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//! Multimodal multi-purpose model combining Gemma-based language model with SigLIP image understanding
//!
//! See PaLiGemma details at:
//! - [Paper](https://arxiv.org/abs/2402.05257)
//! - [Google Blog Post](https://blog.research.google/2024/02/paligemma-scaling-language-image.html)
//!
//! The model is a multimodal combination of:
//! - SigLIP vision encoder
//! - Gemma language model
//! - Cross-projection layers
//!
//! References:
//! - [HuggingFace Implementation](https://huggingface.co/google/paligemma-3b)
//! - [Paper: PaLI-3 and Beyond: Scaling Language-Image Learning](https://arxiv.org/abs/2402.05257)
//!
use crate::models::{gemma, siglip};
use candle::{Module, Result, Tensor};
use candle_nn::{linear, Linear, VarBuilder};
#[derive(serde::Deserialize, Clone, Debug)]
pub struct Config {
pub vision_config: siglip::VisionConfig,
pub text_config: gemma::Config,
pub projection_dim: usize,
}
impl Config {
pub fn paligemma_3b_224() -> Self {
// https://huggingface.co/google/paligemma-3b-pt-224/blob/main/config.json
Self {
vision_config: siglip::VisionConfig::paligemma_3b_224(),
text_config: gemma::Config {
hidden_size: 2048,
intermediate_size: 16384,
num_attention_heads: 8,
num_hidden_layers: 18,
num_key_value_heads: 1,
vocab_size: 257216,
// Default values.
rope_theta: 10000.,
head_dim: 256,
hidden_act: Some(candle_nn::Activation::GeluPytorchTanh),
hidden_activation: None,
attention_bias: false,
max_position_embeddings: 8192,
rms_norm_eps: 1e-6,
},
projection_dim: 2048,
}
}
pub fn paligemma_3b_448() -> Self {
Self {
vision_config: siglip::VisionConfig::paligemma_3b_448(),
text_config: gemma::Config {
hidden_size: 2048,
intermediate_size: 16384,
num_attention_heads: 8,
num_hidden_layers: 18,
num_key_value_heads: 1,
// Default values.
rope_theta: 10000.,
head_dim: 256,
hidden_act: Some(candle_nn::Activation::GeluPytorchTanh),
hidden_activation: None,
attention_bias: false,
max_position_embeddings: 8192,
rms_norm_eps: 1e-6,
vocab_size: 257216,
},
projection_dim: 2048,
}
}
}
#[derive(Clone, Debug)]
pub struct MultiModalProjector {
linear: Linear,
}
impl MultiModalProjector {
fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
let linear = linear(
cfg.vision_config.hidden_size,
cfg.projection_dim,
vb.pp("linear"),
)?;
Ok(Self { linear })
}
}
impl Module for MultiModalProjector {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
xs.apply(&self.linear)
}
}
#[derive(Clone, Debug)]
pub struct Model {
pos: usize,
vision_tower: siglip::VisionModel,
multi_modal_projector: MultiModalProjector,
language_model: gemma::Model,
}
impl Model {
pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
let vision_tower = siglip::VisionModel::new(
&cfg.vision_config,
false,
vb.pp("vision_tower.vision_model"),
)?;
let multi_modal_projector = MultiModalProjector::new(cfg, vb.pp("multi_modal_projector"))?;
let language_model = gemma::Model::new(false, &cfg.text_config, vb.pp("language_model"))?;
Ok(Self {
pos: 0,
language_model,
vision_tower,
multi_modal_projector,
})
}
pub fn setup(&mut self, pixel_values: &Tensor, input_ids: &Tensor) -> Result<Tensor> {
self.clear_kv_cache();
let image_features = self
.vision_tower
.forward(pixel_values)?
.apply(&self.multi_modal_projector)?;
let image_features = crate::models::clip::div_l2_norm(&image_features)?;
let text_features = self.language_model.embed_tokens().forward(input_ids)?;
let input_embeds = Tensor::cat(&[image_features, text_features], 1)?;
self.pos = input_embeds.dim(1)?;
self.language_model.forward_embeds(&input_embeds, None, 0)
}
pub fn forward(&mut self, input_ids: &Tensor) -> Result<Tensor> {
let pos = self.pos;
let seq_len = input_ids.dim(1)?;
self.pos = pos + seq_len;
self.language_model.forward(input_ids, pos)
}
pub fn forward_without_projection(&mut self, input_ids: &Tensor) -> Result<Tensor> {
self.clear_kv_cache();
let input_embeds = self.language_model.embed_tokens().forward(input_ids)?;
self.language_model
.forward_embeds_without_projection(&input_embeds, None, 0)
}
pub fn setup_without_projection(
&mut self,
pixel_values: &Tensor,
input_ids: &Tensor,
) -> Result<Tensor> {
self.clear_kv_cache();
let image_features = self
.vision_tower
.forward(pixel_values)?
.apply(&self.multi_modal_projector)?;
let image_features = crate::models::clip::div_l2_norm(&image_features)?;
let text_features = self.language_model.embed_tokens().forward(input_ids)?;
let input_embeds = Tensor::cat(&[image_features, text_features], 1)?;
self.language_model
.forward_embeds_without_projection(&input_embeds, None, 0)
}
pub fn clear_kv_cache(&mut self) {
self.pos = 0;
self.language_model.clear_kv_cache()
}
}
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