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Run Yolo using TVM and rust frontend on NVIDIA jetson agx xavier or PC

Build TVM

Following the offical docs to build TVM

Please get source code using git. We will merge some patch using git later.

Build darknet (Yolo v3)

TVM currently only support yolo v3. We need to checkout a history commit, master branch don't work.

git clone https://github.com/pjreddie/darknet.git
cd darknet
git checkout f6d861736038da22c9eb0739dca84003c5a5e275
make -j8

We would get a libdarknet.so after built it.

Download the weight file.

wget https://pjreddie.com/media/files/yolov3.weights

Compile yolov3 using TVM

#!/usr/bin/env python3

import numpy as np
import tvm
from tvm import relay
from tvm.relay.testing.darknet import __darknetffi__

cfg_path = './cfg/yolov3.cfg'
weights_path = './yolov3.weights'
lib_path = './libdarknet.so'

DARKNET_LIB = __darknetffi__.dlopen(lib_path)
net = DARKNET_LIB.load_network(cfg_path.encode('utf-8'), weights_path.encode('utf-8'), 0)
dtype = 'float32'
batch_size = 1
data = np.empty([batch_size, net.c, net.h, net.w], dtype)
print("Converting darknet to relay functions...")
mod, params = relay.frontend.from_darknet(net, dtype=dtype, shape=data.shape)
target = 'llvm'
target_host = 'llvm'

print("Compiling the model...")
with tvm.transform.PassContext(opt_level=3):
    graph, lib, params = relay.build(mod, target=target, target_host=target_host, params=params)

lib.export_library('deploy_lib.so')

with open("deploy_graph.json", "w") as fo:
    fo.write(graph)

with open("deploy_param.params", "wb") as fo:
    fo.write(relay.save_param_dict(params))

Rust frontend

const WIDTH: u32 = 416;
const HEIGHT: u32 = 416;

fn run(img: ArrayD<f32>) -> Vec<NDArray> {
    // get the global TVM graph runtime function
    let runtime_create_fn = Function::get("tvm.graph_runtime.create").unwrap();
    let lib = Module::load(&Path::new("deploy_lib.so")).unwrap();
    let graph = fs::read_to_string("deploy_graph.json").unwrap();
    let runtime_create_fn_ret = runtime_create_fn.invoke(vec![
        graph.into(),
        (&lib).into(),
        (DeviceType::CPU as i32).into(),
        0i32.into(),
    ]);
    // get graph runtime module
    let gmod: Module = runtime_create_fn_ret.unwrap().try_into().unwrap();
    let ref load_param_fn = gmod.get_function("load_params", false).unwrap();
    // parse parameters and convert to TVMByteArray
    let params: Vec<u8> = fs::read("deploy_param.params").unwrap();
    let barr = ByteArray::from(&params);
    load_param_fn.invoke(vec![(&barr).into()]).unwrap();
    let now = std::time::Instant::now();
    // get the set_input function
    let ref set_input_fn = gmod.get_function("set_input", false).unwrap();

    let input = NDArray::from_rust_ndarray(
        &img,
        Context::new(DeviceType::CPU, 0),
        DataType::float(32, 1),
    )
    .unwrap();
    println!(
        "input size is {:?}",
        input.shape().expect("cannot get the input shape")
    );
    set_input_fn
        .invoke(vec!["data".into(), (&input).into()])
        .unwrap();

    // get `run` function from runtime module
    let ref run_fn = gmod.get_function("run", false).unwrap();
    // execute the run function. Note that it has no argument
    run_fn.invoke(vec![]).unwrap();
    // get the `get_output` function from runtime module
    let ref get_output_fn = gmod.get_function("get_output", false).unwrap();

    // prepare to get the output
    let mut outputs = Vec::with_capacity(12);
    for i in 0..12 {
        let dtype = if i % 2 == 0 {
            DataType::float(32, 1)
        } else {
            DataType::int(32, 1)
        };
        let output_shape: SmallVec<[usize; 4]> = match i % 4 {
            0 => smallvec![
                1,
                255,
                HEIGHT as usize >> (3 + (i / 4)),
                WIDTH as usize >> (3 + (i / 4))
            ],
            1 => smallvec![3],
            2 => smallvec![18],
            3 => smallvec![6],
            _ => unreachable!(),
        };
        let output = NDArray::empty(&output_shape, Context::new(DeviceType::CPU, 0), dtype);
        get_output_fn
            .invoke(vec![i.into(), (&output).into()])
            .unwrap();
        outputs.push(output);
    }
    println!("TVM run in {:?}", now.elapsed());
    outputs
}

Build in release mode and check the binary.

$ ldd target/release/yolov3-rs
	linux-vdso.so.1 (0x0000007fadaf5000)
	libtvm.so => not found
	libdl.so.2 => /lib/aarch64-linux-gnu/libdl.so.2 (0x0000007fad94a000)
	libpthread.so.0 => /lib/aarch64-linux-gnu/libpthread.so.0 (0x0000007fad91e000)
	libgcc_s.so.1 => /lib/aarch64-linux-gnu/libgcc_s.so.1 (0x0000007fad8fa000)
	libc.so.6 => /lib/aarch64-linux-gnu/libc.so.6 (0x0000007fad7a0000)
	libm.so.6 => /lib/aarch64-linux-gnu/libm.so.6 (0x0000007fad6e7000)
	/lib/ld-linux-aarch64.so.1 (0x0000007fadaca000)

This libtvm.so is in ~/tvm/build. Either copy this .so file to /usr/local/lib or using LD_LIBRARY_PATH environment variable to add it to library search path.

Run on agx xavier:

$ LD_LIBRARY_PATH=~/tvm/build RUST_BACKTRACE=1 cargo run --release ../darknet/data/dog.jpg
    Finished release [optimized] target(s) in 0.12s
     Running `target/release/yolov3-rs ../darknet/data/dog.jpg`
input size is [1, 3, 416, 416]
TVM run in 1.236270534s
15 result before nms sort
x:164 y:112 w:400 h:331 [1 0.9958774],
x:127 y:222 w:187 h:316 [16 0.99073726],
x:472 y:85 w:216 h:85 [7 0.9385087],

Run on 10900k:

input size is [1, 3, 416, 416]
TVM run in 273.787502ms
15 result before nms sort
x:164 y:112 w:400 h:331 [1 0.9958774],
x:127 y:222 w:187 h:316 [16 0.99073726],
x:472 y:85 w:216 h:85 [7 0.9385088],

run on GPU

Change target = 'llvm' to target = 'cuda -libs=cudnn' in python script and run it.

Change DeviceType::CPU to DeviceType::GPU in rust code. Keep the DeviceType of get_output to CPU because we need access output in main RAM not VRAM.

Run on agx xavier:

input size is [1, 3, 416, 416]
TVM run in 22.744051ms
15 result before nms sort
x:164 y:112 w:400 h:331 [1 0.9958774],
x:127 y:222 w:187 h:316 [16 0.99073726],
x:472 y:85 w:216 h:85 [7 0.9385089],

Run on 2080ti:

input size is [1, 3, 416, 416]
TVM run in 10.635469ms
15 result before nms sort
x:164 y:112 w:400 h:331 [1 0.9958774],
x:127 y:222 w:187 h:316 [16 0.99073726],
x:472 y:85 w:216 h:85 [7 0.9385089],

using new module interface

You may notice this warning:

DeprecationWarning: legacy graph runtime behaviour of producing json / lib / params will be  removed in the next release. Please see documents of tvm.contrib.graph_runtime.GraphModule for the  new recommended usage.
  graph, lib, params = relay.build(mod, target=target, target_host=target_host, params=params)

Change python script:

with tvm.transform.PassContext(opt_level=3):
    lib = relay.build(mod, target=target, target_host=target_host, params=params)

lib.export_library('deploy_lib.so')

We will get a single huge deploy_lib.so

Change rust code:

let lib = Module::load(&Path::new("deploy_lib.so")).unwrap();
let default = lib.get_function("default", true).unwrap();
let ctx = Context::gpu(0);
let ret = default.invoke(vec![ctx.into()]);
let gmod: Module = ret.unwrap().try_into().unwrap();

// get the set_input function
let ref set_input_fn = gmod.get_function("set_input", false).unwrap();
...

using yolov4

Same as yolov3, we need to checkout a fork version, master branch don't work.

git clone https://github.com/12101111/darknet.git
cd darknet
./build.sh
wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights -o weights/yolov4.weights

We also need to checkout a fork version of TVM.

cd ~/tvm
git remote add yolov4 https://github.com/12101111/incubator-tvm.git
git fetch yolov4
git checkout yolov4

Then compile yolov4 using TVM.

#!/usr/bin/env python3

import numpy as np
import tvm
from tvm import relay
from tvm.relay.testing.darknet import __darknetffi__

cfg_path = './cfg/yolov4.cfg'
weights_path = './weights/yolov4.weights'
lib_path = './libdark.so'

DARKNET_LIB = __darknetffi__.dlopen(lib_path)
net = DARKNET_LIB.load_network_custom(cfg_path.encode('utf-8'), weights_path.encode('utf-8'), 0, 1)
dtype = 'float32'
batch_size = 1
data = np.empty([batch_size, net.c, net.h, net.w], dtype)
print("Converting darknet to relay functions...")
mod, params = relay.frontend.from_darknet(net, dtype=dtype, shape=data.shape)
target = 'cuda --libs=cudnn'
target_host = 'llvm'

print("Compiling the model...")
with tvm.transform.PassContext(opt_level=3):
    lib = relay.build(mod, target=target, target_host=target_host, params=params)

# save the model artifacts
lib.export_library('libyolov4.so')

The input and output shape of yolov4 is different with yolov3, we need to modify it in rust.

const WIDTH: u32 = 608;
const HEIGHT: u32 = 608;

let output_shape: SmallVec<[usize; 4]> = match i % 4 {
    0 => smallvec![
        1,
        255,
        HEIGHT as usize >> (5 - (i / 4)),
        WIDTH as usize >> (5 - (i / 4))
    ,
    1 => smallvec![3],
    2 => smallvec![18],
    3 => smallvec![6],
    _ => unreachable!(),
};

Run on agx xavier:

input size is [1, 3, 608, 608]
TVM run in 64.129044ms
19 result before nms sort
x:131 y:225 w:179 h:315 [16 0.9820385],
x:121 y:122 w:448 h:302 [1 0.9248689],
x:466 y:75 w:216 h:95 [7 0.9171608],

Run on 2080ti:

input size is [1, 3, 608, 608]
TVM run in 24.377104ms
19 result before nms sort
x:131 y:225 w:179 h:315 [16 0.9820385],
x:121 y:122 w:448 h:302 [1 0.9248689],
x:466 y:75 w:216 h:95 [7 0.9171608],

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Yolov3 & Yolov4 with TVM and rust

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