[Example] MLX: add documentation

wasmedge-mlx/README.md

@@ -0,0 +1,81 @@
+# MLX example with WasmEdge WASI-NN MLX plugin
+
+This example demonstrates using WasmEdge WASI-NN MLX plugin to perform an inference task with LLM model.
+
+## Supported Models
+
+| Family | Models |
+|--------|--------|
+| LLaMA 2 | llama_2_7b_chat_hf |
+| LLaMA 3 | llama_3_8b |
+| TinyLLaMA | tiny_llama_1.1B_chat_v1.0 |
+
+## Install WasmEdge with WASI-NN MLX plugin
+
+The MLX backend relies on [MLX](https://github.com/ml-explore/mlx), but we will auto-download MLX when you build WasmEdge. You do not need to install it yourself. If you want to custom MLX, install it yourself or set the `CMAKE_PREFIX_PATH` variable when configuring cmake.
+
+Build and install WasmEdge from source:
+
+``` bash
+cd <path/to/your/wasmedge/source/folder>
+
+cmake -GNinja -Bbuild -DCMAKE_BUILD_TYPE=Release -DWASMEDGE_PLUGIN_WASI_NN_BACKEND="mlx"
+cmake --build build
+
+# For the WASI-NN plugin, you should install this project.
+cmake --install build
+```
+
+Then you will have an executable `wasmedge` runtime under `/usr/local/bin` and the WASI-NN with MLX backend plug-in under `/usr/local/lib/wasmedge/libwasmedgePluginWasiNN.so` after installation.
+
+## Download the model and tokenizer
+
+In this example, we will use `tiny_llama_1.1B_chat_v1.0`, which you can change to `llama_2_7b_chat_hf` or `llama_3_8b`.
+
+``` bash
+# Download model weight
+wget https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0/resolve/main/model.safetensors
+# Download tokenizer
+wget https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0/resolve/main/tokenizer.json
+```
+
+## Build wasm
+
+Run the following command to build wasm, the output WASM file will be at `target/wasm32-wasi/release/`
+
+```bash
+cargo build --target wasm32-wasi --release
+```
+## Execute 
+
+Execute the WASM with the `wasmedge` using nn-preload to load model. 
+
+``` bash
+wasmedge --dir .:. \
+ --nn-preload default:mlx:AUTO:model.safetensors \
+  ./target/wasm32-wasi/release/wasmedge-mlx.wasm default
+
+```
+
+If your model has multiple weight files, you need to provide all in the nn-preload.
+
+For example:
+``` bash
+wasmedge --dir .:. \
+ --nn-preload default:mlx:AUTO:llama2-7b/model-00001-of-00002.safetensors:llama2-7b/model-00002-of-00002.safetensors \
+  ./target/wasm32-wasi/release/wasmedge-mlx.wasm default
+```
+
+## Other 
+
+There are some metadata for MLX plugin you can set.
+
+- model_type (required): LLM model type.
+- tokenizer (required): tokenizer.json path
+- max_token (option): maximum generate token number, default is 1024.
+- enable_debug_log (option): if print debug log, default is false.
+
+``` rust
+let graph = GraphBuilder::new(GraphEncoding::Mlx, ExecutionTarget::AUTO)
+        .config(serde_json::to_string(&json!({"model_type": "tiny_llama_1.1B_chat_v1.0", "tokenizer":tokenizer_path, "max_token":100}))
+```

wasmedge-mlx/src/main.rs

@@ -1,42 +1,41 @@
-use tokenizers::tokenizer::Tokenizer;
 use serde_json::json;
+use std::env;
 use wasmedge_wasi_nn::{
-    self, ExecutionTarget, GraphBuilder, GraphEncoding, GraphExecutionContext,
-    TensorType,
+    self, ExecutionTarget, GraphBuilder, GraphEncoding, GraphExecutionContext, TensorType,
 };
-use std::env;
-fn get_data_from_context(context: &GraphExecutionContext, index: usize) -> Vec<u8> {
-    // Preserve for 4096 tokens with average token length 8
-    const MAX_OUTPUT_BUFFER_SIZE: usize = 4096 * 8;
+fn get_data_from_context(context: &GraphExecutionContext, index: usize) -> String {
+    // Preserve for 4096 tokens with average token length 6
+    const MAX_OUTPUT_BUFFER_SIZE: usize = 4096 * 6;
     let mut output_buffer = vec![0u8; MAX_OUTPUT_BUFFER_SIZE];
-    let _ = context
+    let mut output_size = context
         .get_output(index, &mut output_buffer)
         .expect("Failed to get output");
+    output_size = std::cmp::min(MAX_OUTPUT_BUFFER_SIZE, output_size);
 
-    return output_buffer;
+    return String::from_utf8_lossy(&output_buffer[..output_size]).to_string();
 }
 
-fn get_output_from_context(context: &GraphExecutionContext) -> Vec<u8> {
+fn get_output_from_context(context: &GraphExecutionContext) -> String {
     get_data_from_context(context, 0)
 }
 fn main() {
-        let tokenizer_path = "tokenizer.json";
-        let prompt = "Once upon a time, there existed a little girl,";
-
-        let graph = GraphBuilder::new(GraphEncoding::Mlx, ExecutionTarget::AUTO)
-        .config(serde_json::to_string(&json!({"tokenizer":tokenizer_path})).expect("Failed to serialize options"))
+    let tokenizer_path = "./tokenizer.json";
+    let prompt = "Once upon a time, there existed a little girl,";
+    let args: Vec<String> = env::args().collect();
+    let model_name: &str = &args[1];
+    let graph = GraphBuilder::new(GraphEncoding::Mlx, ExecutionTarget::AUTO)
+        .config(serde_json::to_string(&json!({"model_type": "tiny_llama_1.1B_chat_v1.0", "tokenizer":tokenizer_path, "max_token":100})).expect("Failed to serialize options"))
         .build_from_cache(model_name)
         .expect("Failed to build graph");
-        let mut context = graph
-            .init_execution_context()
-            .expect("Failed to init context");
-        let tensor_data = prompt.as_bytes().to_vec();
-        context
-            .set_input(0, TensorType::U8, &[1], &tensor_data)
-            .expect("Failed to set input");
-        context.compute().expect("Failed to compute");
-        let output_bytes = get_output_from_context(&context);
+    let mut context = graph
+        .init_execution_context()
+        .expect("Failed to init context");
+    let tensor_data = prompt.as_bytes().to_vec();
+    context
+        .set_input(0, TensorType::U8, &[1], &tensor_data)
+        .expect("Failed to set input");
+    context.compute().expect("Failed to compute");
+    let output = get_output_from_context(&context);
 
-        println!("{}", output.trim());
-
-}
+    println!("{}", output.trim());
+}