use crate::models::vit::{Config, Embeddings, Encoder};
use candle::{DType, Result, Tensor};
use candle_nn::{
embedding, layer_norm, linear_no_bias, Embedding, LayerNorm, Linear, Module, VarBuilder,
};
fn default_tie_word_embeddings() -> bool {
true
}
fn default_use_learned_position_embeddings() -> bool {
true
}
#[derive(Debug, Clone, PartialEq, serde::Deserialize)]
pub struct TrOCRConfig {
pub vocab_size: usize,
pub d_model: usize,
pub cross_attention_hidden_size: usize,
pub decoder_layers: usize,
pub decoder_attention_heads: usize,
pub decoder_ffn_dim: usize,
pub activation_function: candle_nn::Activation,
pub max_position_embeddings: usize,
pub dropout: f64,
pub attention_dropout: f64,
pub activation_dropout: f64,
pub decoder_start_token_id: u32,
pub init_std: f64,
pub decoder_layerdrop: f64,
pub use_cache: bool,
pub scale_embedding: bool,
pub pad_token_id: usize,
pub bos_token_id: usize,
pub eos_token_id: u32,
pub decoder_vocab_size: Option<usize>,
#[serde(default = "default_use_learned_position_embeddings")]
pub use_learned_position_embeddings: bool,
#[serde(default = "default_tie_word_embeddings")]
pub tie_word_embeddings: bool,
}
impl Default for TrOCRConfig {
fn default() -> Self {
Self {
vocab_size: 50265,
d_model: 1024,
cross_attention_hidden_size: 768,
decoder_layers: 12,
decoder_attention_heads: 16,
decoder_ffn_dim: 4096,
activation_function: candle_nn::Activation::Gelu,
max_position_embeddings: 512,
dropout: 0.1,
attention_dropout: 0.0,
activation_dropout: 0.0,
decoder_start_token_id: 2,
init_std: 0.02,
decoder_layerdrop: 0.0,
use_cache: true,
scale_embedding: false,
pad_token_id: 1,
bos_token_id: 0,
eos_token_id: 2,
decoder_vocab_size: Some(50265),
use_learned_position_embeddings: true,
tie_word_embeddings: true,
}
}
}
#[derive(Debug, Clone)]
struct TrOCRLearnedPositionalEmbedding {
offset: usize,
weights: Embedding,
}
impl TrOCRLearnedPositionalEmbedding {
fn load(vb: VarBuilder, cfg: &TrOCRConfig) -> Result<Self> {
let offset: usize = 2;
let num_embeddings = cfg.max_position_embeddings;
let embedding_dim = cfg.d_model;
let weights = embedding(num_embeddings + offset, embedding_dim, vb)?;
Ok(Self { offset, weights })
}
fn new_sinusoidal(vb: VarBuilder, cfg: &TrOCRConfig) -> Result<Self> {
let embedding_dim = cfg.d_model;
let half_dim = embedding_dim / 2;
let num_positions = cfg.max_position_embeddings + cfg.pad_token_id + 1;
let dev = vb.device();
let inv_freq: Vec<_> = (0..half_dim)
.map(|i| 1f32 / 10000f32.powf(i as f32 / (half_dim - 1) as f32))
.collect();
let inv_freq_len = inv_freq.len();
let inv_freq = Tensor::from_vec(inv_freq, (1, inv_freq_len), dev)?;
let t = Tensor::arange(0u32, num_positions as u32, dev)?
.to_dtype(DType::F32)?
.reshape((num_positions, 1))?;
let freqs = t.matmul(&inv_freq)?;
let emb = Tensor::cat(&[freqs.sin()?, freqs.cos()?], 1)?;
let emb = Tensor::cat(
&[
emb.narrow(0, 0, cfg.pad_token_id)?,
Tensor::zeros((1, embedding_dim), DType::F32, dev)?,
emb.narrow(0, cfg.pad_token_id + 1, cfg.max_position_embeddings)?,
],
0,
)?
.contiguous()?;
let emb = Embedding::new(emb, embedding_dim);
Ok(Self {
offset: cfg.pad_token_id + 1,
weights: emb,
})
}
fn forward(&mut self, input_ids: &Tensor, past_key_values_length: u32) -> Result<Tensor> {
let (b_sz, seq_len) = input_ids.dims2()?;
let positions = Tensor::arange(
past_key_values_length,
seq_len as u32 + past_key_values_length,
input_ids.device(),
)?
.expand((b_sz, seq_len))?;
let positions =
positions.broadcast_add(&Tensor::new(self.offset as u32, input_ids.device())?)?;
self.weights.forward(&positions)
}
}
#[derive(Debug, Clone)]
struct TrOCRAttention {
head_dim: usize,
num_heads: usize,
is_decoder: bool,
scaling: f64,
k_proj: Linear,
v_proj: Linear,
q_proj: Linear,
out_proj: Linear,
kv_cache: Option<(Tensor, Tensor)>,
}
impl TrOCRAttention {
fn load(
vb: VarBuilder,
cfg: &TrOCRConfig,
kdim: Option<usize>,
vdim: Option<usize>,
) -> Result<Self> {
let embed_dim = cfg.d_model;
let num_heads = cfg.decoder_attention_heads;
let head_dim = embed_dim / num_heads;
let kdim = kdim.unwrap_or(embed_dim);
let vdim = vdim.unwrap_or(embed_dim);
let k_proj = linear_no_bias(kdim, embed_dim, vb.pp("k_proj"))?;
let v_proj = linear_no_bias(vdim, embed_dim, vb.pp("v_proj"))?;
let q_proj = linear_no_bias(embed_dim, embed_dim, vb.pp("q_proj"))?;
let out_proj = linear_no_bias(embed_dim, embed_dim, vb.pp("out_proj"))?;
Ok(Self {
head_dim,
num_heads,
is_decoder: true,
scaling: 1. / (head_dim as f64).sqrt(),
k_proj,
v_proj,
q_proj,
out_proj,
kv_cache: None,
})
}
fn reset_kv_cache(&mut self) {
self.kv_cache = None
}
fn _shape(&self, tensor: &Tensor, bsz: usize) -> Result<Tensor> {
tensor
.reshape((bsz, (), self.num_heads, self.head_dim))?
.transpose(1, 2)?
.contiguous()
}
fn forward(
&mut self,
xs: &Tensor,
kv_states: Option<&Tensor>,
attn_mask: Option<&Tensor>,
) -> Result<Tensor> {
let (b_sz, tgt_len, _) = xs.dims3()?;
let query_states = (xs.apply(&self.q_proj)? * self.scaling)?;
let (key_states, value_states) = match kv_states {
None => {
let key_states = self._shape(&xs.apply(&self.k_proj)?, b_sz)?;
let value_states = self._shape(&xs.apply(&self.v_proj)?, b_sz)?;
if self.is_decoder {
let kv_states = match &self.kv_cache {
None => (key_states, value_states),
Some((p_key_states, p_value_states)) => {
let key_states = Tensor::cat(&[p_key_states, &key_states], 2)?;
let value_states = Tensor::cat(&[p_value_states, &value_states], 2)?;
(key_states, value_states)
}
};
self.kv_cache = Some(kv_states.clone());
kv_states
} else {
(key_states, value_states)
}
}
Some(kv_states) => {
let key_states = self._shape(&kv_states.apply(&self.k_proj)?, b_sz)?;
let value_states = self._shape(&kv_states.apply(&self.v_proj)?, b_sz)?;
(key_states, value_states)
}
};
let proj_shape = (b_sz * self.num_heads, (), self.head_dim);
let query_states = self._shape(&query_states, b_sz)?.reshape(proj_shape)?;
let key_states = key_states.reshape(proj_shape)?;
let value_states = value_states.reshape(proj_shape)?;
let attn_weights = query_states.matmul(&key_states.transpose(1, 2)?)?;
let attn_weights = match attn_mask {
None => attn_weights,
Some(attn_mask) => attn_weights.broadcast_add(attn_mask)?,
};
let attn_probs = candle_nn::ops::softmax_last_dim(&attn_weights)?;
let attn_output = attn_probs.matmul(&value_states)?;
attn_output
.reshape((b_sz, self.num_heads, tgt_len, self.head_dim))?
.transpose(1, 2)?
.reshape((b_sz, tgt_len, self.head_dim * self.num_heads))?
.apply(&self.out_proj)
}
}
#[derive(Debug, Clone)]
struct TrOCRDecoderLayer {
self_attn: TrOCRAttention,
activation_fn: candle_nn::Activation,
self_attn_layer_norm: LayerNorm,
encoder_attn: TrOCRAttention,
encoder_attn_layer_norm: LayerNorm,
fc1: Linear,
fc2: Linear,
final_layer_norm: LayerNorm,
}
impl TrOCRDecoderLayer {
fn load(vb: VarBuilder, cfg: &TrOCRConfig) -> Result<Self> {
let embed_dim = cfg.d_model;
let self_attn = TrOCRAttention::load(vb.pp("self_attn"), cfg, None, None)?;
let self_attn_layer_norm = layer_norm(embed_dim, 1e-5, vb.pp("self_attn_layer_norm"))?;
let encoder_attn = TrOCRAttention::load(
vb.pp("encoder_attn"),
cfg,
Some(cfg.cross_attention_hidden_size),
Some(cfg.cross_attention_hidden_size),
)?;
let encoder_attn_layer_norm =
layer_norm(embed_dim, 1e-5, vb.pp("encoder_attn_layer_norm"))?;
let fc1 = linear_no_bias(embed_dim, cfg.decoder_ffn_dim, vb.pp("fc1"))?;
let fc2 = linear_no_bias(cfg.decoder_ffn_dim, embed_dim, vb.pp("fc2"))?;
let final_layer_norm = layer_norm(embed_dim, 1e-5, vb.pp("final_layer_norm"))?;
Ok(Self {
self_attn,
activation_fn: cfg.activation_function,
self_attn_layer_norm,
encoder_attn,
encoder_attn_layer_norm,
fc1,
fc2,
final_layer_norm,
})
}
fn reset_kv_cache(&mut self) {
self.self_attn.reset_kv_cache();
}
fn forward(
&mut self,
xs: &Tensor,
attention_mask: &Tensor,
encoder_hidden_states: Option<&Tensor>,
) -> Result<Tensor> {
let residual = xs.clone();
let xs = self.self_attn.forward(xs, None, Some(attention_mask))?;
let xs = (xs + residual)?;
let mut xs = self.self_attn_layer_norm.forward(&xs)?;
if let Some(encoder_hidden_states) = &encoder_hidden_states {
let residual = xs.clone();
let encoder_attention_mask = attention_mask.clone(); xs = self.encoder_attn.forward(
&xs,
Some(encoder_hidden_states),
Some(&encoder_attention_mask),
)?;
xs = (xs + residual)?;
xs = self.encoder_attn_layer_norm.forward(&xs)?
}
let residual = xs.clone();
let xs = self.fc1.forward(&xs)?;
let xs = self.activation_fn.forward(&xs)?;
let xs = self.fc2.forward(&xs)?;
let xs = (xs + residual)?;
let xs = self.final_layer_norm.forward(&xs)?;
Ok(xs)
}
}
#[derive(Debug, Clone)]
pub struct TrOCRDecoder {
layers: Vec<TrOCRDecoderLayer>,
embed_scale: Option<f64>,
embed_tokens: Embedding,
embed_positions: TrOCRLearnedPositionalEmbedding,
}
impl TrOCRDecoder {
fn new(cfg: &TrOCRConfig, vb: VarBuilder) -> Result<Self> {
let vb = vb.pp("decoder.model.decoder");
let embed_tokens = embedding(cfg.vocab_size, cfg.d_model, vb.pp("embed_tokens"))?;
let embed_positions = if cfg.use_learned_position_embeddings {
TrOCRLearnedPositionalEmbedding::load(vb.pp("embed_positions"), cfg)?
} else {
TrOCRLearnedPositionalEmbedding::new_sinusoidal(vb.pp("embed_positions"), cfg)?
};
let mut layers = Vec::with_capacity(cfg.decoder_layers);
let vb_l = vb.pp("layers");
for idx in 0..cfg.decoder_layers {
let layer = TrOCRDecoderLayer::load(vb_l.pp(idx), cfg)?;
layers.push(layer)
}
let embed_scale = if cfg.scale_embedding {
Some((cfg.d_model as f64).sqrt())
} else {
None
};
Ok(Self {
layers,
embed_scale,
embed_tokens,
embed_positions,
})
}
fn reset_kv_cache(&mut self) {
self.layers.iter_mut().for_each(|l| l.reset_kv_cache())
}
pub fn forward(
&mut self,
xs: &Tensor,
encoder_xs: Option<&Tensor>,
past_kv_len: usize,
attn_mask: &Tensor,
) -> Result<Tensor> {
let embed_pos = self.embed_positions.forward(xs, past_kv_len as u32)?;
let xs = xs.apply(&self.embed_tokens)?;
let xs = match self.embed_scale {
None => xs,
Some(scale) => (xs * scale)?,
};
let mut xs = xs.broadcast_add(&embed_pos)?;
for layer in self.layers.iter_mut() {
xs = layer.forward(&xs, attn_mask, encoder_xs)?;
}
Ok(xs)
}
}
#[derive(Debug, Clone)]
pub struct TrOCREncoder {
embeddings: Embeddings,
encoder: Encoder,
layernorm: LayerNorm,
}
impl TrOCREncoder {
pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
let vb_v = vb.pp("encoder");
let embeddings = Embeddings::new(cfg, false, vb_v.pp("embeddings"))?;
let encoder = Encoder::new(cfg, vb_v.pp("encoder"))?;
let layernorm = layer_norm(cfg.hidden_size, cfg.layer_norm_eps, vb_v.pp("layernorm"))?;
Ok(Self {
embeddings,
encoder,
layernorm,
})
}
pub fn forward(&self, xs: &Tensor) -> Result<Tensor> {
let embedding_output = self.embeddings.forward(xs, None, false)?;
let encoder_outputs = self.encoder.forward(&embedding_output)?;
self.layernorm.forward(&encoder_outputs)
}
}
#[derive(Debug, Clone)]
pub struct TrOCRForCausalLM {
decoder: TrOCRDecoder,
output_projection: Linear,
}
impl TrOCRForCausalLM {
pub fn new(decoder_cfg: &TrOCRConfig, vb: VarBuilder) -> Result<Self> {
let decoder = TrOCRDecoder::new(decoder_cfg, vb.clone())?;
let output_projection = if decoder_cfg.tie_word_embeddings {
candle_nn::Linear::new(decoder.embed_tokens.embeddings().clone(), None)
} else {
candle_nn::linear_no_bias(
decoder_cfg.d_model,
decoder_cfg.vocab_size,
vb.pp("decoder.output_projection"),
)?
};
Ok(Self {
decoder,
output_projection,
})
}
pub fn forward(
&mut self,
xs: &Tensor,
encoder_xs: Option<&Tensor>,
past_kv_len: usize,
attn_mask: &Tensor,
) -> Result<Tensor> {
let xs = self
.decoder
.forward(xs, encoder_xs, past_kv_len, attn_mask)?;
let xs = xs.apply(&self.output_projection)?;
Ok(xs)
}
fn reset_kv_cache(&mut self) {
self.decoder.reset_kv_cache();
}
}
#[derive(Debug, Clone)]
pub struct TrOCRModel {
encoder: TrOCREncoder,
decoder: TrOCRForCausalLM,
}
impl TrOCRModel {
pub fn new(encoder_cfg: &Config, decoder_cfg: &TrOCRConfig, vb: VarBuilder) -> Result<Self> {
let encoder = TrOCREncoder::new(encoder_cfg, vb.clone())?;
let decoder = TrOCRForCausalLM::new(decoder_cfg, vb)?;
Ok(Self { encoder, decoder })
}
pub fn encoder(&mut self) -> &mut TrOCREncoder {
&mut self.encoder
}
pub fn decoder(&mut self) -> &mut TrOCRForCausalLM {
&mut self.decoder
}
pub fn decode(
&mut self,
xs: &Tensor,
encoder_xs: &Tensor,
past_kv_len: usize,
) -> Result<Tensor> {
let seq_len = xs.dim(1)?;
let mask: Vec<_> = (0..seq_len)
.flat_map(|i| (0..seq_len).map(move |j| if j > i { f32::NEG_INFINITY } else { 0f32 }))
.collect();
let mask = Tensor::from_vec(mask, (seq_len, seq_len), xs.device())?;
self.decoder
.forward(xs, Some(encoder_xs), past_kv_len, &mask)
}
pub fn reset_kv_cache(&mut self) {
self.decoder.reset_kv_cache();
}
}