ExecuTorch: Run PyTorch programs on mobile and edge devices | Hacker News Reader

ExecuTorch: Run PyTorch programs on mobile and edge devices

pytorch.org

94 pointsresolve-ev25 comments

There is also PyTorch mobile [1]. I'm curious how these two relate.

[Edit] And here is the answer [2]

> PyTorch Mobile uses TorchScript to allow PyTorch models to run on devices with limited resources. ExecuTorch has a significantly smaller memory size and a dynamic memory footprint resulting in superior performance and portability compared to PyTorch Mobile. Also, ExecuTorch does not rely on TorchScript, and instead leverages PyTorch 2 compiler and export functionality for on-device execution of PyTorch models.

[1] https://pytorch.org/mobile/home/ [2] https://pytorch.org/executorch/stable/intro-overview.html#ho...

Yeah it's a bit weird to see the difference.

> Also, ExecuTorch does not rely on TorchScript, and instead leverages PyTorch 2 compiler and export functionality for on-device execution of PyTorch models.

I think this is the major change: AFAIU you could consider ExecuTorch as ""just a rewrite"" of PyTorch mobile using PyTorch 2 compiler.

BUT this is should still be pretty major. It was pretty hard to retarget models to be compatible with TorchScript. But most recent models will want to target PyTorch 2 compiler for the performance boost. Which means they'll automatically get ExecuTorch support.

As someone who got burned several times in trying to port models to TorchScript and run them on Androids, I'm happy they did that.

Hey guys, we are ExecuTorch team, we're excited about launching this! Please ask us anything :)

It looks like executorch is for edge devices (phones / IoT / etc).

I'm currently doing inference on GPUs with libtorch and have a few concerns: (1) It seems like libtorch/torchscript are on a path to getting deprecated and (2) libtorch/torchscript pull in enormously bloated libraries. Should I be looking at executorch? I currently don't see an nvidia backend / integration with tensor rt in https://github.com/pytorch/executorch/tree/main/backends , but seems like it might be possible. Is this something you are thinking about?

Yes ExecuTorch is currently targeted at Edge devices. The runtime is written in C++ with 50KB binary size (without kernels) and should run in most of platforms. You are right that we have not integrated to Nvidia backend yet. Have you tried torch.compile() in PyTorch 2.0? It would do the Nvidia optimization for you without Torchscript. If you have specific binary size or edge specific request, feel free to file issues in https://github.com/pytorch/executorch/issues

torch.compile only works with python from what I understand. Many people need a native way to run GPU models, but don't want the bloat of full libtorch.

We like Rust for its build tooling and safe concurrency primitives. Thus we are eyeballing with candle [1].

OTOH, writing platform specific backends is a huge undertaking. Do you think backends such as MPS may become a shared effort?

[1] https://github.com/huggingface/candle [2] https://github.com/huggingface/candle/issues/313

Is it possible to execute a light weight language model, perhaps this https://github.com/facebookresearch/llama using ExecuTorch to run on smartphone in real time for a chatbot app ? Please share some guidance.

Looks cool! Does the Vulkan backend work on PC? MLC-LLM proves it can work well and it would be cool to have a cross platform, minimal runtime, GPU-agnostic backend for PC too, not just mobile.

The Vulkan backend does work on PC, as the only requirement is that Vulkan drivers are present. However, it was developed with mobile use-cases in mind and we haven't validated/optimized performance for PC.

As an aside, the Vulkan backend is tied to TorchScript at the moment, so it is not yet compatible with ExecuTorch. However, we are also planning to introduce a Vulkan delegate for ExecuTorch which will enable GPU delegation through ExecuTorch.

hey - this might be a rather specific use case, but our applications need to run "real-time" and allocation-free - are there any guarantees on that from your side? assuming fixed shapes, pre-allocating everything for the forward pass should probably be possible in theory, but i guess that wasn't really a relevant factor in the design of it all

Nice tool with ugly name

It's one of those names that sound better said aloud than spelled out. Excited to see where this goes though!

Very cool. A few notes:

1. MPS backend uses MPSGraph exclusively, might hit some performance ceilings limited by MPSGraph. s4nnc moved more and more ops from MPSGraph to Metal directly to have better control on both allocation and some erratic behaviors from MPSGraph.

2. CoreML backend uses coremltools, thus it carries all the baggage of that: requiring to generate CoreML model AOT because Python dependency, have no control over memory planning, weight quantization scheme, or where to put the weights. Dynamic shape might further making memory planning worse as static shape is the main use-case of CoreML so far and far better tested.

I would love to see an updated port of coremltools either in Swift or C++ (ONNX's coremltools implementation is in v3 I believe, and coremltools moved to v7 spec?).

The Android example is still too low level and full of CLI stuff, it would be easier for adoption, if it was more focused on Android Studio and building AARs.

Saw this at pytorch con. Very cool! Was used to build the hand tracking that shipped in quest 3 apparently

ONNX Runtime has supported this for years - https://onnxruntime.ai/blogs/pytorch-on-the-edge

One difference here is that there is no conversion for ExecuTorch. In general conversions introduce inconsistency and are difficult to maintain, so ExecuTorch eliminates that step.

This is very cool.

Any plan for web support? :)

There's wasi-nn for that I think. Whatever the original question means

Could you elaborate? What kind of web support you are looking for?

interference or inference?

We've changed the title now to use language from the article itself.

(submitted title was "ExecuTorch: Enabling On-Device interference for embedded devices")

Edit: I switched "anywhere" to "mobile and edge devices" per https://pytorch.org/blog/pytorch-edge/. One does not get away with saying things like "anywhere" in HN titles...

Obviously the poster meant to write inference. Maybe autocorrect messed up the headline?

It wouldn't be the first time autocorrect has messed up a headline on HN.

EDIT: dang fixed the headline. Thanks dang!

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