1 files changed, 227 insertions, 0 deletions
diff --git a/candle-examples/examples/llama2-c/qmodel.rs b/candle-examples/examples/llama2-c/qmodel.rs
new file mode 100644
index 00000000..07db146e
--- /dev/null
+++ b/candle-examples/examples/llama2-c/qmodel.rs
@@ -0,0 +1,227 @@
+use super::model::{Cache, Config};
+use candle::{DType, IndexOp, Module, Result, Tensor, D};
+use candle_transformers::quantized_nn::{linear_no_bias as linear, Embedding, Linear, RmsNorm};
+pub use candle_transformers::quantized_var_builder::VarBuilder;
+
+fn silu(xs: &Tensor) -> Result<Tensor> {
+    xs / (xs.neg()?.exp()? + 1.0)?
+}
+
+struct CausalSelfAttention {
+    q_proj: Linear,
+    k_proj: Linear,
+    v_proj: Linear,
+    o_proj: Linear,
+    n_head: usize,
+    n_key_value_head: usize,
+    head_dim: usize,
+    cache: Cache,
+}
+
+impl CausalSelfAttention {
+    fn apply_rotary_emb(&self, x: &Tensor, index_pos: usize) -> Result<Tensor> {
+        let (b_sz, seq_len, h, n_embd) = x.dims4()?;
+        let cos = self.cache.cos.i(index_pos..index_pos + seq_len)?;
+        let sin = self.cache.sin.i(index_pos..index_pos + seq_len)?;
+        let cos = cos.unsqueeze(1)?;
+        let sin = sin.unsqueeze(1)?;
+        let cos = cos.broadcast_as((b_sz, seq_len, 1, n_embd / 2, 1))?;
+        let sin = sin.broadcast_as((b_sz, seq_len, 1, n_embd / 2, 1))?;
+        let x = x.reshape((b_sz, seq_len, h, n_embd / 2, 2))?;
+        let x0 = x.narrow(D::Minus1, 0, 1)?;
+        let x1 = x.narrow(D::Minus1, 1, 1)?;
+        let dst0 = (x0.broadcast_mul(&cos)? - x1.broadcast_mul(&sin)?)?;
+        let dst1 = (x0.broadcast_mul(&sin)? + x1.broadcast_mul(&cos)?)?;
+        let rope = Tensor::cat(&[&dst0, &dst1], D::Minus1)?.reshape((b_sz, seq_len, h, n_embd))?;
+        Ok(rope)
+    }
+
+    fn forward(&self, x: &Tensor, index_pos: usize, block_idx: usize) -> Result<Tensor> {
+        let (b_sz, seq_len, n_embd) = x.dims3()?;
+        let q = self.q_proj.forward(x)?;
+        let k = self.k_proj.forward(x)?;
+        let v = self.v_proj.forward(x)?;
+
+        let q = q.reshape((b_sz, seq_len, self.n_head, self.head_dim))?;
+        let k = k.reshape((b_sz, seq_len, self.n_key_value_head, self.head_dim))?;
+        let mut v = v.reshape((b_sz, seq_len, self.n_key_value_head, self.head_dim))?;
+
+        let q = self.apply_rotary_emb(&q, index_pos)?;
+        let mut k = self.apply_rotary_emb(&k, index_pos)?;
+
+        if self.cache.use_kv_cache {
+            let mut cache = self.cache.kvs.lock().unwrap();
+            if let Some((cache_k, cache_v)) = &cache[block_idx] {
+                k = Tensor::cat(&[cache_k, &k], 1)?.contiguous()?;
+                v = Tensor::cat(&[cache_v, &v], 1)?.contiguous()?;
+            }
+            cache[block_idx] = Some((k.clone(), v.clone()))
+        }
+
+        let k = self.repeat_kv(k)?;
+        let v = self.repeat_kv(v)?;
+
+        let q = q.transpose(1, 2)?.contiguous()?;
+        let k = k.transpose(1, 2)?.contiguous()?;
+        let v = v.transpose(1, 2)?.contiguous()?;
+
+        let att = (q.matmul(&k.t()?)? / (self.head_dim as f64).sqrt())?;
+        let mask = self.cache.mask(seq_len)?.broadcast_as(att.shape())?;
+        let att = masked_fill(&att, &mask, f32::NEG_INFINITY)?;
+        let att = candle_nn::ops::softmax(&att, D::Minus1)?;
+        // Convert to contiguous as matmul doesn't support strided vs for now.
+        let y = att.matmul(&v.contiguous()?)?;
+        let y = y.transpose(1, 2)?.reshape(&[b_sz, seq_len, n_embd])?;
+        let y = self.o_proj.forward(&y)?;
+        Ok(y)
+    }
+
+    fn repeat_kv(&self, x: Tensor) -> Result<Tensor> {
+        let n_rep = self.n_head / self.n_key_value_head;
+        if n_rep == 1 {
+            Ok(x)
+        } else {
+            let (b_sz, seq_len, n_kv_head, head_dim) = x.dims4()?;
+            let x = x
+                .unsqueeze(3)?
+                .expand((b_sz, seq_len, n_kv_head, n_rep, head_dim))?
+                .reshape((b_sz, seq_len, n_kv_head * n_rep, head_dim))?;
+            Ok(x)
+        }
+    }
+
+    fn load(vb: VarBuilder, cache: &Cache, cfg: &Config) -> Result<Self> {
+        let size_in = cfg.dim;
+        let size_q = (cfg.dim / cfg.n_heads) * cfg.n_heads;
+        let size_kv = (cfg.dim / cfg.n_heads) * cfg.n_kv_heads;
+        let q_proj = linear(size_in, size_q, vb.pp("q_proj"))?;
+        let k_proj = linear(size_in, size_kv, vb.pp("k_proj"))?;
+        let v_proj = linear(size_in, size_kv, vb.pp("v_proj"))?;
+        let o_proj = linear(size_q, size_in, vb.pp("o_proj"))?;
+        Ok(Self {
+            q_proj,
+            k_proj,
+            v_proj,
+            o_proj,
+            n_head: cfg.n_heads,
+            n_key_value_head: cfg.n_kv_heads,
+            head_dim: cfg.dim / cfg.n_heads,
+            cache: cache.clone(),
+        })
+    }
+}
+
+fn masked_fill(on_false: &Tensor, mask: &Tensor, on_true: f32) -> Result<Tensor> {
+    let shape = mask.shape();
+    let on_true = Tensor::new(on_true, on_false.device())?.broadcast_as(shape.dims())?;
+    let m = mask.where_cond(&on_true, on_false)?;
+    Ok(m)
+}
+
+struct Mlp {
+    c_fc1: Linear,
+    c_fc2: Linear,
+    c_proj: Linear,
+}
+
+impl Mlp {
+    fn new(c_fc1: Linear, c_fc2: Linear, c_proj: Linear) -> Self {
+        Self {
+            c_fc1,
+            c_fc2,
+            c_proj,
+        }
+    }
+
+    fn forward(&self, x: &Tensor) -> Result<Tensor> {
+        let x = (silu(&self.c_fc1.forward(x)?)? * self.c_fc2.forward(x)?)?;
+        self.c_proj.forward(&x)
+    }
+
+    fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
+        let h_size = cfg.dim;
+        let i_size = cfg.hidden_dim;
+        let c_fc1 = linear(h_size, i_size, vb.pp("gate_proj"))?;
+        let c_fc2 = linear(h_size, i_size, vb.pp("up_proj"))?;
+        let c_proj = linear(i_size, h_size, vb.pp("down_proj"))?;
+        Ok(Self::new(c_fc1, c_fc2, c_proj))
+    }
+}
+
+struct Block {
+    rms_1: RmsNorm,
+    attn: CausalSelfAttention,
+    rms_2: RmsNorm,
+    mlp: Mlp,
+}
+
+impl Block {
+    fn new(rms_1: RmsNorm, attn: CausalSelfAttention, rms_2: RmsNorm, mlp: Mlp) -> Self {
+        Self {
+            rms_1,
+            attn,
+            rms_2,
+            mlp,
+        }
+    }
+
+    fn forward(&self, x: &Tensor, index_pos: usize, block_idx: usize) -> Result<Tensor> {
+        let residual = x;
+        let x = self.rms_1.forward(x)?;
+        let x = (self.attn.forward(&x, index_pos, block_idx)? + residual)?;
+        let residual = &x;
+        let x = (self.mlp.forward(&self.rms_2.forward(&x)?)? + residual)?;
+        Ok(x)
+    }
+
+    fn load(vb: VarBuilder, cache: &Cache, cfg: &Config) -> Result<Self> {
+        let attn = CausalSelfAttention::load(vb.pp("self_attn"), cache, cfg)?;
+        let mlp = Mlp::load(vb.pp("mlp"), cfg)?;
+        let input_layernorm = RmsNorm::new(cfg.dim, cfg.norm_eps, vb.pp("input_layernorm"))?;
+        let post_attention_layernorm =
+            RmsNorm::new(cfg.dim, cfg.norm_eps, vb.pp("post_attention_layernorm"))?;
+        Ok(Self::new(
+            input_layernorm,
+            attn,
+            post_attention_layernorm,
+            mlp,
+        ))
+    }
+}
+
+pub struct QLlama {
+    wte: Embedding,
+    blocks: Vec<Block>,
+    ln_f: RmsNorm,
+    lm_head: Linear,
+    pub config: Config,
+}
+
+impl QLlama {
+    pub fn forward(&self, x: &Tensor, index_pos: usize) -> Result<Tensor> {
+        let (_b_sz, _seq_len) = x.dims2()?;
+        let mut x = self.wte.forward(x)?;
+        for (block_idx, block) in self.blocks.iter().enumerate() {
+            x = block.forward(&x, index_pos, block_idx)?;
+        }
+        let x = self.ln_f.forward(&x)?;
+        let logits = self.lm_head.forward(&x)?;
+        logits.to_dtype(DType::F32)
+    }
+
+    pub fn load(vb: VarBuilder, cache: &Cache, cfg: Config) -> Result<Self> {
+        let wte = Embedding::new(cfg.vocab_size, cfg.dim, vb.pp("model.embed_tokens"))?;
+        let lm_head = linear(cfg.dim, cfg.vocab_size, vb.pp("lm_head"))?;
+        let ln_f = RmsNorm::new(cfg.dim, cfg.norm_eps, vb.pp("model.norm"))?;
+        let blocks: Vec<_> = (0..cfg.n_layers)
+            .map(|i| Block::load(vb.pp(format!("model.layers.{i}")), cache, &cfg).unwrap())
+            .collect();
+        Ok(Self {
+            wte,
+            blocks,
+            ln_f,
+            lm_head,
+            config: cfg,
+        })
+    }
+}