Separate quantized phi-3 implementation. (#2157)

* Separate quantized phi-3 implementation.

* Integrate the quantized phi3 model.=

* Small fixes, get the generation to work properly.

* Keep the old llama implementation around.

* Change the default.

7 files changed, 323 insertions, 12 deletions

diff --git a/candle-core/src/metal_backend/mod.rs b/candle-core/src/metal_backend/mod.rs
index c0f6a844..e00566ca 100644
--- a/candle-core/src/metal_backend/mod.rs
+++ b/candle-core/src/metal_backend/mod.rs
@@ -676,9 +676,6 @@ impl BackendStorage for MetalStorage {
             }
         }
 
-        if layout.is_contiguous() {
-        } else {
-        }
         Ok(Self::new(buffer, device.clone(), el_count, dtype))
     }
 
diff --git a/candle-core/src/sort.rs b/candle-core/src/sort.rs
index 6bfa3ca7..614a37fe 100644
--- a/candle-core/src/sort.rs
+++ b/candle-core/src/sort.rs
@@ -178,7 +178,7 @@ impl crate::CustomOp1 for ArgSort {
             device.metal_device(),
             &command_buffer,
             kernels,
-            &name,
+            name,
             nrows,
             ncols,
             ncols_pad,
diff --git a/candle-examples/examples/quantized-phi/main.rs b/candle-examples/examples/quantized-phi/main.rs
index 7d255f58..e2211844 100644
--- a/candle-examples/examples/quantized-phi/main.rs
+++ b/candle-examples/examples/quantized-phi/main.rs
@@ -13,8 +13,9 @@ use candle::Tensor;
 use candle_transformers::generation::{LogitsProcessor, Sampling};
 
 use candle_examples::token_output_stream::TokenOutputStream;
-use candle_transformers::models::quantized_llama::ModelWeights as Phi3;
+use candle_transformers::models::quantized_llama::ModelWeights as Phi3b;
 use candle_transformers::models::quantized_phi::ModelWeights as Phi2;
+use candle_transformers::models::quantized_phi3::ModelWeights as Phi3;
 
 const DEFAULT_PROMPT: &str = "Write a function to count prime numbers up to N. ";
 
@@ -24,6 +25,9 @@ enum Which {
     Phi2,
     #[value(name = "phi-3")]
     Phi3,
+    /// Alternative implementation of phi-3, based on llama.
+    #[value(name = "phi-3b")]
+    Phi3b,
 }
 
 #[derive(Parser, Debug)]
@@ -84,7 +88,7 @@ struct Args {
     repeat_last_n: usize,
 
     /// The model size to use.
-    #[arg(long, default_value = "phi-2")]
+    #[arg(long, default_value = "phi-3b")]
     which: Which,
 }
 
@@ -96,7 +100,7 @@ impl Args {
                 let api = hf_hub::api::sync::Api::new()?;
                 let repo = match self.which {
                     Which::Phi2 => "microsoft/phi-2",
-                    Which::Phi3 => "microsoft/Phi-3-mini-4k-instruct",
+                    Which::Phi3 | Which::Phi3b => "microsoft/Phi-3-mini-4k-instruct",
                 };
                 let api = api.model(repo.to_string());
                 api.get("tokenizer.json")?
@@ -114,6 +118,11 @@ impl Args {
                     Which::Phi3 => (
                         "microsoft/Phi-3-mini-4k-instruct-gguf",
                         "Phi-3-mini-4k-instruct-q4.gguf",
+                        "main",
+                    ),
+                    Which::Phi3b => (
+                        "microsoft/Phi-3-mini-4k-instruct-gguf",
+                        "Phi-3-mini-4k-instruct-q4.gguf",
                         "5eef2ce24766d31909c0b269fe90c817a8f263fb",
                     ),
                 };
@@ -145,6 +154,7 @@ fn format_size(size_in_bytes: usize) -> String {
 enum Model {
     Phi2(Phi2),
     Phi3(Phi3),
+    Phi3b(Phi3b),
 }
 
 impl Model {
@@ -152,6 +162,7 @@ impl Model {
         match self {
             Self::Phi2(m) => m.forward(xs, pos),
             Self::Phi3(m) => m.forward(xs, pos),
+            Self::Phi3b(m) => m.forward(xs, pos),
         }
     }
 }
@@ -203,6 +214,7 @@ fn main() -> anyhow::Result<()> {
         match args.which {
             Which::Phi2 => Model::Phi2(Phi2::from_gguf(model, &mut file, &device)?),
             Which::Phi3 => Model::Phi3(Phi3::from_gguf(model, &mut file, &device)?),
+            Which::Phi3b => Model::Phi3b(Phi3b::from_gguf(model, &mut file, &device)?),
         }
     };
     println!("model built");
diff --git a/candle-metal-kernels/src/lib.rs b/candle-metal-kernels/src/lib.rs
index c08e44fe..814ca0b9 100644
--- a/candle-metal-kernels/src/lib.rs
+++ b/candle-metal-kernels/src/lib.rs
@@ -350,7 +350,7 @@ pub fn call_unary_contiguous_tiled(
     let pipeline = kernels.load_pipeline(device, Source::Unary, kernel_name.0)?;
     let encoder = command_buffer.new_compute_command_encoder();
     let tile_size = 2;
-    let tiles = length.div_ceil(tile_size);
+    let tiles = (length + tile_size - 1) / tile_size;
 
     encoder.set_compute_pipeline_state(&pipeline);
 
diff --git a/candle-transformers/src/models/mod.rs b/candle-transformers/src/models/mod.rs
index 02f84158..de2430a2 100644
--- a/candle-transformers/src/models/mod.rs
+++ b/candle-transformers/src/models/mod.rs
@@ -40,6 +40,7 @@ pub mod quantized_mixformer;
 pub mod quantized_moondream;
 pub mod quantized_mpt;
 pub mod quantized_phi;
+pub mod quantized_phi3;
 pub mod quantized_recurrent_gemma;
 pub mod quantized_rwkv_v5;
 pub mod quantized_rwkv_v6;
diff --git a/candle-transformers/src/models/phi3.rs b/candle-transformers/src/models/phi3.rs
index d305e175..a5e3e9a9 100644
--- a/candle-transformers/src/models/phi3.rs
+++ b/candle-transformers/src/models/phi3.rs
@@ -24,19 +24,19 @@ pub struct Config {
 }
 
 impl Config {
-    fn head_dim(&self) -> usize {
+    pub fn head_dim(&self) -> usize {
         self.hidden_size / self.num_attention_heads
     }
 }
 
 #[derive(Debug, Clone)]
-struct RotaryEmbedding {
+pub struct RotaryEmbedding {
     sin: Tensor,
     cos: Tensor,
 }
 
 impl RotaryEmbedding {
-    fn new(dtype: DType, cfg: &Config, dev: &Device) -> Result<Self> {
+    pub fn new(dtype: DType, cfg: &Config, dev: &Device) -> Result<Self> {
         let dim = cfg.head_dim();
         let max_seq_len = cfg.max_position_embeddings;
         let inv_freq: Vec<_> = (0..dim)
@@ -55,7 +55,7 @@ impl RotaryEmbedding {
         })
     }
 
-    fn apply_rotary_emb_qkv(
+    pub fn apply_rotary_emb_qkv(
         &self,
         q: &Tensor,
         k: &Tensor,
diff --git a/candle-transformers/src/models/quantized_phi3.rs b/candle-transformers/src/models/quantized_phi3.rs
new file mode 100644
index 00000000..ef404ca0
--- /dev/null
+++ b/candle-transformers/src/models/quantized_phi3.rs
@@ -0,0 +1,301 @@
+use std::collections::HashMap;
+
+use candle::quantized::gguf_file;
+use candle::quantized::QTensor;
+use candle::{DType, Device, IndexOp, Module, Result, Tensor, D};
+use candle_nn::{Embedding, RmsNorm};
+
+pub const MAX_SEQ_LEN: usize = 4096;
+
+#[derive(Debug, Clone)]
+struct QLinear {
+    inner: candle::quantized::QMatMul,
+    span: tracing::Span,
+}
+
+impl QLinear {
+    fn new<R: std::io::Read + std::io::Seek>(
+        ct: &gguf_file::Content,
+        r: &mut R,
+        name: &str,
+        device: &Device,
+    ) -> Result<Self> {
+        let span = tracing::span!(tracing::Level::TRACE, "qmatmul");
+        let w = ct.tensor(r, &format!("{name}.weight"), device)?;
+        let inner = candle::quantized::QMatMul::from_qtensor(w)?;
+        Ok(Self { inner, span })
+    }
+}
+
+impl Module for QLinear {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        self.inner.forward(xs)
+    }
+}
+
+#[derive(Debug, Clone)]
+struct Mlp {
+    ffn_up: QLinear,
+    ffn_down: QLinear,
+    i_size: usize,
+}
+
+impl Module for Mlp {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let up_states = xs.apply(&self.ffn_up)?;
+        let gate = up_states.narrow(D::Minus1, 0, self.i_size)?;
+        let up_states = up_states.narrow(D::Minus1, self.i_size, self.i_size)?;
+        let up_states = (up_states * gate.silu()?)?;
+        up_states.apply(&self.ffn_down)
+    }
+}
+
+fn rms_norm(w: QTensor, eps: f64) -> Result<RmsNorm> {
+    let w = w.dequantize(&w.device())?;
+    let rms = RmsNorm::new(w, eps);
+    Ok(rms)
+}
+
+#[derive(Debug, Clone)]
+struct LayerWeights {
+    attn_qkv: QLinear,
+    attn_output: QLinear,
+    attn_norm: RmsNorm,
+    ffn_norm: RmsNorm,
+    mlp: Mlp,
+    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,
+}
+
+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, xs: &Tensor, index_pos: usize) -> Result<Tensor> {
+        let _enter = self.span_rot.enter();
+        let (_b_sz, _h, seq_len, _n_embd) = xs.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(&xs.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 qkv = self.attn_qkv.forward(x)?;
+
+        let query_pos = self.n_head * self.head_dim;
+        let q = qkv.narrow(D::Minus1, 0, query_pos)?;
+        let k = qkv.narrow(D::Minus1, query_pos, self.n_kv_head * self.head_dim)?;
+        let v = qkv.narrow(
+            D::Minus1,
+            query_pos + self.n_kv_head * self.head_dim,
+            self.n_kv_head * self.head_dim,
+        )?;
+
+        let q = q
+            .reshape((b_sz, seq_len, self.n_head, self.head_dim))?
+            .transpose(1, 2)?;
+        let k = k
+            .reshape((b_sz, seq_len, self.n_head, self.head_dim))?
+            .transpose(1, 2)?;
+        let v = v
+            .reshape((b_sz, seq_len, self.n_kv_head, self.head_dim))?
+            .transpose(1, 2)?;
+
+        let q = self.apply_rotary_emb(&q, index_pos)?.contiguous()?;
+        let k = self.apply_rotary_emb(&k, index_pos)?;
+
+        let (k, v) = match &self.kv_cache {
+            None => (k.contiguous()?, v.contiguous()?),
+            Some((k_cache, v_cache)) => {
+                if index_pos == 0 {
+                    (k.contiguous()?, v.contiguous()?)
+                } else {
+                    let k = Tensor::cat(&[k_cache, &k], 2)?;
+                    let v = Tensor::cat(&[v_cache, &v], 2)?;
+                    (k.contiguous()?, v.contiguous()?)
+                }
+            }
+        };
+        self.kv_cache = Some((k.clone(), v.clone()));
+
+        let k = crate::utils::repeat_kv(k, self.n_head / self.n_kv_head)?;
+        let v = crate::utils::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.attn_output.forward(&y)?;
+        Ok(y)
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct ModelWeights {
+    tok_embeddings: Embedding,
+    layers: Vec<LayerWeights>,
+    output_norm: RmsNorm,
+    output: QLinear,
+    masks: HashMap<usize, Tensor>,
+    span: tracing::Span,
+    span_output: tracing::Span,
+}
+
+fn precomput_freqs_cis(
+    head_dim: usize,
+    freq_base: f32,
+    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, MAX_SEQ_LEN as u32, device)?
+        .to_dtype(DType::F32)?
+        .reshape((MAX_SEQ_LEN, 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),
+        };
+
+        // Parameter extraction from metadata.
+        let head_count = md_get("phi3.attention.head_count")?.to_u32()? as usize;
+        let head_count_kv = md_get("phi3.attention.head_count_kv")?.to_u32()? as usize;
+        let block_count = md_get("phi3.block_count")?.to_u32()? as usize;
+        let embedding_length = md_get("phi3.embedding_length")?.to_u32()? as usize;
+        let i_size = md_get("phi3.feed_forward_length")?.to_u32()? as usize;
+        let rope_dim = md_get("phi3.rope.dimension_count")?.to_u32()? as usize;
+        let rms_eps = md_get("phi3.attention.layer_norm_rms_epsilon")?.to_f32()? as f64;
+        let (cos, sin) = precomput_freqs_cis(rope_dim, 10_000., device)?;
+        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 output_norm = rms_norm(ct.tensor(reader, "output_norm.weight", device)?, rms_eps)?;
+        let output = QLinear::new(&ct, reader, "output", device)?;
+        let mut layers = Vec::with_capacity(block_count);
+        for layer_idx in 0..block_count {
+            let prefix = format!("blk.{layer_idx}");
+            let ffn_up = QLinear::new(&ct, reader, &format!("{prefix}.ffn_up"), device)?;
+            let ffn_down = QLinear::new(&ct, reader, &format!("{prefix}.ffn_down"), device)?;
+            let mlp = Mlp {
+                ffn_up,
+                ffn_down,
+                i_size,
+            };
+            let attn_norm = rms_norm(
+                ct.tensor(reader, &format!("{prefix}.attn_norm.weight"), device)?,
+                rms_eps,
+            )?;
+            let ffn_norm = rms_norm(
+                ct.tensor(reader, &format!("{prefix}.ffn_norm.weight"), device)?,
+                rms_eps,
+            )?;
+            let span_attn = tracing::span!(tracing::Level::TRACE, "attn");
+            let span_rot = tracing::span!(tracing::Level::TRACE, "attn-rot");
+            layers.push(LayerWeights {
+                attn_qkv: QLinear::new(&ct, reader, &format!("{prefix}.attn_qkv"), device)?,
+                attn_output: QLinear::new(&ct, reader, &format!("{prefix}.attn_output"), device)?,
+                attn_norm,
+                ffn_norm,
+                mlp,
+                n_head: head_count,
+                n_kv_head: head_count_kv,
+                head_dim: embedding_length / head_count,
+                cos: cos.clone(),
+                sin: sin.clone(),
+                neg_inf: neg_inf.clone(),
+                kv_cache: None,
+                span_attn,
+                span_rot,
+            })
+        }
+        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,
+            output_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, xs: &Tensor, index_pos: usize) -> Result<Tensor> {
+        let (_b_sz, seq_len) = xs.dims2()?;
+        let mask = if seq_len == 1 {
+            None
+        } else {
+            Some(self.mask(seq_len, xs.device())?)
+        };
+        let _enter = self.span.enter();
+        let mut xs = self.tok_embeddings.forward(xs)?;
+        for layer in self.layers.iter_mut() {
+            let residual = &xs;
+            let ys = xs.apply(&layer.attn_norm)?;
+            let ys = layer.forward_attn(&ys, mask.as_ref(), index_pos)?;
+            let ys = (ys + residual)?;
+            let residual = &ys;
+            let ys = ys.apply(&layer.ffn_norm)?;
+            let ys = layer.mlp.forward(&ys)?;
+            xs = (ys + residual)?
+        }
+        let xs = xs.apply(&self.output_norm)?.i((.., seq_len - 1, ..))?;
+        let _enter = self.span_output.enter();
+        self.output.forward(&xs)
+    }
+}