add models of rwkv v6 and quantized rwkv v6 (#1781)

* add models of rwkv v6 and quantized rwkv v6

* fix ci clippy fail

candle-transformers/src/models/mod.rs

@@ -32,12 +32,14 @@ pub mod quantized_mistral;
 pub mod quantized_mixformer;
 pub mod quantized_mpt;
 pub mod quantized_rwkv_v5;
+pub mod quantized_rwkv_v6;
 pub mod quantized_stable_lm;
 pub mod quantized_t5;
 pub mod qwen2;
 pub mod repvgg;
 pub mod resnet;
 pub mod rwkv_v5;
+pub mod rwkv_v6;
 pub mod segment_anything;
 pub mod stable_diffusion;
 pub mod stable_lm;

candle-transformers/src/models/quantized_rwkv_v6.rs

@@ -0,0 +1,332 @@
+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_x: Tensor,
+    time_mix_w: Tensor,
+    time_mix_key: Tensor,
+    time_mix_value: Tensor,
+    time_mix_receptance: Tensor,
+    time_decay: Tensor,
+    time_faaaa: Tensor,
+    time_mix_gate: Tensor,
+    time_decay_w1: Tensor,
+    time_decay_w2: Tensor,
+    time_mix_w1: Tensor,
+    time_mix_w2: 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_x = vb
+            .get((1, 1, cfg.hidden_size), "time_mix_x")?
+            .dequantize(vb.device())?;
+        let time_mix_w = vb
+            .get((1, 1, cfg.hidden_size), "time_mix_w")?
+            .dequantize(vb.device())?;
+        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((1, 1, cfg.hidden_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())?;
+        let time_decay_w1 = vb
+            .get((cfg.hidden_size, n_attn_heads * 2), "time_decay_w1")?
+            .dequantize(vb.device())?;
+        let time_decay_w2 = vb
+            .get((n_attn_heads * 2, cfg.hidden_size), "time_decay_w2")?
+            .dequantize(vb.device())?;
+        let time_mix_w1 = vb
+            .get((cfg.hidden_size, n_attn_heads * 5), "time_mix_w1")?
+            .dequantize(vb.device())?;
+        let time_mix_w2 = vb
+            .get((5, n_attn_heads, cfg.hidden_size), "time_mix_w2")?
+            .dequantize(vb.device())?;
+        Ok(Self {
+            key,
+            value,
+            receptance,
+            gate,
+            output,
+            ln_x,
+            time_mix_x,
+            time_mix_w,
+            time_mix_key,
+            time_mix_value,
+            time_mix_receptance,
+            time_decay,
+            time_faaaa,
+            time_mix_gate,
+            time_decay_w1,
+            time_decay_w2,
+            time_mix_w1,
+            time_mix_w2,
+            layer_id,
+            n_attn_heads,
+        })
+    }
+
+    pub fn forward(&self, xs: &Tensor, state: &mut State) -> Result<Tensor> {
+        let h = self.n_attn_heads;
+        let (b, t, s) = xs.dims3()?;
+        let s = s / h;
+        let (receptance, key, value, gate, w) = {
+            // 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 sx = (&shifted - xs)?;
+            let xxx = (xs + &sx * &self.time_mix_x)?;
+            let xxx = xxx
+                .broadcast_matmul(&self.time_mix_w1)?
+                .tanh()?
+                .reshape((b * t, 5, ()))?
+                .transpose(0, 1)?;
+
+            let xxx = xxx.matmul(&self.time_mix_w2)?.reshape((5, b, t, ()))?;
+
+            let (mw, mk, mv, mr, mg) = (xxx.i(0)?, xxx.i(1)?, xxx.i(2)?, xxx.i(3)?, xxx.i(4)?);
+
+            let xw = (xs + &sx * (&self.time_mix_w + &mw)?)?;
+            let xk = (xs + &sx * (&self.time_mix_key + &mk)?)?;
+            let xv = (xs + &sx * (&self.time_mix_value + &mv)?)?;
+            let xr = (xs + &sx * (&self.time_mix_receptance + &mr)?)?;
+            let xg = (xs + &sx * (&self.time_mix_gate + &mg)?)?;
+
+            let w = (&self.time_decay
+                + xw.broadcast_matmul(&self.time_decay_w1)?
+                    .tanh()?
+                    .broadcast_matmul(&self.time_decay_w2)?)?
+            .reshape(((), 1, 1))?
+            .reshape((self.n_attn_heads, (), 1))?;
+
+            let key = self.key.forward(&xk)?;
+            let value = self.value.forward(&xv)?;
+            let receptance = self.receptance.forward(&xr)?;
+            let gate = candle_nn::ops::silu(&self.gate.forward(&xg)?)?;
+            state.per_layer[self.layer_id].extract_key_value = xs.i((.., t - 1))?;
+            (receptance, key, value, gate, w)
+        };
+
+        // 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 w = w.exp()?.neg()?.exp()?;
+
+        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 + w.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
+            .broadcast_sub(xs)?;
+        let key = (xs + shifted.broadcast_mul(&self.time_mix_key)?)?;
+        let receptance = (xs + shifted.broadcast_mul(&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_v6.rs

@@ -0,0 +1,295 @@
+use super::with_tracing::{layer_norm, linear_no_bias as linear, LayerNorm, Linear};
+use candle::{IndexOp, Result, Tensor};
+use candle_nn::{embedding, Embedding, Module, VarBuilder};
+
+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_x: Tensor,
+    time_mix_w: Tensor,
+    time_mix_key: Tensor,
+    time_mix_value: Tensor,
+    time_mix_receptance: Tensor,
+    time_decay: Tensor,
+    time_faaaa: Tensor,
+    time_mix_gate: Tensor,
+    time_decay_w1: Tensor,
+    time_decay_w2: Tensor,
+    time_mix_w1: Tensor,
+    time_mix_w2: 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 ln_x = candle_nn::group_norm(
+            hidden_size / cfg.head_size,
+            hidden_size,
+            1e-5,
+            vb.pp("ln_x"),
+        )?;
+
+        let time_mix_x = vb.get((1, 1, cfg.hidden_size), "time_mix_x")?;
+        let time_mix_w = vb.get((1, 1, cfg.hidden_size), "time_mix_w")?;
+        let time_mix_key = vb.get((1, 1, cfg.hidden_size), "time_mix_key")?;
+        let time_mix_value = vb.get((1, 1, cfg.hidden_size), "time_mix_value")?;
+        let time_mix_receptance = vb.get((1, 1, cfg.hidden_size), "time_mix_receptance")?;
+        let n_attn_heads = cfg.hidden_size / cfg.head_size;
+        let time_decay = vb.get((1, 1, cfg.hidden_size), "time_decay")?;
+        let time_faaaa = vb.get((n_attn_heads, cfg.head_size), "time_faaaa")?;
+        let time_mix_gate = vb.get((1, 1, cfg.hidden_size), "time_mix_gate")?;
+        let time_decay_w1 = vb.get((cfg.hidden_size, n_attn_heads * 2), "time_decay_w1")?;
+        let time_decay_w2 = vb.get((n_attn_heads * 2, cfg.hidden_size), "time_decay_w2")?;
+        let time_mix_w1 = vb.get((cfg.hidden_size, n_attn_heads * 5), "time_mix_w1")?;
+        let time_mix_w2 = vb.get((5, n_attn_heads, cfg.hidden_size), "time_mix_w2")?;
+        Ok(Self {
+            key,
+            value,
+            receptance,
+            gate,
+            output,
+            ln_x,
+            time_mix_x,
+            time_mix_w,
+            time_mix_key,
+            time_mix_value,
+            time_mix_receptance,
+            time_decay,
+            time_faaaa,
+            time_mix_gate,
+            time_decay_w1,
+            time_decay_w2,
+            time_mix_w1,
+            time_mix_w2,
+            layer_id,
+            n_attn_heads,
+        })
+    }
+
+    pub fn forward(&self, xs: &Tensor, state: &mut State) -> Result<Tensor> {
+        let h = self.n_attn_heads;
+        let (b, t, s) = xs.dims3()?;
+        let s = s / h;
+        let (receptance, key, value, gate, w) = {
+            // 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 sx = (&shifted - xs)?;
+            let xxx = (xs + &sx * &self.time_mix_x)?;
+            let xxx = xxx
+                .broadcast_matmul(&self.time_mix_w1)?
+                .tanh()?
+                .reshape((b * t, 5, ()))?
+                .transpose(0, 1)?;
+
+            let xxx = xxx.matmul(&self.time_mix_w2)?.reshape((5, b, t, ()))?;
+
+            let (mw, mk, mv, mr, mg) = (xxx.i(0)?, xxx.i(1)?, xxx.i(2)?, xxx.i(3)?, xxx.i(4)?);
+
+            let xw = (xs + &sx * (&self.time_mix_w + &mw)?)?;
+            let xk = (xs + &sx * (&self.time_mix_key + &mk)?)?;
+            let xv = (xs + &sx * (&self.time_mix_value + &mv)?)?;
+            let xr = (xs + &sx * (&self.time_mix_receptance + &mr)?)?;
+            let xg = (xs + &sx * (&self.time_mix_gate + &mg)?)?;
+
+            let w = (&self.time_decay
+                + xw.broadcast_matmul(&self.time_decay_w1)?
+                    .tanh()?
+                    .broadcast_matmul(&self.time_decay_w2)?)?
+            .reshape(((), 1, 1))?
+            .reshape((self.n_attn_heads, (), 1))?;
+
+            let key = self.key.forward(&xk)?;
+            let value = self.value.forward(&xv)?;
+            let receptance = self.receptance.forward(&xr)?;
+            let gate = candle_nn::ops::silu(&self.gate.forward(&xg)?)?;
+            state.per_layer[self.layer_id].extract_key_value = xs.i((.., t - 1))?;
+            (receptance, key, value, gate, w)
+        };
+
+        // 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 w = w.exp()?.neg()?.exp()?;
+
+        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 + w.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")?;
+        let time_mix_receptance = vb.get((1, 1, cfg.hidden_size), "time_mix_receptance")?;
+        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
+            .broadcast_sub(xs)?;
+        let key = (xs + shifted.broadcast_mul(&self.time_mix_key)?)?;
+        let receptance = (xs + shifted.broadcast_mul(&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(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)
+    }
+}