Add TVM application extension with WASM runtime

Signed-off-by: leonwanghui <wanghui71leon@gmail.com>

apps/README.md

@@ -26,3 +26,4 @@ If you are interested in writing optimized kernels with TVM, checkout [TOPI: TVM
 - [android_rpc](android_rpc) Android RPC server.
 - [benchmark](benchmark) Example end to end compilation benchmarks
 - [howto_deploy](howto_deploy) Tutorial on how to deploy TVM with minimum code dependency.
+- [wasm-dlbackend-tvm](wasm-dlbackend-tvm) WebAssembly backend for deep learning framework with TVM runtime.

apps/wasm-dlbackend-tvm/.cargo/config

@@ -0,0 +1,3 @@
+[build]
+target = "wasm32-wasi"
+rustflags = ["-C", "link-arg=--whole-archive", "-C", "link-arg=-lops_wasm32"]

apps/wasm-dlbackend-tvm/.gitignore

@@ -0,0 +1,8 @@
+# Built packages
+**/lib/
+
+
+#Added by cargo
+
+**/target/
+**/Cargo.lock

apps/wasm-dlbackend-tvm/Cargo.toml

@@ -0,0 +1,30 @@
+[package]
+name = "wasm-dlbackend-tvm"
+version = "0.1.0"
+authors = ["leonwanghui <wanghui71leon@gmail.com>"]
+edition = "2018"
+description = "WebAssembly backend to deep learning framework using TVM runtime"
+readme = "README.md"
+repository = "https://github.com/apache/incubator-tvm"
+license = "Apache-2.0"
+keywords = ["wasm", "machine learning", "tvm"]
+
+[package.metadata]
+wasm-opt = true
+wasm-name-section = false
+wasm-producers-section = false
+
+[profile.release]
+lto = true
+opt-level = 's'
+
+[lib]
+crate-type = ['cdylib']
+
+[dependencies]
+serde = "1.0.53"
+serde_derive = "1.0.53"
+serde_json = "1.0.53"
+ndarray = "0.12"
+tvm-common = { version = "0.1", path = "../../rust/common" }
+tvm-runtime = { version = "0.1", path = "../../rust/runtime" }

apps/wasm-dlbackend-tvm/README.md

@@ -0,0 +1,137 @@
+# WebAssembly Backend for Deep Learning Framework with TVM Runtime
+
+#### Experimental notice: This project is still *experimental* and only serves as a proof of concept for running deep learning frameworks (such like [MindSpore](https://github.com/mindspore-ai/mindspore)) on [WebAssembly runtime](https://github.com/bytecodealliance/wasmtime) with [TVM stack](https://tvm.apache.org/).
+
+- [WebAssembly Backend for Deep Learning Framework with TVM Runtime](#webassembly-backend-for-deep-learning-framework-with-tvm-runtime)
+    - [Motivation](#motivation)
+    - [Framework Landscape](#framework-landscape)
+    - [Project Status](#project-status)
+    - [PoC Guidelines](#poc-guidelines)
+        - [Pre-installation](#pre-installation)
+        - [Build wasm-dlbackend-tvm package](#build-wasm-dlbackend-tvm-package)
+        - [Test](#test)
+    - [Future Work](#future-work)
+        - [Operator enhancement](#operator-enhancement)
+        - [Performance benchmark](#performance-benchmark)
+        - [Native TVM Rust runtime support](#native-tvm-rust-runtime-support)
+    - [Appendix](#appendix)
+        - [System packages install](#system-packages-install)
+    - [Contribution](#contribution)
+
+## Motivation
+
+<img src="https://github.com/dmlc/web-data/raw/master/tvm/tutorial/tvm_support_list.png" alt="TVM hardware support" width="600"/>
+
+As demonstrated in TVM runtime [tutorials](https://tvm.apache.org/docs/tutorials/relay_quick_start.html), TVM already supports WASM as the optional hardware backend, so we can leverage the features of WebAssembly (portability, security) and TVM runtime (domain-specific, optimization) to build a flexible and auto-optimized operator backend for all deep learning frameworks.
+
+## Framework Landscape
+
+The figure below demonstrates the whole landscape of running deep learning framework on WASM runtime with TVM compiler stack.
+```
+ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+|                               |                       _ _ _ _ _ _ _ _ _ _ _
+| Framework Frontend Expression |                      |                     |
+|_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _|                      | TVM (TE) Python API |
+              ||                                       |_ _ _ _ _ _ _ _ _ _ _|
+              \/                                                 ||
+   _ _ _ _ _ _ _ _ _ _ _ _ _ _                                   \/
+  |                           |                         _ _ _ _ _ _ _ _ _ _ _
+  |  Framework WASM Backend   |                        |                     |
+  |      (WASM runtime)       |                        |  TVM Compiler Stack |
+  |_ _ _ _ _ _ _ _ _ _ _ _ _ _|                        |_ _ _ _ _ _ _ _ _ _ _|
+              ||                                                 ||
+              \/                                                 \/
+        _ _ _ _ _ _ _ _        _ _ _ _ _ _ _ _ _            _ _ _ _ _ _ _
+       |               |      |                 |  llvm-ar |             |
+       |  TVM Runtime  | <--- | libops_wasm32.a | <------- | add.o sub.o |
+       |_ _ _ _ _ _ _ _|      |_ _ _ _ _ _ _ _ _|          |_ _ _ _ _ _ _|
+```
+
+## Project Status
+
+This project should be considered **experimental** at the very early stage, all rich features are under active development. Here is the current operator support matrix:
+
+| Operator Name | FP32 | INT32 | INT8 |
+| ------------- | ---- | ----- | ---- |
+| Add | ✔️ | <center>&mdash;</center> | <center>&mdash;</center> |
+| Sub | ✔️ | <center>&mdash;</center> | <center>&mdash;</center> |
+
+**NOTICE**: Currently this project is ONLY tested on Ubuntu system, so `Ubuntu 16.04+` should be prepared as the testing environment.
+
+## PoC Guidelines
+
+### Pre-installation
+
+* Rust
+
+    Before running this demo, please make sure [Rust](#system-packages-install) has been installed.
+
+    After Rust installed, execute the code below to add `wasm32-wasi` target:
+    ```shell
+    rustup target add wasm32-wasi
+    cargo install cargo-wasi
+    ```
+
+* TVM
+
+    Please follow TVM [installations](https://tvm.apache.org/docs/install/index.html), `export TVM_HOME=/path/to/tvm` and add `libtvm_runtime` to your `LD_LIBRARY_PATH`.
+
+    *Note:* To run the end-to-end examples and tests, `tvm` and `topi` need to be added to your `PYTHONPATH` or it's automatic via an Anaconda environment when it is installed individually.
+
+### Build wasm-dlbackend-tvm package
+
+```shell
+cd wasm-dlbackend-tvm && cargo wasi build --release
+```
+
+### Test
+
+Run the command below to install the frontend package for testing (`rust` REQUIRED):
+```shell
+cd wasm-dlfrontend/ && cargo build --release
+cp ./target/release/wasm-dlfrontend /usr/local/bin/
+```
+
+Check the usage of `wasm-dlfrontend`:
+
+```shell
+~# wasm-dlfrontend -h
+
+Usage: wasm-dlfrontend [options]
+
+Options:
+    -c, --ms-backend-config FILE_PATH
+                        set wasm backend config file
+    -o, --op-type VALUE set the operator type, currently ONLY support Add and
+                        Sub, default: Add.
+    -h, --help          print this help menu
+```
+
+## Future Work
+
+### Operator enhancement
+TODO
+
+### Performance benchmark
+TODO
+
+### Native TVM Rust runtime support
+TODO
+
+## Appendix
+
+### System packages install
+
+* Rust (latest version)
+
+    If you are running Windows, to install Rust, download and run the [RUST-INIT.EXE](https://win.rustup.rs/), and then follow the onscreen instructions.
+
+    If you are a Linux user, run the following in your terminal, then follow the on-screen instructions to install Rust.
+
+    ```shell
+    curl https://sh.rustup.rs -sSf | sh
+    ```
+
+## Contribution
+
+Lastly very thanks [@kazum](https://github.com/kazum) for having offered a lot of help when implementing this project.

apps/wasm-dlbackend-tvm/build.rs

@@ -0,0 +1,32 @@
+use std::{path::PathBuf, process::Command};
+
+fn main() {
+    let mut out_dir = PathBuf::from(env!("CARGO_MANIFEST_DIR"));
+    out_dir.push("lib");
+
+    if !out_dir.is_dir() {
+        std::fs::create_dir(&out_dir).unwrap();
+    }
+
+    Command::new(concat!(
+        env!("CARGO_MANIFEST_DIR"),
+        "/tools/build_ops_lib.py"
+    ))
+    .arg(&out_dir)
+    .output()
+    .expect("Failed to execute command!");
+
+    let ar = option_env!("LLVM_AR").unwrap_or("llvm-ar-10");
+    let add_obj_file = out_dir.join("add.o");
+    let sub_obj_file = out_dir.join("sub.o");
+    let lib_file = out_dir.join("libops_wasm32.a");
+    Command::new(ar)
+        .arg("rcs")
+        .arg(&lib_file)
+        .arg(&add_obj_file)
+        .arg(&sub_obj_file)
+        .output()
+        .expect("Failed to execute command!");
+
+    println!("cargo:rustc-link-search=native={}", out_dir.display());
+}

apps/wasm-dlbackend-tvm/src/lib.rs

@@ -0,0 +1,31 @@
+#[macro_use]
+extern crate serde_derive;
+#[macro_use]
+extern crate tvm_runtime;
+
+mod ops;
+use ops::types::Status;
+mod utils;
+
+#[no_mangle]
+pub extern "C" fn run(op_type: i32, in_addr: i32, in_size: i32, out_addr: i32) -> i32 {
+    let inputs = utils::load_inputs(in_addr, in_size as usize);
+    if ops::validate_inputs(&inputs) != Status::Succeed {
+        return 0i32;
+    }
+
+    let op_instance = ops::operator_instantiate(op_type);
+    let (a_shape, b_shape, c_shape) = ops::parse_inputs_shape(&inputs);
+    if op_instance.init(a_shape, b_shape, c_shape) != Status::Succeed {
+        return 0i32;
+    };
+
+    let (in_tensors, out_tensor) = ops::parse_inputs_tensor(&inputs);
+    let (stat, output) = op_instance.launch(in_tensors, out_tensor);
+    if stat != Status::Succeed {
+        return 0i32;
+    }
+
+    let out_size = utils::store_output(out_addr, output);
+    out_size as i32
+}

apps/wasm-dlbackend-tvm/src/ops/add.rs

@@ -0,0 +1,63 @@
+use super::types::*;
+use tvm_runtime::{Module as _, SystemLibModule};
+
+extern "C" {
+    fn __wasm_call_ctors();
+}
+
+pub struct TVMAddOp {}
+
+impl TVMAddOp {
+    pub fn new() -> Self {
+        Self {}
+    }
+}
+
+impl Operator for TVMAddOp {
+    fn init(&self, a_shape: Vec<i64>, b_shape: Vec<i64>, c_shape: Vec<i64>) -> Status {
+        if !((a_shape.len() == b_shape.len()
+            && a_shape
+                .iter()
+                .zip(&b_shape)
+                .filter(|&(a, b)| a == b)
+                .count()
+                == a_shape.len())
+            && (b_shape.len() == c_shape.len()
+                && b_shape
+                    .iter()
+                    .zip(&c_shape)
+                    .filter(|&(b, c)| b == c)
+                    .count()
+                    == c_shape.len()))
+        {
+            println!("Both dimension size and shape for Add operator should be equal!");
+            return Status::InitFailed;
+        }
+
+        println!("TVM Add operator init success!");
+        Status::Succeed
+    }
+
+    fn launch(&self, mut inputs: Vec<Tensor>, output: Tensor) -> (Status, Tensor) {
+        if inputs.len() != 2 {
+            println!("Inputs tensor length should be 2!");
+            return (Status::LaunchFailed, Tensor::default());
+        }
+        let mut l_tensor = inputs.get_mut(0).unwrap().as_dltensor();
+        let mut r_tensor = inputs.get_mut(1).unwrap().as_dltensor();
+        let mut out_tensor = output.as_dltensor();
+
+        unsafe {
+            // This is necessary to invoke TVMBackendRegisterSystemLibSymbol
+            // API calls.
+            __wasm_call_ctors();
+        }
+        let syslib = SystemLibModule::default();
+        let add = syslib.get_function("add").expect("add function not found!");
+        call_packed!(add, &mut l_tensor, &mut r_tensor, &mut out_tensor).unwrap();
+
+        let output: Tensor = out_tensor.into();
+        println!("TVM Add operator run success!");
+        (Status::Succeed, output)
+    }
+}

apps/wasm-dlbackend-tvm/src/ops/mod.rs

@@ -0,0 +1,46 @@
+mod add;
+use add::TVMAddOp;
+mod sub;
+use sub::TVMSubOp;
+pub mod types;
+use types::*;
+
+use std::boxed::Box;
+
+pub fn operator_instantiate(op_type: i32) -> Box<dyn Operator> {
+    match OpType::from(op_type) {
+        OpType::Add => Box::new(TVMAddOp::new()),
+        OpType::Sub => Box::new(TVMSubOp::new()),
+    }
+}
+
+pub fn validate_inputs(inputs: &Vec<Tensor>) -> Status {
+    if (inputs.len() == 3
+        && !(inputs[0].dtype() == inputs[1].dtype() && inputs[0].dtype() == inputs[2].dtype()))
+        || (inputs.len() == 2 && inputs[0].dtype() != inputs[1].dtype())
+    {
+        println!("The dtype of inputs and outputs is not equal!");
+        Status::ValidateFailed
+    } else {
+        Status::Succeed
+    }
+}
+
+pub fn parse_inputs_shape(inputs: &Vec<Tensor>) -> (Vec<i64>, Vec<i64>, Vec<i64>) {
+    if inputs.len() == 3 {
+        (inputs[0].shape(), inputs[1].shape(), inputs[2].shape())
+    } else {
+        (inputs[0].shape(), inputs[1].shape(), Vec::new())
+    }
+}
+
+pub fn parse_inputs_tensor(inputs: &Vec<Tensor>) -> (Vec<Tensor>, Tensor) {
+    if inputs.len() == 3 {
+        (
+            vec![inputs[0].clone(), inputs[1].clone()],
+            inputs[2].clone(),
+        )
+    } else {
+        (vec![inputs[0].clone()], inputs[1].clone())
+    }
+}