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From "Lupesko, Hagay" <lupe...@gmail.com>
Subject Re: Request for suggestions- Supporting onnx in mxnet
Date Wed, 18 Oct 2017 23:57:48 GMT
Roshani – this is an exciting initiative, ONNX support on MXNet will enable more users to
ramp up on MXNet, which is great.

Tianqi – a few questions and thoughts about your note:
- “More hardware backends to mxnet” – MXNet users get the same benefit of HW support
implementing ONNX import on top of MXNet symbolic, right?
- “NNVM Compiler now received contributions from AWS, UW and many other folks in MXNet community.”
– agreed it is ramping up, but when you look at the data, it is clear that it is very early
on for NNVM. Looking at the repo, it has overall 223 commits, 0 releases. Compare it to MXNet
with 6136 commits and 32 releases. It seems to be still early on for NNVM, and for a more
reliable initial implementation building the import on top of MXNet is easier, faster and
safer. MXNet has lots of users already using the Symbolic API which hopefully mean that is
a mature API that is not likely to have breaking changes or major issues.

I’m supportive option 1 proposed by Roshani (building serde on top of MXNet symbolic), but
to do it as an encapsulated implementation detail, so the implementation can be migrated to
NNVM or another implementation in the future, if at that point it seems like the right thing
to do.

Interested in hearing other opinions though…

Hagay

On 10/18/17, 14:13, "Tianqi Chen" <workcrow@gmail.com on behalf of tqchen@cs.washington.edu>
wrote:

    I am strongly recommending going through the nnvm/top. One major reason in
    here is that the support of nnvm/top layer NOT ONLY mean compatibility of
    model format with onnx. These are the major benefits:
    
    
    - More hardware backends to mxnet, including opencl, metal, Raspberry Pi,
    web browser. These things are automatically enabled by going through this
    layer. In general, we design nnvm/tvm stack to resolve the challenge of
    current mxnet's weakness in terms deploying to more hardware backends.
    
    - More frontend capabilities, nnvm's gluon style IR ingests now from
    CoreML, ONNX and in future keras. Supporting those will reduce the amount
    of engineering effort needed.
    
    - Future compatibility. We all agree that the future being migrated to
    gluon's API. NNVM/top tries to look ahead by directly adopting the symbolic
    API to be gluon.
    
    
    I would also like to correct some of the mentioned facts with regard to
    nnvm/tvm stack
    
    1.   Nascent project with few contributors
    
    NNVM Compiler now received contributions from AWS, UW and many other folks
    in MXNet community. NNVM itself is already being used by MXNet.
    MXNet's internal IR is migrating toward gluon, and its final form being
    nnvm/top
    
    3.   Does not support all operators that exist in MXNet Symbolic API
    
    Neither NNVM/top or onnx support all operators that exist in mxnet symbolic
    API. The end goal here is mainly to make nnvm/top onnx compatible, which is
    a more reasonable goal.
    
    4.  No CI Pipeline and testcases
    
    NNVM already contains a compiler contains unittests and ci tested with
    integration  https://github.com/dmlc/nnvm, with a CI pipline that is well
    tested on CPU and GPU cases for front-ends.
    
    Tianqi
    
    
    On Wed, Oct 18, 2017 at 1:41 PM, Roshani Nagmote <roshaninagmote2@gmail.com>
    wrote:
    
    > Hi guys,
    >
    >
    > I am working on supporting ONNX <https://github.com/onnx/onnx> pre-trained
    > models in Apache MXNet and would like to seek your opinion on the choice of
    > implementation. I also have created a GitHub issue
    > <https://github.com/apache/incubator-mxnet/issues/8319>. Supporting ONNX
    > in
    > MXNet will enable users to move between frameworks with their models, this
    > will also enable MXNet project to be a part of the ONNX open standard and
    > steer the direction of ONNX.
    >
    >
    > For those who don’t know ONNX, ONNX is an open source format for AI models
    > which enables models to be transferred between frameworks. Refer to
    > https://github.com/onnx/onnx for more details.
    >
    >
    > To implement the import/export functionality in MXNet, I propose to expose
    > a MXNet python module “serde”(name taken from Apache Hive project) with the
    > following methods supporting different formats:
    >
    > sym, params = mxnet.serde.import(other_format_file, other_format=‘onnx’)
    >
    > other_format_file =  mxnet.serde.export(mxnet_sym, mxnet_params, ‘onnx’)
    >
    >
    > The implementation under the hood can be done in two ways:
    >
    >
    > 1) Implement at the MXNet layer by parsing the ONNX model(in protobuf
    > format) and turn into MXNet Symbolic operators and build MXNet model
    > directly. Similarly, I can convert the MXNet model to ONNX format at this
    > layer.
    >
    >
    > 2) The DMLC community has released the nnvm/tvm complier and an
    > intermediate representation of the models, refer:
    > http://www.tvmlang.org/2017/10/06/nnvm/tvm-compiler-announcement.html
    > <http://www.tvmlang.org/2017/10/06/nnvm-compiler-announcement.html>
    >
    > Based on the conversation on the GitHub issue
    > <https://github.com/apache/incubator-mxnet/issues/8319> I opened, Mu
    > mentioned that MXNet would use nnvm/tvm as the backend in the future.
    >
    >
    > We could hook into this layer to implement the import/export functionality.
    > nnvm/tvm has ONNX 0.1 version import implemented.
    >
    > For import,
    >
    >    1.
    >
    >    I will need to enhance nnvm/tvm’s importer to support ONNX 0.2
    >    2.
    >
    >    Implement nnvm/tvm->mxnet symbolic operators.
    >
    > For export:
    >
    >
    >    1.
    >
    >    mxnet->nnvm/tvm ( nnvm/tvm provides this implementation already)
    >    2.
    >
    >    I will need to Implement nnvm/tvm>onnx.
    >
    >
    > These are the pros and cons I see in the above approaches:
    >
    >    1.
    >
    >    Import/export at mxnet layer
    >
    > Pros:
    >
    >    1.
    >
    >    Stable APIs currently used by users.
    >    2.
    >
    >    Larger Apache MXNet community of contributors.
    >    3.
    >
    >    CI pipeline to catch bugs.
    >    4.
    >
    >    Comparatively less time to implement and put it in the hands of the
    >    users.
    >
    > Cons:
    >
    >    1.
    >
    >    In the future we may have to reimplement at the nnvm/tvm layer, in case
    >    MXNet moves to the nnvm/tvm backend(assuming it will move).
    >
    >
    >
    >    1.
    >
    >    Import/export at nnvm/tvm layer
    >
    > Pros:
    >
    >    1.
    >
    >    Less engineering work in case mxnet moves to nnvm/tvm
    >    2.
    >
    >    nnvm/tvm would become a hub to convert to different formats.
    >    3.
    >
    >    nnvm operators are more in parity with mxnet’s gluon APIs this could be
    >    useful in case Gluon becomes the only standard that MXNet will support.
    >
    > Cons:
    >
    >    1.
    >
    >    Nascent project with few contributors
    >    2.
    >
    >    Does not support all operators that exist in MXNet Symbolic API
    >    3.
    >
    >    No CI Pipeline
    >    4.
    >
    >    Current Apache MXNet project does not use nnvm/tvm backend
    >    5.
    >
    >    mxnet->nnvm/tvm backend needs more testing and user feedback.
    >
    >
    > Any suggestions on both of these approaches? From user's perspective, this
    > will be an implementation detail that is not exposed.
    >
    > Thanks,
    >
    > Roshani
    >
    



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