PyTorch Monarch
32 comments
·October 23, 2025pjmlp
dhrt12327
Multiple sources say that it is an experimental framework around PyTorch, not a replacement. People will still get to enjoy a circular graph using std::shared_ptr with memory leaks.
It's a pity they don't do a complete rewrite with a functional language as the driver.
gaogao
> It's a pity they don't do a complete rewrite with a functional language as the driver.
It's open source, so seeing such an extension would be quite cool. There's much that could be done with native Rust actors and code that get maybe at what you want, but nothing precludes mixing PyTorch and other backends.
For example, you could wrap a C++ inference engine as part of one of the actors generating data for other actors doing distributed training.
pjmlp
Interesting, by the way, you can replicate the experience in Rust.
galangalalgol
This is a new project right? Not the oxidation of an existing one.
gaogao
Yup, hyperreactor, one of the new crates that's part of it, does some particularly interesting things for efficient parallel distributed channels.
null
chandureddyvari
Interesting - this seems to target a different layer than services like Tinker (https://thinkingmachines.ai/blog/announcing-tinker/). Monarch provides the infrastructure primitives while Tinker is a managed finetuning service. Could someone build something like Tinker on top of Monarch?
gaogao
Yup, there's stuff like https://pytorch.org/blog/introducing-torchforge/ on top of it now
chandureddyvari
Nice, so the open source equivalent now exists. Meta basically commoditized Tinker's($12B valuation) value prop by giving away the infra (Monarch) and the RL framework (TorchForge). Will be interesting to see how a managed service competes with free + open source at this layer.
alyxya
I made my own single controller PyTorch extension [1], though mines doesn't yet support cross node communication. I found it interesting to compare how Monarch makes things performant. I believe Monarch also uses cloudpickle for code to be shared among all nodes, which is probably the only way to performantly have various nodes execute work as that ends up being a one time setup cost. I found the fanning out of sending messages from the single controller to be really interesting, so the controller is unlikely to be the bottleneck besides any synchronous operations.
As far as things that might be a performance loss here, one thing I'm wondering is if custom kernels are supported. I'm also wondering how much granularity of control there is with communication between different actors calling a function. Overall, I really like this project and hope to see it used over multi-controller setups.
gaogao
> As far as things that might be a performance loss here, one thing I'm wondering is if custom kernels are supported
Yeah, you might end up needing some changes to remote worker initialization, but you can generally bake in whatever kernels and other system code you need.
SomaticPirate
"Our Rust-based backend facilitates our performance, scale, and robustness — we amply use Rust’s fearless concurrency in Monarch’s implementation"
Found a few typo's. The em dash makes me suspect an LLM was involved in proofreading
whimsicalism
that it is surrounded by spaces makes this less likely
fadedsignal
It is a nice project. I have questions.
- Is this similar to openMPI?
- How is a mesh established? Do they need to be on the same host?
valzam
I assume this is similar to Ray?
unnah
There's also Dask, which can do distributed pandas and numpy operations etc. However it was originally developed for traditional HPC systems and has only limited support for GPU computing. https://www.dask.org/
disattention
I had the same thought, especially because of their recent collaboration.
https://pytorch.org/blog/pytorch-foundation-welcomes-ray-to-...
lairv
I'm also curious what's the use case of this over Ray. Tighter integration with PyTorch/tensors abstractions?
porridgeraisin
That.
Also, it has RDMA. Last I checked, Ray did not support RDMA.
There are probably other differences as well, but the lack of RDMA immediately splits the world into things you can do with ray and things you cannot do with ray
zacmps
Not currently, but it is being worked on https://github.com/ray-project/ray/issues/53976.
porridgeraisin
> This lets us avoid single-host bottlenecks, effectively using the whole mesh as a distributed cluster for message forwarding. (Cite scalability numbers here.)
In case someone that can fix this is reading here
milancurcic
Cool! Essentially Fortran coarrays from 2008.
philipallstar
Or Hadoop from 2006? But you don't need to write MapReduce or Fortran, so it's probably far nicer.
logicchains
This seems strictly less powerful than Jax, which comes with a powerful compiler that optimises how cross-node communication is conducted.
gaogao
Nah, focusing on a different controller paradigm. Jax is focused on multi-controller SPMD, while this is focused on a single-controller setup. Both have their place, with single-controller being generally easier to reason about, and multi-controller more optimal for certain dataflows. There's also some interesting mixes of the two control paradigms.
jonapro
Beowulf then.
nothrowaways
FB should create a pytorch foundation and set it free before they fuck it up.
Apparently PyTorch oxidation has started.
> Monarch is split into a Python-based frontend, and a backend implemented in Rust.
Other than that, looks like a quite interesting project.