toka toimiva vedos

network-poc/node/Cargo.toml

@@ -1,7 +1,7 @@
 [package]
 name = "node"
 version = "0.1.0"
-edition = "2021"
+edition = "2024"
 
 [lib]
 crate-type = ["cdylib"]
@@ -17,6 +17,12 @@ web-sys = { version = "0.3.68", features = [
     "MessageEvent",
     "Performance",
     "console",
+    "Request",
+    "RequestInit",
+    "Response",
+    "Headers",
+    "ReadableStream",
+    "ReadableStreamDefaultReader",
 ] }
 serde = { version = "1.0", features = ["derive"] }
 serde_json = "1.0"
@@ -29,4 +35,8 @@ reqwest = { version = "0.12", default-features = false, features = ["json"] }
 tokenizers = { version = "0.19.1", default-features = false, features = ["unstable_wasm"] }
 rexie = "0.6"
 log = "0.4"
+candle-core = { version = "0.8" }
+candle-nn = "0.8"
+candle-transformers = "0.8"
+getrandom = { version = "0.3", features = ["wasm_js"] }
 

network-poc/node/src/lib.rs

@@ -1,5 +1,5 @@
 use wasm_bindgen::prelude::*;
-use web_sys::{console, WebSocket, MessageEvent};
+use web_sys::{WebSocket, MessageEvent};
 use std::cell::RefCell;
 use std::rc::Rc;
 use std::sync::atomic::{AtomicU32, AtomicBool, Ordering};
@@ -7,15 +7,17 @@ use burn::tensor::Tensor;
 use burn::backend::{Wgpu, NdArray};
 
 pub mod storage;
+pub mod smollm;
 
+#[macro_export]
 macro_rules! console_log {
-    ($($t:tt)*) => (console::log_1(&format_args!($($t)*).to_string().into()))
+    ($($t:tt)*) => (web_sys::console::log_1(&format_args!($($t)*).to_string().into()))
 }
 
-// Globaali muuttuja GPU Load Sliderille (25-100%)
 static GPU_LOAD_PERCENT: AtomicU32 = AtomicU32::new(50);
-// Onko WebGPU käytettävissä — asetetaan JS-puolelta käynnistyksessä
 static HAS_WEBGPU: AtomicBool = AtomicBool::new(true);
+// Valittu tehtävä: 0=tokenize, 1=smollm-135m, 2=qwen-05b, 3=phi3-mini
+static SELECTED_TASK: AtomicU32 = AtomicU32::new(0);
 
 #[wasm_bindgen]
 pub fn set_gpu_load(load: u32) {
@@ -148,12 +150,15 @@ async fn run_pair_comparison(en_text: String, fi_text: String, ws: Rc<RefCell<We
 }
 
 #[wasm_bindgen]
-pub async fn start_agent_node(hub_url: String, has_webgpu: bool, device_info_json: String) -> Result<(), JsValue> {
+pub async fn start_agent_node(hub_url: String, has_webgpu: bool, device_info_json: String, task_id: u32) -> Result<(), JsValue> {
     console_error_panic_hook::set_once();
 
     HAS_WEBGPU.store(has_webgpu, Ordering::SeqCst);
+    SELECTED_TASK.store(task_id, Ordering::SeqCst);
     let backend_name = if has_webgpu { "WebGPU" } else { "CPU (NdArray)" };
-    console_log!("Kipinä Agent Node käynnistyy — backend: {}", backend_name);
+    let task_names = ["tokenize", "smollm-135m", "qwen-05b", "phi3-mini"];
+    let task_name = task_names.get(task_id as usize).unwrap_or(&"tokenize");
+    console_log!("Kipinä Agent Node käynnistyy — backend: {} | tehtävä: {}", backend_name, task_name);
 
     let device_info = device_info_json.clone();
     
@@ -182,7 +187,10 @@ pub async fn start_agent_node(hub_url: String, has_webgpu: bool, device_info_jso
         if let Ok(txt) = e.data().dyn_into::<js_sys::JsString>() {
             let msg: String = txt.into();
             
-            if msg.contains("pair_task") {
+            let current_task = SELECTED_TASK.load(Ordering::SeqCst);
+
+            if msg.contains("pair_task") && current_task == 0 {
+                // Vain tokenisaatiosolmut käsittelevät pair_task-viestejä
                 if let Ok(task) = serde_json::from_str::<serde_json::Value>(&msg) {
                     let en = task.get("en").and_then(|v| v.as_str()).unwrap_or("").to_string();
                     let fi = task.get("fi").and_then(|v| v.as_str()).unwrap_or("").to_string();
@@ -193,6 +201,18 @@ pub async fn start_agent_node(hub_url: String, has_webgpu: bool, device_info_jso
                         });
                     }
                 }
+            } else if msg.contains("llm_prompt") && current_task == 1 {
+                // Vain SmolLM-solmut käsittelevät llm_prompt-viestejä
+                if let Ok(task) = serde_json::from_str::<serde_json::Value>(&msg) {
+                    let prompt = task.get("prompt").and_then(|v| v.as_str()).unwrap_or("").to_string();
+                    let model = task.get("model").and_then(|v| v.as_str()).unwrap_or("").to_string();
+                    if !prompt.is_empty() && model == "smollm-135m" {
+                        let ws_for_async = ws_clone.clone();
+                        wasm_bindgen_futures::spawn_local(async move {
+                            smollm::run_smollm_inference(prompt, ws_for_async).await;
+                        });
+                    }
+                }
             } else if msg.contains("ai_task") {
                 console_log!("Hub task vastaanotettu, ajetaan GPU:lla...");
                 let ws_for_async = ws_clone.clone();

network-poc/node/src/smollm.rs

@@ -0,0 +1,246 @@
+use candle_core::{Device, Tensor, DType};
+use candle_nn::VarBuilder;
+use candle_transformers::models::llama::{Llama, LlamaConfig, LlamaEosToks, Cache};
+use candle_transformers::generation::LogitsProcessor;
+use wasm_bindgen::JsCast;
+use std::cell::RefCell;
+use std::rc::Rc;
+use web_sys::WebSocket;
+
+use crate::storage;
+
+macro_rules! console_log {
+    ($($t:tt)*) => (web_sys::console::log_1(&format_args!($($t)*).to_string().into()))
+}
+
+const MODEL_URL: &str = "https://huggingface.co/HuggingFaceTB/SmolLM-135M-Instruct/resolve/main/model.safetensors";
+const TOKENIZER_URL: &str = "https://huggingface.co/HuggingFaceTB/SmolLM-135M-Instruct/resolve/main/tokenizer.json";
+
+/// Lataa tiedosto HuggingFacesta streaming-latauksella (progress-ilmoitukset) ja tallentaa IndexedDB:hen
+async fn ensure_cached(key: &str, url: &str, ws: &Rc<RefCell<WebSocket>>) -> Result<Vec<u8>, String> {
+    if let Ok(Some(bytes)) = storage::load_from_idb(key).await {
+        console_log!("[SmolLM] {} löytyi välimuistista ({} MB)", key, bytes.len() / 1024 / 1024);
+        send_progress(ws, key, 100, bytes.len(), bytes.len());
+        return Ok(bytes);
+    }
+
+    console_log!("[SmolLM] Ladataan {}...", key);
+    send_progress(ws, key, 0, 0, 0);
+
+    // Fetch API:lla saadaan Content-Length ja streaming-luku
+    let window = web_sys::window().unwrap();
+    let resp_val = wasm_bindgen_futures::JsFuture::from(window.fetch_with_str(url))
+        .await.map_err(|e| format!("Fetch epäonnistui: {:?}", e))?;
+    let resp: web_sys::Response = resp_val.dyn_into().map_err(|_| "Ei Response-objekti".to_string())?;
+
+    if !resp.ok() {
+        return Err(format!("HTTP {}", resp.status()));
+    }
+
+    // Kokonaiskoko Content-Length-headerista
+    let total_size: usize = resp.headers()
+        .get("content-length").ok().flatten()
+        .and_then(|s| s.parse().ok())
+        .unwrap_or(0);
+
+    let body = resp.body().ok_or("Ei bodyä")?;
+    let reader = body.get_reader();
+    let reader: web_sys::ReadableStreamDefaultReader = reader.dyn_into().map_err(|_| "Ei ReadableStreamDefaultReader".to_string())?;
+
+    let mut data: Vec<u8> = Vec::with_capacity(total_size);
+    let mut last_pct: u32 = 0;
+
+    loop {
+        let chunk = wasm_bindgen_futures::JsFuture::from(reader.read())
+            .await.map_err(|e| format!("Luku epäonnistui: {:?}", e))?;
+
+        let done = js_sys::Reflect::get(&chunk, &"done".into())
+            .map_err(|_| "done-kenttä puuttuu".to_string())?
+            .as_bool().unwrap_or(true);
+
+        if done { break; }
+
+        let value = js_sys::Reflect::get(&chunk, &"value".into())
+            .map_err(|_| "value-kenttä puuttuu".to_string())?;
+        let array = js_sys::Uint8Array::new(&value);
+        let mut buf = vec![0u8; array.length() as usize];
+        array.copy_to(&mut buf);
+        data.extend_from_slice(&buf);
+
+        // Progress-päivitys (joka 5%)
+        if total_size > 0 {
+            let pct = ((data.len() as f64 / total_size as f64) * 100.0) as u32;
+            if pct >= last_pct + 5 || pct == 100 {
+                last_pct = pct;
+                console_log!("[SmolLM] {} lataus: {}% ({}/{} MB)", key, pct, data.len() / 1024 / 1024, total_size / 1024 / 1024);
+                send_progress(ws, key, pct, data.len(), total_size);
+            }
+        }
+    }
+
+    console_log!("[SmolLM] Tallennetaan {} ({} MB) IndexedDB:hen...", key, data.len() / 1024 / 1024);
+    let _ = storage::save_to_idb(key, &data).await;
+    console_log!("[SmolLM] {} tallennettu!", key);
+    send_progress(ws, key, 100, data.len(), data.len());
+
+    Ok(data)
+}
+
+fn send_progress(ws: &Rc<RefCell<WebSocket>>, file: &str, pct: u32, loaded: usize, total: usize) {
+    let msg = serde_json::json!({
+        "type": "download_progress",
+        "file": file,
+        "pct": pct,
+        "loaded_mb": loaded / 1024 / 1024,
+        "total_mb": total / 1024 / 1024,
+    });
+    let _ = ws.borrow().send_with_str(&msg.to_string());
+}
+
+/// Lataa malli ja tokenizer, suorita inferenssi ja streamaa tokenit hubille
+pub async fn run_smollm_inference(prompt: String, ws: Rc<RefCell<WebSocket>>) {
+    let perf = web_sys::window().unwrap().performance().unwrap();
+
+    // 1. Lataa tokenizer
+    let tok_bytes = match ensure_cached("smollm-tokenizer.json", TOKENIZER_URL, &ws).await {
+        Ok(b) => b,
+        Err(e) => { console_log!("[SmolLM] Tokenizer-virhe: {}", e); return; }
+    };
+
+    let tokenizer = match tokenizers::Tokenizer::from_bytes(&tok_bytes) {
+        Ok(t) => t,
+        Err(e) => { console_log!("[SmolLM] Tokenizer-parsinta epäonnistui: {}", e); return; }
+    };
+
+    // 2. Lataa mallin painot
+    let model_bytes = match ensure_cached("smollm-model.safetensors", MODEL_URL, &ws).await {
+        Ok(b) => b,
+        Err(e) => { console_log!("[SmolLM] Malli-virhe: {}", e); return; }
+    };
+
+    console_log!("[SmolLM] Rakennetaan mallia...");
+    let start_load = perf.now();
+
+    let device = Device::Cpu;
+    let dtype = DType::F32;
+
+    // Parsitaan safetensors
+    let tensors = match candle_core::safetensors::load_buffer(&model_bytes, &device) {
+        Ok(t) => t,
+        Err(e) => { console_log!("[SmolLM] Safetensors-parsinta epäonnistui: {}", e); return; }
+    };
+
+    let vb = VarBuilder::from_tensors(tensors, dtype, &device);
+
+    // SmolLM-135M config (Llama-arkkitehtuuri)
+    let config = LlamaConfig {
+        hidden_size: 576,
+        intermediate_size: 1536,
+        vocab_size: 49152,
+        num_hidden_layers: 30,
+        num_attention_heads: 9,
+        num_key_value_heads: Some(3),
+        rms_norm_eps: 1e-5,
+        rope_theta: 10000.0,
+        max_position_embeddings: 2048,
+        tie_word_embeddings: Some(true),
+        bos_token_id: Some(1u32),
+        eos_token_id: Some(LlamaEosToks::Single(2)),
+        rope_scaling: None,
+    };
+
+    let llama_config = config.into_config(false); // false = ei flash attention
+    let mut cache = Cache::new(true, dtype, &llama_config, &device).unwrap();
+
+    let model = match Llama::load(vb, &llama_config) {
+        Ok(m) => m,
+        Err(e) => { console_log!("[SmolLM] Mallin lataus epäonnistui: {}", e); return; }
+    };
+
+    let load_time = perf.now() - start_load;
+    console_log!("[SmolLM] Malli ladattu ({:.0}ms). Generoidaan...", load_time);
+
+    // 3. Tokenisoi syöte
+    let encoding = match tokenizer.encode(prompt.as_str(), true) {
+        Ok(e) => e,
+        Err(e) => { console_log!("[SmolLM] Tokenisointivirhe: {}", e); return; }
+    };
+
+    let input_ids: Vec<u32> = encoding.get_ids().to_vec();
+    let input_len = input_ids.len();
+    console_log!("[SmolLM] Syöte: {} tokenia", input_len);
+
+    // 4. Generoi tokeneita
+    let start_gen = perf.now();
+    let mut logits_processor = LogitsProcessor::new(42, Some(0.8), Some(0.95));
+    let mut all_tokens = input_ids.clone();
+    let max_new_tokens = 64;
+    let mut generated_text = String::new();
+
+    for i in 0..max_new_tokens {
+        let context_tokens = if i == 0 {
+            all_tokens.as_slice()
+        } else {
+            std::slice::from_ref(all_tokens.last().unwrap())
+        };
+
+        let input = Tensor::new(context_tokens, &device).unwrap().unsqueeze(0).unwrap();
+        let seq_len = input.dim(1).unwrap();
+
+        let logits = match model.forward(&input, input_len + i - seq_len, &mut cache) {
+            Ok(l) => l,
+            Err(e) => { console_log!("[SmolLM] Forward-virhe stepissä {}: {}", i, e); break; }
+        };
+
+        // Viimeisen tokenin logitit
+        let logits = logits.squeeze(0).unwrap();
+        let last_dim = logits.dim(0).unwrap();
+        let logits = if last_dim > 1 {
+            logits.get(last_dim - 1).unwrap()
+        } else {
+            logits.get(0).unwrap()
+        };
+
+        let next_token = logits_processor.sample(&logits).unwrap();
+
+        // EOS-tarkistus
+        if next_token == 2 {
+            break;
+        }
+
+        all_tokens.push(next_token);
+
+        // Dekoodaa token tekstiksi
+        if let Ok(text) = tokenizer.decode(&[next_token], true) {
+            generated_text.push_str(&text);
+
+            // Streamaa token hubille
+            let chunk = serde_json::json!({
+                "type": "llm_chunk",
+                "token": text,
+                "is_last": false,
+                "prompt": prompt,
+                "model": "SmolLM-135M"
+            });
+            let _ = ws.borrow().send_with_str(&chunk.to_string());
+        }
+    }
+
+    let gen_time = perf.now() - start_gen;
+    let tokens_generated = all_tokens.len() - input_len;
+    let tokens_per_sec = if gen_time > 0.0 { (tokens_generated as f64 / gen_time) * 1000.0 } else { 0.0 };
+
+    console_log!("[SmolLM] Generoitu {} tokenia | {:.0}ms | {:.1} tok/s", tokens_generated, gen_time, tokens_per_sec);
+
+    let done = serde_json::json!({
+        "type": "llm_done",
+        "prompt": prompt,
+        "model": "SmolLM-135M-Instruct",
+        "response": generated_text,
+        "tokens_generated": tokens_generated,
+        "duration_ms": (gen_time * 100.0).round() / 100.0,
+        "tokens_per_sec": (tokens_per_sec * 10.0).round() / 10.0,
+        "load_time_ms": (load_time * 100.0).round() / 100.0,
+    });
+    let _ = ws.borrow().send_with_str(&done.to_string());
+}