I want to offer one last thing in terms of technical details. I mentioned two trends in the deep learning systems. There is one last thing that is omitted. How should we build a good deploy end for deep learning models.
There is always a paradox to this problem: - On one hand, the deployment end needs to be lightweight and portable. - We want a lot of optimizations (memory layout compute) and feature support, this makes the project big. All the existing systems suffer from this problem. The solution is simple, separates the optimization part from the actual runtime and compiles the things down to a bare metal module. And this is the solution nnvm/top compiler pipeline offer, which I believe will become a standard practice of deployment and where all systems go to Tianqi On Wed, Oct 18, 2017 at 10:03 PM, Tianqi Chen <tqc...@cs.washington.edu> wrote: > OK, there is some miscommunication in here I guess. We only need to do a > "canonization" step in python API that goes a symbol to symbol translation > layer. It can be done in purely in python, and there is no need for going > "down" into c++ to do this. > > For example, the current nnvm.from_mxnet API takes Module or Gluon module > and get you back nnvm/top graph in python. > > All we are asking for is to decomposing it into > > def mxnet_to_onnx(module): > nnvm_graph, params = nnvm_from_mxnet(module) > onnx = nnvm_to_onnx(nnvm_graph, params) > return onnx > > This allows nnvm_from_mxnet to be reused for other purposes, like > compiling API to deployable modules > > Tianqi > > On Wed, Oct 18, 2017 at 9:55 PM, Lupesko, Hagay <lupe...@gmail.com> wrote: > >> Tianqi: >> Thanks for detailing the trends. I fully agree that ONNX is just a graph >> serialization format – nothing more, nothing less. I also think we all >> agree that this simple mechanism holds lots of value to DL users since it >> allows them to move between frameworks easily (e.g. train with MXNet, >> deploy on a mobile device with Caffe2, or the other way around). >> As you said, In Memory IR is different than serialization formats such as >> ONNX. They are designed to make the runtime execution as efficient as >> possible, leveraging software and hardware optimizations. They are indeed >> complex, and where the “meat” is. >> (BTW ONNX regards itself as an “IR” format, but not in the same sense as >> NNVM). >> >> At the end of the day, Roshani is aiming to deliver a simple >> functionality to MXNet users: (1) take an ONNX file, and load it into MXNet >> so you get a graph+weights you can work with (2) Given a trained model, >> save it as an ONNX file. Since MXNet users do not interact with NNVM >> directly, but rather interact with MXNet API (MXNet Module), isn’t the >> simplest thing to do is just to construct the Module “on the fly” using >> MXNet API? Taking the other approach, we will go from the top level MXNet >> “load” API, go “down” to NNVM to construct the graph, go back up to MXNet >> to expose it as a Module. This seems to complex and does not add any >> benefit. In whatever way we construct the MXNet Module object, NNVM will >> always be the underlying in memory IR that is being executed, so why not >> take the simpler route? >> >> Hagay >> >> On 10/18/17, 19:42, "Tianqi Chen" <workc...@gmail.com on behalf of >> tqc...@cs.washington.edu> wrote: >> >> Hi Chris: >> >> There is no intention to move things away from mxnet. The reduction of >> lines of code by having a better design in general, and usually, you >> write >> less redundant code by benefiting from better design. As I may quote: >> "the >> best design is not achieved not when there is nothing to add, but when >> there is nothing to be taken away." >> >> MXNet has always benefited from this philosophy and improves with the >> new >> designs and proper modularization. For example, we see such reduction >> and >> convenience happening when migrating from MXNet's legacy op to the >> NNVM's mechanism. The new mechanism now enables things like sparse >> aware >> support and other stuff which would be much harder to support. >> >> The nnvm/tvm stack comes brings the same benefit(if not more) and it >> will >> only add more features to MXNet itself. Offering more hardware >> backends and >> optimization, allowing us to write less code and spent less time to >> optimize for each backend by going through TVM >> >> Tianqi >> >> On Wed, Oct 18, 2017 at 7:15 PM, Chris Olivier <cjolivie...@gmail.com >> > >> wrote: >> >> > Reduce code base of mxnet? By increasing scope of the dmlc modules? >> Is the >> > intent to make mxnet a thin language wrapper around a group of dmlc >> > modules? >> > >> > >> > On Wed, Oct 18, 2017 at 6:58 PM Tianqi Chen < >> tqc...@cs.washington.edu> >> > wrote: >> > >> > > To better answer Hagay's question, I would like to dive down a >> bit deeper >> > > on the relation between MXNet, NNVM and model exchange format >> like ONNX. >> > > >> > > There are two major trends in deep learning systems now: >> > > >> > > - Common serializable formats, like ONNX and CoreML, that defines >> the >> > model >> > > exchange format. >> > > - The in-memory graph IR for quick optimization and JIT. NNVM, >> > Tensorflow's >> > > XLA falls into this category. >> > > >> > > The exchange formats are great, it only poses a layer of >> conversion, >> > which >> > > is good for exchange. The real meat still comes from the >> compilation and >> > > JIT pipeline you have to offer. For that, we will need an >> in-memory IR, >> > > because of the cost of constructing, serialize could be high for >> the >> > > exchange formats like protobuf. And usually, the exchange >> formats are >> > > designed in a minimalistic fashion, making it less easy to extend >> more >> > > information to support in-depth optimization like automatic >> quantization, >> > > accelerator support. >> > > >> > > The current MXNet relies on NNVM for in-memory IR manipulation >> but does >> > not >> > > contain a compilation component that compiles to the hardware >> backends. >> > > Doing export to an exchange format and then back into NNVM run the >> > > compilation poses too much burden that JIT compiler could pay. >> Using the >> > > same in-memory graph IR as the compilation stack give much more >> advantage >> > > in terms of this. >> > > >> > > The newly introduces nnvm/top and compiler offers in-memory graph >> > > optimization and compilation and offers more hardware backend >> directly >> > via >> > > TVM. We already see promising results in edge deployments with a >> much >> > lower >> > > overhead of runtime. We will further benefit quickly from more >> graph >> > > optimizations that it has to offer. >> > > >> > > Building support around this new paradigm offers us advantage of >> being >> > > future compatible and takes full benefit of the points I >> mentioned above >> > > >> > > Tianqi >> > > >> > > >> > > >> > > On Wed, Oct 18, 2017 at 4:57 PM, Lupesko, Hagay < >> lupe...@gmail.com> >> > wrote: >> > > >> > > > 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" <workc...@gmail.com on >> behalf of >> > > > tqc...@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 < >> > > > roshaninagmo...@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 >> > > > > >> > > > >> > > > >> > > > >> > > > >> > > >> > >> >> >> >> >
