feat: intergrate chinese clip and add example (#2555)

* start to impl chinese clip

* impl vision model

* copy code from bert

* refactor use

* refactor use again

* fix text model

* refactor

* try to fix text model

* tuning

* tuning chinese clip

* delete useless code

* revert code

* Clippy fixes.

* Also apply cargo fmt.

---------

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

candle-examples/examples/chinese_clip/main.rs

@@ -0,0 +1,224 @@
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
+use candle::{DType, Device, Tensor};
+use candle_nn as nn;
+use candle_transformers::models::chinese_clip::{ChineseClipConfig, ChineseClipModel};
+use clap::Parser;
+use tokenizers::Tokenizer;
+
+#[derive(Parser)]
+struct Args {
+    #[arg(long)]
+    model: Option<String>,
+
+    #[arg(long)]
+    tokenizer: Option<String>,
+
+    #[arg(long, use_value_delimiter = true)]
+    images: Option<Vec<String>>,
+
+    #[arg(long)]
+    cpu: bool,
+
+    #[arg(long, use_value_delimiter = true)]
+    sequences: Option<Vec<String>>,
+}
+
+fn main() -> anyhow::Result<()> {
+    let args = Args::parse();
+
+    tracing_subscriber::fmt::init();
+
+    let device = candle_examples::device(args.cpu)?;
+    let var = load_weights(args.model, &device)?;
+    let clip_model = ChineseClipModel::new(var, &ChineseClipConfig::clip_vit_base_patch16())?;
+    tracing::info!("Transformer loaded. ");
+
+    let (pixel_values, vec_imgs) = load_images(args.images, &device)?;
+    tracing::info!("Images loaded. ");
+
+    let tokenizer = load_tokenizer()?;
+    let (input_ids, type_ids, attention_mask, text_sequences) =
+        tokenize_sequences(args.sequences, &tokenizer, &device)?;
+
+    tracing::info!("Computing ... ");
+    let (_logits_per_text, logits_per_image) = clip_model.forward(
+        &pixel_values,
+        &input_ids,
+        Some(&type_ids),
+        Some(&attention_mask),
+    )?;
+    let softmax_image = nn::ops::softmax(&logits_per_image, 1)?;
+
+    let softmax_image_vec = softmax_image.flatten_all()?.to_vec1::<f32>()?;
+
+    let probability_vec = softmax_image_vec
+        .iter()
+        .map(|v| v * 100.0)
+        .collect::<Vec<f32>>();
+
+    let probability_per_image = probability_vec.len() / vec_imgs.len();
+
+    for (i, img) in vec_imgs.iter().enumerate() {
+        let start = i * probability_per_image;
+        let end = start + probability_per_image;
+        let prob = &probability_vec[start..end];
+        tracing::info!("\n\nResults for image: {}\n", img);
+
+        for (i, p) in prob.iter().enumerate() {
+            tracing::info!("Probability: {:.4}% Text: {} ", p, text_sequences[i]);
+        }
+    }
+
+    Ok(())
+}
+
+pub fn load_weights(model: Option<String>, device: &Device) -> anyhow::Result<nn::VarBuilder> {
+    let model_file = match model {
+        None => {
+            let api = hf_hub::api::sync::Api::new()?;
+            let repo = hf_hub::Repo::with_revision(
+                "OFA-Sys/chinese-clip-vit-base-patch16".to_string(),
+                hf_hub::RepoType::Model,
+                "refs/pr/3".to_string(),
+            );
+            let api = api.repo(repo);
+            api.get("model.safetensors")?
+        }
+        Some(model) => model.into(),
+    };
+
+    Ok(unsafe { nn::VarBuilder::from_mmaped_safetensors(&[model_file], DType::F32, device)? })
+}
+
+pub fn load_tokenizer() -> anyhow::Result<Tokenizer> {
+    let tokenizer_file = {
+        let api = hf_hub::api::sync::Api::new()?;
+        let repo = hf_hub::Repo::with_revision(
+            "OFA-Sys/chinese-clip-vit-base-patch16".to_string(),
+            hf_hub::RepoType::Model,
+            "refs/pr/3".to_string(),
+        );
+        let api = api.repo(repo);
+        api.get("tokenizer.json")?
+    };
+
+    Tokenizer::from_file(tokenizer_file).map_err(anyhow::Error::msg)
+}
+
+pub fn tokenize_sequences(
+    sequences: Option<Vec<String>>,
+    tokenizer: &Tokenizer,
+    device: &Device,
+) -> anyhow::Result<(Tensor, Tensor, Tensor, Vec<String>)> {
+    let vec_seq = match sequences {
+        Some(seq) => seq,
+        None => vec![
+            "自行车比赛".to_string(),
+            "两只猫咪".to_string(),
+            "拿着蜡烛的机器人".to_string(),
+        ],
+    };
+
+    let mut input_ids = vec![];
+    let mut type_ids = vec![];
+    let mut attention_mask = vec![];
+    let mut max_len = 0;
+
+    for seq in vec_seq.clone() {
+        let encoding = tokenizer.encode(seq, true).map_err(anyhow::Error::msg)?;
+        input_ids.push(encoding.get_ids().to_vec());
+        type_ids.push(encoding.get_type_ids().to_vec());
+        attention_mask.push(encoding.get_attention_mask().to_vec());
+        if encoding.get_ids().len() > max_len {
+            max_len = encoding.get_ids().len();
+        }
+    }
+
+    let pad_id = *tokenizer
+        .get_vocab(true)
+        .get("[PAD]")
+        .ok_or(anyhow::Error::msg("No pad token"))?;
+
+    let input_ids: Vec<Vec<u32>> = input_ids
+        .iter_mut()
+        .map(|item| {
+            item.extend(vec![pad_id; max_len - item.len()]);
+            item.to_vec()
+        })
+        .collect();
+
+    let type_ids: Vec<Vec<u32>> = type_ids
+        .iter_mut()
+        .map(|item| {
+            item.extend(vec![0; max_len - item.len()]);
+            item.to_vec()
+        })
+        .collect();
+
+    let attention_mask: Vec<Vec<u32>> = attention_mask
+        .iter_mut()
+        .map(|item| {
+            item.extend(vec![0; max_len - item.len()]);
+            item.to_vec()
+        })
+        .collect();
+
+    let input_ids = Tensor::new(input_ids, device)?;
+    let type_ids = Tensor::new(type_ids, device)?;
+    let attention_mask = Tensor::new(attention_mask, device)?;
+
+    Ok((input_ids, type_ids, attention_mask, vec_seq))
+}
+
+pub fn load_images(
+    images: Option<Vec<String>>,
+    device: &Device,
+) -> anyhow::Result<(Tensor, Vec<String>)> {
+    let vec_imgs = match images {
+        Some(imgs) => imgs,
+        None => vec![
+            "candle-examples/examples/stable-diffusion/assets/stable-diffusion-xl.jpg".to_string(),
+            "candle-examples/examples/yolo-v8/assets/bike.jpg".to_string(),
+        ],
+    };
+
+    let mut images = vec![];
+
+    for path in vec_imgs.iter() {
+        let tensor = load_image(path, 224, device)?;
+        images.push(tensor);
+    }
+
+    let images = Tensor::stack(&images, 0)?.to_device(device)?;
+    Ok((images, vec_imgs))
+}
+
+fn load_image<T: AsRef<std::path::Path>>(
+    path: T,
+    image_size: usize,
+    device: &Device,
+) -> anyhow::Result<Tensor> {
+    let img = image::ImageReader::open(path)?.decode()?;
+    let (height, width) = (image_size, image_size);
+    let img = img.resize_to_fill(
+        width as u32,
+        height as u32,
+        image::imageops::FilterType::Triangle,
+    );
+
+    let img = img.to_rgb8().into_raw();
+    let img = Tensor::from_vec(img, (height, width, 3), device)?.permute((2, 0, 1))?;
+    let mean = Tensor::new(&[0.48145466f32, 0.4578275, 0.40821073], device)?.reshape((3, 1, 1))?;
+    let std =
+        Tensor::new(&[0.26862954f32, 0.261_302_6, 0.275_777_1], device)?.reshape((3, 1, 1))?;
+    let img = (img.to_dtype(DType::F32)? / 255.)?
+        .broadcast_sub(&mean)?
+        .broadcast_div(&std)?;
+
+    Ok(img)
+}