Quantized version of mistral. (#1009)

* Quantized version of mistral.

* Integrate the quantized mistral variant.

* Use the quantized weight files.

* Tweak the quantization command.

* Fix the dtype when computing the rotary embeddings.

* Update the readme with the quantized version.

* Fix the decoding of the remaining tokens.

candle-core/examples/tensor-tools.rs

@@ -103,8 +103,10 @@ enum Command {
 
     Quantize {
         /// The input file, in gguf format.
-        in_file: std::path::PathBuf,
+        in_file: Vec<std::path::PathBuf>,
+
         /// The output file, in gguf format.
+        #[arg(long)]
         out_file: std::path::PathBuf,
 
         /// The quantization schema to apply.
@@ -218,12 +220,16 @@ fn run_ls(file: &std::path::PathBuf, format: Option<Format>, verbose: bool) -> R
 }
 
 fn run_quantize_safetensors(
-    in_file: std::path::PathBuf,
+    in_files: &[std::path::PathBuf],
     out_file: std::path::PathBuf,
     q: Quantization,
 ) -> Result<()> {
     let mut out_file = std::fs::File::create(out_file)?;
-    let tensors = candle_core::safetensors::load(in_file, &Device::Cpu)?;
+    let mut tensors = std::collections::HashMap::new();
+    for in_file in in_files.iter() {
+        let in_tensors = candle_core::safetensors::load(in_file, &Device::Cpu)?;
+        tensors.extend(in_tensors)
+    }
     println!("tensors: {}", tensors.len());
 
     let quantize_fn = match q {
@@ -280,20 +286,32 @@ fn run_quantize_safetensors(
 }
 
 fn run_quantize(
-    in_file: std::path::PathBuf,
+    in_files: &[std::path::PathBuf],
     out_file: std::path::PathBuf,
     q: Quantization,
     qmode: QuantizationMode,
 ) -> Result<()> {
-    if let Some(extension) = in_file.extension() {
+    if in_files.is_empty() {
+        candle_core::bail!("no specified input files")
+    }
+    if let Some(extension) = out_file.extension() {
         if extension == "safetensors" {
-            return run_quantize_safetensors(in_file, out_file, q);
+            candle_core::bail!("the generated file cannot use the safetensors extension")
         }
     }
+    if let Some(extension) = in_files[0].extension() {
+        if extension == "safetensors" {
+            return run_quantize_safetensors(in_files, out_file, q);
+        }
+    }
+
+    if in_files.len() != 1 {
+        candle_core::bail!("only a single in-file can be used when quantizing gguf files")
+    }
 
     // Open the out file early so as to fail directly on missing directories etc.
     let mut out_file = std::fs::File::create(out_file)?;
-    let mut in_ = std::fs::File::open(&in_file)?;
+    let mut in_ = std::fs::File::open(&in_files[0])?;
     let content = gguf_file::Content::read(&mut in_)?;
     println!("tensors: {}", content.tensor_infos.len());
 
@@ -319,7 +337,7 @@ fn run_quantize(
         .par_iter()
         .map(|(name, _)| {
             println!("  quantizing {name}");
-            let mut in_file = std::fs::File::open(&in_file)?;
+            let mut in_file = std::fs::File::open(&in_files[0])?;
             let tensor = content.tensor(&mut in_file, name)?;
             let tensor = qmode.quantize(name, tensor, quantize_fn)?;
             Ok((name, tensor))
@@ -360,7 +378,7 @@ fn main() -> anyhow::Result<()> {
             out_file,
             quantization,
             mode,
-        } => run_quantize(in_file, out_file, quantization, mode)?,
+        } => run_quantize(&in_file, out_file, quantization, mode)?,
     }
     Ok(())
 }

candle-examples/examples/mistral/README.md

@@ -6,6 +6,9 @@ as of 2023-09-28. Weights (and the original Python model code) are released unde
 - [Blog post](https://mistral.ai/news/announcing-mistral-7b/) from Mistral announcing the model release.
 - [Model card](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the
   HuggingFace Hub.
+This example supports the initial model as well as a quantized variant.
+
+## Running the example
 
 ```bash
 $ cargo run --example mistral --release --features cuda -- --prompt 'Write helloworld code in Rust' --sample-len 150
@@ -38,3 +41,50 @@ fn main() {
 
 This example is released under the terms
 ```
+
+## Running the quantized version of the model
+
+```bash
+$ cargo run --example mistral --features accelerate --release -- \
+$   --prompt "Here is a sample quick sort implementation in rust " --quantized -n 400
+avx: false, neon: true, simd128: false, f16c: false
+temp: 0.00 repeat-penalty: 1.10 repeat-last-n: 64
+retrieved the files in 562.292µs
+loaded the model in 1.100323667s
+Here is a sample quick sort implementation in rust
+
+``rust
+fn quick_sort(arr: &mut [i32]) {
+    if arr.len() <= 1 {
+        return;
+    }
+
+    let pivot = arr[0];
+    let mut left = vec![];
+    let mut right = vec![];
+
+    for i in 1..arr.len() {
+        if arr[i] < pivot {
+            left.push(arr[i]);
+        } else {
+            right.push(arr[i]);
+        }
+    }
+
+    quick_sort(&mut left);
+    quick_sort(&mut right);
+
+    let mut i = 0;
+    for _ in &left {
+        arr[i] = left.pop().unwrap();
+        i += 1;
+    }
+
+    for _ in &right {
+        arr[i] = right.pop().unwrap();
+        i += 1;
+    }
+}
+``
+226 tokens generated (10.91 token/s)
+```

candle-examples/examples/mistral/main.rs

@@ -7,7 +7,8 @@ extern crate accelerate_src;
 use anyhow::{Error as E, Result};
 use clap::Parser;
 
-use candle_transformers::models::mistral::{Config, Model};
+use candle_transformers::models::mistral::{Config, Model as Mistral};
+use candle_transformers::models::quantized_mistral::Model as QMistral;
 
 use candle::{DType, Device, Tensor};
 use candle_examples::token_output_stream::TokenOutputStream;
@@ -16,6 +17,11 @@ use candle_transformers::generation::LogitsProcessor;
 use hf_hub::{api::sync::Api, Repo, RepoType};
 use tokenizers::Tokenizer;
 
+enum Model {
+    Mistral(Mistral),
+    Quantized(QMistral),
+}
+
 struct TextGeneration {
     model: Model,
     device: Device,
@@ -76,7 +82,10 @@ impl TextGeneration {
             let start_pos = tokens.len().saturating_sub(context_size);
             let ctxt = &tokens[start_pos..];
             let input = Tensor::new(ctxt, &self.device)?.unsqueeze(0)?;
-            let logits = self.model.forward(&input, start_pos)?;
+            let logits = match &mut self.model {
+                Model::Mistral(m) => m.forward(&input, start_pos)?,
+                Model::Quantized(m) => m.forward(&input, start_pos)?,
+            };
             let logits = logits.squeeze(0)?.squeeze(0)?.to_dtype(DType::F32)?;
             let logits = if self.repeat_penalty == 1. {
                 logits
@@ -101,8 +110,9 @@ impl TextGeneration {
             }
         }
         let dt = start_gen.elapsed();
-        let rest = self.tokenizer.decode_rest().map_err(E::msg)?;
-        print!("{rest}");
+        if let Some(rest) = self.tokenizer.decode_rest().map_err(E::msg)? {
+            print!("{rest}");
+        }
         std::io::stdout().flush()?;
         println!(
             "\n{generated_tokens} tokens generated ({:.2} token/s)",
@@ -211,24 +221,39 @@ fn main() -> Result<()> {
             .split(',')
             .map(std::path::PathBuf::from)
             .collect::<Vec<_>>(),
-        None => vec![
-            repo.get("pytorch_model-00001-of-00002.safetensors")?,
-            repo.get("pytorch_model-00002-of-00002.safetensors")?,
-        ],
+        None => {
+            if args.quantized {
+                vec![repo.get("model-q4k.gguf")?]
+            } else {
+                vec![
+                    repo.get("pytorch_model-00001-of-00002.safetensors")?,
+                    repo.get("pytorch_model-00002-of-00002.safetensors")?,
+                ]
+            }
+        }
     };
     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::config_7b_v0_1(args.use_flash_attn);
-    let device = candle_examples::device(args.cpu)?;
-    let dtype = if device.is_cuda() {
-        DType::BF16
+    let (model, device) = if args.quantized {
+        let filename = &filenames[0];
+        let vb = candle_transformers::quantized_var_builder::VarBuilder::from_gguf(filename)?;
+        let model = QMistral::new(&config, vb)?;
+        (Model::Quantized(model), Device::Cpu)
     } else {
-        DType::F32
+        let device = candle_examples::device(args.cpu)?;
+        let dtype = if device.is_cuda() {
+            DType::BF16
+        } else {
+            DType::F32
+        };
+        let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
+        let model = Mistral::new(&config, vb)?;
+        (Model::Mistral(model), device)
     };
-    let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
-    let model = Model::new(&config, vb)?;
+
     println!("loaded the model in {:?}", start.elapsed());
 
     let mut pipeline = TextGeneration::new(

candle-examples/src/token_output_stream.rs

@@ -50,8 +50,20 @@ impl TokenOutputStream {
         }
     }
 
-    pub fn decode_rest(&self) -> Result<String> {
-        self.decode(&self.tokens[self.prev_index..])
+    pub fn decode_rest(&self) -> 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)?
+        };
+        let text = self.decode(&self.tokens[self.prev_index..])?;
+        if text.len() > prev_text.len() {
+            let text = text.split_at(prev_text.len());
+            Ok(Some(text.1.to_string()))
+        } else {
+            Ok(None)
+        }
     }
 
     pub fn decode_all(&self) -> Result<String> {

candle-transformers/src/models/mistral.rs

@@ -6,18 +6,18 @@ use std::sync::Arc;
 
 #[derive(Debug, Clone, PartialEq)]
 pub struct Config {
-    vocab_size: usize,
-    hidden_size: usize,
-    intermediate_size: usize,
-    num_hidden_layers: usize,
-    num_attention_heads: usize,
-    num_key_value_heads: usize,
-    hidden_act: Activation,
-    max_position_embeddings: usize,
-    rms_norm_eps: f64,
-    rope_theta: f64,
-    sliding_window: usize,
-    use_flash_attn: bool,
+    pub(crate) vocab_size: usize,
+    pub(crate) hidden_size: usize,
+    pub(crate) intermediate_size: usize,
+    pub(crate) num_hidden_layers: usize,
+    pub(crate) num_attention_heads: usize,
+    pub(crate) num_key_value_heads: usize,
+    pub(crate) hidden_act: Activation,
+    pub(crate) max_position_embeddings: usize,
+    pub(crate) rms_norm_eps: f64,
+    pub(crate) rope_theta: f64,
+    pub(crate) sliding_window: usize,
+    pub(crate) use_flash_attn: bool,
 }
 
 impl Config {

candle-transformers/src/models/mod.rs

@@ -7,6 +7,7 @@ pub mod llama;
 pub mod mistral;
 pub mod mixformer;
 pub mod quantized_llama;
+pub mod quantized_mistral;
 pub mod quantized_mixformer;
 pub mod quantized_t5;
 pub mod segment_anything;

candle-transformers/src/models/quantized_mistral.rs

@@ -0,0 +1,364 @@
+use crate::models::quantized_t5::Embedding;
+use crate::models::with_tracing::QMatMul;
+pub use crate::quantized_var_builder::VarBuilder;
+use candle::{DType, Device, Module, Result, Tensor, D};
+use candle_nn::Activation;
+use std::sync::Arc;
+
+pub use crate::models::mistral::Config;
+
+#[derive(Debug)]
+struct Linear {
+    weight: QMatMul,
+}
+
+impl Module for Linear {
+    fn forward(&self, x: &Tensor) -> candle::Result<Tensor> {
+        x.apply(&self.weight)
+    }
+}
+
+fn linear_no_bias(in_dim: usize, out_dim: usize, vb: VarBuilder) -> Result<Linear> {
+    let weight = QMatMul::new(in_dim, out_dim, vb)?;
+    Ok(Linear { weight })
+}
+
+#[derive(Debug)]
+struct RmsNorm {
+    inner: candle_nn::RmsNorm,
+    span: tracing::Span,
+}
+
+impl RmsNorm {
+    fn new(size: usize, eps: f64, vb: VarBuilder) -> Result<Self> {
+        let span = tracing::span!(tracing::Level::TRACE, "rms-norm");
+        let weight = vb.get(size, "weight")?.dequantize(vb.device())?;
+        let inner = candle_nn::RmsNorm::new(weight, eps);
+        Ok(Self { inner, span })
+    }
+}
+
+impl Module for RmsNorm {
+    fn forward(&self, x: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        self.inner.forward(x)
+    }
+}
+
+#[derive(Debug)]
+struct RotaryEmbedding {
+    sin: Tensor,
+    cos: Tensor,
+}
+
+fn rotate_half(xs: &Tensor) -> Result<Tensor> {
+    let last_dim = xs.dim(D::Minus1)?;
+    let xs1 = xs.narrow(D::Minus1, 0, last_dim / 2)?;
+    let xs2 = xs.narrow(D::Minus1, last_dim / 2, last_dim - last_dim / 2)?;
+    Tensor::cat(&[&xs2.neg()?, &xs1], D::Minus1)
+}
+
+impl RotaryEmbedding {
+    fn new(cfg: &Config, dev: &Device) -> Result<Self> {
+        let dim = cfg.hidden_size / cfg.num_attention_heads;
+        let max_seq_len = cfg.max_position_embeddings;
+        let inv_freq: Vec<_> = (0..dim)
+            .step_by(2)
+            .map(|i| 1f32 / 10000f32.powf(i as f32 / dim as f32))
+            .collect();
+        let inv_freq_len = inv_freq.len();
+        let inv_freq = Tensor::from_vec(inv_freq, (1, inv_freq_len), dev)?;
+        let t = Tensor::arange(0u32, max_seq_len as u32, dev)?
+            .to_dtype(DType::F32)?
+            .reshape((max_seq_len, 1))?;
+        let freqs = t.matmul(&inv_freq)?;
+        let freqs = Tensor::cat(&[&freqs, &freqs], D::Minus1)?;
+        Ok(Self {
+            sin: freqs.sin()?,
+            cos: freqs.cos()?,
+        })
+    }
+
+    fn apply_rotary_emb_qkv(
+        &self,
+        q: &Tensor,
+        k: &Tensor,
+        seqlen_offset: usize,
+    ) -> Result<(Tensor, Tensor)> {
+        let (_b_sz, _h, seq_len, _n_embd) = q.dims4()?;
+        let cos = self.cos.narrow(0, seqlen_offset, seq_len)?;
+        let sin = self.sin.narrow(0, seqlen_offset, seq_len)?;
+        let cos = cos.unsqueeze(0)?.unsqueeze(0)?; // (1, 1, seq_len, dim)
+        let sin = sin.unsqueeze(0)?.unsqueeze(0)?; // (1, 1, seq_len, dim)
+        let q_embed = (q.broadcast_mul(&cos)? + rotate_half(q)?.broadcast_mul(&sin))?;
+        let k_embed = (k.broadcast_mul(&cos)? + rotate_half(k)?.broadcast_mul(&sin))?;
+        Ok((q_embed, k_embed))
+    }
+}
+
+#[derive(Debug)]
+#[allow(clippy::upper_case_acronyms)]
+struct MLP {
+    gate_proj: Linear,
+    up_proj: Linear,
+    down_proj: Linear,
+    act_fn: Activation,
+}
+
+impl MLP {
+    fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let hidden_sz = cfg.hidden_size;
+        let intermediate_sz = cfg.intermediate_size;
+        let gate_proj = linear_no_bias(hidden_sz, intermediate_sz, vb.pp("gate_proj"))?;
+        let up_proj = linear_no_bias(hidden_sz, intermediate_sz, vb.pp("up_proj"))?;
+        let down_proj = linear_no_bias(intermediate_sz, hidden_sz, vb.pp("down_proj"))?;
+        Ok(Self {
+            gate_proj,
+            up_proj,
+            down_proj,
+            act_fn: cfg.hidden_act,
+        })
+    }
+}
+
+impl Module for MLP {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let lhs = xs.apply(&self.gate_proj)?.apply(&self.act_fn)?;
+        let rhs = xs.apply(&self.up_proj)?;
+        (lhs * rhs)?.apply(&self.down_proj)
+    }
+}
+
+#[derive(Debug)]
+struct Attention {
+    q_proj: Linear,
+    k_proj: Linear,
+    v_proj: Linear,
+    o_proj: Linear,
+    num_heads: usize,
+    num_kv_heads: usize,
+    num_kv_groups: usize,
+    head_dim: usize,
+    hidden_size: usize,
+    rotary_emb: Arc<RotaryEmbedding>,
+    kv_cache: Option<(Tensor, Tensor)>,
+}
+
+impl Attention {
+    fn new(rotary_emb: Arc<RotaryEmbedding>, cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let hidden_sz = cfg.hidden_size;
+        let num_heads = cfg.num_attention_heads;
+        let num_kv_heads = cfg.num_key_value_heads;
+        let num_kv_groups = num_heads / num_kv_heads;
+        let head_dim = hidden_sz / num_heads;
+        let q_proj = linear_no_bias(hidden_sz, num_heads * head_dim, vb.pp("q_proj"))?;
+        let k_proj = linear_no_bias(hidden_sz, num_kv_heads * head_dim, vb.pp("k_proj"))?;
+        let v_proj = linear_no_bias(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,
+            k_proj,
+            v_proj,
+            o_proj,
+            num_heads,
+            num_kv_heads,
+            num_kv_groups,
+            head_dim,
+            hidden_size: hidden_sz,
+            rotary_emb,
+            kv_cache: None,
+        })
+    }
+
+    fn repeat_kv(&self, xs: Tensor) -> Result<Tensor> {
+        let n_rep = self.num_kv_groups;
+        if n_rep == 1 {
+            Ok(xs)
+        } else {
+            let (b_sz, num_kv_heads, seq_len, head_dim) = xs.dims4()?;
+            xs.unsqueeze(2)?
+                .expand((b_sz, num_kv_heads, n_rep, seq_len, head_dim))?
+                .reshape((b_sz, num_kv_heads * n_rep, seq_len, head_dim))
+        }
+    }
+
+    fn forward(
+        &mut self,
+        xs: &Tensor,
+        attention_mask: Option<&Tensor>,
+        seqlen_offset: usize,
+    ) -> Result<Tensor> {
+        let (b_sz, q_len, _) = xs.dims3()?;
+
+        let query_states = self.q_proj.forward(xs)?;
+        let key_states = self.k_proj.forward(xs)?;
+        let value_states = self.v_proj.forward(xs)?;
+
+        let query_states = query_states
+            .reshape((b_sz, q_len, self.num_heads, self.head_dim))?
+            .transpose(1, 2)?;
+        let key_states = key_states
+            .reshape((b_sz, q_len, self.num_kv_heads, self.head_dim))?
+            .transpose(1, 2)?;
+        let value_states = value_states
+            .reshape((b_sz, q_len, self.num_kv_heads, self.head_dim))?
+            .transpose(1, 2)?;
+
+        let (query_states, key_states) =
+            self.rotary_emb
+                .apply_rotary_emb_qkv(&query_states, &key_states, seqlen_offset)?;
+
+        let (key_states, value_states) = match &self.kv_cache {
+            None => (key_states, value_states),
+            Some((prev_k, prev_v)) => {
+                let key_states = Tensor::cat(&[prev_k, &key_states], 2)?;
+                let value_states = Tensor::cat(&[prev_v, &value_states], 2)?;
+                (key_states, value_states)
+            }
+        };
+        self.kv_cache = Some((key_states.clone(), value_states.clone()));
+
+        let key_states = self.repeat_kv(key_states)?;
+        let value_states = self.repeat_kv(value_states)?;
+
+        let attn_output = {
+            let scale = 1f64 / f64::sqrt(self.head_dim as f64);
+            let attn_weights = (query_states.matmul(&key_states.transpose(2, 3)?)? * scale)?;
+
+            let attn_weights = match attention_mask {
+                None => attn_weights,
+                Some(mask) => attn_weights.broadcast_add(mask)?,
+            };
+            let attn_weights = candle_nn::ops::softmax_last_dim(&attn_weights)?;
+            attn_weights.matmul(&value_states)?
+        };
+        attn_output
+            .transpose(1, 2)?
+            .reshape((b_sz, q_len, self.hidden_size))?
+            .apply(&self.o_proj)
+    }
+}
+
+#[derive(Debug)]
+struct DecoderLayer {
+    self_attn: Attention,
+    mlp: MLP,
+    input_layernorm: RmsNorm,
+    post_attention_layernorm: RmsNorm,
+}
+
+impl DecoderLayer {
+    fn new(rotary_emb: Arc<RotaryEmbedding>, cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let self_attn = Attention::new(rotary_emb, cfg, vb.pp("self_attn"))?;
+        let mlp = MLP::new(cfg, vb.pp("mlp"))?;
+        let input_layernorm =
+            RmsNorm::new(cfg.hidden_size, cfg.rms_norm_eps, vb.pp("input_layernorm"))?;
+        let post_attention_layernorm = RmsNorm::new(
+            cfg.hidden_size,
+            cfg.rms_norm_eps,
+            vb.pp("post_attention_layernorm"),
+        )?;
+        Ok(Self {
+            self_attn,
+            mlp,
+            input_layernorm,
+            post_attention_layernorm,
+        })
+    }
+
+    fn forward(
+        &mut self,
+        xs: &Tensor,
+        attention_mask: Option<&Tensor>,
+        seqlen_offset: usize,
+    ) -> Result<Tensor> {
+        let residual = xs;
+        let xs = self.input_layernorm.forward(xs)?;
+        let xs = self.self_attn.forward(&xs, attention_mask, seqlen_offset)?;
+        let xs = (xs + residual)?;
+        let residual = &xs;
+        let xs = xs.apply(&self.post_attention_layernorm)?.apply(&self.mlp)?;
+        residual + xs
+    }
+}
+
+#[derive(Debug)]
+pub struct Model {
+    embed_tokens: Embedding,
+    layers: Vec<DecoderLayer>,
+    norm: RmsNorm,
+    lm_head: Linear,
+    sliding_window: usize,
+    device: Device,
+}
+
+impl Model {
+    pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let vb_m = vb.pp("model");
+        let embed_tokens =
+            Embedding::new(cfg.vocab_size, cfg.hidden_size, vb_m.pp("embed_tokens"))?;
+        let rotary_emb = Arc::new(RotaryEmbedding::new(cfg, vb_m.device())?);
+        let mut layers = Vec::with_capacity(cfg.num_hidden_layers);
+        let vb_l = vb_m.pp("layers");
+        for layer_idx in 0..cfg.num_hidden_layers {
+            let layer = DecoderLayer::new(rotary_emb.clone(), cfg, vb_l.pp(layer_idx))?;
+            layers.push(layer)
+        }
+        let norm = RmsNorm::new(cfg.hidden_size, cfg.rms_norm_eps, vb_m.pp("norm"))?;
+        let lm_head = linear_no_bias(cfg.hidden_size, cfg.vocab_size, vb.pp("lm_head"))?;
+        Ok(Self {
+            embed_tokens,
+            layers,
+            norm,
+            lm_head,
+            sliding_window: cfg.sliding_window,
+            device: vb.device().clone(),
+        })
+    }
+
+    fn prepare_decoder_attention_mask(
+        &self,
+        b_size: usize,
+        tgt_len: usize,
+        seqlen_offset: usize,
+    ) -> Result<Tensor> {
+        // Sliding window mask?
+        let mask: Vec<_> = (0..tgt_len)
+            .flat_map(|i| {
+                (0..tgt_len).map(move |j| {
+                    if i < j || j + self.sliding_window < i {
+                        f32::NEG_INFINITY
+                    } else {
+                        0.
+                    }
+                })
+            })
+            .collect();
+        let mask = Tensor::from_slice(&mask, (tgt_len, tgt_len), &self.device)?;
+        let mask = if seqlen_offset > 0 {
+            let mask0 = Tensor::zeros((tgt_len, seqlen_offset), DType::F32, &self.device)?;
+            Tensor::cat(&[&mask0, &mask], D::Minus1)?
+        } else {
+            mask
+        };
+        mask.expand((b_size, 1, tgt_len, tgt_len + seqlen_offset))?
+            .to_dtype(DType::F32)
+    }
+
+    pub fn forward(&mut self, input_ids: &Tensor, seqlen_offset: usize) -> Result<Tensor> {
+        let (b_size, seq_len) = input_ids.dims2()?;
+        let attention_mask = if seq_len <= 1 {
+            None
+        } else {
+            let mask = self.prepare_decoder_attention_mask(b_size, seq_len, seqlen_offset)?;
+            Some(mask)
+        };
+        let mut xs = self.embed_tokens.forward(input_ids)?;
+        for layer in self.layers.iter_mut() {
+            xs = layer.forward(&xs, attention_mask.as_ref(), seqlen_offset)?
+        }
+        xs.narrow(1, seq_len - 1, 1)?
+            .apply(&self.norm)?
+            .apply(&self.lm_head)
+    }
+}