Introduction

ONNC (Open Neural Network Compiler)

The ONNC project aims to provide a compiler to connect Open Neural Network Exchange Format (ONNX) to every Deep Learning Accelerator (DLA). ONNX represents deep learning models that enables models to be correctly transferred among frameworks such as TensorFlow. ONNC guarantees executability across every DLA by means of transforming ONNX models into DLA specific binary forms and leveraging the intermediate representation (IR) design of ONNX along with effective algorithms to eliminate the overhead of data movement.

With the aid of ONNC, DLA vendors can specify customizecd physical cost model and avoid re-inventing these intricate optimization algorithms.

Current Status

• v0.9

How to contribute

• Please see contribute guidline

Directory Structure

• README.md - This document
• docs - documents
• include - header files for libonnc
• lib - implementation for libonnc
• tools - tools based on libonnc

Supported platforms

ONNC supports Ubuntu/x86_64 and MacOSX.

Here is a list of verified versions:

• Ubuntu/x86_64

  • 16.04

• MacOSX

  • High Sierra

Getting Started

Download and build ONNC.

The simplest way to download and build ONNC is follow the instructions of README.md in onnc-umbrella. Here is the version of external library we are using in ONNC.

Basic Installation

Here is general installation instructions.

cd ${ONNC}   # go to the source
./autogen.sh # generate GNU Autotools input files
cd ..
mkdir build  # create placeholder for build
cd build
../${ONNC}/configure --prefix=${INSTALL} \ # configure
                     --with-llvm=${LLVM_DIR} \
                     --with-onnx=${ONNX_DIR} \
                     --with-skypat=${SKYPAT} \
                     --with-target=x86
make
make install

The first step is to generate GNU Autotools input files in your package. There is a script autogen.sh in the top-level source directory. You can easily run it to update your project's output files, and rebuild the project:

cd ${ONNC} # go to the top-level source directory
./autogen.sh

The configure shell script attempts to guess correct values for various system-dependent variables used during compilation. It uses those values to create a Makefile in each directory of the package. It may also create one or more .h files containing system-dependent definitions. Finally, it creates a shell script config.status that you can run in the future to recreate the current configuration, and a file config.log containing compiler output (useful mainly for debugging configure).

It can also use an optional file (typically called config.cache and enabled with --cache-file=config.cache or simply -C) that saves the results of its tests to speed up reconfiguring. (Caching is disabled by default to prevent problems with accidental use of stale cache files.)

If you need to do unusual things to compile the package, please try to figure out how configure could check whether to do them, and mail diffs or instructions to the address given in the README' so they can be considered for the next release. If you are using the cache, and at some point config.cache` contains results you don't want to keep, you may remove or edit it.

The file configure.ac (or configure.in) is used to create configure by a program called autoconf. You only need configure.ac if you want to change it or regenerate configure using a newer version of autoconf.

The simplest way to compile this package is:

1. cd to the directory containing the package's source code and type ./configure to configure the package for your system. If you're using csh on an old version of System V, you might need to type sh ./configure instead to prevent csh from trying to execute configure itself.

   Running configure takes awhile. While running, it prints some Running configure takes awhile. While running, it prints some messages telling which features it is checking for.

2. Type make to compile the package.

3. Optionally, type make check to run any self-tests that come with the package.

4. Type make install to install the programs and any data files and documentation.

5. You can remove the program binaries and object files from the source code directory by typing make clean. To also remove the files that configure created (so you can compile the package for a different kind of computer), type make distclean. There is also a make maintainer-clean target, but that is intended mainly for the package's developers. If you use it, you may have to get all sorts of other programs in order to regenerate files that came with the distribution.

x86 interpreter runtime -- onni

Run onni:

$ cd ${ONNC_BUILD_FOLDER}/tools/onni && ./onni --help
NAME
        onni -- [Experimental] ONNI is the interpreter of ONNC

SYNOPSIS
        onni [options ...]

DESCRIPTION
        model        <path>       The onnx model file                               
        input        <path>       The input file                                    
        -o           <string>     The output file                                   
        --help                    Show this manual.                                 
        --verbose    =<number>    Set verbose level to <number> (default is 1).     
        -v                        One -v increases one verbose level.               
        --quiet                   Set verbose level to 0.                           
        --dry-run                 Do not do the inference, just print statistics.   
        --onnx-opt                Enable onnx optimizer                             
        -mquadruple  <string>     target quadruple                                  
        -march       <string>     target architecture                               

COPYRIGHT
        onni version 0.1.0
        https://onnc.ai

Use onni to inference model from ONNX model zoo:

$ mkdir -p ~/onnx_model_zoo
$ cd ~/onnx_model_zoo && wget https://s3.amazonaws.com/download.onnx/models/opset_8/bvlc_alexnet.tar.gz
$ tar xvf bvlc_alexnet.tar.gz
$ cd ${ONNC_BUILD_FOLDER}/tools/onnc
$ ./onni ~/onnx_model_zoo/bvlc_alexnet/model.onnx ~/onnx_model_zoo/bvlc_alexnet/test_data_set_0/input_0.pb 

# you can also try verbose=4 to see details.