1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147

//! Contrastive Language-Image Pre-Training
//!
//! Contrastive Language-Image Pre-Training (CLIP) is an architecture trained on
//! pairs of images with related texts.
//!
//! https://github.com/openai/CLIP
//! https://github.com/huggingface/transformers/tree/f6fa0f0bf0796ac66f201f23bdb8585de1609add/src/transformers/models/clip

use candle::{IndexOp, Result, Shape, Tensor, D};
use candle_nn as nn;
use candle_nn::Module;
use nn::Conv2dConfig;

use super::{
    text_model::{Activation, ClipEncoder},
    EncoderConfig,
};

#[derive(Debug, Clone)]
pub struct ClipVisionConfig {
    pub embed_dim: usize,
    pub activation: Activation,
    pub intermediate_size: usize,
    pub num_hidden_layers: usize,
    pub num_attention_heads: usize,
    #[allow(dead_code)]
    pub projection_dim: usize,
    pub num_channels: usize,
    pub image_size: usize,
    pub patch_size: usize,
}

impl ClipVisionConfig {
    // The config details can be found in the "vision_config" section of this json file:
    // https://huggingface.co/openai/clip-vit-large-patch14/blob/main/config.json
    pub fn vit_base_patch32() -> Self {
        Self {
            embed_dim: 768,
            activation: Activation::QuickGelu,
            intermediate_size: 3072,
            num_hidden_layers: 12,
            num_attention_heads: 12,
            projection_dim: 512,
            num_channels: 3,
            image_size: 224,
            patch_size: 32,
        }
    }
}

// https://github.com/huggingface/transformers/blob/f6fa0f0bf0796ac66f201f23bdb8585de1609add/src/transformers/models/clip/modeling_clip.py#L112
#[derive(Clone, Debug)]
struct ClipVisionEmbeddings {
    patch_embedding: candle_nn::Conv2d,
    position_ids: Tensor,
    class_embedding: Tensor,
    position_embedding: candle_nn::Embedding,
}

impl ClipVisionEmbeddings {
    fn new(vs: candle_nn::VarBuilder, c: &ClipVisionConfig) -> Result<Self> {
        // originally nn.Parameter
        let class_embedding = if vs.contains_tensor("class_embedding") {
            vs.get(c.embed_dim, "class_embedding")?
        } else {
            Tensor::randn(0f32, 1f32, c.embed_dim, vs.device())?
        };

        let num_patches = (c.image_size / c.patch_size).pow(2);
        let num_positions = num_patches + 1;
        let position_ids = Tensor::arange(0, num_positions as i64, vs.device())?;

        let conv2dconfig = Conv2dConfig {
            stride: c.patch_size,
            ..Default::default()
        };
        let position_embedding =
            candle_nn::embedding(num_positions, c.embed_dim, vs.pp("position_embedding"))?;
        let patch_embedding = candle_nn::conv2d_no_bias(
            c.num_channels,
            c.embed_dim,
            c.patch_size,
            conv2dconfig,
            vs.pp("patch_embedding"),
        )?;
        Ok(Self {
            patch_embedding,
            position_ids,
            class_embedding,
            position_embedding,
        })
    }
}

impl Module for ClipVisionEmbeddings {
    fn forward(&self, pixel_values: &Tensor) -> Result<Tensor> {
        let batch_size = pixel_values.shape().dims();
        let patch_embeds = self
            .patch_embedding
            .forward(pixel_values)?
            .flatten_from(2)?
            .transpose(1, 2)?;
        let shape = Shape::from((batch_size[0], 1, self.class_embedding.dim(D::Minus1)?));
        let class_embeds = self.class_embedding.expand(shape)?;
        let embeddings = Tensor::cat(&[class_embeds, patch_embeds], 1)?;
        let position_embedding = self.position_embedding.forward(&self.position_ids)?;
        embeddings.broadcast_add(&position_embedding)
    }
}

// https://github.com/huggingface/transformers/blob/f6fa0f0bf0796ac66f201f23bdb8585de1609add/src/transformers/models/clip/modeling_clip.py#L743
#[derive(Clone, Debug)]
pub struct ClipVisionTransformer {
    embeddings: ClipVisionEmbeddings,
    encoder: ClipEncoder,
    pre_layer_norm: candle_nn::LayerNorm,
    final_layer_norm: candle_nn::LayerNorm,
}

impl ClipVisionTransformer {
    pub fn new(vs: candle_nn::VarBuilder, c: &ClipVisionConfig) -> Result<Self> {
        let embeddings = ClipVisionEmbeddings::new(vs.pp("embeddings"), c)?;
        let pre_layer_norm = candle_nn::layer_norm(c.embed_dim, 1e-5, vs.pp("pre_layrnorm"))?;
        let encoder = ClipEncoder::new(vs.pp("encoder"), &EncoderConfig::Vision(c.clone()))?;
        let final_layer_norm = candle_nn::layer_norm(c.embed_dim, 1e-5, vs.pp("post_layernorm"))?;
        Ok(Self {
            embeddings,
            encoder,
            final_layer_norm,
            pre_layer_norm,
        })
    }
}

impl Module for ClipVisionTransformer {
    fn forward(&self, pixel_values: &Tensor) -> Result<Tensor> {
        let hidden_states = pixel_values
            .apply(&self.embeddings)?
            .apply(&self.pre_layer_norm)?;

        let encoder_outputs = self.encoder.forward(&hidden_states, None)?;
        // https://github.com/huggingface/transformers/blob/f6fa0f0bf0796ac66f201f23bdb8585de1609add/src/transformers/models/clip/modeling_clip.py#L787
        // pooled_output = encoder_outputs[:, 0, :]
        let pooled_output = encoder_outputs.i((.., 0, ..))?;
        self.final_layer_norm.forward(&pooled_output)
    }
}