let head_dim = cfg.head_dim();
         let num_heads = cfg.num_attention_heads;
         let num_kv_heads = cfg.num_key_value_heads;
-        let q_proj = linear_no_bias(hidden_sz, num_heads * head_dim, vb.pp("q_proj"))?;
+        let linear_layer = if cfg.use_qkv_bias {
-        let k_proj = linear_no_bias(hidden_sz, num_kv_heads * head_dim, vb.pp("k_proj"))?;
+            linear
-        let v_proj = linear_no_bias(hidden_sz, num_kv_heads * head_dim, vb.pp("v_proj"))?;
+        } else {
+            linear_no_bias
+        };
+
+        let q_proj = linear_layer(hidden_sz, num_heads * head_dim, vb.pp("q_proj"))?;
+        let k_proj = linear_layer(hidden_sz, num_kv_heads * head_dim, vb.pp("k_proj"))?;
+        let v_proj = linear_layer(hidden_sz, num_kv_heads * head_dim, vb.pp("v_proj"))?;
         let o_proj = linear_no_bias(num_heads * head_dim, hidden_sz, vb.pp("o_proj"))?;
         Ok(Self {
             q_proj,