add quantized qwen2 (#2329)

* add quantized version of qwen2 and corresponding example for qwen2-instruct

* fix quantized qwen2 clippy error

2 files changed, 324 insertions, 0 deletions

diff --git a/candle-transformers/src/models/mod.rs b/candle-transformers/src/models/mod.rs
index 86a0ec08..7baa12e6 100644
--- a/candle-transformers/src/models/mod.rs
+++ b/candle-transformers/src/models/mod.rs
@@ -47,6 +47,7 @@ pub mod quantized_moondream;
 pub mod quantized_mpt;
 pub mod quantized_phi;
 pub mod quantized_phi3;
+pub mod quantized_qwen2;
 pub mod quantized_recurrent_gemma;
 pub mod quantized_rwkv_v5;
 pub mod quantized_rwkv_v6;
diff --git a/candle-transformers/src/models/quantized_qwen2.rs b/candle-transformers/src/models/quantized_qwen2.rs
new file mode 100644
index 00000000..addfab2b
--- /dev/null
+++ b/candle-transformers/src/models/quantized_qwen2.rs
@@ -0,0 +1,323 @@
+use crate::{quantized_nn::RmsNorm, utils::repeat_kv};
+use candle::{
+    quantized::{gguf_file, QMatMul},
+    DType, Device, IndexOp, Result, Tensor,
+};
+use candle_nn::{Embedding, Module};
+use std::collections::HashMap;
+
+#[derive(Debug, Clone)]
+struct Mlp {
+    feed_forward_w1: QMatMul,
+    feed_forward_w2: QMatMul,
+    feed_forward_w3: QMatMul,
+}
+
+impl Module for Mlp {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let w1 = self.feed_forward_w1.forward(xs)?;
+        let w3 = self.feed_forward_w3.forward(xs)?;
+        self.feed_forward_w2
+            .forward(&(candle_nn::ops::silu(&w1)? * w3)?)
+    }
+}
+
+#[derive(Debug, Clone)]
+struct LayerWeights {
+    attention_wq: QMatMul,
+    attention_wk: QMatMul,
+    attention_wv: QMatMul,
+    attention_bq: Tensor,
+    attention_bk: Tensor,
+    attention_bv: Tensor,
+    attention_wo: QMatMul,
+    attention_norm: RmsNorm,
+    mlp: Mlp,
+    ffn_norm: RmsNorm,
+    n_head: usize,
+    n_kv_head: usize,
+    head_dim: usize,
+    cos: Tensor,
+    sin: Tensor,
+    neg_inf: Tensor,
+    kv_cache: Option<(Tensor, Tensor)>,
+    span_attn: tracing::Span,
+    span_rot: tracing::Span,
+    span_mlp: tracing::Span,
+}
+
+fn masked_fill(on_false: &Tensor, mask: &Tensor, on_true: &Tensor) -> Result<Tensor> {
+    let shape = mask.shape();
+    let m = mask.where_cond(&on_true.broadcast_as(shape.dims())?, on_false)?;
+    Ok(m)
+}
+
+impl LayerWeights {
+    fn apply_rotary_emb(&self, x: &Tensor, index_pos: usize) -> Result<Tensor> {
+        let _enter = self.span_rot.enter();
+        let (_b_sz, _n_head, seq_len, _n_embd) = x.dims4()?;
+        let cos = self.cos.narrow(0, index_pos, seq_len)?;
+        let sin = self.sin.narrow(0, index_pos, seq_len)?;
+        candle_nn::rotary_emb::rope(&x.contiguous()?, &cos, &sin)
+    }
+
+    fn forward_attn(
+        &mut self,
+        x: &Tensor,
+        mask: Option<&Tensor>,
+        index_pos: usize,
+    ) -> Result<Tensor> {
+        let _enter = self.span_attn.enter();
+        let (b_sz, seq_len, n_embd) = x.dims3()?;
+
+        let q = self.attention_wq.forward(x)?;
+        let k = self.attention_wk.forward(x)?;
+        let v = self.attention_wv.forward(x)?;
+
+        let q = q.broadcast_add(&self.attention_bq)?;
+        let k = k.broadcast_add(&self.attention_bk)?;
+        let v = v.broadcast_add(&self.attention_bv)?;
+
+        let q = q
+            .reshape((b_sz, seq_len, self.n_head, self.head_dim))?
+            .transpose(1, 2)?
+            .contiguous()?;
+        let k = k
+            .reshape((b_sz, seq_len, self.n_kv_head, self.head_dim))?
+            .transpose(1, 2)?
+            .contiguous()?;
+        let v = v
+            .reshape((b_sz, seq_len, self.n_kv_head, self.head_dim))?
+            .transpose(1, 2)?
+            .contiguous()?;
+
+        // let (q, k) = self
+        //     .rotary_embedding
+        //     .apply_rotary_emb_qkv(&q, &k, index_pos)?;
+        let q = self.apply_rotary_emb(&q, index_pos)?;
+        let k = self.apply_rotary_emb(&k, index_pos)?;
+
+        let (k, v) = match &self.kv_cache {
+            None => (k, v),
+            Some((k_cache, v_cache)) => {
+                if index_pos == 0 {
+                    (k, v)
+                } else {
+                    let k = Tensor::cat(&[k_cache, &k], 2)?;
+                    let v = Tensor::cat(&[v_cache, &v], 2)?;
+                    (k, v)
+                }
+            }
+        };
+        self.kv_cache = Some((k.clone(), v.clone()));
+
+        // Support for MQA, useful for 70B models and mistral.
+        let k = repeat_kv(k, self.n_head / self.n_kv_head)?;
+        let v = repeat_kv(v, self.n_head / self.n_kv_head)?;
+
+        let att = (q.matmul(&k.t()?)? / (self.head_dim as f64).sqrt())?;
+        let att = match mask {
+            None => att,
+            Some(mask) => {
+                let mask = mask.broadcast_as(att.shape())?;
+                masked_fill(&att, &mask, &self.neg_inf)?
+            }
+        };
+        let att = candle_nn::ops::softmax_last_dim(&att)?;
+        // 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.attention_wo.forward(&y)?;
+        Ok(y)
+    }
+}
+
+pub struct ModelWeights {
+    tok_embeddings: Embedding,
+    layers: Vec<LayerWeights>,
+    norm: RmsNorm,
+    output: QMatMul,
+    masks: HashMap<usize, Tensor>,
+    span: tracing::Span,
+    span_output: tracing::Span,
+}
+
+fn precomput_freqs_cis(
+    head_dim: usize,
+    freq_base: f32,
+    context_length: usize,
+    device: &Device,
+) -> Result<(Tensor, Tensor)> {
+    let theta: Vec<_> = (0..head_dim)
+        .step_by(2)
+        .map(|i| 1f32 / freq_base.powf(i as f32 / head_dim as f32))
+        .collect();
+    let theta = Tensor::new(theta.as_slice(), device)?;
+    let idx_theta = Tensor::arange(0, context_length as u32, device)?
+        .to_dtype(DType::F32)?
+        .reshape((context_length, 1))?
+        .matmul(&theta.reshape((1, theta.elem_count()))?)?;
+    let cos = idx_theta.cos()?;
+    let sin = idx_theta.sin()?;
+    Ok((cos, sin))
+}
+
+impl ModelWeights {
+    pub fn from_gguf<R: std::io::Seek + std::io::Read>(
+        ct: gguf_file::Content,
+        reader: &mut R,
+        device: &Device,
+    ) -> Result<Self> {
+        let md_get = |s: &str| match ct.metadata.get(s) {
+            None => candle::bail!("cannot find {s} in metadata"),
+            Some(v) => Ok(v),
+        };
+
+        let head_count = md_get("qwen2.attention.head_count")?.to_u32()? as usize;
+        let head_count_kv = md_get("qwen2.attention.head_count_kv")?.to_u32()? as usize;
+        let embedding_length = md_get("qwen2.embedding_length")?.to_u32()? as usize;
+        let context_length = md_get("qwen2.context_length")?.to_u32()? as usize;
+        let block_count = md_get("qwen2.block_count")?.to_u32()? as usize;
+        let rms_norm_eps = md_get("qwen2.attention.layer_norm_rms_epsilon")?.to_f32()? as f64;
+        let rope_freq_base = md_get("qwen2.rope.freq_base")
+            .and_then(|m| m.to_f32())
+            .unwrap_or(10000f32);
+
+        let head_dim = embedding_length / head_count;
+
+        let neg_inf = Tensor::new(f32::NEG_INFINITY, device)?;
+
+        let tok_embeddings = ct.tensor(reader, "token_embd.weight", device)?;
+        let tok_embeddings = tok_embeddings.dequantize(device)?;
+        let norm = RmsNorm::from_qtensor(
+            ct.tensor(reader, "output_norm.weight", device)?,
+            rms_norm_eps,
+        )?;
+        let output = match ct.tensor(reader, "output.weight", device) {
+            Ok(v) => QMatMul::from_qtensor(v)?,
+            _ => {
+                // use tie_word_embeddings
+                QMatMul::from_qtensor(ct.tensor(reader, "token_embd.weight", device)?)?
+            }
+        };
+
+        let (cos, sin) = precomput_freqs_cis(head_dim, rope_freq_base, context_length, device)?;
+
+        let mut layers = Vec::with_capacity(block_count);
+
+        for layer_idx in 0..block_count {
+            let prefix = format!("blk.{layer_idx}");
+            let attention_wq = ct.tensor(reader, &format!("{prefix}.attn_q.weight"), device)?;
+            let attention_wk = ct.tensor(reader, &format!("{prefix}.attn_k.weight"), device)?;
+            let attention_wv = ct.tensor(reader, &format!("{prefix}.attn_v.weight"), device)?;
+
+            let attention_bq = ct.tensor(reader, &format!("{prefix}.attn_q.bias"), device)?;
+            let attention_bk = ct.tensor(reader, &format!("{prefix}.attn_k.bias"), device)?;
+            let attention_bv = ct.tensor(reader, &format!("{prefix}.attn_v.bias"), device)?;
+
+            let attention_wo =
+                ct.tensor(reader, &format!("{prefix}.attn_output.weight"), device)?;
+
+            let mlp = {
+                let feed_forward_w1 =
+                    ct.tensor(reader, &format!("{prefix}.ffn_gate.weight"), device)?;
+                let feed_forward_w2 =
+                    ct.tensor(reader, &format!("{prefix}.ffn_down.weight"), device)?;
+                let feed_forward_w3 =
+                    ct.tensor(reader, &format!("{prefix}.ffn_up.weight"), device)?;
+                Mlp {
+                    feed_forward_w1: QMatMul::from_qtensor(feed_forward_w1)?,
+                    feed_forward_w2: QMatMul::from_qtensor(feed_forward_w2)?,
+                    feed_forward_w3: QMatMul::from_qtensor(feed_forward_w3)?,
+                }
+            };
+
+            let attention_norm =
+                ct.tensor(reader, &format!("{prefix}.attn_norm.weight"), device)?;
+            let ffn_norm = ct.tensor(reader, &format!("{prefix}.ffn_norm.weight"), device)?;
+
+            let span_attn = tracing::span!(tracing::Level::TRACE, "attn");
+            let span_rot = tracing::span!(tracing::Level::TRACE, "attn-rot");
+            let span_mlp = tracing::span!(tracing::Level::TRACE, "attn-mlp");
+
+            layers.push(LayerWeights {
+                attention_wq: QMatMul::from_qtensor(attention_wq)?,
+                attention_wk: QMatMul::from_qtensor(attention_wk)?,
+                attention_wv: QMatMul::from_qtensor(attention_wv)?,
+                attention_bq: attention_bq.dequantize(device)?,
+                attention_bk: attention_bk.dequantize(device)?,
+                attention_bv: attention_bv.dequantize(device)?,
+                attention_wo: QMatMul::from_qtensor(attention_wo)?,
+                attention_norm: RmsNorm::from_qtensor(attention_norm, rms_norm_eps)?,
+                cos: cos.clone(),
+                sin: sin.clone(),
+                mlp,
+                ffn_norm: RmsNorm::from_qtensor(ffn_norm, rms_norm_eps)?,
+                n_head: head_count,
+                n_kv_head: head_count_kv,
+                head_dim,
+                neg_inf: neg_inf.clone(),
+                kv_cache: None,
+                span_attn,
+                span_rot,
+                span_mlp,
+            });
+        }
+
+        let span = tracing::span!(tracing::Level::TRACE, "model");
+        let span_output = tracing::span!(tracing::Level::TRACE, "output");
+
+        Ok(Self {
+            tok_embeddings: Embedding::new(tok_embeddings, embedding_length),
+            layers,
+            norm,
+            output,
+            masks: HashMap::new(),
+            span,
+            span_output,
+        })
+    }
+
+    fn mask(&mut self, t: usize, device: &Device) -> Result<Tensor> {
+        if let Some(mask) = self.masks.get(&t) {
+            Ok(mask.clone())
+        } else {
+            let mask: Vec<_> = (0..t)
+                .flat_map(|i| (0..t).map(move |j| u8::from(j > i)))
+                .collect();
+            let mask = Tensor::from_slice(&mask, (t, t), device)?;
+            self.masks.insert(t, mask.clone());
+            Ok(mask)
+        }
+    }
+
+    pub fn forward(&mut self, x: &Tensor, index_pos: usize) -> Result<Tensor> {
+        let (_b_sz, seq_len) = x.dims2()?;
+        let mask = if seq_len == 1 {
+            None
+        } else {
+            Some(self.mask(seq_len, x.device())?)
+        };
+        let _enter = self.span.enter();
+        let mut layer_in = self.tok_embeddings.forward(x)?;
+        for layer in self.layers.iter_mut() {
+            let x = layer_in;
+            let residual = &x;
+            let x = layer.attention_norm.forward(&x)?;
+            let attn = layer.forward_attn(&x, mask.as_ref(), index_pos)?;
+            let x = (attn + residual)?;
+
+            // MLP
+            let _enter = layer.span_mlp.enter();
+            let residual = &x;
+            let x = layer.ffn_norm.forward(&x)?;
+            let x = layer.mlp.forward(&x)?;
+            let x = (x + residual)?;
+            layer_in = x
+        }
+        let x = self.norm.forward(&layer_in)?;
+        let x = x.i((.., seq_len - 1, ..))?;
+        let _enter = self.span_output.enter();
+        self.output.forward(&x)
+    }
+}