I'm very excited to see this proposal! -Gon
On Mon, Feb 18, 2019 at 9:06 AM Sebastian <s...@apache.org> wrote: > I have also volunteered as a potential mentor for the TVM project and I > am very excited about it :) > > Best, > Sebastian > > On 17.02.19 09:02, Kevin A. McGrail wrote: > > +1 binding with a caveat: > > > > You need mentors and champions from Apache who are available and ideally > > active in the incubator. Markus had to step down on hivemail last > > year. Has his situation changed? > > > > Some comments: > > The hardware artifacts being donated is interesting and something I > > would support helping. We might want to loop in the secretary and legal > > vps to discuss. > > > > The reviewer status is something the pmc can elect to do. They might > > end up with the same karma as committers on any repos if they need that > > karma is the only hurdle I can think of. But we like a model of trust > > for people so it should be a good thing. > > > > But otherwise looks like a great start! > > KAM > > On Fri, Feb 15, 2019, 13:42 Markus Weimer <wei...@apache.org > > <mailto:wei...@apache.org> wrote: > > > > Hi, > > > > we'd like to start the discussion of accepting TVM into the > incubator. > > Please see the proposal below. I'd like to highlight a few things for > > our discussion: > > > > (1) The project already follows many Apache ways like meritocracy, > > open development and such. > > > > (2) The project recognizes an in-between state of "reviewer" that it > > nominates people for between contributor and committer status. We'd > > like to learn if and how to maintain that in the future. > > > > (3) The project contains hardware as a software artifact. We are not > > aware of another ASF project like that and wonder if and how it > > affects its acceptance into the incubator. > > > > Thanks! > > > > Markus > > > > === Proposal === > > > > We propose to incubate the TVM project the Apache Software > > Foundation. TVM is a > > full stack open deep learning compiler stack for CPUs, GPUs, and > > specialized > > accelerators. It aims to close the gap between the > > productivity-focused deep > > learning frameworks, and the performance- or efficiency-oriented > > hardware > > backends. > > > > === Background === > > > > There is an increasing need to bring machine learning to a wide > > diversity of > > hardware devices. Current frameworks rely on vendor-specific > > operator libraries > > and optimize for a narrow range of server-class GPUs. Deploying > > workloads to new > > platforms -- such as mobile phones, embedded devices, and > > accelerators (e.g., > > FPGAs, ASICs) -- requires significant manual effort. TVM is an end > > to end deep > > learning a compiler that exposes graph-level and operator-level > > optimizations to > > provide performance portability to deep learning workloads across > > diverse > > hardware back-ends. TVM solves optimization challenges specific to > deep > > learning, such as high-level operator fusion, mapping to arbitrary > > hardware > > primitives, and memory latency hiding. It also automates > optimization of > > low-level programs to hardware characteristics by employing a novel, > > learning-based cost modeling method for rapid exploration of program > > optimizations. > > > > Moreover, there is increasing interest in designing specialized > > hardware which > > accelerates machine learning. Towards this goal, TVM introduces VTA, > > an open > > source deep learning accelerator as part of its stack. The open > > source VTA > > driver and hardware design is a crucial step toward building > > software support > > for future ASICs. The TVM-VTA flow acts as a is the great frontier > for > > researchers and practitioners to explore specialized hardware > designs. > > > > > > === Rationale === > > > > Deep learning compilation will be the next frontier of machine > > learning systems. > > TVM is already one of the leading open source projects pursuing this > > direction. > > > > Specifically, TVM provides infrastructure to use machine learning to > > automatically optimize deployment of deep learning programs on > > diverse hardware > > backends. > > > > > > === VTA: Open Source Hardware Design === > > > > TVM also contains open source hardware as part of its stack. The VTA > > hardware > > design is a fully open sourced deep learning accelerator that allows > > us to > > experiment with compiler, driver, runtime, and execute the code on > > FPGA. VTA > > provides a path to target future ASICs, and build software-driven > > solutions to > > co-design future deep learning accelerators. > > > > Having an open source hardware design in an ASF project is rare and > > perhaps > > unprecedented. We put some of our rationale on why it is necessary > > for the > > community. > > > > Deep learning specialized ASICs are going to be at the center of the > AI > > revolution. However, given its early shape, there is no open > > standard, or even > > any available information hardware interface that allows an open > > source software > > to target to. VTA provides such open source hardware abstraction > > layer and > > allows us to build in abstractions that can be effectively used to > > target other > > deep learning accelerators. > > > > Moreover, there is an increasing need for co-designing future of > machine > > learning systems with the hardware abstraction. Having a co-designed > > open source > > hardware stack along with the software creates a path for this > > route. In short, > > we need open-source hardware to build the best open source software. > > > > Finally, we can still view VTA design as “software”, as its source > > code is > > written in source description language and can generate “binary” > > which can run > > on FPGA and possibly simulators. > > > > > > === Current Status === > > > > TVM is open sourced under the Apache License for one and half years. > > See the > > current project website (https://tvm.ai/), Github > > (https://github.com/dmlc/tvm/), as well as TVM Conference > > (https://sampl.cs.washington.edu/tvmconf/#about-tvmconf) > > > > TVM has already been used in production, some highlights are AWS > > (Sagemaker > > Neo), Huawei (AI Chip compilation) and Facebook (mobile > > optimization). We > > anticipate the list of adopters to grow over the next few years. > > > > === Meritocracy === > > > > The TVM stack began as a research project of the SAMPL group at Paul > > G. Allen > > School of Computer Science & Engineering, University of Washington. > > The project > > is now driven by an open source community involving multiple > > industry and > > academic institutions. The project is currently governed by the > > Apache Way > > (https://docs.tvm.ai/contribute/community.html). The project now > has 14 > > committers and 6 PMCs, and the list is actively growing. The PMCs > > uses a google > > group mail-list to vote in new committers/PMCs, which will be moved > > to private@ > > after incubation. > > > > The community highly values open collaboration among contributors > > from different > > backgrounds.The current committers come from UW, Berkeley, Cornell, > > SJTU, AMD, > > AWS, Huawei, Google, Facebook, Ziosoft. > > > > > > === Community === > > > > The project currently has 173 contributors. As per the Apache way, > > all the > > discussions are conducted in publicly archivable places. > > > > - Github issues are used to track development activities and RFC. > > - The roadmap is public and encourages participation from everyone > > in the > > community. > > - Discussion forums for general discussions. https://discuss.tvm.ai > > - The content of the discourse forum can be considered as a public > > archive > > as it is searchable with all the content > > - We also created a mail-list archive of the forum, which we will > > forward to > > an Apache mail-list after incubation > > https://groups.google.com/forum/#!forum/tvm-discuss-archive > > > > - See https://tvm.ai/community > > - See https://github.com/dmlc/tvm/releases for past releases. > > > > Currently, Github issue serves as dev@ channel. Notably, major > > features always > > start from RFCs discussions to encourage broad participation in the > > community. > > > > The community recognizes potential committers early by bringing > > contributors as > > code reviewers and encourages them to participate in code reviews. > > Code reviews > > and high-quality code are fundamental to the long-term success of > > the project. > > The reviewer mechanism in the community serves a way to highlight > > this aspect as > > well as helping the community find good candidates to promote to > > committers. > > > > > > > > ==== Development and Decision Process ==== > > > > See > > > https://docs.tvm.ai/contribute/community.html#general-development-process > > for the current development guideline. The key points are: Open > > public roadmap > > during development, which turns into release notes Major features > > start with an > > RFC, everything happens in public Encourage public discussion via > > archivable > > channels Strive to reach a consensus on technical decisions through > > discussion > > Moderation from committers and encourage everyone’s participation > > > > Example Roadmap: https://github.com/dmlc/tvm/issues/1170 > > The idea is to keep an active list of roadmaps that can be turned > > directly > > into a release note. Public roadmap helps to encourage general > > participation > > from all contributors. > > > > Example 1: > > Recently a major proposal in the community is to bring in a new > > high-level IR, RFC thread: https://github.com/dmlc/tvm/issues/1673 > > The pull > > request: https://github.com/dmlc/tvm/pull/1672 Everyone who > > participated in the > > RFC is invited to review the code as well - Follow up features are > > proposed as > > follow up RFCs. > > > > Example 2: Community guideline improvements > > RFC thread: https://github.com/dmlc/tvm/issues/2017 > > Slack channel setup as per community suggestion, but still encourage > the > > community to only use it for quick communication and use publicly > > archived > > channels for development: https://github.com/dmlc/tvm/issues/2174 > > > > Example 3: Python3 timeline proposal > > RFC thread: https://github.com/dmlc/tvm/issues/1602 > > Finished with the decision to respect backward compatibility and > > keep python2 > > support. > > > > See > > > https://github.com/dmlc/tvm/issues?utf8=%E2%9C%93&q=label%3A%22status%3A+RFC%22+ > > for a full list of RFCs. > > > > > > === Alignment === > > > > TVM is useful for building deep learning deployment solutions. It is > > perhaps > > also the first Apache incubator proposal that includes both open > > source software > > and hardware system design. > > > > It has the potential to benefit existing related ML projects such as > > MXNet, > > Singa, SystemML, and Mahout by providing powerful low-level > > primitives for > > matrix operations. > > > > > > === Known Risks === > > > > ==== Orphaned products ==== > > > > The project has a diverse contributor base. As an example, the > current > > committers come from: UW, Berkeley, Cornell, SJTU, AMD, AWS, Google, > > Facebook, > > Ziosoft, Huawei. We are actively growing this list. Given that the > > project has > > already been used in production, there is a minimum risk of the > > project being > > abandoned. > > > > ==== Inexperience with Open Source ==== > > > > The TVM community has extensive experience in open source. Three of > > current five > > PMCs are already PPMCs of existing Apache projects. Over the course > of > > development, the community already has a good way bringing RFCs, > > discussions and > > most importantly, welcoming new contributors in the Apache way. > > > > ==== Homogenous Developers ==== > > > > The project has a diverse contributor base. As an example, the > current > > committers comes from: UW, Berkeley, Cornell, SJTU, AMD, AWS, > > Huawei, Google, > > Facebook, Ziosoft. The community actively seeks to collaborative > > broadly. The > > PMCs followed a principle to *only* nominate committers outside > > their own > > organizations. > > > > > > === Reliance on Salaried Developers === > > > > Most of the current committers are volunteers. > > > > === Relationships with Other Apache Products === > > > > TVM can serve as a fundamental compiler stack for deep learning and > > machine > > learning in general. We expect it can benefit projects like MXNet, > > Spark, Flink, > > Mahout, and SystemML. > > > > === Documentation === > > > > See https://tvm.ai/ > > > > === Initial Source === > > > > https://github.com/dmlc/tvm > > > > We plan to move our repository to > > https://github.com/apache/incubator-tvm > > > > > > === Source and Intellectual Property Submission Plan === > > > > TVM source code is available under Apache V2 license. We will work > > with the > > committers to get ICLAs signed. > > > > === External Dependencies === > > > > We put all the source level dependencies under > > https://github.com/dmlc/tvm/tree/master/3rdparty > > > > - dmlc-core (Apache2): https://github.com/dmlc/dmlc-core > > - dlpack (Apache2): https://github.com/dmlc/dlpack > > - HalideIR (MIT): https://github.com/dmlc/HalideIR > > - range(Unlicense): https://github.com/agauniyal/rang > > - Compiler-RT (BSD) > > - LLVM > > > > All of the current he dependencies are stable, which means that the > > current TVM > > repo is standalone and main development activities only happen at > > the TVM repo. > > The dependencies are periodically updated in the rate about once a > > month when > > necessary. For source level dependencies, we will always point to a > > stable > > release version for software release in the future. > > > > > > === External Dependencies on DMLC projects === > > > > There are three dependencies to dmlc projects in the 3rdparty. The > > current > > proposal is to keep the current dependencies in the 3rdparty. We > > elaborate on > > the background of these dependencies below: > > > > - dmlc-core: is a minimum module for logging and memory > > serialization. It is > > currently used by projects including ApacheMXNet, TVM, and XGBoost. > The > > project is relatively stable, with around one change a week(most > recent > > changes comes from XGBoost project). TVM’s dependency on dmlc-core > > is minimum > > and only uses its feature for logging. > > - dlpack: is a minimum consensus standard for in-memory Tensor > > format. It is > > currently used by PyTorch, ApacheMXNet, Chainer, and a few other > > projects. > > - HalideIR: is a minimum IR data structure that is isolated from a > > fork of > > Halide project. We keep the license to be MIT to respect the > > original license > > and its origin. A common consensus in the TVM project is that we > > keep the old > > derived code in HalideIR (which are stable), and all new > > developments happen > > in the TVM repo. > > > > The main reason to propose keep these dependencies are: > > - Each of the dependencies has the user and developer community of > > its own > > which is larger than the TVM community or different license > > options(MIT in > > HalideIR) > > - These dependencies are stable and update at a monthly rate. > > > > While it is possible to fork the code in the tvm repo, given that > > the current > > tvm repo is self-contained, and community development is > > stand-alone, we feel > > that there are have enough justifications to treat these as 3rdparty > > dependencies. > > > > > > === Required Resources === > > > > ==== Mailing List: ==== > > The usual mailing lists are expected to be set up when entering > > incubation: > > > > * priv...@tvm.apache.org <mailto:priv...@tvm.apache.org> > > * d...@tvm.apache.org <mailto:d...@tvm.apache.org> , subscribe github > > issues. > > * discuss-arch...@tvm.apache.org > > <mailto:discuss-arch...@tvm.apache.org>, Archive the discuss content > > of the > > discourse user forum > > > > > > Currently, we only use issues for developments and encourage > > community to use > > discuss forums when possible. As a result, the current github issues > > serves > > similar purposes as dev@, so we propose to subscribe github issues > > to dev@ after > > incubation. > > > > The current community use https://discuss.tvm.ai/ for general > > technical and > > support discussions. The community forum is maintained by PMCs. We > > propose to > > continue to use the forum and archive the posts to an Apache > > mail-list. We > > already have the mechanism to do so (see > > https://groups.google.com/forum/#!forum/tvm-discuss-archive) > > > > > > > > ==== Git Repositories: ==== > > > > Upon entering incubation, we plan to transfer the existing repo from > > https://github.com/dmlc/tvm to > https://github.com/apache/incubator-tvm. > > > > > > > > > > ==== Issue Tracking: ==== > > > > TVM currently uses GitHub to track issues. We would like to continue > > to do so > > while we discuss migration possibilities with the ASF Infra team. > > > > ==== URL: ==== > > > > Current project website: https://tvm.ai/, as we proceed website will > > migrate to > > https://tvm.incubator.apache.org and hopefully > https://tvm.apache.org > > > > === Initial Committers and PMCs === > > > > As the project has already followed the Apache way of development(in > > terms of > > meritocracy, community, and archive of public discussion). We plan > > to transition > > the current PMCs to PPMCs , and committers to apache committers. > > There are also > > ongoing votes and discussions in the current tvm PMC private > > mail-list about new > > committers/PMCs(we also invited our tentative mentors as observers > > to the > > mail-list). We plan to migrate the discussions to private@ after the > > proposal > > has been accepted and bring in the new committers/PPMCs according to > the > > standard Apache community procedure. > > > > > > Initial PPMCs > > - Tianqi Chen tqc...@apache.org <mailto:tqc...@apache.org> > > - Ziheng Jiang zih...@apache.org <mailto:zih...@apache.org> > > - Yizhi Liu liuyi...@apache.org <mailto:liuyi...@apache.org> > > - Thierry Moreau mor...@cs.washington.edu > > <mailto:mor...@cs.washington.edu> > > - Haichen Shen shenhaic...@gmail.com <mailto:shenhaic...@gmail.com> > > - Lianmin Zheng lianminzh...@gmail.com <mailto: > lianminzh...@gmail.com> > > - Markus Weimer wei...@apache.org <mailto:wei...@apache.org> > > - Sebastian Schelter > > - Byung-Gon Chun > > > > Initial Committers (Including PPMCs) > > - Aditya Atluri aditya.atl...@amd.com <mailto:aditya.atl...@amd.com> > AMD > > - Tianqi Chen tqc...@apache.org <mailto:tqc...@apache.org> > > University of Washington > > - Yuwei Hu huyuwei1...@gmail.com <mailto:huyuwei1...@gmail.com> > Cornell > > - Nick Hynes nhy...@berkeley.edu <mailto:nhy...@berkeley.edu> UC > > Berkeley > > - Ziheng Jiang zih...@apache.org <mailto:zih...@apache.org> > > University of Washington > > - Yizhi Liu liuyi...@apache.org <mailto:liuyi...@apache.org> AWS > > - Thierry Moreau mor...@cs.washington.edu > > <mailto:mor...@cs.washington.edu> University of Washington > > - Siva srk.i...@gmail.com <mailto:srk.i...@gmail.com> Huawei > > - Haichen Shen shenhaic...@gmail.com <mailto:shenhaic...@gmail.com> > AWS > > - Masahiro Masuda masahi...@gmail.com <mailto:masahi...@gmail.com> > > Ziosoft > > - Zhixun Tan phisi...@gmail.com <mailto:phisi...@gmail.com> Google > > - Leyuan Wang laura...@gmail.com <mailto:laura...@gmail.com> AWS > > - Eddie Yan e...@cs.washington.edu <mailto:e...@cs.washington.edu> > > University of Washington > > - Lianming Zheng lianminzh...@gmail.com > > <mailto:lianminzh...@gmail.com> Shanghai Jiao Tong University > > > > > > === Sponsors: === > > > > ==== Champion: ==== > > * Markus Weimer, Microsoft > > > > ==== Mentors: ==== > > * Sebastian Schelter, New York University > > * Byung-Gon Chun, Seoul National University > > > > ==== Sponsoring Entity ==== > > We are requesting the Incubator to sponsor this project. > > > > --------------------------------------------------------------------- > > To unsubscribe, e-mail: general-unsubscr...@incubator.apache.org > > <mailto:general-unsubscr...@incubator.apache.org> > > For additional commands, e-mail: general-h...@incubator.apache.org > > <mailto:general-h...@incubator.apache.org> > > > -- Byung-Gon Chun
