Olmo 2 model (#2954)

* OLMo 2 model

* Update olmo-2 to example

* Clippy fix.

---------

Co-authored-by: laurent <laurent.mazare@gmail.com>

candle-examples/examples/olmo/README.md

@@ -3,7 +3,7 @@
 OLMo is a series of Open Language Models designed to enable the science of language models.
 
 - **Project Page:** https://allenai.org/olmo
-- **Paper:** [Link](https://arxiv.org/abs/2402.00838)
+- **Papers:** [OLMo](https://arxiv.org/abs/2402.00838) [OLMo 2](https://arxiv.org/abs/2501.00656)
 - **Technical blog post:** https://blog.allenai.org/olmo-open-language-model-87ccfc95f580
 - **W&B Logs:** https://wandb.ai/ai2-llm/OLMo-1B/reports/OLMo-1B--Vmlldzo2NzY1Njk1
 <!-- - **Press release:** TODO -->

candle-examples/examples/olmo/main.rs

@@ -8,6 +8,7 @@ use anyhow::{Error as E, Result};
 use clap::{Parser, ValueEnum};
 
 use candle_transformers::models::olmo::{Config, Model as OLMo};
+use candle_transformers::models::olmo2::{Config as Config2, Model as OLMo2};
 
 use candle::{DType, Device, Tensor};
 use candle_examples::token_output_stream::TokenOutputStream;
@@ -18,6 +19,7 @@ use tokenizers::Tokenizer;
 
 enum Model {
     OLMo(OLMo),
+    OLMo2(OLMo2),
 }
 
 struct TextGeneration {
@@ -82,6 +84,7 @@ impl TextGeneration {
             let input = Tensor::new(ctxt, &self.device)?.unsqueeze(0)?;
             let logits = match &mut self.model {
                 Model::OLMo(m) => m.forward(&input, start_pos)?,
+                Model::OLMo2(m) => m.forward(&input, start_pos)?,
             };
             let logits = logits.squeeze(0)?.squeeze(0)?.to_dtype(DType::F32)?;
             let logits = if self.repeat_penalty == 1. {
@@ -129,6 +132,8 @@ enum Which {
     W7bTwin2T,
     #[value(name = "1.7-7b")]
     V1_7W7b,
+    #[value(name = "2-1b")]
+    V2W1b,
 }
 
 #[derive(Parser, Debug)]
@@ -220,6 +225,7 @@ fn main() -> Result<()> {
             Which::W7b => "allenai/OLMo-7B-hf".to_string(),
             Which::W7bTwin2T => "allenai/OLMo-7B-Twin-2T-hf".to_string(),
             Which::V1_7W7b => "allenai/OLMo-1.7-7B-hf".to_string(),
+            Which::V2W1b => "allenai/OLMo-2-0425-1B-Instruct".to_string(),
         },
     };
 
@@ -238,33 +244,36 @@ fn main() -> Result<()> {
             .map(std::path::PathBuf::from)
             .collect::<Vec<_>>(),
         None => match args.model {
-            Which::W1b => {
+            Which::W1b | Which::V2W1b => {
                 vec![repo.get("model.safetensors")?]
             }
             _ => candle_examples::hub_load_safetensors(&repo, "model.safetensors.index.json")?,
         },
     };
 
+    let config_filename = repo.get("config.json")?;
     println!("retrieved the files in {:?}", start.elapsed());
+
     let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
-
     let start = std::time::Instant::now();
-    let config = {
-        let config_filename = repo.get("config.json")?;
-        let config: Config = serde_json::from_slice(&std::fs::read(config_filename)?)?;
-        config
-    };
-
     let device = candle_examples::device(args.cpu)?;
-    let model = {
-        let dtype = if device.is_cuda() {
-            DType::BF16
-        } else {
-            DType::F32
-        };
-        let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
-        let model = OLMo::new(&config, vb)?;
-        Model::OLMo(model)
+    let dtype = if device.is_cuda() {
+        DType::BF16
+    } else {
+        DType::F32
+    };
+    let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
+    let model = match args.model {
+        Which::W1b | Which::W7b | Which::W7bTwin2T | Which::V1_7W7b => {
+            let config: Config = serde_json::from_slice(&std::fs::read(config_filename)?)?;
+            let model = OLMo::new(&config, vb)?;
+            Model::OLMo(model)
+        }
+        Which::V2W1b => {
+            let config: Config2 = serde_json::from_slice(&std::fs::read(config_filename)?)?;
+            let model = OLMo2::new(&config, vb)?;
+            Model::OLMo2(model)
+        }
     };
 
     println!("loaded the model in {:?}", start.elapsed());

candle-transformers/src/models/mod.rs

@@ -70,6 +70,7 @@ pub mod moondream;
 pub mod mpt;
 pub mod nvembed_v2;
 pub mod olmo;
+pub mod olmo2;
 pub mod openclip;
 pub mod paligemma;
 pub mod parler_tts;

candle-transformers/src/models/olmo2.rs

@@ -0,0 +1,348 @@
+//! OLMo 2 (Open Language Model) implementation
+//!
+//! See OLMo 2 model details at:
+//! - [Hugging Face Collection](https://huggingface.co/collections/allenai/olmo-2-674117b93ab84e98afc72edc)
+//! - [OLMo 2 Paper](https://arxiv.org/abs/2501.00656)
+//!
+//!
+use candle::{DType, Device, Module, Result, Tensor, D};
+use candle_nn::{linear_b, linear_no_bias, rms_norm, Activation, Linear, RmsNorm, VarBuilder};
+use std::sync::Arc;
+
+#[derive(Debug, Clone, serde::Deserialize)]
+pub struct Config {
+    pub vocab_size: usize,
+    pub hidden_size: usize,
+    pub intermediate_size: usize,
+    pub attention_bias: bool,
+    pub num_hidden_layers: usize,
+    pub num_attention_heads: usize,
+    pub num_key_value_heads: usize,
+    pub rms_norm_eps: f64,
+    pub hidden_act: candle_nn::Activation,
+    pub max_position_embeddings: usize,
+    pub rope_theta: f64,
+    pub tie_word_embeddings: bool,
+    pub clip_qkv: Option<f64>,
+}
+
+#[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,
+        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.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,
+    q_norm: RmsNorm,
+    k_norm: RmsNorm,
+    num_heads: usize,
+    num_kv_heads: usize,
+    num_kv_groups: usize,
+    head_dim: usize,
+    hidden_size: usize,
+    rotary_emb: Arc<RotaryEmbedding>,
+    kv_cache: Option<(Tensor, Tensor)>,
+}
+
+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 b = cfg.attention_bias;
+        let q_proj = linear_b(hidden_sz, num_heads * head_dim, b, vb.pp("q_proj"))?;
+        let k_proj = linear_b(hidden_sz, num_kv_heads * head_dim, b, vb.pp("k_proj"))?;
+        let v_proj = linear_b(hidden_sz, num_kv_heads * head_dim, b, vb.pp("v_proj"))?;
+        let o_proj = linear_b(num_heads * head_dim, hidden_sz, b, vb.pp("o_proj"))?;
+        let q_norm = rms_norm(hidden_sz, cfg.rms_norm_eps, vb.pp("q_norm"))?;
+        let k_norm = rms_norm(num_kv_heads * head_dim, cfg.rms_norm_eps, vb.pp("k_norm"))?;
+        Ok(Self {
+            q_proj,
+            k_proj,
+            v_proj,
+            o_proj,
+            q_norm,
+            k_norm,
+            num_heads,
+            num_kv_heads,
+            num_kv_groups,
+            head_dim,
+            hidden_size: hidden_sz,
+            rotary_emb,
+            kv_cache: None,
+        })
+    }
+
+    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 = self.q_norm.forward(&query_states)?;
+        let key_states = self.k_norm.forward(&key_states)?;
+
+        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, seqlen_offset)?;
+
+        let (key_states, value_states) = match &self.kv_cache {
+            None => (key_states, value_states),
+            Some((prev_k, prev_v)) => {
+                let key_states = Tensor::cat(&[prev_k, &key_states], 2)?;
+                let value_states = Tensor::cat(&[prev_v, &value_states], 2)?;
+                (key_states, value_states)
+            }
+        };
+        self.kv_cache = Some((key_states.clone(), value_states.clone()));
+
+        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)
+    }
+
+    fn clear_kv_cache(&mut self) {
+        self.kv_cache = None
+    }
+}
+
+#[derive(Debug, Clone)]
+struct DecoderLayer {
+    self_attn: Attention,
+    mlp: MLP,
+    post_attention_layernorm: RmsNorm,
+    post_feedforward_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 post_feedforward_layernorm = rms_norm(
+            cfg.hidden_size,
+            cfg.rms_norm_eps,
+            vb.pp("post_feedforward_layernorm"),
+        )?;
+        let post_attention_layernorm = rms_norm(
+            cfg.hidden_size,
+            cfg.rms_norm_eps,
+            vb.pp("post_attention_layernorm"),
+        )?;
+        Ok(Self {
+            self_attn,
+            mlp,
+            post_attention_layernorm,
+            post_feedforward_layernorm,
+        })
+    }
+
+    fn forward(
+        &mut self,
+        xs: &Tensor,
+        attention_mask: Option<&Tensor>,
+        seqlen_offset: usize,
+    ) -> Result<Tensor> {
+        let residual = xs;
+        let xs = self.self_attn.forward(xs, attention_mask, seqlen_offset)?;
+        let xs = self.post_attention_layernorm.forward(&xs)?;
+        let xs = (xs + residual)?;
+        let residual = &xs;
+        let xs = self.mlp.forward(&xs)?;
+        let xs = self.post_feedforward_layernorm.forward(&xs)?;
+        residual + xs
+    }
+
+    fn clear_kv_cache(&mut self) {
+        self.self_attn.clear_kv_cache()
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct Model {
+    embed_tokens: candle_nn::Embedding,
+    layers: Vec<DecoderLayer>,
+    norm: RmsNorm,
+    lm_head: Linear,
+    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 = rms_norm(cfg.hidden_size, cfg.rms_norm_eps, vb_m.pp("norm"))?;
+        let lm_head = if cfg.tie_word_embeddings {
+            Linear::new(embed_tokens.embeddings().clone(), None)
+        } else {
+            linear_no_bias(cfg.hidden_size, cfg.vocab_size, vb.pp("lm_head"))?
+        };
+        Ok(Self {
+            embed_tokens,
+            layers,
+            norm,
+            lm_head,
+            device: vb.device().clone(),
+            dtype: vb.dtype(),
+        })
+    }
+
+    fn prepare_decoder_attention_mask(
+        &self,
+        b_size: usize,
+        tgt_len: usize,
+        seqlen_offset: usize,
+    ) -> Result<Tensor> {
+        // Sliding window mask?
+        let mask: Vec<_> = (0..tgt_len)
+            .flat_map(|i| (0..tgt_len).map(move |j| if i < j { f32::NEG_INFINITY } else { 0. }))
+            .collect();
+        let mask = Tensor::from_slice(&mask, (tgt_len, tgt_len), &self.device)?;
+        let mask = if seqlen_offset > 0 {
+            let mask0 = Tensor::zeros((tgt_len, seqlen_offset), self.dtype, &self.device)?;
+            Tensor::cat(&[&mask0, &mask], D::Minus1)?
+        } else {
+            mask
+        };
+        mask.expand((b_size, 1, tgt_len, tgt_len + seqlen_offset))?
+            .to_dtype(self.dtype)
+    }
+
+    pub fn forward(&mut self, input_ids: &Tensor, seqlen_offset: usize) -> Result<Tensor> {
+        let (b_size, seq_len) = input_ids.dims2()?;
+        let attention_mask = if seq_len <= 1 {
+            None
+        } else {
+            let mask = self.prepare_decoder_attention_mask(b_size, seq_len, seqlen_offset)?;
+            Some(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(), seqlen_offset)?
+        }
+        xs.narrow(1, seq_len - 1, 1)?
+            .apply(&self.norm)?
+            .apply(&self.lm_head)
+    }
+
+    pub fn clear_kv_cache(&mut self) {
+        for layer in self.layers.iter_mut() {
+            layer.clear_kv_cache()
+        }
+    }
+}