Adds support for Stella_en_v5 embedding model - 1.5B variant (#2551)

* Stella_en_1.5B_v5

* Separated  creation. This is a critical step for numerical accuracy and would be documented in the readme

* EmbedDim would require clone and copy

* WIP: example

* Examples added

* a litte more in README

2 files changed, 400 insertions, 0 deletions

diff --git a/candle-transformers/src/models/mod.rs b/candle-transformers/src/models/mod.rs
index 6ed7a8b5..23edf349 100644
--- a/candle-transformers/src/models/mod.rs
+++ b/candle-transformers/src/models/mod.rs
@@ -84,6 +84,7 @@ pub mod siglip;
 pub mod stable_diffusion;
 pub mod stable_lm;
 pub mod starcoder2;
+pub mod stella_en_v5;
 pub mod t5;
 pub mod trocr;
 pub mod vgg;
diff --git a/candle-transformers/src/models/stella_en_v5.rs b/candle-transformers/src/models/stella_en_v5.rs
new file mode 100644
index 00000000..9d933fad
--- /dev/null
+++ b/candle-transformers/src/models/stella_en_v5.rs
@@ -0,0 +1,399 @@
+use crate::models::with_tracing::{linear, linear_no_bias, Linear, RmsNorm};
+use candle::{DType, Device, IndexOp, Module, Result, Tensor};
+use candle_nn::{Activation, VarBuilder};
+use std::sync::Arc;
+
+// Same as `qwen2` family of models with the exception being the `embed_head`
+// The final `output` causal modelling head is swapped with a learned `dense` layer, `embed_head`
+#[derive(Debug, Clone, PartialEq, serde::Deserialize)]
+pub struct Config {
+    pub vocab_size: usize,
+    pub hidden_size: usize,
+    pub intermediate_size: usize,
+    pub num_hidden_layers: usize,
+    pub num_attention_heads: usize,
+    pub num_key_value_heads: usize,
+    pub max_position_embeddings: usize,
+    pub max_window_layers: usize,
+    pub tie_word_embeddings: bool,
+    pub rope_theta: f64,
+    pub rms_norm_eps: f64,
+    pub hidden_act: Activation,
+    pub embed_head: EmbedHead,
+}
+
+// Excerpt from `stella` model card:
+// `Stella_en_1.5B_v5` models have been trained on [MRL](https://arxiv.org/abs/2205.13147) enabling multiple output dimensions
+// Embed head represents the config for various embedding dims supported
+#[derive(Debug, Clone, PartialEq, serde::Deserialize)]
+pub struct EmbedHead {
+    pub in_features: usize,
+    pub out_features: usize,
+}
+
+/// An enum variant representing the Embedding head dimensions `stella` is trained on
+/// As the [model-card](https://huggingface.co/dunzhang/stella_en_1.5B_v5#introduction) suggests, D1024 is good enough for most cases
+#[derive(Debug, Clone, Copy)]
+pub enum EmbedDim {
+    Dim256,
+    Dim768,
+    Dim1024,
+    Dim2048,
+    Dim4096,
+    Dim6144,
+    Dim8192,
+}
+
+impl Default for EmbedDim {
+    fn default() -> Self {
+        Self::Dim1024
+    }
+}
+
+impl EmbedDim {
+    pub fn config(&self) -> EmbedHead {
+        EmbedHead {
+            in_features: 1536,
+            out_features: match &self {
+                Self::Dim256 => 256,
+                Self::Dim768 => 768,
+                Self::Dim1024 => 1024,
+                Self::Dim2048 => 2048,
+                Self::Dim4096 => 4096,
+                Self::Dim6144 => 6144,
+                Self::Dim8192 => 8192,
+            },
+        }
+    }
+}
+
+// Initialize a new `stella_en` model - with 400M variant or 1.5B variant
+impl Config {
+    /// Initialize a new `stella_en_1.5B_v5`` model with given embedding dim
+    pub fn new_1_5_b_v5(embed_dim: EmbedDim) -> Self {
+        // Representing config.json at https://huggingface.co/dunzhang/stella_en_1.5B_v5/blob/main/config.json
+        // Removed `sliding_window` related config which is basically being carried forward from `qwen2` but not used here
+        Self {
+            hidden_act: candle_nn::Activation::Silu,
+            vocab_size: 151646,
+            hidden_size: 1536,
+            intermediate_size: 8960,
+            num_hidden_layers: 28,
+            num_attention_heads: 12,
+            num_key_value_heads: 2,
+            max_position_embeddings: 131072,
+            max_window_layers: 21,
+            tie_word_embeddings: false,
+            rope_theta: 1000000.,
+            rms_norm_eps: 1e-06,
+            embed_head: embed_dim.config(),
+        }
+    }
+}
+
+#[derive(Debug, Clone)]
+struct RotaryEmbedding {
+    sin: Tensor,
+    cos: Tensor,
+}
+
+impl RotaryEmbedding {
+    fn new(dtype: DType, cfg: &Config, dev: &Device) -> Result<Self> {
+        let dim = cfg.hidden_size / cfg.num_attention_heads;
+        let max_seq_len = cfg.max_position_embeddings;
+        let inv_freq: Vec<_> = (0..dim)
+            .step_by(2)
+            .map(|i| 1f32 / cfg.rope_theta.powf(i as f64 / dim as f64) as f32)
+            .collect();
+        let inv_freq_len = inv_freq.len();
+        let inv_freq = Tensor::from_vec(inv_freq, (1, inv_freq_len), dev)?.to_dtype(dtype)?;
+        let t = Tensor::arange(0u32, max_seq_len as u32, dev)?
+            .to_dtype(dtype)?
+            .reshape((max_seq_len, 1))?;
+        let freqs = t.matmul(&inv_freq)?;
+        Ok(Self {
+            sin: freqs.sin()?,
+            cos: freqs.cos()?,
+        })
+    }
+
+    fn apply_rotary_emb_qkv(&self, q: &Tensor, k: &Tensor) -> Result<(Tensor, Tensor)> {
+        let (_b_sz, _h, seq_len, _n_embd) = q.dims4()?;
+        let cos = self.cos.narrow(0, 0, seq_len)?;
+        let sin = self.sin.narrow(0, 0, seq_len)?;
+        let q_embed = candle_nn::rotary_emb::rope(&q.contiguous()?, &cos, &sin)?;
+        let k_embed = candle_nn::rotary_emb::rope(&k.contiguous()?, &cos, &sin)?;
+        Ok((q_embed, k_embed))
+    }
+}
+
+#[derive(Debug, Clone)]
+#[allow(clippy::upper_case_acronyms)]
+struct MLP {
+    gate_proj: Linear,
+    up_proj: Linear,
+    down_proj: Linear,
+    act_fn: Activation,
+}
+
+impl MLP {
+    fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let hidden_sz = cfg.hidden_size;
+        let intermediate_sz = cfg.intermediate_size;
+        let gate_proj = linear_no_bias(hidden_sz, intermediate_sz, vb.pp("gate_proj"))?;
+        let up_proj = linear_no_bias(hidden_sz, intermediate_sz, vb.pp("up_proj"))?;
+        let down_proj = linear_no_bias(intermediate_sz, hidden_sz, vb.pp("down_proj"))?;
+        Ok(Self {
+            gate_proj,
+            up_proj,
+            down_proj,
+            act_fn: cfg.hidden_act,
+        })
+    }
+}
+
+impl Module for MLP {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let lhs = xs.apply(&self.gate_proj)?.apply(&self.act_fn)?;
+        let rhs = xs.apply(&self.up_proj)?;
+        (lhs * rhs)?.apply(&self.down_proj)
+    }
+}
+
+#[derive(Debug, Clone)]
+struct Attention {
+    q_proj: Linear,
+    k_proj: Linear,
+    v_proj: Linear,
+    o_proj: Linear,
+    num_heads: usize,
+    num_kv_heads: usize,
+    num_kv_groups: usize,
+    head_dim: usize,
+    hidden_size: usize,
+    rotary_emb: Arc<RotaryEmbedding>,
+}
+
+impl Attention {
+    fn new(rotary_emb: Arc<RotaryEmbedding>, cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let hidden_sz = cfg.hidden_size;
+        let num_heads = cfg.num_attention_heads;
+        let num_kv_heads = cfg.num_key_value_heads;
+        let num_kv_groups = num_heads / num_kv_heads;
+        let head_dim = hidden_sz / num_heads;
+        let q_proj = linear(hidden_sz, num_heads * head_dim, vb.pp("q_proj"))?;
+        let k_proj = linear(hidden_sz, num_kv_heads * head_dim, vb.pp("k_proj"))?;
+        let v_proj = linear(hidden_sz, num_kv_heads * head_dim, vb.pp("v_proj"))?;
+        let o_proj = linear_no_bias(num_heads * head_dim, hidden_sz, vb.pp("o_proj"))?;
+        Ok(Self {
+            q_proj,
+            k_proj,
+            v_proj,
+            o_proj,
+            num_heads,
+            num_kv_heads,
+            num_kv_groups,
+            head_dim,
+            hidden_size: hidden_sz,
+            rotary_emb,
+        })
+    }
+
+    fn forward(&mut self, xs: &Tensor, attention_mask: Option<&Tensor>) -> Result<Tensor> {
+        let (b_sz, q_len, _) = xs.dims3()?;
+
+        let query_states = self.q_proj.forward(xs)?;
+        let key_states = self.k_proj.forward(xs)?;
+        let value_states = self.v_proj.forward(xs)?;
+
+        let query_states = query_states
+            .reshape((b_sz, q_len, self.num_heads, self.head_dim))?
+            .transpose(1, 2)?;
+        let key_states = key_states
+            .reshape((b_sz, q_len, self.num_kv_heads, self.head_dim))?
+            .transpose(1, 2)?;
+        let value_states = value_states
+            .reshape((b_sz, q_len, self.num_kv_heads, self.head_dim))?
+            .transpose(1, 2)?;
+
+        let (query_states, key_states) = self
+            .rotary_emb
+            .apply_rotary_emb_qkv(&query_states, &key_states)?;
+
+        let key_states = crate::utils::repeat_kv(key_states, self.num_kv_groups)?.contiguous()?;
+        let value_states =
+            crate::utils::repeat_kv(value_states, self.num_kv_groups)?.contiguous()?;
+
+        let attn_output = {
+            let scale = 1f64 / f64::sqrt(self.head_dim as f64);
+            let attn_weights = (query_states.matmul(&key_states.transpose(2, 3)?)? * scale)?;
+
+            let attn_weights = match attention_mask {
+                None => attn_weights,
+                Some(mask) => attn_weights.broadcast_add(mask)?,
+            };
+            let attn_weights = candle_nn::ops::softmax_last_dim(&attn_weights)?;
+            attn_weights.matmul(&value_states)?
+        };
+        attn_output
+            .transpose(1, 2)?
+            .reshape((b_sz, q_len, self.hidden_size))?
+            .apply(&self.o_proj)
+    }
+}
+
+#[derive(Debug, Clone)]
+struct DecoderLayer {
+    self_attn: Attention,
+    mlp: MLP,
+    input_layernorm: RmsNorm,
+    post_attention_layernorm: RmsNorm,
+}
+
+impl DecoderLayer {
+    fn new(rotary_emb: Arc<RotaryEmbedding>, cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let self_attn = Attention::new(rotary_emb, cfg, vb.pp("self_attn"))?;
+        let mlp = MLP::new(cfg, vb.pp("mlp"))?;
+        let input_layernorm =
+            RmsNorm::new(cfg.hidden_size, cfg.rms_norm_eps, vb.pp("input_layernorm"))?;
+        let post_attention_layernorm = RmsNorm::new(
+            cfg.hidden_size,
+            cfg.rms_norm_eps,
+            vb.pp("post_attention_layernorm"),
+        )?;
+        Ok(Self {
+            self_attn,
+            mlp,
+            input_layernorm,
+            post_attention_layernorm,
+        })
+    }
+
+    fn forward(&mut self, xs: &Tensor, attention_mask: Option<&Tensor>) -> Result<Tensor> {
+        let residual = xs;
+        let xs = self.input_layernorm.forward(xs)?;
+        let xs = self.self_attn.forward(&xs, attention_mask)?;
+        let xs = (xs + residual)?;
+        let residual = &xs;
+        let xs = xs.apply(&self.post_attention_layernorm)?.apply(&self.mlp)?;
+        residual + xs
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct Model {
+    embed_tokens: candle_nn::Embedding,
+    layers: Vec<DecoderLayer>,
+    norm: RmsNorm,
+    device: Device,
+    dtype: DType,
+}
+
+impl Model {
+    pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let vb_m = vb.pp("model");
+        let embed_tokens =
+            candle_nn::embedding(cfg.vocab_size, cfg.hidden_size, vb_m.pp("embed_tokens"))?;
+        let rotary_emb = Arc::new(RotaryEmbedding::new(vb.dtype(), cfg, vb_m.device())?);
+        let mut layers = Vec::with_capacity(cfg.num_hidden_layers);
+        let vb_l = vb_m.pp("layers");
+        for layer_idx in 0..cfg.num_hidden_layers {
+            let layer = DecoderLayer::new(rotary_emb.clone(), cfg, vb_l.pp(layer_idx))?;
+            layers.push(layer)
+        }
+        let norm = RmsNorm::new(cfg.hidden_size, cfg.rms_norm_eps, vb_m.pp("norm"))?;
+        Ok(Self {
+            embed_tokens,
+            layers,
+            norm,
+            // sliding_window: 0,
+            device: vb.device().clone(),
+            dtype: vb.dtype(),
+        })
+    }
+
+    fn prepare_attention_mask(&self, attn_mask: &Tensor) -> Result<Tensor> {
+        let (b_sz, sql_len) = attn_mask.dims2()?;
+        let mut mask: Vec<Tensor> = vec![];
+        for b in 0..b_sz {
+            mask.push(attn_mask.i((b, ..))?.expand((1, 1, sql_len, sql_len))?);
+        }
+        let mask = Tensor::cat(&mask, 0)?;
+        let on_true = mask.zeros_like()?.to_dtype(self.dtype)?;
+        let on_false = Tensor::new(f32::NEG_INFINITY, &self.device)?
+            .broadcast_as(mask.shape())?
+            .to_dtype(self.dtype)?;
+        mask.where_cond(&on_true, &on_false)
+    }
+
+    pub fn forward(&mut self, input_ids: &Tensor, mask: &Tensor) -> Result<Tensor> {
+        let (_, seq_len) = input_ids.dims2()?;
+        let attention_mask = if seq_len <= 1 {
+            None
+        } else {
+            // This is not a `causal language modelling` task, we'll need to prepare a `non-causal` attention
+            Some(self.prepare_attention_mask(mask)?)
+        };
+
+        let mut xs = self.embed_tokens.forward(input_ids)?;
+        for layer in self.layers.iter_mut() {
+            xs = layer.forward(&xs, attention_mask.as_ref())?
+        }
+        xs.apply(&self.norm)
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct EmbeddingModel {
+    base_model: Model,
+    lm_head: Linear,
+}
+
+impl EmbeddingModel {
+    pub fn new(cfg: &Config, base_vb: VarBuilder, embed_vb: VarBuilder) -> Result<Self> {
+        let base_model = Model::new(cfg, base_vb.clone())?;
+        let lm_head = linear(
+            cfg.embed_head.in_features,
+            cfg.embed_head.out_features,
+            embed_vb.pp("linear"),
+        )?;
+
+        Ok(Self {
+            base_model,
+            lm_head,
+        })
+    }
+
+    pub fn forward(&mut self, input_ids: &Tensor, mask: &Tensor) -> Result<Tensor> {
+        let x = self.base_model.forward(input_ids, mask)?;
+        let x = self.pool(&x, mask)?;
+
+        // No matter what keeping the final activations as F32 helps with the accuracy
+        self.lm_head.forward(&x.to_dtype(DType::F32)?) // [B_sz, dim_size]
+    }
+
+    /// Same as forward pass but normalizes the output
+    pub fn forward_norm(&mut self, input_ids: &Tensor, mask: &Tensor) -> Result<Tensor> {
+        let x = self.forward(input_ids, mask)?;
+        // Normalize
+        x.broadcast_div(&x.sqr()?.sum_keepdim(1)?.sqrt()?)
+    }
+
+    fn pool(&self, x: &Tensor, mask: &Tensor) -> Result<Tensor> {
+        let mask = mask.to_dtype(x.dtype())?; // [B_Sz, Seq_len]
+        let (batch_size, seq_len, hidden_dim) = x.dims3()?;
+        // expanding the shape of the mask from [B_Sz, Seq_len] -> [B_Sz, Seq_len, Hidden_size]
+        let mask_expanded = mask
+            .unsqueeze(2)?
+            .broadcast_as((batch_size, seq_len, hidden_dim))?; // [B_Sz, Seq_len, Hidden_dim]
+
+        let x = (x * &mask_expanded)?;
+
+        // Sum
+        let sum_mask = mask
+            .sum(1)?
+            .unsqueeze(1)?
+            .expand((batch_size, hidden_dim))?;
+        x.sum(1)? / sum_mask
+    }
+}