functorch
TensorRT
| functorch | TensorRT | |
|---|---|---|
| 11 | 5 | |
| 1,372 | 2,343 | |
| 0.4% | 1.9% | |
| 0.0 | 9.5 | |
| 1 day ago | 1 day ago | |
| Jupyter Notebook | Python | |
| BSD 3-clause "New" or "Revised" License | BSD 3-clause "New" or "Revised" License |
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functorch
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What is the most efficient approach to ensemble a pytorch actor-critic model?
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).
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[P] Multidimensional array batch indexing for pytorch and numpy
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
- Functorch: Jax-like composable function transforms for PyTorch
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Jax vs. Julia (Vs PyTorch)
Tangentially related but there is an effort to get some of the features of JAX into PyTorch: https://pytorch.org/functorch/
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[D] Current State of JAX vs Pytorch?
Fwiw, composable vmap and stuff like that have also been implemented in PyTorch now - see functorch :) https://github.com/pytorch/functorch
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[D] Ideal deep learning library
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
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[P] Made Some Pytorch Modules For Agent Systems
You may find vmap from functorch to be quite useful: https://github.com/pytorch/functorch
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[D] Are you using PyTorch or TensorFlow going into 2022?
If you're interested in function transformations in PyTorch, try out functorch :) https://github.com/pytorch/functorch
- PyTorch: Where we are headed and why it looks a lot like Julia (but not exactly)
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Show HN: How does Jax allocate memory on a TPU? An interactive C++ walkthrough
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.)
TensorRT
- Learn TensorRT optimization
- I made TensorRT example. I hope this will help beginners. And I also have a question about TensorRT best practice.
- [P] [D] I made TensorRT example. I hope this will help beginners. And I also have a question about TensorRT best practice.
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[P] 4.5 times faster Hugging Face transformer inference by modifying some Python AST
Have you tried the new Torch-TensorRT compiler from NVIDIA?
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PyTorch 1.10
You can quantize your model to FP16 or Int8 using PTQ as well and it should give you an additional speed up inference wise.
Here is a tutorial[2] to leverage TRTorch.
[1] https://github.com/NVIDIA/TRTorch/tree/master/core
What are some alternatives?
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
torch2trt - An easy to use PyTorch to TensorRT converter
nn - 🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
onnx-simplifier - Simplify your onnx model
cutlass - CUDA Templates for Linear Algebra Subroutines
BinaryBuilder.jl - Binary Dependency Builder for Julia
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
py2many - Transpiler of Python to many other languages
transformer-deploy - Efficient, scalable and enterprise-grade CPU/GPU inference server for 🤗 Hugging Face transformer models 🚀