add quantized rwkv v5 model (#1743)

* and quantized rwkv v5 model

* Integrate the quantized rwkv model in the initial example.

---------

Co-authored-by: laurent <laurent.mazare@gmail.com>

candle-examples/examples/rwkv/main.rs

@@ -7,13 +7,28 @@ extern crate accelerate_src;
 use anyhow::Result;
 use clap::{Parser, ValueEnum};
 
-use candle_transformers::models::rwkv_v5::{Config, Model, State, Tokenizer};
+use candle_transformers::models::quantized_rwkv_v5::Model as Q;
+use candle_transformers::models::rwkv_v5::{Config, Model as M, State, Tokenizer};
 
 use candle::{DType, Device, Tensor};
 use candle_nn::VarBuilder;
 use candle_transformers::generation::LogitsProcessor;
 use hf_hub::{api::sync::Api, Repo, RepoType};
 
+enum Model {
+    M(M),
+    Q(Q),
+}
+
+impl Model {
+    fn forward(&self, xs: &Tensor, state: &mut State) -> candle::Result<Tensor> {
+        match self {
+            Self::M(m) => m.forward(xs, state),
+            Self::Q(m) => m.forward(xs, state),
+        }
+    }
+}
+
 struct TextGeneration {
     model: Model,
     config: Config,
@@ -176,6 +191,9 @@ struct Args {
     #[arg(long)]
     config_file: Option<String>,
 
+    #[arg(long)]
+    quantized: bool,
+
     /// Penalty to be applied for repeating tokens, 1. means no penalty.
     #[arg(long, default_value_t = 1.1)]
     repeat_penalty: f32,
@@ -236,7 +254,16 @@ fn main() -> Result<()> {
             .map(std::path::PathBuf::from)
             .collect::<Vec<_>>(),
         None => {
-            vec![repo.get("model.safetensors")?]
+            if args.quantized {
+                let file = match args.which {
+                    Which::World1b5 => "world1b5-q4k.gguf",
+                    Which::World3b => "world3b-q4k.gguf",
+                    Which::Eagle7b => "eagle7b-q4k.gguf",
+                };
+                vec![api.model("lmz/candle-rwkv".to_string()).get(file)?]
+            } else {
+                vec![repo.get("model.safetensors")?]
+            }
         }
     };
     println!("retrieved the files in {:?}", start.elapsed());
@@ -245,8 +272,15 @@ fn main() -> Result<()> {
     let start = std::time::Instant::now();
     let config: Config = serde_json::from_slice(&std::fs::read(config_filename)?)?;
     let device = candle_examples::device(args.cpu)?;
-    let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, DType::F32, &device)? };
-    let model = Model::new(&config, vb)?;
+    let model = if args.quantized {
+        let filename = &filenames[0];
+        let vb =
+            candle_transformers::quantized_var_builder::VarBuilder::from_gguf(filename, &device)?;
+        Model::Q(Q::new(&config, vb)?)
+    } else {
+        let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, DType::F32, &device)? };
+        Model::M(M::new(&config, vb)?)
+    };
     println!("loaded the model in {:?}", start.elapsed());
 
     let mut pipeline = TextGeneration::new(

candle-transformers/src/models/mod.rs

@@ -30,6 +30,7 @@ pub mod quantized_llama2_c;
 pub mod quantized_mistral;
 pub mod quantized_mixformer;
 pub mod quantized_mpt;
+pub mod quantized_rwkv_v5;
 pub mod quantized_stable_lm;
 pub mod quantized_t5;
 pub mod qwen2;

candle-transformers/src/models/quantized_rwkv_v5.rs

@@ -0,0 +1,286 @@
+use crate::{
+    quantized_nn::{layer_norm, linear_no_bias as linear, Embedding, Linear},
+    quantized_var_builder::VarBuilder,
+};
+use candle::{IndexOp, Result, Tensor};
+use candle_nn::{GroupNorm, LayerNorm, Module};
+
+pub use crate::models::rwkv_v5::{Config, State, Tokenizer};
+
+#[derive(Debug, Clone)]
+struct SelfAttention {
+    key: Linear,
+    receptance: Linear,
+    value: Linear,
+    gate: Linear,
+    output: Linear,
+    ln_x: candle_nn::GroupNorm,
+    time_mix_key: Tensor,
+    time_mix_value: Tensor,
+    time_mix_receptance: Tensor,
+    time_decay: Tensor,
+    time_faaaa: Tensor,
+    time_mix_gate: Tensor,
+    layer_id: usize,
+    n_attn_heads: usize,
+}
+
+impl SelfAttention {
+    fn new(layer_id: usize, cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let hidden_size = cfg.hidden_size;
+        let attn_hidden_size = cfg.attention_hidden_size;
+        let key = linear(hidden_size, attn_hidden_size, vb.pp("key"))?;
+        let receptance = linear(hidden_size, attn_hidden_size, vb.pp("receptance"))?;
+        let value = linear(hidden_size, attn_hidden_size, vb.pp("value"))?;
+        let gate = linear(hidden_size, attn_hidden_size, vb.pp("gate"))?;
+        let output = linear(attn_hidden_size, hidden_size, vb.pp("output"))?;
+
+        let vb_x = vb.pp("ln_x");
+        let ln_x_weight = vb_x.get(hidden_size, "weight")?.dequantize(vb.device())?;
+        let ln_x_bias = vb_x.get(hidden_size, "bias")?.dequantize(vb.device())?;
+
+        let ln_x = GroupNorm::new(
+            ln_x_weight,
+            ln_x_bias,
+            hidden_size,
+            hidden_size / cfg.head_size,
+            1e-5,
+        )?;
+
+        let time_mix_key = vb
+            .get((1, 1, cfg.hidden_size), "time_mix_key")?
+            .dequantize(vb.device())?;
+        let time_mix_value = vb
+            .get((1, 1, cfg.hidden_size), "time_mix_value")?
+            .dequantize(vb.device())?;
+        let time_mix_receptance = vb
+            .get((1, 1, cfg.hidden_size), "time_mix_receptance")?
+            .dequantize(vb.device())?;
+        let n_attn_heads = cfg.hidden_size / cfg.head_size;
+        let time_decay = vb
+            .get((n_attn_heads, cfg.head_size), "time_decay")?
+            .dequantize(vb.device())?;
+        let time_faaaa = vb
+            .get((n_attn_heads, cfg.head_size), "time_faaaa")?
+            .dequantize(vb.device())?;
+        let time_mix_gate = vb
+            .get((1, 1, cfg.hidden_size), "time_mix_gate")?
+            .dequantize(vb.device())?;
+        Ok(Self {
+            key,
+            value,
+            receptance,
+            gate,
+            output,
+            ln_x,
+            time_mix_key,
+            time_mix_value,
+            time_mix_receptance,
+            time_decay,
+            time_faaaa,
+            time_mix_gate,
+            layer_id,
+            n_attn_heads,
+        })
+    }
+
+    pub fn forward(&self, xs: &Tensor, state: &mut State) -> Result<Tensor> {
+        let h = self.time_decay.dim(0)?;
+        let (b, t, s) = xs.dims3()?;
+        let s = s / h;
+        let (receptance, key, value, gate) = {
+            // extract key-value
+            let shifted = state.per_layer[self.layer_id].extract_key_value.clone();
+            let shifted = if shifted.rank() == 2 {
+                shifted.unsqueeze(1)?
+            } else {
+                shifted
+            };
+            let key = ((xs * &self.time_mix_key)? + &shifted * (1.0 - &self.time_mix_key)?)?;
+            let value = ((xs * &self.time_mix_value)? + &shifted * (1.0 - &self.time_mix_value)?)?;
+            let receptance = ((xs * &self.time_mix_receptance)?
+                + &shifted * (1.0 - &self.time_mix_receptance)?)?;
+            let gate = ((xs * &self.time_mix_gate)? + &shifted * (1.0 - &self.time_mix_gate)?)?;
+
+            let key = self.key.forward(&key)?;
+            let value = self.value.forward(&value)?;
+            let receptance = self.receptance.forward(&receptance)?;
+            let gate = candle_nn::ops::silu(&self.gate.forward(&gate)?)?;
+            state.per_layer[self.layer_id].extract_key_value = xs.i((.., t - 1))?;
+            (receptance, key, value, gate)
+        };
+        // linear attention
+        let mut state_ = state.per_layer[self.layer_id].linear_attention.clone();
+        let key = key.reshape((b, t, h, s))?.permute((0, 2, 3, 1))?;
+        let value = value.reshape((b, t, h, s))?.transpose(1, 2)?;
+        let receptance = receptance.reshape((b, t, h, s))?.transpose(1, 2)?;
+
+        let time_decay = self
+            .time_decay
+            .exp()?
+            .neg()?
+            .exp()?
+            .reshape(((), 1, 1))?
+            .reshape((self.n_attn_heads, (), 1))?;
+        let time_faaaa =
+            self.time_faaaa
+                .reshape(((), 1, 1))?
+                .reshape((self.n_attn_heads, (), 1))?;
+
+        let mut out: Vec<Tensor> = Vec::with_capacity(t);
+        for t_ in 0..t {
+            let rt = receptance.i((.., .., t_..t_ + 1))?.contiguous()?;
+            let kt = key.i((.., .., .., t_..t_ + 1))?.contiguous()?;
+            let vt = value.i((.., .., t_..t_ + 1))?.contiguous()?;
+            let at = kt.matmul(&vt)?;
+            let rhs = (time_faaaa.broadcast_mul(&at)? + &state_)?;
+            let out_ = rt.matmul(&rhs)?.squeeze(2)?;
+            state_ = (&at + time_decay.broadcast_mul(&state_))?;
+            out.push(out_)
+        }
+        let out = Tensor::cat(&out, 1)?.reshape((b * t, h * s, 1))?;
+        let out = out.apply(&self.ln_x)?.reshape((b, t, h * s))?;
+        let out = (out * gate)?.apply(&self.output)?;
+        state.per_layer[self.layer_id].linear_attention = state_;
+        Ok(out)
+    }
+}
+
+#[derive(Debug, Clone)]
+struct FeedForward {
+    time_mix_key: Tensor,
+    time_mix_receptance: Tensor,
+    key: Linear,
+    receptance: Linear,
+    value: Linear,
+    layer_id: usize,
+}
+
+impl FeedForward {
+    fn new(layer_id: usize, cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let int_size = cfg
+            .intermediate_size
+            .unwrap_or(((cfg.hidden_size as f64 * 3.5) as usize) / 32 * 32);
+        let key = linear(cfg.hidden_size, int_size, vb.pp("key"))?;
+        let receptance = linear(cfg.hidden_size, cfg.hidden_size, vb.pp("receptance"))?;
+        let value = linear(int_size, cfg.hidden_size, vb.pp("value"))?;
+        let time_mix_key = vb
+            .get((1, 1, cfg.hidden_size), "time_mix_key")?
+            .dequantize(vb.device())?;
+        let time_mix_receptance = vb
+            .get((1, 1, cfg.hidden_size), "time_mix_receptance")?
+            .dequantize(vb.device())?;
+        Ok(Self {
+            key,
+            receptance,
+            value,
+            time_mix_key,
+            time_mix_receptance,
+            layer_id,
+        })
+    }
+
+    fn forward(&self, xs: &Tensor, state: &mut State) -> Result<Tensor> {
+        let shifted = &state.per_layer[self.layer_id].feed_forward;
+        let key = (xs.broadcast_mul(&self.time_mix_key)?
+            + shifted.broadcast_mul(&(1.0 - &self.time_mix_key)?)?)?;
+        let receptance = (xs.broadcast_mul(&self.time_mix_receptance)?
+            + shifted.broadcast_mul(&(1.0 - &self.time_mix_receptance)?)?)?;
+        let key = key.apply(&self.key)?.relu()?.sqr()?;
+        let value = key.apply(&self.value)?;
+        let receptance = candle_nn::ops::sigmoid(&receptance.apply(&self.receptance)?)?;
+        state.per_layer[self.layer_id].feed_forward = xs.i((.., xs.dim(1)? - 1))?;
+        let xs = (receptance * value)?;
+        Ok(xs)
+    }
+}
+
+#[derive(Debug, Clone)]
+struct Block {
+    pre_ln: Option<LayerNorm>,
+    ln1: LayerNorm,
+    ln2: LayerNorm,
+    attention: SelfAttention,
+    feed_forward: FeedForward,
+}
+
+impl Block {
+    fn new(layer_id: usize, cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let ln1 = layer_norm(cfg.hidden_size, cfg.layer_norm_epsilon, vb.pp("ln1"))?;
+        let ln2 = layer_norm(cfg.hidden_size, cfg.layer_norm_epsilon, vb.pp("ln2"))?;
+        let pre_ln = if layer_id == 0 {
+            let ln = layer_norm(cfg.hidden_size, cfg.layer_norm_epsilon, vb.pp("pre_ln"))?;
+            Some(ln)
+        } else {
+            None
+        };
+        let attention = SelfAttention::new(layer_id, cfg, vb.pp("attention"))?;
+        let feed_forward = FeedForward::new(layer_id, cfg, vb.pp("feed_forward"))?;
+        Ok(Self {
+            pre_ln,
+            ln1,
+            ln2,
+            attention,
+            feed_forward,
+        })
+    }
+
+    fn forward(&self, xs: &Tensor, state: &mut State) -> Result<Tensor> {
+        let xs = match self.pre_ln.as_ref() {
+            None => xs.clone(),
+            Some(pre_ln) => xs.apply(pre_ln)?,
+        };
+        let attention = self.attention.forward(&xs.apply(&self.ln1)?, state)?;
+        let xs = (xs + attention)?;
+        let feed_forward = self.feed_forward.forward(&xs.apply(&self.ln2)?, state)?;
+        let xs = (xs + feed_forward)?;
+        Ok(xs)
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct Model {
+    embeddings: Embedding,
+    blocks: Vec<Block>,
+    ln_out: LayerNorm,
+    head: Linear,
+    rescale_every: usize,
+    layers_are_rescaled: bool,
+}
+
+impl Model {
+    pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let vb_m = vb.pp("rwkv");
+        let embeddings = Embedding::new(cfg.vocab_size, cfg.hidden_size, vb_m.pp("embeddings"))?;
+        let mut blocks = Vec::with_capacity(cfg.num_hidden_layers);
+        let vb_b = vb_m.pp("blocks");
+        for block_index in 0..cfg.num_hidden_layers {
+            let block = Block::new(block_index, cfg, vb_b.pp(block_index))?;
+            blocks.push(block)
+        }
+        let ln_out = layer_norm(cfg.hidden_size, 1e-5, vb_m.pp("ln_out"))?;
+        let head = linear(cfg.hidden_size, cfg.vocab_size, vb.pp("head"))?;
+        Ok(Self {
+            embeddings,
+            blocks,
+            ln_out,
+            head,
+            rescale_every: cfg.rescale_every,
+            layers_are_rescaled: false, // This seem to only happen for the f16/bf16 dtypes.
+        })
+    }
+
+    pub fn forward(&self, xs: &Tensor, state: &mut State) -> Result<Tensor> {
+        let (_b_size, _seq_len) = xs.dims2()?;
+        let mut xs = xs.apply(&self.embeddings)?;
+        for (block_idx, block) in self.blocks.iter().enumerate() {
+            xs = block.forward(&xs, state)?;
+            if self.layers_are_rescaled && (block_idx + 1) % self.rescale_every == 0 {
+                xs = (xs / 2.)?
+            }
+        }
+        let xs = xs.apply(&self.ln_out)?.apply(&self.head)?;
+        state.pos += 1;
+        Ok(xs)
+    }
+}

candle-transformers/src/models/rwkv_v5.rs

@@ -124,7 +124,7 @@ impl SelfAttention {
         let (b, t, s) = xs.dims3()?;
         let s = s / h;
         let (receptance, key, value, gate) = {
-            // exctract key-value
+            // extract key-value
             let shifted = state.per_layer[self.layer_id].extract_key_value.clone();
             let shifted = if shifted.rank() == 2 {
                 shifted.unsqueeze(1)?
@@ -164,7 +164,6 @@ impl SelfAttention {
 
         let mut out: Vec<Tensor> = Vec::with_capacity(t);
         for t_ in 0..t {
-            //
             let rt = receptance.i((.., .., t_..t_ + 1))?.contiguous()?;
             let kt = key.i((.., .., .., t_..t_ + 1))?.contiguous()?;
             let vt = value.i((.., .., t_..t_ + 1))?.contiguous()?;