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Add TVM application extension with WASM runtime (apache#5892)
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* Refactor wasm runtime module and resovle conflict errors

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

* Fix some cargo clippy warnings

Signed-off-by: leonwanghui <wanghui71leon@gmail.com>
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leonwanghui authored and Trevor Morris committed Aug 26, 2020
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1 change: 1 addition & 0 deletions apps/README.md
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Expand Up @@ -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_standalone](tvm-standalone) WebAssembly standalone for deep learning framework with TVM runtime.
8 changes: 8 additions & 0 deletions apps/wasm-standalone/.gitignore
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# Built packages
**/lib/


#Added by cargo

**/target/
**/Cargo.lock
202 changes: 202 additions & 0 deletions apps/wasm-standalone/README.md
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<!--- Licensed to the Apache Software Foundation (ASF) under one -->
<!--- or more contributor license agreements. See the NOTICE file -->
<!--- distributed with this work for additional information -->
<!--- regarding copyright ownership. The ASF licenses this file -->
<!--- to you under the Apache License, Version 2.0 (the -->
<!--- "License"); you may not use this file except in compliance -->
<!--- with the License. You may obtain a copy of the License at -->

<!--- http://www.apache.org/licenses/LICENSE-2.0 -->

<!--- Unless required by applicable law or agreed to in writing, -->
<!--- software distributed under the License is distributed on an -->
<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
<!--- KIND, either express or implied. See the License for the -->
<!--- specific language governing permissions and limitations -->
<!--- under the License. -->

# WebAssembly Standalone 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 on [WebAssembly runtime](https://github.com/bytecodealliance/wasmtime) with [TVM stack](https://tvm.apache.org/).

- [WebAssembly Standalone for Deep Learning Framework with TVM Runtime](#webassembly-standalone-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 ResNet50 model](#build-resnet50-model)
- [Build wasm-graph package](#build-wasm-graph-package)
- [Test](#test)
- [Future Work](#future-work)
- [More networks support](#more-networks-support)
- [Performance benchmark](#performance-benchmark)
- [Native TVM Rust runtime support](#native-tvm-rust-runtime-support)
- [Appendix](#appendix)
- [System packages install](#system-packages-install)

## 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 graph compiler for all deep learning frameworks.

## Framework Landscape

The figures below demonstrate the whole landscape of running deep learning frameworks on WASM runtime with TVM compiler stack.

* WASM graph generation
```
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
| | | | | |
| Framework Model | ---> | ONNX Model | ---> | TVM Relay Python API |
|_ _ _ _ _ _ _ _ _ _| |_ _ _ _ _ _ _| |_ _ _ _ _ _ _ _ _ _ _ _|
||
\/
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
| | | |
| WASM Graph Builder | | TVM Compiler Stack |
| (TVM runtime) | |_ _ _ _ _ _ _ _ _ _ _|
|_ _ _ _ _ _ _ _ _ _ _| ||
|| \/
_ _ _ _ _ _ _ _ _ || _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
| | \/ | | llvm-ar | |
| wasm_graph.wasm | <--- | libgraph_wasm32.a | <------- | graph.o |
|_ _ _ _ _ _ _ _ _| |_ _ _ _ _ _ _ _ _ _| |_ _ _ _ _|
```

* WASM graph loading
```
_ _ _ _ _ _ _ _ _ _ _
| |
| WASM Graph Loader |
| (WASM runtime) |
|_ _ _ _ _ _ _ _ _ _ _|
||
\/
_ _ _ _ _ _ _ _ _ _
| |
| wasm_graph.wasm |
|_ _ _ _ _ _ _ _ _ _|
```

## 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:

| Model Name | Status |
| ---------- | ------ |
| ResNet50 | ✔️ |
| LeNet | <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
```

* TVM

Please follow TVM [installations](https://tvm.apache.org/docs/install/index.html) for the detailed instruction.

* LLVM

`LLVM 10.0` or later is REQUIRED.

### Build ResNet50 model

- Build DL library in the WebAssembly format.

- Download model

```
cd wasm-graph/tools && wget https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v1/resnet50v1.onnx
```

- Compile

```
LLVM_AR=llvm-ar-10 python ./build_graph_lib.py -O3 ./resnet50v1.onnx
```

### Build wasm-graph package

```shell
cd wasm-graph && cargo build --release
cp ./target/wasm32-wasi/release/wasm_graph.wasm ./lib/wasm_graph_resnet50.wasm
```

### Test

Before running this demo, please make sure [`Rust`](#system-packages-install) has been installed.

Next run the command below to install the runtime package for testing (`rust` REQUIRED):

```shell
cd wasm-runtime/tests/test_graph_resnet50 && cargo build
```

Check the usage of `test_graph_resnet50`:

```shell
~# ./target/debug/test_graph_resnet50 -h
Usage: ./target/debug/test_graph_resnet50 [options]
Options:
-g, --wasm-graph-file FILE_PATH
set the path to wasm graph file
-i, --input-data-file FILE_PATH
set the path to input image file
-l, --label-class-file FILE_PATH
set the path to label class file
-h, --help print this help menu
```

Next perform model inference using these commands below:
```
$ cp ../../../wasm-graph/lib/wasm_graph_resnet50.wasm ./
$ wget -O cat.png https://github.com/dmlc/mxnet.js/blob/master/data/cat.png?raw=true
$ wget -O synset.csv https://raw.githubusercontent.com/kazum/tvm-wasm/master/synset.csv
$ ./target/debug/test_graph_resnet50 -g ./wasm_graph_resnet50.wasm -i ./cat.png -l ./synset.csv
original image dimensions: (256, 256)
resized image dimensions: (224, 224)
input image belongs to the class `tabby, tabby cat`
```

## Future Work

### More networks support
TODO

### Performance benchmark

We are working on several improvements on performances:
* WebAssembly simd128 support (**Done**)
* Auto-tvm enhancement for llvm target

### 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
```
3 changes: 3 additions & 0 deletions apps/wasm-standalone/wasm-graph/.cargo/config
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[build]
target = "wasm32-wasi"
rustflags = ["-C", "link-arg=--whole-archive", "-C", "link-arg=-lgraph_wasm32"]
43 changes: 43 additions & 0 deletions apps/wasm-standalone/wasm-graph/Cargo.toml
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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.

[package]
name = "wasm-graph"
version = "0.1.0"
authors = ["TVM Contributors"]
edition = "2018"
description = "WebAssembly graph to deep learning frameworks using TVM"
readme = "README.md"
repository = "https://github.com/apache/incubator-tvm"
license = "Apache-2.0"
keywords = ["wasm", "machine learning", "tvm"]

[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-sys = { path = "../../../rust/tvm-sys" }
tvm-graph-rt = { path = "../../../rust/tvm-graph-rt" }
lazy_static = "1.1.1"
24 changes: 24 additions & 0 deletions apps/wasm-standalone/wasm-graph/build.rs
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/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*/

fn main() {
let out_dir = concat!(env!("CARGO_MANIFEST_DIR"), "/lib");

println!("cargo:rustc-link-search=native={}", out_dir);
}
83 changes: 83 additions & 0 deletions apps/wasm-standalone/wasm-graph/src/lib.rs
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/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*/

#[macro_use]
extern crate lazy_static;
#[macro_use]
extern crate serde_derive;

mod types;
mod utils;

use std::{collections::HashMap, convert::TryFrom, env, sync::Mutex};

use tvm_graph_rt::{Graph, GraphExecutor, SystemLibModule, Tensor as TVMTensor};

use types::Tensor;

extern "C" {
fn __wasm_call_ctors();
}

lazy_static! {
static ref SYSLIB: SystemLibModule = SystemLibModule::default();
static ref GRAPH_EXECUTOR: Mutex<GraphExecutor<'static, 'static>> = {
unsafe {
// This is necessary to invoke TVMBackendRegisterSystemLibSymbol
// API calls.
__wasm_call_ctors();
}
let graph = Graph::try_from(include_str!(concat!(
env!("CARGO_MANIFEST_DIR"),
"/lib/graph.json"
)))
.unwrap();
let params_bytes =
include_bytes!(concat!(env!("CARGO_MANIFEST_DIR"), "/lib/graph.params"));
let params = tvm_graph_rt::load_param_dict(params_bytes)
.unwrap()
.into_iter()
.map(|(k, v)| (k, v.to_owned()))
.collect::<HashMap<String, TVMTensor<'static>>>();

let mut exec = GraphExecutor::new(graph, &*SYSLIB).unwrap();
exec.load_params(params);

Mutex::new(exec)
};
}

#[no_mangle]
pub extern "C" fn run(wasm_addr: i32, in_size: i32) -> i32 {
let in_tensor = unsafe { utils::load_input(wasm_addr, in_size as usize) };
let input: TVMTensor = in_tensor.as_dltensor().into();

GRAPH_EXECUTOR.lock().unwrap().set_input("data", input);
GRAPH_EXECUTOR.lock().unwrap().run();
let output = GRAPH_EXECUTOR
.lock()
.unwrap()
.get_output(0)
.unwrap()
.as_dltensor(false);

let out_tensor: Tensor = output.into();
let out_size = unsafe { utils::store_output(wasm_addr, out_tensor) };
out_size as i32
}
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