Setup RPC System

Remote procedure call (RPC) is a very important and useful feature of Apache TVM, it allows us to run compiled Neural Network (NN) models on the real hardware without need to touch the remote device, the output result will be passed back automatically through network.

By eliminating the manual work like, dumping input data to file, copying the exported NN model to remote device, setuping the device user environment, copying the output result to host development environment, RPC improve the development efficiency extremely.

In addition, because only the execution part of the compiled NN model is run on the remote device, all other parts are run on host development environment, so any Python packages can be used to do the preprocess and postprocess works.

RPC is very helpful in below 2 situations

  • Hardware resources are limited

    RPC’s queue and resource management mechanism can make the hardware devices serve many developers and test jobs to run the compiled NN models correctly.

  • Early-stage end to end evaluation

    Except the compiled NN model, all other parts are executed on the host development environment, so the complex preprocess or postprocess can be implemented easily.

Suggested Architecture

Apache TVM RPC contains 3 tools, RPC tracker, RPC proxy, and PRC server. The RPC server is the necessary one, an RPC system can work correctly without RPC proxy and RPC tracker. RPC proxy is needed when you can’t access the RPC server directly. RPC tracker is strongly suggested to be added in your RPC system, because it provides many useful features, e.g., queue capability, multiple RPC servers management, manage RPC server through key instead of IP address.

https://raw.githubusercontent.com/tlc-pack/web-data/main/images/dev/how-to/rpc_system_suggested_arch.svg

As above figure shown, because there aren’t physical connection channels between machine A and machine C, D, so we set up a RPC proxy on machine B. The RPC tracker manage a request queue per RPC key, each user can request an RPC server from RPC tracker by a RPC key at anytime, if there is a idle RPC server with the same RPC key, then RPC tracker assign the RPC server to the user, if there isn’t a idle RPC server for the moment, the request will be put into the request queue of that RPC key, and check for it later.

Setup RPC Tracker and RPC Proxy

In general, RPC tracker and RPC proxy only need to be run on host machine, e.g., development server or PC, they needn’t depend on any enironment of device machine, so the only work need to do for setting up them is executing below commands on the corresponding machine after installing Apache TVM according to the official document https://tvm.apache.org/docs/install/index.html.

  • RPC Tracker

    $ python3 -m tvm.exec.rpc_tracker --host RPC_TRACKER_IP --port 9190 --port-end 9191

  • RPC Proxy

    $ python3 -m tvm.exec.rpc_proxy --host RPC_PROXY_IP --port 9090 --port-end 9091 --tracker RPC_TRACKER_IP:RPC_TRACKER_PORT

Please modify the RPC_TRACKER_IP, RPC_TRACKER_PORT, RPC_PROXY_IP, and the port numbers in above commands according to your concrete environment, the option port-end can be used to avoid the service start with an unexpected port number, which may cause other service can’t be connected correctly, this is important especially for auto testing system.

Setup RPC Server

In our community, there is multiple RPC server implementations, e.g., apps/android_rpc, apps/cpp_rpc, apps/ios_rpc, below content only focus on the Python version RPC server which is implemented by python/tvm/exec/rpc_server.py, for the setup instruction of other version RPC server please refer to the document of its corresponding directory.

RPC server need to be run on device machine, and it usually will depend on xPU driver, the enhanced TVM runtime with xPU support, and other libraries, so please setup the dependent components first, e.g., install the KMD driver, ensure the required dynamic libraries can be found from environment variable LD_LIBRARY_PATH.

If the required compilation environment can be setup on your device machine, i.e., you needn’t to do the cross compilation, then just follow the instruction of https://tvm.apache.org/docs/install/from_source.html to compile the TVM runtime and directly jump to the step 3. Luanch RPC Server.

1. Cross Compile TVM Runtime

We use CMake to manage the compile process, for cross compilation, CMake need a toolchain file to get the required information, so you need to prepare this file according to your device platform, below is a example for the device machine which CPU is 64bit ARM architecture and the operating system is Linux.

set(CMAKE_SYSTEM_NAME Linux)
set(root_dir "/XXX/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu")

set(CMAKE_C_COMPILER "${root_dir}/bin/aarch64-linux-gnu-gcc")
set(CMAKE_CXX_COMPILER "${root_dir}/bin/aarch64-linux-gnu-g++")
set(CMAKE_SYSROOT "${root_dir}/aarch64-linux-gnu/libc")

set(CMAKE_FIND_ROOT_PATH_MODE_PROGRAM NEVER)
set(CMAKE_FIND_ROOT_PATH_MODE_LIBRARY ONLY)
set(CMAKE_FIND_ROOT_PATH_MODE_INCLUDE ONLY)
set(CMAKE_FIND_ROOT_PATH_MODE_PACKAGE ONLY)

After executing commands like something below under the root directory of TVM repository, the runtime will be cross compiled successfully, please enable other needed options in file config.cmake according to your concrete requirement.

$ mkdir cross_build
$ cd cross_build
$ cp ../cmake/config.cmake ./

# You maybe need to enable other options, e.g., USE_OPENCL, USE_xPU.
$ sed -i "s|USE_LLVM.*)|USE_LLVM OFF)|" config.cmake
$ sed -i "s|USE_LIBBACKTRACE.*)|USE_LIBBACKTRACE OFF)|" config.cmake
$ sed -i "s|USE_MICRO.*)|USE_MICRO OFF)|" config.cmake

$ cmake -DCMAKE_TOOLCHAIN_FILE=/YYY/aarch64-linux-gnu.cmake -DCMAKE_BUILD_TYPE=Release ..
$ cmake --build . -j -- runtime
$ cd ..

2. Pack and Deploy to Device Machine

Pack the Python version RPC server through the commands like something below.

$ git clean -dxf python
$ cp cross_build/libtvm_runtime.so python/tvm/
$ tar -czf tvm_runtime.tar.gz python

Then copy the compress package tvm_runtime.tar.gz to your concrete device machine, and setting the environment variable PYTHONPATH correctly through the commands like something below on your device machine.

$ tar -xzf tvm_runtime.tar.gz
$ export PYTHONPATH=`pwd`/python:${PYTHONPATH}

3. Luanch RPC Server

The RPC server can be launched on your device machine through the commands like something below, please modify the RPC_TRACKER_IP, RPC_TRACKER_PORT, RPC_PROXY_IP, RPC_PROXY_PORT, and RPC_KEY according to your concrete environment.

# Use this if you use RPC proxy.
$ python3 -m tvm.exec.rpc_server --host RPC_PROXY_IP --port RPC_PROXY_PORT --through-proxy --key RPC_KEY
# Use this if you needn't use RPC proxy.
$ python3 -m tvm.exec.rpc_server --tracker RPC_TRACKER_IP:RPC_TRACKER_PORT --key RPC_KEY

Validate RPC System

$ python3 -m tvm.exec.query_rpc_tracker --host RPC_TRACKER_IP --port RPC_TRACKER_PORT

Through the above command, we can query all available RPC servers and the queue status, if you have 3 RPC servers that connected to the RPC tracker through RPC proxy, the output should be something like below.

Tracker address RPC_TRACKER_IP:RPC_TRACKER_PORT

Server List
----------------------------
server-address  key
----------------------------
RPC_PROXY_IP:RPC_PROXY_PORT       server:proxy[RPC_KEY0,RPC_KEY1,RPC_KEY2]
----------------------------

Queue Status
---------------------------------------
key               total  free  pending
---------------------------------------
RPC_KEY0          0      0     3
---------------------------------------

Troubleshooting

1. The lack of numpy on device machine caused the RPC server can’t be launched.

The package numpy is imported in some Python files which RPC server dependent on, and eliminating the import relationship is difficult, for some devices cross compiling numpy is very hard to do too.

But acturally the TVM runtime doesn’t really dependent on numpy, so a very simple workaround is create a dummy numpy, just need to copy the below content into a file named numpy.py and place it into directory like /usr/local/lib/python3.8/site-packages.

class bool_:
  pass
class int8:
    pass
class int16:
    pass
class int32:
    pass
class int64:
    pass
class uint8:
    pass
class uint16:
    pass
class uint32:
    pass
class uint64:
    pass
class float16:
    pass
class float32:
    pass
class float64:
    pass
class float_:
    pass

class dtype:
    def __init__(self, *args, **kwargs):
        pass

class ndarray:
    pass

def sqrt(*args, **kwargs):
    pass

def log(*args, **kwargs):
    pass

def tanh(*args, **kwargs):
    pass

def power(*args, **kwargs):
    pass

def exp(*args, **kwargs):
    pass

2. The lack of cloudpickle on device machine caused the RPC server can’t be launched.

Because cloudpickle package is a pure Python package, so just copying it from other machine to the directory like /usr/local/lib/python3.8/site-packages of the device machine will resolve the problem.