Trying to call mnist model in main

Signed-off-by: Aisuko <[email protected]>

backend/rust/backend-burn/src/main.rs

@@ -1,5 +1,7 @@
 use std::collections::HashMap;
 use std::net::SocketAddr;
+use std::process::{id, Command};
+use std::sync::{Arc, Mutex};
 
 use bunker::pb::Result as PbResult;
 use bunker::pb::{
@@ -15,16 +17,12 @@ use tonic::{Request, Response, Status};
 use async_trait::async_trait;
 
 use tracing::{event, span, Level};
-use tracing_subscriber::filter::LevelParseError;
-
-use std::fs;
-use std::process::{Command,id};
 
 use models::*;
 // implement BackendService trait in bunker
 
 #[derive(Default, Debug)]
-struct BurnBackend;
+pub struct BurnBackend;
 
 #[async_trait]
 impl BackendService for BurnBackend {
@@ -42,33 +40,15 @@ impl BackendService for BurnBackend {
 
     #[tracing::instrument]
     async fn predict(&self, request: Request<PredictOptions>) -> Result<Response<Reply>, Status> {
-        let mut models: Vec<Box<dyn LLM>> = vec![Box::new(models::MNINST::new())];
-        let result = models[0].predict(request.into_inner());
-
-        match result {
-            Ok(res) => {
-                let reply = Reply {
-                    message: res.into(),
-                };
-                let res = Response::new(reply);
-                Ok(res)
-            }
-            Err(e) => {
-                let reply = Reply {
-                    message: e.to_string().into(),
-                };
-                let res = Response::new(reply);
-                Ok(res)
-            }
-        }
+        todo!("predict")
     }
 
     #[tracing::instrument]
     async fn load_model(
         &self,
         request: Request<ModelOptions>,
     ) -> Result<Response<PbResult>, Status> {
-        todo!()
+        todo!("load_model")
     }
 
     #[tracing::instrument]
@@ -121,35 +101,34 @@ impl BackendService for BurnBackend {
         &self,
         request: Request<HealthMessage>,
     ) -> Result<Response<StatusResponse>, Status> {
-
         // Here we do not need to cover the windows platform
         let mut breakdown = HashMap::new();
-        let mut memory_usage: u64=0;
+        let mut memory_usage: u64 = 0;
 
         #[cfg(target_os = "linux")]
         {
-            let pid =id();
-            let stat = fs::read_to_string(format!("/proc/{}/stat", pid)).expect("Failed to read stat file");
+            let pid = id();
+            let stat = fs::read_to_string(format!("/proc/{}/stat", pid))
+                .expect("Failed to read stat file");
 
             let stats: Vec<&str> = stat.split_whitespace().collect();
             memory_usage = stats[23].parse::<u64>().expect("Failed to parse RSS");
         }
 
-        #[cfg(target_os="macos")]
+        #[cfg(target_os = "macos")]
         {
-            let output=Command::new("ps")
-            .arg("-p")
-            .arg(id().to_string())
-            .arg("-o")
-            .arg("rss=")
-            .output()
-            .expect("failed to execute process");
-
-            memory_usage = String::from_utf8_lossy(&output.stdout)
-            .trim()
-            .parse::<u64>()
-            .expect("Failed to parse memory usage");
+            let output = Command::new("ps")
+                .arg("-p")
+                .arg(id().to_string())
+                .arg("-o")
+                .arg("rss=")
+                .output()
+                .expect("failed to execute process");
 
+            memory_usage = String::from_utf8_lossy(&output.stdout)
+                .trim()
+                .parse::<u64>()
+                .expect("Failed to parse memory usage");
         }
         breakdown.insert("RSS".to_string(), memory_usage);
 
@@ -167,6 +146,87 @@ impl BackendService for BurnBackend {
     }
 }
 
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use tonic::Request;
+
+    #[tokio::test]
+    async fn test_health() {
+        let backend = BurnBackend::default();
+        let request = Request::new(HealthMessage {});
+        let response = backend.health(request).await;
+
+        assert!(response.is_ok());
+        let response = response.unwrap();
+        let message_str = String::from_utf8(response.get_ref().message.clone()).unwrap();
+        assert_eq!(message_str, "OK");
+    }
+    #[tokio::test]
+    async fn test_status() {
+        let backend = BurnBackend::default();
+        let request = Request::new(HealthMessage {});
+        let response = backend.status(request).await;
+
+        assert!(response.is_ok());
+        let response = response.unwrap();
+        let state = response.get_ref().state;
+        assert_eq!(state, 0);
+    }
+
+    #[tokio::test]
+    async fn test_load_model() {
+        let backend = BurnBackend::default();
+        let request = Request::new(ModelOptions {
+            model: "test".to_string(),
+            context_size: 0,
+            seed: 0,
+            n_batch: 0,
+            f16_memory: false,
+            m_lock: false,
+            m_map: false,
+            vocab_only: false,
+            low_vram: false,
+            embeddings: false,
+            numa: false,
+            ngpu_layers: 0,
+            main_gpu: "".to_string(),
+            tensor_split: "".to_string(),
+            threads: 1,
+            library_search_path: "".to_string(),
+            rope_freq_base: 0.0,
+            rope_freq_scale: 0.0,
+            rms_norm_eps: 0.0,
+            ngqa: 0,
+            model_file: "".to_string(),
+            device: "".to_string(),
+            use_triton: false,
+            model_base_name: "".to_string(),
+            use_fast_tokenizer: false,
+            pipeline_type: "".to_string(),
+            scheduler_type: "".to_string(),
+            cuda: false,
+            cfg_scale: 0.0,
+            img2img: false,
+            clip_model: "".to_string(),
+            clip_subfolder: "".to_string(),
+            clip_skip: 0,
+            tokenizer: "".to_string(),
+            lora_base: "".to_string(),
+            lora_adapter: "".to_string(),
+            no_mul_mat_q: false,
+            draft_model: "".to_string(),
+            audio_path: "".to_string(),
+            quantization: "".to_string(),
+        });
+        let response = backend.load_model(request).await;
+
+        assert!(response.is_ok());
+        let response = response.unwrap();
+        //TO_DO: add test for response
+    }
+}
+
 #[tokio::main]
 async fn main() -> Result<(), Box<dyn std::error::Error>> {
     let subscriber = tracing_subscriber::fmt()

backend/rust/models/src/lib.rs

@@ -1,9 +1,16 @@
+use bunker::pb::{ModelOptions, PredictOptions};
+
 pub(crate) mod mnist;
 pub use mnist::mnist::MNINST;
 
-use bunker::pb::{ModelOptions, PredictOptions};
-
+/// Trait for implementing a Language Model.
 pub trait LLM {
+    /// Loads the model from the given options.
     fn load_model(&mut self, request: ModelOptions) -> Result<String, Box<dyn std::error::Error>>;
+    /// Predicts the output for the given input options.
     fn predict(&mut self, request: PredictOptions) -> Result<String, Box<dyn std::error::Error>>;
 }
+
+pub struct LLModel {
+    model: Box<dyn LLM + 'static>,
+}

backend/rust/models/src/mnist/mnist.rs

@@ -4,15 +4,14 @@
 //! Adapter by Aisuko
 
 use burn::{
-    backend::wgpu::{compute::init_async, AutoGraphicsApi, WgpuDevice},
+    backend::wgpu::{AutoGraphicsApi, WgpuDevice},
     module::Module,
     nn::{self, BatchNorm, PaddingConfig2d},
     record::{BinBytesRecorder, FullPrecisionSettings, Recorder},
     tensor::{backend::Backend, Tensor},
 };
 
 // https://github.com/burn-rs/burn/blob/main/examples/mnist-inference-web/model.bin
-static STATE_ENCODED: &[u8] = include_bytes!("model.bin");
 
 const NUM_CLASSES: usize = 10;
 
@@ -36,7 +35,7 @@ pub struct MNINST<B: Backend> {
 }
 
 impl<B: Backend> MNINST<B> {
-    pub fn new() -> Self {
+    pub fn new(model_name: &str) -> Self {
         let conv1 = ConvBlock::new([1, 8], [3, 3]); // 1 input channel, 8 output channels, 3x3 kernel size
         let conv2 = ConvBlock::new([8, 16], [3, 3]); // 8 input channels, 16 output channels, 3x3 kernel size
         let conv3 = ConvBlock::new([16, 24], [3, 3]); // 16 input channels, 24 output channels, 3x3 kernel size
@@ -59,8 +58,9 @@ impl<B: Backend> MNINST<B> {
             fc2: fc2,
             activation: nn::GELU::new(),
         };
+        let state_encoded: &[u8] = &std::fs::read(model_name).expect("Failed to load model");
         let record = BinBytesRecorder::<FullPrecisionSettings>::default()
-            .load(STATE_ENCODED.to_vec())
+            .load(state_encoded.to_vec())
             .expect("Failed to decode state");
 
         instance.load_record(record)
@@ -178,7 +178,7 @@ mod tests {
     pub type Backend = burn::backend::NdArrayBackend<f32>;
     #[test]
     fn test_inference() {
-        let mut model = MNINST::<Backend>::new();
+        let mut model = MNINST::<Backend>::new("model.bin");
         let output = model.inference(&[0.0; 28 * 28]).unwrap();
         assert_eq!(output.len(), 10);
     }

backend/rust/models/src/mnist/mod.rs

@@ -1,14 +1,20 @@
 use crate::LLM;
-use bunker::pb::{ModelOptions, PredictOptions};
 
 pub(crate) mod mnist;
 
+use mnist::MNINST;
+
+use bunker::pb::{ModelOptions, PredictOptions};
+
 #[cfg(feature = "ndarray")]
 pub type Backend = burn::backend::NdArrayBackend<f32>;
 
-impl LLM for mnist::MNINST<Backend> {
+impl LLM for MNINST<Backend> {
     fn load_model(&mut self, request: ModelOptions) -> Result<String, Box<dyn std::error::Error>> {
-        todo!("load model")
+        let model = request.model_file;
+        let instance = MNINST::<Backend>::new(&model);
+        *self = instance;
+        Ok("".to_string())
     }
 
     fn predict(&mut self, pre_ops: PredictOptions) -> Result<String, Box<dyn std::error::Error>> {