[Example] Add Qwen example (#137)

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

Author: jokemanfire

wasmedge-ggml/qwen/Cargo.toml

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+[package]
+name = "wasmedge-ggml-qwen"
+version = "0.1.0"
+edition = "2021"
+
+# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
+
+[dependencies]
+serde_json = "1.0"
+wasmedge-wasi-nn = "0.7.1"

wasmedge-ggml/qwen/README.md

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+# `通义千问`
+
+## Execute - Tong Yi Qwen
+
+### Model Download Link
+
+```console
+wget https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat-GGUF/resolve/main/qwen1_5-0_5b-chat-q2_k.gguf
+```
+
+### Execution Command
+
+Please make sure you have the `qwen1_5-0_5b-chat-q2_k.gguf` file in the current directory.
+If you want to enable GPU support, please set the `n_gpu_layers` environment variable.
+You can also change the `ctx_size` to have a larger context window via `--env ctx_size=8192`. The default value is 1024.
+
+```console
+$ wasmedge --dir .:. \
+  --env n_gpu_layers=10 \
+  --nn-preload default:GGML:AUTO:qwen1_5-0_5b-chat-q2_k.gguf \
+  wasmedge-ggml-qwen.wasm default
+
+USER:
+你好
+ASSISTANT:
+你好！有什么我能帮你的吗？
+USER:
+你是谁
+ASSISTANT:
+我是一个人工智能助手，我叫通义千问。有什么我可以帮助你的吗？
+USER:
+能帮助我写Rust代码吗？                                          
+ASSISTANT:
+当然可以！我可以帮助你使用Rust语言编写代码，帮助你理解和编写出高质量的代码。我可以帮你编写函数、函数和类，提供使用Python解释器编译和运行代码的建议，提供使用Node.js、Python或Java的代码示例，以及更多关于如何使用Python、Java或C++的代码示例。
+```

wasmedge-ggml/qwen/src/main.rs

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+use serde_json::json;
+use serde_json::Value;
+use std::env;
+use std::io;
+use wasmedge_wasi_nn::{
+    self, BackendError, Error, ExecutionTarget, GraphBuilder, GraphEncoding, GraphExecutionContext,
+    TensorType,
+};
+
+fn read_input() -> String {
+    loop {
+        let mut answer = String::new();
+        io::stdin()
+            .read_line(&mut answer)
+            .expect("Failed to read line");
+        if !answer.is_empty() && answer != "\n" && answer != "\r\n" {
+            return answer.trim().to_string();
+        }
+    }
+}
+
+fn get_options_from_env() -> Value {
+    let mut options = json!({});
+    if let Ok(val) = env::var("enable_log") {
+        options["enable-log"] = serde_json::from_str(val.as_str())
+            .expect("invalid value for enable-log option (true/false)")
+    } else {
+        options["enable-log"] = serde_json::from_str("false").unwrap()
+    }
+    if let Ok(val) = env::var("n_gpu_layers") {
+        options["n-gpu-layers"] =
+            serde_json::from_str(val.as_str()).expect("invalid ngl value (unsigned integer")
+    } else {
+        options["n-gpu-layers"] = serde_json::from_str("0").unwrap()
+    }
+    options["ctx-size"] = serde_json::from_str("1024").unwrap();
+
+    options
+}
+
+fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec<u8>) -> Result<(), Error> {
+    context.set_input(0, TensorType::U8, &[1], &data)
+}
+
+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 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);
+
+    String::from_utf8((output_buffer[..output_size]).to_vec())
+        .unwrap()
+        .to_string()
+}
+
+fn get_output_from_context(context: &GraphExecutionContext) -> String {
+    get_data_from_context(context, 0)
+}
+
+fn main() {
+    let args: Vec<String> = env::args().collect();
+    let model_name: &str = &args[1];
+
+    // Set options for the graph. Check our README for more details:
+    // https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters
+    let options = get_options_from_env();
+
+    // Create graph and initialize context.
+    let graph = GraphBuilder::new(GraphEncoding::Ggml, ExecutionTarget::AUTO)
+        .config(serde_json::to_string(&options).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 mut saved_prompt =
+        String::from("<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n");
+
+    loop {
+        println!("USER:");
+        let input = read_input();
+
+        saved_prompt = format!("{}\n<|im_start|>user\n{}<|im_end|>\n", saved_prompt, input);
+        let contex_prompt = saved_prompt.clone() + "<|im_start|>assistant\n";
+
+        // Set prompt to the input tensor.
+        set_data_to_context(&mut context, contex_prompt.as_bytes().to_vec())
+            .expect("Failed to set input");
+
+        // Execute the inference.
+        let mut reset_prompt = false;
+        match context.compute() {
+            Ok(_) => (),
+            Err(Error::BackendError(BackendError::ContextFull)) => {
+                println!("\n[INFO] Context full, we'll reset the context and continue.");
+                reset_prompt = true;
+            }
+            Err(Error::BackendError(BackendError::PromptTooLong)) => {
+                println!("\n[INFO] Prompt too long, we'll reset the context and continue.");
+                reset_prompt = true;
+            }
+            Err(err) => {
+                println!("\n[ERROR] {}", err);
+            }
+        }
+
+        // Retrieve the output.
+        let mut output = get_output_from_context(&context);
+        println!("ASSISTANT:\n{}", output.trim());
+
+        // Update the saved prompt.
+        if reset_prompt {
+            saved_prompt.clear();
+        } else {
+            output = output.trim().to_string();
+            saved_prompt = format!("{} {}", saved_prompt, output);
+        }
+    }
+}