9 ms·

So what's gonna happen to MLIR and TensorFlow Swift now? Dead?

by m0zg 7y ago

eklavya 7y ago
Depends on the community, more and more communities are proving to be more reliable when it comes to core tech pieces.
DiffProg 7y ago
By far the #1 committer and founding engineer of Swift for Tensorflow also recently left the project: http://rxwei.me/about/ http://rxwei.me/about/ . From the committer list, it looks like Dan Zheng is the only individual who is actively working on it, and he's was an intern until he formally started January 2019. I don't know the ins and outs of Google culture, but it looks at least like every senior engineer has moved onto something else.
- auggierose 7y ago
  Doesn't mean Swift for Tensorflow is dead. Could mean Swift for Tensorflow will properly support Metal soon :-D
- jph00 7y ago
  There are medium sized teams at Google for each of MLIR and s4tf. There's a weekly open design meeting for each of them. Many different folks have presented their work at both meetings.
  - rezahussain 7y ago
    Yea but they cancelled this week's meeting, and I guess this is why
fmap 7y ago
Wild speculation, but my guess is that both will continue to be developed. As far as I understand, the MLIR project predates its machine learning application and was originally intended as a new IR for clang. In that capacity it makes a lot of sense. MLIR is also currently experimental in Tensorflow, although I have no idea how mature the implementation is. Similarly, there has been significant investment into Swift for Tensorflow, so it's probably here to stay. On the other hand, from a language design perspective Swift is not a particularly good choice for automatic differentiation and translation into Tensorflow graphs (imperative, exposing many details of the underlying machine, etc.). Without a lot of investment into this project it might just be overtaken by a better engineered competitor, or more likely, fail to gain sufficient mind-share over the "good enough" python solution that already exists.
nochance 7y ago
Not clear with MLIR, since it seems to have some traction beyond S4TF. But yes, I think S4TF has had zero interest among people actually doing ML and is probably dead. It might take Google a while to actually kill it off, though. From conversations I’ve had with people doing ML, if they know about S4TF at all they actively dislike certain aspects (for example that it’s based on a statically typed language), or are dismissive of others (like support for differentiation, which is a neat parlor trick but saves a tiny bit of effort).
- m0zg 7y ago
  Not "zero" IMO. I, for one, was excited about its potential, I was just waiting for it to mature (wisely in retrospect). Python is really bursting at the seams for ML/DL at this point and the ecosystem is in need of a proper compiled language for implementing deep learning systems. This language must be easier on the newcomers than C++. Imagine a future where you could tell if your shit's broken by recompiling it, and your "deployment" would be just putting some RPC in front of your existing code.
  - jph00 7y ago
    Most people I know that understand s4tf well (including me) are excited about it. Chris Lattner and I co-taught two video lessons on it - have a look at those and see what you think: https://course.fast.ai/videos/?lesson=13 https://course.fast.ai/videos/?lesson=13 .
    - m0zg 7y ago
      I think you might have misread. I'm excited about it, it just wasn't mature enough for anything client-facing for me.
      - jph00 7y ago
        I didn't misread - I was agreeing with you! :) And disagreeing with the person you were responding to.
Joky 7y ago
I wouldn't be worried for MLIR. MLIR is getting used internally more and more inside TensorFlow, but also by separate team in different projects, like IREE for example (https://github.com/google/iree https://github.com/google/iree ). The TensorFlow lite converter has been replaced by the new MLIR-based one, similarly for the Edge TPU. But more than that, what makes me confident is the traction we are getting outside Google. First, we landed MLIR in LLVM last month: https://github.com/llvm/llvm-project/commit/0f0d0ed1c78f1a80139a1f2133fad5284691a121 https://github.com/llvm/llvm-project/commit/0f0d0ed1c78f1a80... The LLVM Fortran frontend (f18/flang) which will merge soon in the LLVM monorepo is using MLIR for their own IR. It'll be exiting to develop a non-ML MLIR-based frontend within LLVM! In particular Flang is opening an HPC perspective that could be leveraged by other DSLs later. They are adding an OpenMP dialect to MLIR right now: https://llvm.discourse.group/t/rfc-openmp-dialect-in-mlir/397 https://llvm.discourse.group/t/rfc-openmp-dialect-in-mlir/39... Intel has been actively porting their nGraph/PlaidML framework to be based on MLIR (search for "The Stripe dialect" and "nGraph Dialect" here: https://mlir.llvm.org/talks/ https://mlir.llvm.org/talks/ ). I'm less familiar with S4TF, but I know they recently got some nice new hires, including https://twitter.com/DaveAbrahams/status/1207690883782467584 https://twitter.com/DaveAbrahams/status/1207690883782467584
- m0zg 7y ago
  Edge TPU will eventually be canceled as well, I'm pretty sure, which is why, as exciting as it is otherwise, I'm not using it for anything practical. There's just no way to make $1B/yr with it, so it's officially below Google's executive interest threshold.
- enos_feedler 7y ago
  Chris Lattner actually gave a talk at the last MLIR open design meeting. This may or may not ease worrying about MLIR's future.
  - Joky 7y ago
    "2020-01-24: Thoughts on Tensor Code Generation in MLIR" here: https://mlir.llvm.org/talks/ https://mlir.llvm.org/talks/ for reference.
zapnuk 7y ago
My guess is that Swift+Tensorflow on hold. Last I checked, Swift is still years away from 1st class Windows support and adequate tooling. Without those in place, Swift+Tensorflow is still a very niche product for Google and I cannot think of a good enough reason why they should heavily invest into it.
- seanmcdirmid 7y ago
  Why would windows matter so much for tensorflow? Isn’t most ML work these days done under Linux?
  - pjmlp 7y ago
    Because that is what we get on our desks, managed by IT. Python Tensorflow, Julia, ML.NET, Tensorflow for C++, Pytorch, DL4J do all pretty well on Windows.
  - jeffshek 7y ago
    A lot of work from researchers are using *conda on Windows. ML research is happening across all fields, not just computer science.
eklavya 7y ago
I just saw the last commit is from Eugene Burmako, who has previously done awesome work with Scala compiler. I think the project is in good hands.
chrislattner 7y ago
https://twitter.com/JeffDean/status/1222033368700706816 https://twitter.com/JeffDean/status/1222033368700706816

So what's gonna happen to MLIR and TensorFlow Swift now? Dead?

by m0zg 7y ago

So what's gonna happen to MLIR and TensorFlow Swift now? Dead?

eklavya 7y ago

Depends on the community, more and more communities are proving to be more reliable when it comes to core tech pieces.

DiffProg 7y ago

By far the #1 committer and founding engineer of Swift for Tensorflow also recently left the project: http://rxwei.me/about/ http://rxwei.me/about/ . From the committer list, it looks like Dan Zheng is the only individual who is actively working on it, and he's was an intern until he formally started January 2019. I don't know the ins and outs of Google culture, but it looks at least like every senior engineer has moved onto something else.

auggierose 7y ago
Doesn't mean Swift for Tensorflow is dead. Could mean Swift for Tensorflow will properly support Metal soon :-D
jph00 7y ago
There are medium sized teams at Google for each of MLIR and s4tf. There's a weekly open design meeting for each of them. Many different folks have presented their work at both meetings.
- rezahussain 7y ago
  Yea but they cancelled this week's meeting, and I guess this is why

fmap 7y ago

Wild speculation, but my guess is that both will continue to be developed. As far as I understand, the MLIR project predates its machine learning application and was originally intended as a new IR for clang. In that capacity it makes a lot of sense. MLIR is also currently experimental in Tensorflow, although I have no idea how mature the implementation is. Similarly, there has been significant investment into Swift for Tensorflow, so it's probably here to stay. On the other hand, from a language design perspective Swift is not a particularly good choice for automatic differentiation and translation into Tensorflow graphs (imperative, exposing many details of the underlying machine, etc.). Without a lot of investment into this project it might just be overtaken by a better engineered competitor, or more likely, fail to gain sufficient mind-share over the "good enough" python solution that already exists.

nochance 7y ago

Not clear with MLIR, since it seems to have some traction beyond S4TF. But yes, I think S4TF has had zero interest among people actually doing ML and is probably dead. It might take Google a while to actually kill it off, though. From conversations I’ve had with people doing ML, if they know about S4TF at all they actively dislike certain aspects (for example that it’s based on a statically typed language), or are dismissive of others (like support for differentiation, which is a neat parlor trick but saves a tiny bit of effort).

m0zg 7y ago
Not "zero" IMO. I, for one, was excited about its potential, I was just waiting for it to mature (wisely in retrospect). Python is really bursting at the seams for ML/DL at this point and the ecosystem is in need of a proper compiled language for implementing deep learning systems. This language must be easier on the newcomers than C++. Imagine a future where you could tell if your shit's broken by recompiling it, and your "deployment" would be just putting some RPC in front of your existing code.
- jph00 7y ago
  Most people I know that understand s4tf well (including me) are excited about it. Chris Lattner and I co-taught two video lessons on it - have a look at those and see what you think: https://course.fast.ai/videos/?lesson=13 https://course.fast.ai/videos/?lesson=13 .
  - m0zg 7y ago
    I think you might have misread. I'm excited about it, it just wasn't mature enough for anything client-facing for me.
    - jph00 7y ago
      I didn't misread - I was agreeing with you! :) And disagreeing with the person you were responding to.

Joky 7y ago

I wouldn't be worried for MLIR. MLIR is getting used internally more and more inside TensorFlow, but also by separate team in different projects, like IREE for example (https://github.com/google/iree https://github.com/google/iree ). The TensorFlow lite converter has been replaced by the new MLIR-based one, similarly for the Edge TPU. But more than that, what makes me confident is the traction we are getting outside Google. First, we landed MLIR in LLVM last month: https://github.com/llvm/llvm-project/commit/0f0d0ed1c78f1a80139a1f2133fad5284691a121 https://github.com/llvm/llvm-project/commit/0f0d0ed1c78f1a80... The LLVM Fortran frontend (f18/flang) which will merge soon in the LLVM monorepo is using MLIR for their own IR. It'll be exiting to develop a non-ML MLIR-based frontend within LLVM! In particular Flang is opening an HPC perspective that could be leveraged by other DSLs later. They are adding an OpenMP dialect to MLIR right now: https://llvm.discourse.group/t/rfc-openmp-dialect-in-mlir/397 https://llvm.discourse.group/t/rfc-openmp-dialect-in-mlir/39... Intel has been actively porting their nGraph/PlaidML framework to be based on MLIR (search for "The Stripe dialect" and "nGraph Dialect" here: https://mlir.llvm.org/talks/ https://mlir.llvm.org/talks/ ). I'm less familiar with S4TF, but I know they recently got some nice new hires, including https://twitter.com/DaveAbrahams/status/1207690883782467584 https://twitter.com/DaveAbrahams/status/1207690883782467584

m0zg 7y ago
Edge TPU will eventually be canceled as well, I'm pretty sure, which is why, as exciting as it is otherwise, I'm not using it for anything practical. There's just no way to make $1B/yr with it, so it's officially below Google's executive interest threshold.
enos_feedler 7y ago
Chris Lattner actually gave a talk at the last MLIR open design meeting. This may or may not ease worrying about MLIR's future.
- Joky 7y ago
  "2020-01-24: Thoughts on Tensor Code Generation in MLIR" here: https://mlir.llvm.org/talks/ https://mlir.llvm.org/talks/ for reference.

zapnuk 7y ago

My guess is that Swift+Tensorflow on hold. Last I checked, Swift is still years away from 1st class Windows support and adequate tooling. Without those in place, Swift+Tensorflow is still a very niche product for Google and I cannot think of a good enough reason why they should heavily invest into it.

seanmcdirmid 7y ago
Why would windows matter so much for tensorflow? Isn’t most ML work these days done under Linux?
- pjmlp 7y ago
  Because that is what we get on our desks, managed by IT. Python Tensorflow, Julia, ML.NET, Tensorflow for C++, Pytorch, DL4J do all pretty well on Windows.
- jeffshek 7y ago
  A lot of work from researchers are using *conda on Windows. ML research is happening across all fields, not just computer science.

eklavya 7y ago

I just saw the last commit is from Eugene Burmako, who has previously done awesome work with Scala compiler. I think the project is in good hands.

chrislattner 7y ago

https://twitter.com/JeffDean/status/1222033368700706816 https://twitter.com/JeffDean/status/1222033368700706816