Tvm: [DEV] TVM v0.6 Roadmap

[ZihengJiang]

This roadmap for TVM v0.6. TVM is a community-driven project and we love your feedback and proposals on where we should be heading. Please open up discussion in the discussion forum as well as bring RFCs.

• Feel free to volunteer yourself if you are interested in trying out some items(they do not have to be on the list).
• Please also check out the help wanted list in the github issues on things that need help

Features

• Quantization
  
  
  
  • [x] Support for configuring mix-precision
  
  
  • [x] Per-Channel scale
  
  
  • [x] Graph packing
  
  
  • [ ] Smarter calibration algorithm
  
  
  • [ ] Model coverage
  
  
  • [ ] Support importing quantization model from other frameworks
  
  
• Relay
  
  
  
  • [x] Algebra Data Type
  
  
  • [ ] Runtime support for dynamic models
  
  
  • [x] Support Any syntax
  
  
  • [x] Pass Manager
  
  
  • [x] Official text format support
  
  
• Automated tuning and scheduling
  
  
  
  • [ ] graph level automated optimization
  
  
• Ultra low-bit support
  
  
  
  • [ ] tutorials of low-bit ops
  
  
  • [ ] customized accelerator support
  
  
• VTA enhancements
  
  
  
  • [ ] support generic high level models
  
  
  • [ ] Enhanced operator/model coverage
  
  
  • [ ] Ultra-96, ZCU102 support
  
  
  • [ ] Amazon F1 preliminary support
  
  
  • [ ] Low-bit support, bit serial support
  
  
  • [ ] Chisel version
  
  
• Micro-asm kernel exploration
  
  
  
  • [ ] Core micro-asm primitives for certain ops
  
  
• Hybrid python programming model
  
  
  
  • [ ] transition of vision operators to hybrid mode.
  
  
• RPC and Device API
  
  
  
  • [ ] Support a c++ version of cross platform RPC
  
  
• Training
  
  
  
  • [x] First-order auto differentiation
  
  
  • [ ] Gradient operators
  
  
  • [x] High-order auto differentiation
  
  
• Arithmetics
  
  
  
  • [ ] Formalize Integer Arithmetic Analysis
  
  
• Tutorials
  
  
  
  • [ ] Tutorials of low-bit ops using Relay
  
  

[icemelon9]

Does runtime for dynamic model refer to the runtime for Relay? Otherwise, we can also add Relay runtime into 0.6 roadmap.

[ZihengJiang]

@edmBernard Great to hear that! Added!

[ZihengJiang]

@icemelon9 Right, it refers to the runtime for Relay

[icemelon9]

Cool. @jroesch @zhiics @wweic and I can work on runtime for Relay.

[FrozenGene]

Wish TVM v0.6 can finish this item: https://github.com/dmlc/tvm/issues/2351. i.e. Support importing exist quantization TFLite model. This can be a start for supporting importing existing quantization model (i.e. don't restrict TFLite).

[zhiics]

Pass manager for relay, should be able to finish discussion soon, haha

[yzhliu]

does "graph level automated optimization" mean https://github.com/dmlc/tvm/pull/2184 or something else?

[ZihengJiang]

@yzhliu I think so. It is a legacy item from roadmap v0.5

[antinucleon]

Will work together with @icemelon9 on dynamic runtime.

[antinucleon]

I think we should also deprecate nnvm fully in 0.6. So far there are still some legacy code in topi depends on nnvm.

[icemelon9]

Could we also add Any dimension support in Relay to the roadmap? I think it's an important feature to have.

[joshpoll]

I think official text format support should be part of 0.6. i.e. the parser and printer should be able to support all Relay constructs.

[yzhliu]

TVM Monthly - Feb 2019

Community

In Feb 2019, we successfully released TVM v0.5 (release notes) and made the roadmap for v0.6.

The community welcomes new Reviewer Zhao Wu (@FrozenGene), Committer Jared Roesch (@jroesch) and PMC member Lianmin Zheng (@merrymercy)

TVM community has voted through Apache Incubation proposal (#2543). Markus Weimer posted a proposal to [email protected] seeking for ideas and suggestions. The official voting is ongoing on general@ right now.

Features and Improvements

Operator Support

• A special operator annotation.stop_fusion to prevent it being fused with previous expressions (#2624).
• batch_matmul supported (#2561).
• reverse_reshape supported (#2503).
• Faster-RCNN proposal operator for CUDA (#2420).
• Vision operator for YOLO yolo_reorg (#1941).
• slice operator for MXNet (#2662).
• arange supported (#2621).
• Vision operator roi_align (#2618).
• where operator for MXNet (#2647).

User Interface and Frontend

• Introduced HybridModule (#2477) so that normal TVM schedule can be compiled to hybrid target, run and dumped to Hybrid Script.
• Most frameworks have been supported in Relay, including ONNX, Keras, Tensorflow, Caffe2, CoreML, NNVMv1, MXNet (#2246). Siju is working on DarkNet.
• Relay now supports saving and loading parameter dictionaries. (#2620)
• Rust frontend (#2292).
• We are now supporting Tensorflow saved model for NNVM (#2493). Relay support is ongoing (#2586).
• Add max_num_threads to Hybrid Script, which allows users to get max number of threads for GPU targets (#2672).

Runtime and Hardware Support

• RFC for bringing TVM to Bare-Metal devices (#2563)
• Make external library extend TVM's NDArray more easily (#2613).

Performance Improvement

• AlterOpLayout pass is now enabled for x86 on Relay (#2585). It is essential to get decent performance for CNN-based model on Intel CPUs.

Documents and Tutorials

• Tutorials for deep learning frameworks support in Relay.
• Tutorial for running AutoTVM with Relay (#2594).
• Document for Algebraic Data Types (#2575).

High-level Optimizations

• An optimization pass to eliminate expressions which have the same functionality and same inputs (#2639).
• Provided a pass to calculate the number of multiply-accumulate operations in a network (#2609).
• Algebraic Data Types (ADT) support (#2442, #2575). ADT provides an expressive, efficient, and safe way to realize recursive computation (e.g., RNN). Refer to https://docs.tvm.ai/langref/relay_adt.html for more information.
• As a part of Relay's automatic differentiation system, we are adding primal gradients for Relay operators. Please refer to #2562 for tracking the progress. (Help wanted). For automatic differentiation in Relay, also see Automatic differentiation for tensor expressions, Higher order reverse mode automatic differentiation that work with control flow, Tensor Expression level automatic differentiation
• Low-bit quantization supported (#2116). The workflow includes annotation, calibration and transformation. Please also see Data-Aware Calibration for calibration algorithm improvement (Help wanted).

Tensor Expression

• RFC for formalizing Integer Arithmetic Analysis (#2588). It is aiming to perform better context-dependent analysis, bound analysis, centralized arithmetic logic and arithmetic simplification.

Contribution and Commits

Thanks Wei @wweic or providing the tools.

People Who Reviewed Pull Requests

• tqchen Runtime, Relay, Tensor Expression, Document, Frontend, AutoTVM
• were Runtime, Hybrid Script, TOPI
• junrushao1994 Runtime, Relay, Hybrid Script
• ZihengJiang Relay, Rust, Quantization, Tensor Expression
• sgrechanik-h Tensor Expression
• derisavi Tensor Expression
• wweic Relay
• nhynes Rust, Quantization
• ehsanmok Rust
• mjs-arm Pylint
• MarisaKirisame Relay
• srkreddy1238 Relay, Tensorflow Frontend, Golang
• vinx13 Hybrid Script, Relay, TOPI, Quatization
• kazum Relay
• FrozenGene TFLite Frontend, AutoTVM, Quantization
• jroesch Relay, Rust
• zhiics Relay, Runtime, Tensorflow Frontend
• imorinaga Relay(heterogenous annotation)
• merrymercy AutoTVM, Tensor Expression, Quantization
• yzhliu AutoTVM, Relay, Tensor Expression, Frontend
• eqy AutoTVM, Quantization, Relay
• icemelon9 AutoTVM, Tensor Expression, Runtime, Relay, Frontend
• reminisce Runtime
• kevinthesun Tensor Expression, TOPI
• Anthony-Mai Tensor Expression
• slyubomirsky Relay
• joshpoll Relay Document
• eric-haibin-lin Bugfix
• ZhennanQin Bugfix
• grwlf Runtime
• xqdan Tensor Expression, Hybrid Script
• Laurawly Relay, Hybrid Script, TOPI
• masahi Relay, TOPI, Document, CodeGen
• siju-samuel Relay
• PariksheetPinjari909 Operator
• liangfu Quantization
• lixiaoquan Quantization
• ajtulloch Quantization
• zhreshold Operator

People Who Committed

• tqchen CI, Runtime, Tensor Expression, Relay Text Printer
• ruslo Documents
• mjs-arm Pylint, CI
• vinx13 Relay, TOPI, Operator
• lixiaoquan Relay
• wweic Relay Document
• kazum Tutorial and Document
• derisavi Tensor Expression (IntSet)
• antinucleon AutoTVM
• slyubomirsky Relay (ADT, AutoDiff)
• MarisaKirisame Relay
• junrushao1994 Runtime, Build
• were Hybrid Script
• yidawang Relay (MAC Calculation)
• ariwaranosai Operator
• ZihengJiang Relay, Quantization
• jroesch Relay
• hlu1 Runtime
• headupinclouds TOPI
• zhiics Relay, Tensor Expression
• weberlo Relay (param save/load)
• icemelon9 Tensor Expression, Operator
• yzhliu Tensor Expression
• eqy Relay, Quantization, AutoTVM tutorial
• geexie Bug-fix
• abergeron Conda package
• larroy NodeEntry Implement Improvement
• ptrendx Bug-fix
• Anthony-Mai Namespace Fix
• jdavies-huawei Tensor Expression
• srkreddy1238 Tensorflow Frontend, Golang
• yongwww Tensorflow Frontend, Relay Document
• alexeyr Code Improvement
• nhynes Rust
• makihiro Caffe2 Frontend
• kevinthesun AutoTVM
• Huyuwei Tutorial
• ehsanmok Rust
• siju-samuel Operator
• sgrechanik-h Tensor Expression
• haojin2 Operator
• SiNZeRo Document
• denis0x0D CodeGen
• Laurawly Bug-fix
• apivovarov Typo-fix
• take-cheeze Typo-fix

List of Commits

Refer to https://discuss.tvm.ai/t/tvm-monthly-feb-2019/1801

[jroesch]

We (@yongwww, @zhiics and I) will be providing support for loops and conditionals for the TensorFlow frontend soon.

[FrozenGene]

@jroesch Awesome. Using AOT relay runtime?

[ajtulloch]

@jroesch that's wonderful, very excited to see how that turns out :)

[zhiics]

@FrozenGene It uses the interpreter.
@ajtulloch We used something like "pattern matching" to identify the scope/execution frame a control flow op and converted them into Relay recursive call and If constructs. We will send out the PR soon.

[tqchen]

@zhiics @jroesch as per community tradition, it would be great to send out an RFC first to describe the proposal of such major change, before we do the PR

[zhiics]

@tqchen No worries. We will.

[merrymercy]

TVM Monthly - March 2019

https://discuss.tvm.ai/t/tvm-monthly-march-2019/2083

[ZihengJiang]

TVM Monthly - April 2019

https://discuss.tvm.ai/t/tvm-monthly-april-2019/2426

[icemelon9]

TVM Monthly - May 2019

https://discuss.tvm.ai/t/tvm-monthly-may-2019/2793

[yzhliu]

TVM Monthly - June 2019

https://discuss.tvm.ai/t/tvm-monthly-june-2019

[kparzysz-quic]

What is the planned release date for 0.6?

[tqchen]

There has been quite a lot of improvements recently. While it is up to the community, I think we might be able to get out something around Sep

[Lyken17]

• [ ] graph level automated optimization

Does it mean traditional fusion / layout transform, or more recent graph substitution like this paper? If the later one, I would like to port my onnx implementation to TVM.

[MarisaKirisame]

Higher order automatic differentiation was done like half years ago. Please check that.

[merrymercy]

@Lyken17 https://github.com/dmlc/tvm/issues/1585

[icemelon9]

TVM Monthly - July 2019

https://discuss.tvm.ai/t/tvm-monthly-july-2019

[ZihengJiang]

@MarisaKirisame Checked. I listed it here just because that it did not go into the last release cycle

[snowolfhawk]

Hybrid python programming model
transition of vision operators to hybrid mode.

what's plan for this feature?

[zhiics]

TVM Monthly - August 2019

https://discuss.tvm.ai/t/tvm-monthly-august-2019

[icemelon9]

TVM Monthly - September 2019

https://discuss.tvm.ai/t/tvm-monthly-september-2019

[xqdan]

When will we have 0.6 release ? thanks

[yzhliu]

TVM Monthly - October 2019

https://discuss.tvm.ai/t/tvm-monthly-oct-2019

[tqchen]

Move to #4259