torch2trt VS functorch

Main thing you want for server inference is auto batching. It's a feature that's included in onnxruntime, torchserve, nvidia triton inference server and ray serve.

If you have a lot of preprocessing and post logic in your model it can be hard to export it for onnxruntime or triton so I usually recommend starting with Ray Serve (https://docs.ray.io/en/latest/serve/index.html) and using an actor that runs inference with a quantized model or optimized with tensorrt (https://github.com/NVIDIA-AI-IOT/torch2trt)

https://github.com/NVIDIA-AI-IOT/torch2trt <- pretty straightforward https://github.com/jkjung-avt/tensorrt_demos <- this helped me a lot

I find Pytorch easier to work with generally. Nvidia has a Pytorch --> TensorRT converter which yields some significant speedups and has a simple Python API. Convert the Pytorch model on the Nano.

I would suggest checking https://pytorch.org/functorch/ and https://github.com/metaopt/torchopt for efficient inference and training with ensembles (e.g., t be independent actors in a multi-agent setting or multiple critics).

There were some bugs still with advanced indexing in an older release of functorch, I believe they should be fixed now though: https://github.com/pytorch/functorch/pull/862

Tangentially related but there is an effort to get some of the features of JAX into PyTorch: https://pytorch.org/functorch/

Fwiw, composable vmap and stuff like that have also been implemented in PyTorch now - see functorch :) https://github.com/pytorch/functorch

Fwiw, it’s not like Pytorch’s design prevents function transformations from being implemented. See functorch for an example of grad/vmap function transforms: https://github.com/pytorch/functorch

You may find vmap from functorch to be quite useful: https://github.com/pytorch/functorch

If you're interested in function transformations in PyTorch, try out functorch :) https://github.com/pytorch/functorch

The pytorch programming model is just really hard to adapt to an XLA-like compiler. Imperative python code doesn't translate to an ML graph compiler particularly well; Jax's API is functional, so it's easier to translate to the XLA API. By contrast, torch/xla uses "lazy tensors" that record the computation graph and compile when needed. The trouble is, if the compute graph changes from run to run, you end up recompiling a lot.

I guess in Jax you'd just only apply `jax.jit` to the parts where the compute graph is static? I'd be curious to see examples of how this works in practice. Fwiw, there's an offshoot of pytorch that is aiming to provide this sort of API (see https://github.com/pytorch/functorch and look at eager_compilation.py).

(Disclaimer: I worked on this until quite recently.)