meme-search-engine/src/score_model.rs

use anyhow::Result;
use candle_core::{Tensor, Device, safetensors};

pub struct ScoreModel {
    pub device: Device,
    pub up_proj: Tensor,
    pub bias: Tensor,
    pub down_proj: Tensor,
    pub output_channels: usize
}

impl ScoreModel {
    pub fn score_batch(&self, input: &[f32]) -> Result<Vec<f32>> {
        let d_emb = self.up_proj.shape().dim(1).unwrap();
        let d_hidden =  self.up_proj.shape().dim(0).unwrap();
        let scale = d_emb as f32 / d_hidden as f32;
        let batch_size = input.len() / d_emb;

        let input = Tensor::from_slice(input, (batch_size, d_emb), &self.device)?;

        let hidden_state = self.up_proj.matmul(&input.transpose(0, 1)?)?;
        let hidden_state = hidden_state.add(&self.bias.unsqueeze(1)?.expand((d_hidden, batch_size))?)?;
        let hidden_state = hidden_state.silu()?;
        let outputs = self.down_proj.matmul(&hidden_state)?;
        let mut outputs: Vec<f32> = outputs.transpose(0, 1)?.flatten(0, 1)?.to_vec1()?;

        for x in outputs.iter_mut() {
            *x *= scale;
        }

        Ok(outputs)
    }

    pub fn load(path: &str, gpu: Option<usize>) -> Result<Self> {
        let dev = if let Some(gpu) = gpu {
            Device::new_cuda(gpu)?
        } else {
            Device::Cpu
        };
        let tensors = safetensors::load(path, &dev)?;
        let output_channels = tensors["down_proj"].shape().dim(0).unwrap();
        Ok(Self {
            device: dev,
            up_proj: tensors["up_proj"].clone(),
            bias: tensors["bias"].clone(),
            down_proj: tensors["down_proj"].clone(),
            output_channels
        })
    }
}

#[test]
fn run_test_vectors() {
    let score_model = ScoreModel::load("model.safetensors", None).unwrap();
    println!("load complete");
    let input = vec![-1.1441841125488281, -0.3753563463687897, 0.7898056507110596, -1.393734097480774, -0.3568798899650574, 0.9762657284736633, -1.5152218341827393, 0.7137635946273804, 0.9716352224349976, -1.1083635091781616, -0.833057701587677, 0.47789332270622253, 0.5295599699020386, 0.8025445342063904, -1.43166983127594, -0.5103325247764587, -0.2988021671772003, 0.3167966306209564, -1.5173636674880981, -0.4598764479160309, 0.22498439252376556, -0.26091915369033813, -1.1264656782150269, -0.32812589406967163, 0.3076278865337372, -0.41537582874298096, -0.17650583386421204, -0.4513799250125885, 0.48296573758125305, 0.6138008236885071, 0.5470929741859436, 0.662358820438385, -1.5090065002441406, 2.0193638801574707, 1.019300103187561, 0.6488455533981323, 0.24755769968032837, 0.1315346211194992, -0.2465936541557312, -0.5479799509048462, 0.6902561783790588, 0.7232106328010559, -1.1466503143310547, 1.0395011901855469, 2.361029624938965, -0.18545235693454742, -0.0005597859853878617, 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0.5247544050216675, 1.3974066972732544, -0.5880110859870911, 0.6322609782218933, -0.7389049530029297, -1.1852071285247803, 0.30875489115715027, 0.9301239848136902, -0.9930086135864258, -1.9672702550888062, 0.6127535700798035, -0.5060888528823853, 0.8578346967697144, 2.247459650039673, -0.7072847485542297, -0.9808002710342407, 0.2816307842731476, 0.6546081900596619, 0.2289007604122162, -0.24230267107486725, 0.3959594666957855, -1.1941598653793335, -0.7322438359260559, 1.2096837759017944, 1.2835453748703003, 1.7734615802764893, -0.3418593108654022, -0.0902809426188469, 0.3550195097923279, 2.027440071105957, -1.2974379062652588, 0.5419133305549622, 2.6244606971740723, 1.105350375175476, -1.6724731922149658, 0.1750856339931488, 0.21203479170799255, 0.37708204984664917, 0.8567672371864319, 1.1587073802947998, -1.2918639183044434, -0.4195218086242676, 0.32782790064811707, 0.4991025924682617, 0.812545895576477, 1.0040684938430786, 0.10502073168754578];
    let ground_truth = vec![-0.6489750146865845, -5.026195526123047, 5.606937408447266, 0.8131773471832275, -0.1522408127784729, -4.360945701599121, -1.3974180221557617, 11.217863082885742, -8.185169219970703];
    let model_output = score_model.score_batch(&input).unwrap();
    println!("{:?} {:?}", model_output, ground_truth);
    for (x, y) in model_output.iter().zip(ground_truth.iter()) {
        assert!((x - y).abs() < 0.1);
    }
}