Re: [dmlc/tvm] [VOTE] Apache Transition Plan (#2973)
+1 -- You are receiving this because you commented. Reply to this email directly or view it on GitHub: https://github.com/dmlc/tvm/issues/2973#issuecomment-480507855
Re: [dmlc/tvm] [VOTE] Add "Organizations contributing using and contributing to TVM" Section to Community Webpage (#4162)
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Re: [dmlc/tvm] [DEV] TVM v0.6 Roadmap (#2623)
When will we have 0.6 release ? thanks -- You are receiving this because you are subscribed to this thread. Reply to this email directly or view it on GitHub: https://github.com/dmlc/tvm/issues/2623#issuecomment-546780527
Re: [dmlc/tvm] [RFC][DEV] TVM Project Repo Migration (#4212)
Are we going to release 0.6 in new repo? @tqchen -- You are receiving this because you are subscribed to this thread. Reply to this email directly or view it on GitHub: https://github.com/dmlc/tvm/issues/4212#issuecomment-548286904
Re: [dmlc/tvm] [RFC][DEV] TVM Project Repo Migration (#4212)
@tqchen Thanks. Both ok for us, as long as we can get a release in one or two month, is that possible? -- You are receiving this because you are subscribed to this thread. Reply to this email directly or view it on GitHub: https://github.com/dmlc/tvm/issues/4212#issuecomment-548680219
Re: [apache/incubator-tvm] [VOTE] Release Apache TVM (incubating) v0.6.0.rc2 (#4443)
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Re: [apache/incubator-tvm] [RFC] Data-flow Analysis Functionality on TVM IR (#4468)
@tqchen, what's your suggestion? IMO, low level IR has been there for a while, and we've had experience and understanding in low level ir. the post of unified ir to me is just a high level proposal, details needs to be discussed further, such as, The most valuable thing to me is we can make ops white box with unified ir, that is to say, we can analyze into the ops, which is totally different with the current way, we are using ops as black box in graph framework, or separated ir. with white box ops, we don't need to care about the ops name or the formula ops are using, we can optimize them in a general way. see what's XLA doing. The abstraction of high level ir matters. you don't want to lower ops body too much from tensor expression into nested loops, since it's a big cost of analyzing ir with too much context. so I suggest we can keep moving this work on this thread meanwhile we discuss how to reuse the solution. -- You are receiving this because you are subscribed to this thread. Reply to this email directly or view it on GitHub: https://github.com/apache/incubator-tvm/issues/4468#issuecomment-563318743
Re: [apache/tvm-rfcs] Additional Target Hooks RFC (#10)
This is a great disscussion here. Actually, we are supporting a DSA with TVM, let me share my practice. 1, We only re-use some of tvm relay or tir passes, less than 10 passes, such storage flatten, we don't need most of tvm passes, keep them in our flow means wasting compilation time. 2, We develop passes, and enhance tvm passes for our target, such stroage rewrite. 3, We develop a hybrid ir on tir, in which we can do unified memory allocation and schedule insnstruction across operators. So we are excited to see tvm can do these in main line. What we want to have: 1, customzation compilation flow for both relay and tir flow. 2, unified ir to view both graph and tir, supporting inter/intra pass developing. -- You are receiving this because you are subscribed to this thread. Reply to this email directly or view it on GitHub: https://github.com/apache/tvm-rfcs/pull/10#issuecomment-904635448
Re: [apache/tvm-rfcs] [RFC] TVMScript Metaprogramming (PR #79)
@yelite It's a great RFC，and this is what we need right now. the requirements we need: 1) For compute fusion. With TE compute, it's easy to concate TE computes with producer-comsuer relation to get a fused compute. for example, conv + elemwise ops fusion. We should have similar function in TVM script. Which thread is related to this requirement? 2) For conditional lowering. We may have some attributtes in graph/relay level, which will further decide how to lowering into different tir. With old ir builder/TE compute, we can do that. F4 in this RFC will ensure this，correct？ 3) For reducing boilerplate code. F3 is a good idea. Another one is we define a tir function (with or without host python code), and we reuse it other place. We see this in F4 which foucus on conditional lowering, however I think we should define/declare it as standalone Fearture. Looking forward to see this RFC in upstream! -- Reply to this email directly or view it on GitHub: https://github.com/apache/tvm-rfcs/pull/79#issuecomment-1181184802 You are receiving this because you are subscribed to this thread. Message ID:
Re: [apache/tvm] [VOTE] Establish TVM Unity Connection Technical Strategy (Issue #12651)
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Re: [apache/tvm] [VOTE] Clarify Community Strategy Decision Process (Issue #15521)
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Re: [apache/tvm] [VOTE] Transition Main to Unity (Issue #16368)
+1 -- Reply to this email directly or view it on GitHub: https://github.com/apache/tvm/issues/16368#issuecomment-1882134532 You are receiving this because you are subscribed to this thread. Message ID:
[TVM Discuss] [Development] Google lasted work: MLIR Primer
My take is, MLIR is a replacement of HalideIR. 1) compiler infra support, like cfg/dfa/ssa, with these, we can avoid pattern matching style pass on Halide, which is not good for maintaining, 2) other better utilities, like text ir; 3) unified IR for multi-level, graph and tensor. I agree the idea we have a MLIR phase in TVM. if it's indeed better, we can move our work to MLIR gradually, or just write new optimization pass on MLIR. --- [Visit Topic](https://discuss.tvm.ai/t/google-lasted-work-mlir-primer/1721/17) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.tvm.ai/email/unsubscribe/0e24b57e89d3d6edcfa06fda5e8a7e6da1aa6a9e89bb4a188ffb00c38a50be7b). Tianqi Chen, UW, Seattle, WA, 98105, United States http://tracking.discuss.tvm.ai/tracking/unsubscribe?msgid=yrr-J9fBn994G8i-pPhrng2
[TVM Discuss] [Development/RFC] [IR] Unified TVM IR Infra
@tqchen do we have abstractions in TVM’s unfied IR infra?
1, multi-stage ir for relay::Function:
```
c = IRModule A(a, b){
a = a + 1;
b = b + 1;
return a+b;
}
e = IRModule B(c, d){
c = c + 1;
d = d + 1;
return c+d;
}
```
With this abstraction, we can express complex/big ops with limited small ops;
also we can treat big op as white box op, so we can do some computation
optimizations globally.
2, multi-stage ir for tir::PrimFunc:
```
c = IRModule A(a, b){
lowered tir
}
e = IRModule B(c, d){
lowered tir
}
```
with this abstraction, we can do some low level global optimizations, like dma
preload for local buffers.
we may need to break lower into several api, so we can utilize different
property of tir, like before stroageflatten or after.
---
[Visit Topic](https://discuss.tvm.ai/t/ir-unified-tvm-ir-infra/4801/6) to
respond.
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[TVM Discuss] [Development/RFC] [IR] Unified TVM IR Infra
@tqchen That's great! BTW I notice you delete ir dump in recently pr, but this is very very important utility for compiler development in HW projects, do we have other alternatives in tvm? --- [Visit Topic](https://discuss.tvm.ai/t/ir-unified-tvm-ir-infra/4801/8) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.tvm.ai/email/unsubscribe/a86eb74693b834cdc0a185bb83b8bf69dcb711df80e06d83df460989d68d5341).
[TVM Discuss] [Development/RFC] [IR] Unified TVM IR Infra
Do we support round trip ir? which can parser a readable ir and construct ir objects as inputs for compiler. --- [Visit Topic](https://discuss.tvm.ai/t/ir-unified-tvm-ir-infra/4801/10) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.tvm.ai/email/unsubscribe/e82be81fe5b5399ec73d4c0b36d9f94b7e6cdda5a361ddf18f7e2e771d5d7dc1).
[TVM Discuss] [Development/RFC] [RFC] Ansor: An Auto-scheduler for TVM (AutoTVM v2.0)
We have a poly + tvm solution for Davinci, which will be released soon, maybe in the next week. --- [Visit Topic](https://discuss.tvm.ai/t/rfc-ansor-an-auto-scheduler-for-tvm-autotvm-v2-0/7005/19) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.tvm.ai/email/unsubscribe/165772407af889c0ed52ac31b482d68aa08f33b760dff544ca83d0ea7fc3371e).
[TVM Discuss] [Development/RFC] [RFC] Ansor: An Auto-scheduler for TVM (AutoTVM v2.0)
https://gitee.com/mindspore/akg --- [Visit Topic](https://discuss.tvm.ai/t/rfc-ansor-an-auto-scheduler-for-tvm-autotvm-v2-0/7005/20) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.tvm.ai/email/unsubscribe/7b6b126fd8f7135187a8dac6c625752011e33ebf26b856c5d2eb2072ec2efe7b).
[TVM Discuss] [Development/RFC] [RFC] Ansor: An Auto-scheduler for TVM (AutoTVM v2.0)
we do support ascend310 op codegen on AKG side, but not in MindSpore for now. --- [Visit Topic](https://discuss.tvm.ai/t/rfc-ansor-an-auto-scheduler-for-tvm-autotvm-v2-0/7005/23) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.tvm.ai/email/unsubscribe/8e51494271506933809a860b0937a195acff3544f242e7163e53f81f35ae6b2c).
[Apache TVM Discuss] [Development/RFC] [RFC] TensorIR: A schedulable IR for TVM
This is the right way to go. However I have two concern, 1) How to fuse ops as much as possible? Basically fusion is copy propagation optimization in compilers, which is based on data flow analysis, but still lack of programming analysis in TVM now. 2) TE tensorize can not handle some complex pattern matching, see https://github.com/apache/incubator-tvm/pull/1053, can we do 100% pattern matching in tir? --- [Visit Topic](https://discuss.tvm.apache.org/t/rfc-tensorir-a-schedulable-ir-for-tvm/7872/29) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.tvm.apache.org/email/unsubscribe/78c2de28cf50a3f0e21bd234a9fd975d7fd77c870c4627104dab67469571f219).
[Apache TVM Discuss] [Development/RFC] [RFC] TensorIR: A schedulable IR for TVM
Is Fusion in Ansor based on tir? For other transforms, you may checkout here, that's what we've done in AKG. I can explain some if you are intrested. https://github.com/mindspore-ai/akg/blob/master/src/codegen/build_module.cc#L439 --- [Visit Topic](https://discuss.tvm.apache.org/t/rfc-tensorir-a-schedulable-ir-for-tvm/7872/31) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.tvm.apache.org/email/unsubscribe/a7f0d2bafba438aef187410bc3f676663b7b15309b2644f747d34a10d3bc45bd).
[Apache TVM Discuss] [Development/RFC] [RFC] TensorIR: A schedulable IR for TVM
@junrushao1994 It's better to know loops can be vectoried, permutable or distributied, isl can provide these information，so we can do loop optimization and tensorization/vectorization automatically. --- [Visit Topic](https://discuss.tvm.apache.org/t/rfc-tensorir-a-schedulable-ir-for-tvm/7872/32) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.tvm.apache.org/email/unsubscribe/9935959d85972017de17516f48d2c09e3a5b07c0857a9cdcdd3306e512945c9f).
[Apache TVM Discuss] [Development/RFC] [RFC] TensorIR: A schedulable IR for TVM
One issue in old schedule ops is we can not get the accurate bouds with inferbound, what will it be like in new schedule system? thanks. --- [Visit Topic](https://discuss.tvm.apache.org/t/rfc-tensorir-a-schedulable-ir-for-tvm/7872/64) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.tvm.apache.org/email/unsubscribe/a665d101ee258a31107279867294b7a7dc7427c420169cc6f1fed9f459564cc6).
[Apache TVM Discuss] [Application] TVM Community Survey
[quote="hogepodge, post:1, topic:10305"] What platforms are you using TVM for? * [ ] X86 CPU * [ ] ARM CPU * [ ] Other CPU * [ ] NVidia GPU * [ ] AMD GPU * [ ] Other GPU * [ ] Embedded Platform [/quote] We are using TVM for DSA NPU, can you add one option, thanks! --- [Visit Topic](https://discuss.tvm.apache.org/t/tvm-community-survey/10305/2) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.tvm.apache.org/email/unsubscribe/b4ef0750a32d0e34c4c34c0cd4688e804e06895dece4d68d35294a2026e7172c).
