/oneflow-xrt

Primary LanguageC++

OneFlow-XRT

OneFlow-XRT is an OneFlow extension that provides an easy to use, flexible and unified way to integrate third-party computing engines in OneFlow.

OneFlow-XRT has support multiple third-party computing engines, such as XLA and TensorRT. Different engines support different backend hardware. For the same computing graph, XRT allows multiple computing engines to be used in combination to obtain better acceleration effects.

engine device inference training
XRT-XLA X86 CPU + CUDA ✔ ✔
XRT-TensorRT CUDA ✔ ✔ only no weights op
XRT-OpenVINO X86 CPU ✔ ✔ only no weights op
XRT-TVM - - -

Architecture

XRT Architecture

Installation

pip

To install OneFlow-XRT via pip, use the following command:

# TODO
# pip3 install oneflow_xrt

# run the following commands according to your needs
# pip3 install oneflow_xrt_xla
# pip3 install oneflow_xrt_tensorrt
# pip3 install oneflow_xrt_openvino

Building From Source

Prerequisites

  • install cmake
  • install oneflow
  • install CUDA if oneflow supports CUDA device or building TensorRT
  • install bazel if building XLA
  • download and unzip TensorRT if building TensorRT
  • download and unzip OpenVINO runtime if building OpenVINO

Get the OneFlow-XRT Source

git clone https://github.com/Oneflow-Inc/oneflow-xrt

building

Inside OneFlow-XRT source directory, then run the following command to install oneflow_xrt:

python3 setup.py install

The following components are optional, run the command to install it according to your needs,

  • oneflow_xrt_xla
BUILD_XLA=ON python3 setup.py install
  • oneflow_xrt_tensorrt
BUILD_TENSORRT=ON TENSORRT_ROOT=/home/TensorRT-8.4.0.6 python3 setup.py install
  • oneflow_xrt_openvino
BUILD_OPENVINO=ON OPENVINO_ROOT=/home/intel/openvino_2022.1.0.643/runtime python3 setup.py install

Run A Toy Program

# python3

>>> import oneflow as flow
>>> import oneflow_xrt as ofrt
>>> m = flow.nn.Linear(3, 4).to("cuda")
>>> m = ofrt.XRTModule(m, engine=["tensorrt"])
>>> x = flow.randn(4, 3, device="cuda")
>>> y = m(x)
>>> print(y)
tensor([[ 0.2404,  0.7121,  0.4473,  0.4782],
        [-0.8697,  1.5353,  0.2829,  0.4772],
        [-0.3865, -1.2719,  1.0911,  0.1179],
        [ 0.3779,  0.7363,  0.5319,  0.3167]], device='cuda:0', dtype=oneflow.float32)

Documentation

Roadmap

TODO