awesome-tensor-compilers
awesome-machine-learning-in-compilers
| awesome-tensor-compilers | awesome-machine-learning-in-compilers | |
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| 9 | 5 | |
| 2,171 | 1,331 | |
| - | - | |
| 4.4 | 5.7 | |
| 4 months ago | 15 days ago | |
| - | Creative Commons Zero v1.0 Universal |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
awesome-tensor-compilers
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MatX: Faster Chips for LLMs
> So long as Pytorch only practically works with Nvidia GPUs, everything else is little more than a rounding error.
This is changing.
https://github.com/merrymercy/awesome-tensor-compilers
There are more and better projects that can compile an existing PyTorch codebase into a more optimized format for a range of devices. Triton (which is part of PyTorch) TVM and the MLIR based efforts (like torch-MLIR or IREE) are big ones, but there are smaller fish like GGML and Tinygrad, or more narrowly focused projects like Meta's AITemplate (which works on AMD datacenter GPUs).
Hardware is in a strange place now... It feels like everyone but Cerebras and AMD/Intel was squeezed out, but with all the money pouring in, I think this is temporary.
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Run Llama2-70B in Web Browser with WebGPU Acceleration
I think this is true of AI compilation in general. Torch MLIR, AITemplate and really everything here fly under the radar.
https://github.com/merrymercy/awesome-tensor-compilers#open-...
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Ask HN: How to get good as a self taught ML engineer?
> I really want to do some great work and help people.
Have you looked into ML compilation?
https://github.com/merrymercy/awesome-tensor-compilers
IMO there is low hanging fruit in the space between high performance ML compilers/runtimes and the actual projects people use. If you practice porting projects you use to these frameworks, that would give you a massive performance edge.
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Ask HN: What new programming language(s) are you most excited about?
While not all "languages" persay, I am excited about the various ML compilation efforts:
https://github.com/merrymercy/awesome-tensor-compilers
Modern ML training/inference is inefficient, and lacks any portability. These frameworks are how that changes.
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Research Papers on ML in Compilers
You might be interested in this: https://github.com/merrymercy/awesome-tensor-compilers
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The Distributed Tensor Algebra Compiler (2022)
* collection of papers in https://github.com/merrymercy/awesome-tensor-compilers
I also have an interest in the community more widely associated with pandas/dataframes-like languages (e.g. modin/dask/ray/polars/ibis) with substrait/calcite/arrow their choice of IR
- A list of compiler projects and papers for tensor computation and deep learning
- A List of Tensor Compilers
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C-for-Metal: High Performance SIMD Programming on Intel GPUs
Compiling from high-level lang to GPU is a huge problem, and we greatly appreciate efforts to solve it.
If I understand correctly, this (CM) allows for C-style fine-level control over a GPU device as though it were a CPU.
However, it does not appear to address data transit (critical for performance). Compilation and operator fusing to minimize transit is possibly more important. See Graphcore Poplar, Tensorflow XLA, Arrayfire, Pytorch Glow, etc.
Further, this obviously only applies to Intel GPUs, so investing time in utilizing low-level control is possibly a hardware dead-end.
Dream world for programmers is one where data transit and hardware architecture are taken into account without living inside a proprietary DSL Conversely, it is obviously against hardware manufacturers' interests to create this.
Is MLIR / LLVM going to solve this? This list has been interesting to consider:
https://github.com/merrymercy/awesome-tensor-compilers
awesome-machine-learning-in-compilers
- Awesome research papers on ML in Compilers
- Research Papers on ML in Compilers
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Compiler Optimizations Are Hard Because They Forget
This repo is a great collection of various papers & resources on the subject: https://github.com/zwang4/awesome-machine-learning-in-compilers
- Can artificial neural networks make better artificial neural networks than humans can make yet?
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I spent 5 years writing my own operating system
The list goes on.
Genode, unikernels like MirageOS, TempleOS, Singularity OS / Sing#, compiler services like Roslyn and Kotlin, MILEPOST GCC, Tensorflow / TPUs, GPT-3, all of the machine learning in compilers [1] and so much more. I truly think Deep Learning Compilers will be huge.
[1] https://github.com/zwang4/awesome-machine-learning-in-compil...
What are some alternatives?
Arraymancer - A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends
kernel-ml - Machine Learning Framework for Operating Systems - Brings ML to Linux kernel
alpa - Training and serving large-scale neural networks with auto parallelization.
winix - A UNIX-style Operating System for the Waikato RISC Architecture Microprocessor (WRAMP)
Fable - The project has moved to a separate organization. This project provides redirect for old Fable web site.
ZenithOS - The Zenith Operating System is a modernized, professional fork of the 64-bit Temple Operating System.
Distributed-Systems-Guide - Distributed Systems Guide
rexsimulator - a forked copy of https://sourceforge.net/projects/rexsimulator/
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
Parallel-Computing-Guide - Parallel Computing Guide
tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators
unikraft - A next-generation cloud native kernel designed to unlock best-in-class performance, security primitives and efficiency savings.