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TVM (TVM) Execution Provider

TVM is an execution provider for ONNX Runtime that is built on top of Apache TVM. It enables ONNX Runtime users to leverage Apache TVM model optimizations. The TVM EP is currently in “Preview”. It’s been tested to work on a limited set of ONNX models on Linux. Future enhancements will include support on Windows and MacOS.

NOTE: This TVM execution provider was developed as a standalone TVM execution provider. The code repo has references to stvm as the name for this EP.

Contents

Build

To use the TVM EP in ONNX Runtime (ORT), users first need to build Apache TVM and ONNX Runtime.

Note: some python packages may need to be upgraded/downgraded because both TVM and ORT with the TVM EP use the Python API. Alternatively, use modify PYTHONPATH to solve these conflicts.

Build and configure TVM

Install the minimal pre-requisites on Ubuntu/Debian like linux operating systems:

apt-get install -y python3 python3-dev python3-pip python3-setuptools gcc libtinfo-dev zlib1g-dev build-essential cmake libedit-dev libxml2-dev llvm-12
pip3 install numpy decorator attrs

Clone this repo using the --recursive flag to pull all associated dependencies

Build TVM from the tvm_update folder:

cd onnxruntime/cmake/external/tvm_update/
mkdir build
cd ./build
cmake -DCMAKE_BUILD_TYPE=Release -DUSE_LLVM=ON -DUSE_OPENMP=gnu -DUSE_MICRO=ON (If your machine is CUDA enabled -DUSE_CUDA=ON) ..
make -j <number of threads in build machine>

Set the environment variable PYTHONPATH to tell python where to find the TVM library:

export TVM_HOME=<path_to_onnx_runtime>/cmake/external/tvm_update
export PYTHONPATH=$TVM_HOME/python:${PYTHONPATH}

For more details on installing Apache TVM click here

Build ONNX Runtime with the TVM Execution Provider

In order to build ONNXRT you will need to have CMake 3.18 or higher. In Ubuntu 20.04 you can use the following commands to install the latest version of CMake:

sudo apt-get update
sudo apt-get install gpg wget

wget -O - https://apt.kitware.com/keys/kitware-archive-latest.asc 2>/dev/null | gpg --dearmor - | sudo tee /usr/share/keyrings/kitware-archive-keyring.gpg >/dev/null

echo 'deb [signed-by=/usr/share/keyrings/kitware-archive-keyring.gpg] https://apt.kitware.com/ubuntu/ focal main' | sudo tee /etc/apt/sources.list.d/kitware.list >/dev/null
sudo apt-get update

sudo rm /usr/share/keyrings/kitware-archive-keyring.gpg
sudo apt-get install kitware-archive-keyring

sudo apt-get install cmake

Build ONNX Runtime:

./build.sh --config Release --enable_pybind --build_wheel --skip_tests --parallel --use_stvm --skip_onnx_tests

Build the python API for ONNX Runtime instead of using the standard package:

cd <path_to_onnx_runtime>
pip3 uninstall onnxruntime onnxruntime-stvm -y
whl_path=$(find ./build/Linux/Release/dist -name "*.whl")
python3 -m pip install $whl_path

Alternatively, you can set PYTHONPATH to tell python where to find the ONNXRT library:

export ORT_PYTHON_HOME=<path_to_onnx_runtime>/build/Linux/Release
export PYTHONPATH=$ORT_PYTHON_HOME:${PYTHONPATH}

Configuration options

TVM Executor Provider can be configured with the following provider options:

po = [dict(target=client_target,
           target_host=client_target_host,
           opt_level=client_opt_level,
           freeze_weights=freeze,
           tuning_file_path=client_tuning_logfile,
           input_names = input_names_str,
           input_shapes = input_shapes_str)]
tvm_session = onnxruntime.InferenceSession(model_path, providers=["StvmExecutionProvider"], provider_options=po)


  • target and target_host are strings like in TVM (e.g. “llvm –mcpu=avx2”). When using accelerators, target may be something like cuda while target_host may be llvm -mtriple=x86_64-linux-gnu
  • opt_level is TVM optimization level. It is 3 by default
  • freeze_weights means that all model weights are kept on compilation stage otherwise they are downloaded each inference. True is recommended value for the best performance. It is true by default.
  • tuning_type defines the type of TVM tuning logs being used, and can be set to either AutoTVM (1st gen auto tuning logs) or Ansor (2nd gen auto tuning logs). By default this option is set to AutoTVM.
  • tuning_file_path is path to AutoTVM or Ansor tuning file which gives specifications for given model and target for the best performance. (See below for more details).

TVM supports models with fixed graph only. If your model has unknown dimensions in input shapes (excluding batch size) you must provide the shape using the input_names and input_shapes provider options. Below is an example of what must be passed to provider_options:

input_names = "input_1 input_2"
input_shapes = "[1 3 224 224] [1 2]"

Performance Tuning

TVM optimizes machine learning models through an automated tuning process that produces model variants specific to targeted hardware architectures. This process also generates ‘tuning logs’ that the TVM EP relies on to maximize model performance. These logs can be acquired for your model by either using TVM as described here:

AutoTVM: https://tvm.apache.org/docs/how_to/tune_with_autotvm/index.html

Ansor (Autoscheduling): https://tvm.apache.org/docs/how_to/tune_with_autoscheduler/index.html

or by using logs generated through the OctoML platform (https://onnx.octoml.ai) using instructions here

Using the TVM EP with TVM tuning logs also requires users to turn off ONNX Runtime preprocessing. To do this, the following SessionOptions() can be used:

so = onnxruntime.SessionOptions()
so.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_DISABLE_ALL

tvm_session = onnxruntime.InferenceSession(model_path, sess_options=so, providers=["StvmExecutionProvider"], provider_options=po)

Samples

Known issues

  • At this moment, the TVM EP has only been verified on UNIX/Linux systems.
  • CUDA/GPU support is still in pre-alpha mode and results are expected to change. It is recommended that only CPU targets are used.
  • Some compatibility issues have been found between ONNX and Google protobuf. AttributeError: module 'google.protobuf.internal.containers' has no attribute 'MutableMapping'. This usually occurss during import onnx in any python scripts for protobuf version >= 3.19.0 and ONNX version <= 1.8.1. To resolve the issue Google protobuf and ONNX can be reinstalled separately or together using: 
    pip3 uninstall onnx -y
pip3 install onnx==1.10.1
pip3 uninstall protobuf -y
pip3 install protobuf==3.19.1

The following pair of ONNX and protobuf versions have been found to be compatible:

  • 3.17.3 and 1.8.0
  • 3.19.1 and 1.10.1