wasm: feat: support WasmEdge wasi_nn plugin with llm application

Signed-off-by: Zhang Tianyang <burning9699@gmail.com>

.github/workflows/ci.yml

@@ -18,6 +18,9 @@ jobs:
           - directories: wasm
             features: --features=wasmedge
             wasmEdge: 0.13.5
+          - directories: wasm
+            features: --features="wasmedge, wasmedge_wasi_nn"
+            wasmEdge: 0.13.5
           - directories: wasm
             features: --features=wasmtime
     runs-on: ubuntu-latest

docs/wasm/How-to-run-Llama-3-8B-with-Kubernetes.md

@@ -0,0 +1,140 @@
+# How to run a Llama-3-8B inference application in Kubernetes?
+
+## What is LlamaEdge?
+
+The [LlamaEdge](https://github.com/LlamaEdge/LlamaEdge) project makes it easy for you to run LLM inference apps and 
+create OpenAI-compatible API services for the Llama3 series of LLMs locally.
+
+With WasmEdge, you can create and deploy very fast and very lightweight LLM inference applications, see
+details in: https://www.secondstate.io/articles/wasm-runtime-agi/.
+
+## How to run a llm inference application in Kuasar?
+
+Since Kuasar v0.8.0, Kuasar wasm-sandboxer with `wasmedge` and `wasmedge_wasi_nn` 
+features allows your WasmEdge application use the ability of WASI API for
+performing Machine Learning inference: https://github.com/WebAssembly/wasi-nn.
+
+This article is inspired by [Getting Started with Llama-3-8B](https://www.secondstate.io/articles/llama-3-8b/),
+which introducing how to create an OpenAI-compatible API service for Llama-3-8B.
+
+### Prerequisites
+
++ Install WasmEdge and plugins: 
+`curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | bash -s -- -v 0.13.5 --plugins wasi_logging wasi_nn-ggml`
+
+
+### 1. Build docker image
+
+We already have an example docker image on dockerhub: `docker.io/kuasario/llama-api-server:v1`.
+Follow this if you want to build your own docker image with the llm applications, model and other requires.
+
++ Download the Llama-3-8B model GGUF file: Since the size of the model is 5.73 GB，it could take a while to download.
+`curl -LO https://huggingface.co/second-state/Llama-3-8B-Instruct-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-Q5_K_M.gguf`.
+
++ Get your LlamaEdge app: Take the api-server as example, download it by 
+`curl -LO https://github.com/LlamaEdge/LlamaEdge/releases/latest/download/llama-api-server.wasm`. 
+It is a web server providing an OpenAI-compatible API service, as well as an optional web UI, for llama3 models.
+
++ Download the chatbot web UI to interact with the model with a chatbot UI: 
+```bash
+  curl -LO https://github.com/LlamaEdge/chatbot-ui/releases/latest/download/chatbot-ui.tar.gz
+  tar xzf chatbot-ui.tar.gz
+  rm chatbot-ui.tar.gz
+```
+
++ Build it! Here is an example DOCKERFILE:
+```dockerfile
+FROM scratch
+COPY . /
+CMD ["llama-api-server.wasm", "--prompt-template", "llama-3-chat", "--ctx-size", "4096", "--model-name", "Llama-3-8B", "--log-all"]
+```
+Build it with `docker build -t docker.io/kuasario/llama-api-server:v1 .`
+
+### 2. Build and run Kuasar Wasm Sandboxer
+
+```bash
+git clone https://github.com/kuasar-io/kuasar.git
+cd kuasar/wasm
+cargo run --features="wasmedge, wasmedge_wasi_nn" -- --listen /run/wasm-sandboxer.sock --dir /run/kuasar-wasm
+```
+
+### 3. Config and containerd
+Add the following sandboxer config in the containerd config file `/etc/containerd/config.toml`
+```toml
+[proxy_plugins]
+  [proxy_plugins.wasm]
+    type = "sandbox"
+    address = "/run/wasm-sandboxer.sock"
+
+[plugins.'io.containerd.cri.v1.runtime'.containerd.runtimes.kuasar-wasm]
+  runtime_type = "io.containerd.kuasar-wasm.v1"
+  sandboxer = "wasm"
+```
+
+Then, build and run containerd with the environ variable `ENABLE_CRI_SANDBOXES=1`.
+
+### 4. Create Kuasar wasm runtime
+
+Suppose we are in a kubernetes cluster, all the workloads are managed by kubernetes. So how to let container
+engine(containerd) know which runtime the workload should run in?  
+
+[Container Runtimes](https://kubernetes.io/docs/setup/production-environment/container-runtimes/) is designed for launching and
+running containers in Kubernetes. Thus, you should create a new container runtime `kubectl apply -f kuasar-wasm-runtimeclass.yaml`.
+```yaml
+    apiVersion: node.k8s.io/v1
+    handler: kuasar-wasm
+    kind: RuntimeClass
+    metadata:
+      name: kuasar-wasm
+```
+
+OK, the container show know what is `kuasar-wasm` ruintime.
+
+### 5. Deploy your llm workload
+
+The last thing is to deploy the llm workload, you can use the docker image in the step 1.
+
+Run `kubectl apply llama-deploy.yaml` 
+
+Here is an example deploy.yaml
+```yaml
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: llama
+  labels:
+    app: llama
+spec:
+  replicas: 2
+  selector:
+    matchLabels:
+      app: llama
+  template:
+    metadata:
+      labels:
+        app: llama
+    spec:
+      containers:
+      - command:
+        - llama-api-server.wasm
+        args: ["--prompt-template", "llama-3-chat", "--ctx-size", "4096", "--model-name", "Llama-3-8B"]
+        env:
+        - name: io.kuasar.wasm.nn_preload
+          value: default:GGML:AUTO:Meta-Llama-3-8B-Instruct-Q5_K_M.gguf
+        image: docker.io/kuasario/llama-api-server:v1
+        name: llama-api-server
+      runtimeClassName: kuasar-wasm
+```
+Make sure the `runtimeClassName` is the right runtime created in the last step 4.
+
+Please note that we define an env `io.kuasar.wasm.nn_preload`, which will tell kuasar what will be loaded in `wasi_nn`
+plugin. Normally including the alias of model, the inference backend, the execution target and the model file.
+
+## Extension: Try with Kubernetes Service
+
+In Kubernetes, a [Service](https://kubernetes.io/docs/concepts/services-networking/service/) is a method for exposing a 
+network application that is running as one or more Pods in your cluster.
+
+You can create a ClusterIP Service or LoadBalancer Service or whatever you want, and access llm service from outer cluster.
+
+We do not provide examples since it has nothing to do with Kuasar!

wasm/src/wasmedge.rs

@@ -87,14 +87,6 @@ impl Default for WasmEdgeContainerFactory {
         PluginManager::load(None).unwrap();
         let mut host_options = HostRegistrationConfigOptions::default();
         host_options = host_options.wasi(true);
-        #[cfg(all(
-            target_os = "linux",
-            feature = "wasmedge_wasi_nn",
-            target_arch = "x86_64"
-        ))]
-        {
-            host_options = host_options.wasi_nn(true);
-        }
         let config = ConfigBuilder::new(CommonConfigOptions::default())
             .with_host_registration_config(host_options)
             .build()
@@ -161,7 +153,10 @@ impl ContainerFactory<WasmEdgeContainer> for WasmEdgeContainerFactory {
 impl ProcessLifecycle<InitProcess> for WasmEdgeInitLifecycle {
     async fn start(&self, p: &mut InitProcess) -> containerd_shim::Result<()> {
         let spec = &p.lifecycle.spec;
-        let vm = p.lifecycle.prototype_vm.clone();
+        // Allow vm to be mutable since we change it in wasmedge_wasi_nn feature
+        #[allow(unused_mut)]
+        #[allow(unused_assignments)]
+        let mut vm = p.lifecycle.prototype_vm.clone();
         let args = get_args(spec);
         let envs = get_envs(spec);
         let rootfs = get_rootfs(spec).ok_or_else(|| {
@@ -198,6 +193,45 @@ impl ProcessLifecycle<InitProcess> for WasmEdgeInitLifecycle {
                         format!("failed to add task to cgroup: {}", cgroup_path)
                     ))?;
                 }
+                // Only create new VM instance on wasmedge_wasi_nn feature
+                #[cfg(all(
+                    target_os = "linux",
+                    feature = "wasmedge_wasi_nn",
+                    target_arch = "x86_64"
+                ))]
+                {
+                    const NN_PRELOAD_KEY: &str = "io.kuasar.wasm.nn_preload";
+                    if let Some(process) = p.lifecycle.spec.process() {
+                        if let Some(env) = process.env() {
+                            if let Some(v) =
+                                env.iter().find(|k| k.contains(&NN_PRELOAD_KEY.to_string()))
+                            {
+                                if let Some(nn_preload) =
+                                    v.strip_prefix::<&str>(format!("{}=", NN_PRELOAD_KEY).as_ref())
+                                {
+                                    log::info!("found nn_pre_load: {}", nn_preload);
+                                    if let Some(rootfs) = spec.root().as_ref() {
+                                        pre_load_with_new_rootfs(nn_preload, rootfs.path())
+                                            .unwrap();
+                                    }
+                                }
+                            }
+                        }
+                    }
+
+                    let host_options = HostRegistrationConfigOptions::default().wasi(true);
+                    let config = ConfigBuilder::new(CommonConfigOptions::default())
+                        .with_host_registration_config(host_options)
+                        .build()
+                        .map_err(other_error!(e, "generate default wasmedge config"))?;
+
+                    vm = VmBuilder::new()
+                        .with_config(config)
+                        .with_plugin_wasi_nn()
+                        .with_plugin("wasi_logging", None)
+                        .build()
+                        .unwrap();
+                }
                 match run_wasi_func(vm, args, envs, preopens, p) {
                     Ok(_) => exit(0),
                     // TODO add a pipe? to return detailed error message
@@ -461,3 +495,36 @@ pub async fn process_exits<F>(task: &TaskService<F, WasmEdgeContainer>) {
         }
     });
 }
+
+#[cfg(all(
+    target_os = "linux",
+    feature = "wasmedge_wasi_nn",
+    target_arch = "x86_64"
+))]
+fn pre_load_with_new_rootfs(
+    preload: &str,
+    rootfs: &std::path::PathBuf,
+) -> Result<(), WasmEdgeError> {
+    use wasmedge_sdk::plugin::{ExecutionTarget, GraphEncoding};
+    let nn_preload: Vec<&str> = preload.split(':').collect();
+    if nn_preload.len() != 4 {
+        return Err(WasmEdgeError::Operation(format!(
+            "Failed to convert to NNPreload value. Invalid preload string: {}. The correct format is: 'alias:backend:target:path'",
+            preload
+        )));
+    }
+    let (alias, backend, target, path) = (
+        nn_preload[0].to_string(),
+        nn_preload[1]
+            .parse::<GraphEncoding>()
+            .map_err(|err| WasmEdgeError::Operation(err.to_string()))?,
+        nn_preload[2]
+            .parse::<ExecutionTarget>()
+            .map_err(|err| WasmEdgeError::Operation(err.to_string()))?,
+        std::path::Path::new(rootfs).join(nn_preload[3]),
+    );
+    PluginManager::nn_preload(vec![wasmedge_sdk::plugin::NNPreload::new(
+        alias, backend, target, path,
+    )]);
+    Ok(())
+}