Add Nvembed v2 model (#2649)

* Update mod.rs

* Create mod.rs

* Create decoder.rs

* Create model.rs

* Create main.rs

* Create README.md

* Update README.md

* Update main.rs

* Update and rename decoder.rs to embedding.rs

* Update mod.rs

* Update model.rs

4 files changed, 546 insertions, 0 deletions

diff --git a/candle-transformers/src/models/mod.rs b/candle-transformers/src/models/mod.rs
index 571a8861..be1f15c4 100644
--- a/candle-transformers/src/models/mod.rs
+++ b/candle-transformers/src/models/mod.rs
@@ -62,6 +62,7 @@ pub mod mobilenetv4;
 pub mod mobileone;
 pub mod moondream;
 pub mod mpt;
+pub mod nvembed_v2;
 pub mod olmo;
 pub mod openclip;
 pub mod paligemma;
diff --git a/candle-transformers/src/models/nvembed_v2/embedding.rs b/candle-transformers/src/models/nvembed_v2/embedding.rs
new file mode 100644
index 00000000..a52192af
--- /dev/null
+++ b/candle-transformers/src/models/nvembed_v2/embedding.rs
@@ -0,0 +1,294 @@
+/// Mistral LLM, https://github.com/mistralai/mistral-src
+use crate::models::{
+    mistral::Config,
+    with_tracing::{linear_no_bias, Linear, RmsNorm},
+};
+use crate::utils::repeat_kv;
+use candle::{DType, Device, Module, Result, Tensor};
+use candle_nn::{Activation, VarBuilder};
+use std::sync::Arc;
+
+#[derive(Debug, Clone)]
+struct RotaryEmbedding {
+    sin: Tensor,
+    cos: Tensor,
+}
+
+impl RotaryEmbedding {
+    fn new(dtype: DType, cfg: &Config, dev: &Device) -> Result<Self> {
+        let rope_theta = cfg.rope_theta as f32;
+        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 / rope_theta.powf(i as f32 / dim 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,
+        seqlen_offset: usize,
+    ) -> Result<(Tensor, Tensor)> {
+        let (_b_sz, _h, seq_len, _n_embd) = q.dims4()?;
+        let cos = self.cos.narrow(0, seqlen_offset, seq_len)?;
+        let sin = self.sin.narrow(0, seqlen_offset, seq_len)?;
+        let q_embed = candle_nn::rotary_emb::rope(q, &cos, &sin)?;
+        let k_embed = candle_nn::rotary_emb::rope(k, &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_no_bias(hidden_sz, num_heads * head_dim, vb.pp("q_proj"))?;
+        let k_proj = linear_no_bias(hidden_sz, num_kv_heads * head_dim, vb.pp("k_proj"))?;
+        let v_proj = linear_no_bias(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>,
+        seqlen_offset: usize,
+    ) -> 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)?
+            .contiguous()?;
+
+        let key_states = key_states
+            .reshape((b_sz, q_len, self.num_kv_heads, self.head_dim))?
+            .transpose(1, 2)?
+            .contiguous()?;
+        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, seqlen_offset)?;
+
+        let key_states = repeat_kv(key_states, self.num_kv_groups)?;
+        let value_states = repeat_kv(value_states, self.num_kv_groups)?;
+
+        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)?;
+        let attn_output = 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>,
+        seqlen_offset: usize,
+    ) -> Result<Tensor> {
+        let residual = xs;
+        let xs = self.input_layernorm.forward(xs)?;
+
+        let xs = self.self_attn.forward(&xs, attention_mask, seqlen_offset)?;
+
+        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,
+    pub cfg: Config,
+}
+
+impl Model {
+    pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let embed_tokens =
+            candle_nn::embedding(cfg.vocab_size, cfg.hidden_size, vb.pp("embed_tokens"))?;
+        let rotary_emb = Arc::new(RotaryEmbedding::new(vb.dtype(), cfg, vb.device())?);
+        let mut layers = Vec::with_capacity(cfg.num_hidden_layers);
+        let vb_l = vb.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.pp("norm"))?;
+        Ok(Self {
+            embed_tokens,
+            layers,
+            norm,
+            cfg: cfg.clone(),
+        })
+    }
+
+    // Attn mask used to mask out padding tokens
+    pub fn forward(
+        &mut self,
+        attn_mask: &Tensor,
+        input_ids: &Tensor,
+        dtype: DType,
+    ) -> Result<Tensor> {
+        let mut xs = self.embed_tokens.forward(input_ids)?;
+
+        // Expand to 4d mask for sdpa
+        let attn_mask = prepare_4d_attention_mask(attn_mask, dtype, None)?;
+
+        for layer in self.layers.iter_mut() {
+            xs = layer.forward(&xs, Some(&attn_mask), 0)?;
+        }
+
+        // Return hiddens instead of logits
+        xs.apply(&self.norm)
+    }
+}
+
+fn prepare_4d_attention_mask(
+    mask: &Tensor,
+    dtype: DType,
+    tgt_len: Option<usize>,
+) -> Result<Tensor> {
+    let bsz = mask.dims()[0];
+    let src_len = mask.dims()[1];
+    let tgt_len = tgt_len.unwrap_or(src_len);
+
+    let expanded_mask = mask
+        .unsqueeze(1)?
+        .unsqueeze(2)?
+        .expand((bsz, 1, tgt_len, src_len))?
+        .to_dtype(dtype)?;
+
+    let inverted_mask = (1.0 - expanded_mask)?;
+
+    (inverted_mask * get_dtype_min_val(dtype))?.to_dtype(dtype)
+}
+
+fn get_dtype_min_val(dtype: DType) -> f64 {
+    match dtype {
+        DType::F32 => f32::MIN as f64,
+        DType::F64 => f64::MIN,
+        _ => panic!("Unsupported data type"),
+    }
+}
diff --git a/candle-transformers/src/models/nvembed_v2/mod.rs b/candle-transformers/src/models/nvembed_v2/mod.rs
new file mode 100644
index 00000000..8a8f7007
--- /dev/null
+++ b/candle-transformers/src/models/nvembed_v2/mod.rs
@@ -0,0 +1,18 @@
+//! NV-Embed-v2
+//!
+//! NV-Embed-v2 is a text embedding model that combines a Mistral decoder with a latent attention mechanism to produce high-quality text embeddings.
+//!
+//! This implementation is based on the [paper](https://arxiv.org/pdf/2405.17428) and [weights](https://huggingface.co/nvidia/NV-Embed-v2)
+//!
+//! # Query-Passage Retrieval Example
+//! ```bash
+//! cargo run --example nvembed_v2 --release
+//! ```
+//!
+//! # Sentence Embedding Example
+//! ```bash
+//! cargo run --example nvembed_v2 --release -- --prompt "Here is a test sentence"
+//! ```
+
+pub mod embedding;
+pub mod model;
diff --git a/candle-transformers/src/models/nvembed_v2/model.rs b/candle-transformers/src/models/nvembed_v2/model.rs
new file mode 100644
index 00000000..73ef776e
--- /dev/null
+++ b/candle-transformers/src/models/nvembed_v2/model.rs
@@ -0,0 +1,233 @@
+use super::embedding::Model as EmbeddingModel;
+use crate::models::{
+    mistral::Config,
+    with_tracing::{layer_norm, linear, linear_no_bias, LayerNorm, Linear},
+};
+use candle::{DType, Device, Result, Tensor, D};
+use candle_nn::{ops::softmax_last_dim, LayerNormConfig, Module, VarBuilder};
+
+// Geglu and feedforward from candle-transformers/src/models/stable_diffusion/attention.rs
+#[derive(Debug)]
+struct GeGlu {
+    proj: Linear,
+    span: tracing::Span,
+}
+
+impl GeGlu {
+    fn new(vs: VarBuilder, dim_in: usize, dim_out: usize) -> Result<Self> {
+        let proj = linear(dim_in, dim_out * 2, vs)?;
+        let span = tracing::span!(tracing::Level::TRACE, "geglu");
+        Ok(Self { proj, span })
+    }
+}
+
+impl Module for GeGlu {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        let hidden_states_and_gate = self.proj.forward(xs)?.chunk(2, D::Minus1)?;
+        &hidden_states_and_gate[0] * hidden_states_and_gate[1].gelu()?
+    }
+}
+
+#[derive(Debug)]
+struct FeedForward {
+    project_in: GeGlu,
+    linear: Linear,
+    span: tracing::Span,
+}
+
+impl FeedForward {
+    fn new(vs: VarBuilder, dim: usize, dim_out: Option<usize>, mult: usize) -> Result<Self> {
+        let inner_dim = dim * mult;
+        let dim_out = dim_out.unwrap_or(dim);
+        let vs = vs.pp("net");
+        let project_in = GeGlu::new(vs.pp("0"), dim, inner_dim)?;
+        let linear = linear(inner_dim, dim_out, vs.pp("2"))?;
+        let span = tracing::span!(tracing::Level::TRACE, "ff");
+        Ok(Self {
+            project_in,
+            linear,
+            span,
+        })
+    }
+}
+
+impl Module for FeedForward {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        let xs = self.project_in.forward(xs)?;
+        self.linear.forward(&xs)
+    }
+}
+
+// CrossAttention from candle-transformers/src/models/stable_diffusion/attention.rs
+#[derive(Debug)]
+struct CrossAttention {
+    to_q: Linear,
+    to_kv: Linear,
+    to_out: Linear,
+    heads: usize,
+    scale: f64,
+    span: tracing::Span,
+    span_attn: tracing::Span,
+    span_softmax: tracing::Span,
+}
+
+impl CrossAttention {
+    fn new(
+        vs: VarBuilder,
+        query_dim: usize,
+        context_dim: Option<usize>,
+        heads: usize,
+        dim_head: usize,
+    ) -> Result<Self> {
+        let inner_dim = dim_head * heads;
+        let context_dim = context_dim.unwrap_or(query_dim);
+        let scale = 1.0 / f64::sqrt(dim_head as f64);
+        let to_q = linear_no_bias(query_dim, inner_dim, vs.pp("to_q"))?;
+        let to_kv = linear_no_bias(context_dim, inner_dim * 2, vs.pp("to_kv"))?;
+        let to_out = linear_no_bias(inner_dim, query_dim, vs.pp("to_out"))?;
+        let span = tracing::span!(tracing::Level::TRACE, "xa");
+        let span_attn = tracing::span!(tracing::Level::TRACE, "xa-attn");
+        let span_softmax = tracing::span!(tracing::Level::TRACE, "xa-softmax");
+        Ok(Self {
+            to_q,
+            to_kv,
+            to_out,
+            heads,
+            scale,
+            span,
+            span_attn,
+            span_softmax,
+        })
+    }
+
+    fn reshape_heads_to_batch_dim(&self, xs: &Tensor) -> Result<Tensor> {
+        let (batch_size, seq_len, dim) = xs.dims3()?;
+        xs.reshape((batch_size, seq_len, self.heads, dim / self.heads))?
+            .transpose(1, 2)?
+            .reshape((batch_size * self.heads, seq_len, dim / self.heads))
+    }
+
+    fn reshape_batch_dim_to_heads(&self, xs: &Tensor) -> Result<Tensor> {
+        let (batch_size, seq_len, dim) = xs.dims3()?;
+        xs.reshape((batch_size / self.heads, self.heads, seq_len, dim))?
+            .transpose(1, 2)?
+            .reshape((batch_size / self.heads, seq_len, dim * self.heads))
+    }
+
+    fn attention(&self, query: &Tensor, key: &Tensor, value: &Tensor) -> Result<Tensor> {
+        let _enter = self.span_attn.enter();
+
+        let in_dtype = query.dtype();
+        let query = query.to_dtype(DType::F32)?;
+        let key = key.to_dtype(DType::F32)?;
+        let value = value.to_dtype(DType::F32)?;
+        let xs = query.matmul(&(key.t()? * self.scale)?)?;
+        let xs = {
+            let _enter = self.span_softmax.enter();
+            softmax_last_dim(&xs)?
+        };
+        let xs = xs.matmul(&value)?.to_dtype(in_dtype)?;
+
+        self.reshape_batch_dim_to_heads(&xs)
+    }
+
+    fn forward(&self, xs: &Tensor, context: Option<&Tensor>) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        let query = self.to_q.forward(xs)?;
+        let context = context.unwrap_or(xs).contiguous()?;
+        let kv_chunks = self
+            .to_kv
+            .forward(&context)?
+            .chunk(2, context.shape().dims().len() - 1)?;
+        let (key, value) = (kv_chunks[0].clone(), kv_chunks[1].clone());
+        let query = self.reshape_heads_to_batch_dim(&query)?;
+        let key = self.reshape_heads_to_batch_dim(&key)?;
+        let value = self.reshape_heads_to_batch_dim(&value)?;
+
+        let xs = self.attention(&query, &key, &value)?;
+        self.to_out.forward(&xs)
+    }
+}
+
+#[derive(Debug)]
+pub struct Model {
+    embedding_model: EmbeddingModel,
+    cross_attn: CrossAttention,
+    cross_attn_norm: LayerNorm,
+    cross_attn_context_norm: LayerNorm,
+    ff: FeedForward,
+    ff_norm: LayerNorm,
+    latents: Tensor,
+    pub device: Device,
+    pub dtype: DType,
+}
+
+impl Model {
+    pub fn new(vb: VarBuilder) -> Result<Self> {
+        // Embedding model
+        let cfg = Config::config_7b_v0_1(false);
+        let embedding_model = EmbeddingModel::new(&cfg, vb.pp("embedding_model"))?;
+
+        // Latent attention
+        let dim = 4096;
+        let vb = vb.pp("latent_attention_model");
+        let latents = vb.get((512, dim), "latents")?;
+
+        // Cross attend blocks
+        let vb = vb.pp("cross_attend_blocks");
+        let cross_attn_norm = layer_norm(dim, LayerNormConfig::default(), vb.pp("0.norm"))?;
+        let cross_attn_context_norm = layer_norm(
+            dim,
+            candle_nn::LayerNormConfig::default(),
+            vb.pp("0.norm_context"),
+        )?;
+        let cross_attn = CrossAttention::new(vb.pp("0.fn"), dim, None, 8, 4096)?;
+
+        let ff_norm = layer_norm(dim, LayerNormConfig::default(), vb.pp("1.norm"))?;
+        let ff = FeedForward::new(vb.pp("1.fn"), dim, None, 4)?;
+
+        Ok(Self {
+            embedding_model,
+            cross_attn,
+            cross_attn_norm,
+            cross_attn_context_norm,
+            ff,
+            ff_norm,
+            latents,
+            device: vb.device().clone(),
+            dtype: vb.dtype(),
+        })
+    }
+
+    pub fn forward(
+        &mut self,
+        input_ids: &Tensor,
+        attn_mask: &Tensor,
+        pool_mask: &Tensor,
+    ) -> Result<Tensor> {
+        // Embedding model
+        let hiddens = self
+            .embedding_model
+            .forward(attn_mask, input_ids, self.dtype)?;
+
+        // Latent attention
+        let b = hiddens.dims()[0];
+        let x = self.latents.unsqueeze(0)?.repeat((b, 1, 1))?;
+        let original_hiddens = &hiddens;
+
+        let hiddens = self.cross_attn_norm.forward(original_hiddens)?;
+        let x = self.cross_attn_context_norm.forward(&x)?;
+        let cross_hiddens = (self.cross_attn.forward(&hiddens, Some(&x))? + original_hiddens)?;
+
+        let hiddens = self.ff_norm.forward(&cross_hiddens)?;
+        let hiddens = (self.ff.forward(&hiddens)? + cross_hiddens)?;
+
+        // Mean pooling
+        let hiddens_masked = hiddens.broadcast_mul(&pool_mask.unsqueeze(D::Minus1)?)?;
+        let s = hiddens_masked.sum(1)?;
+        let d = pool_mask.sum_keepdim(1)?;
+        s.broadcast_div(&d)
+    }
+}