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

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* 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
This commit is contained in:
Anubhab Bandyopadhyay
2024-10-14 02:39:12 +05:30
committed by GitHub
parent 41ade774e8
commit f553ab5eb4
4 changed files with 804 additions and 0 deletions
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1
candle-transformers/src/models/mod.rs
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@ -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;

399
candle-transformers/src/models/stella_en_v5.rs Normal file
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@ -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
}
}