Fable
awesome-tensor-compilers
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| Fable | awesome-tensor-compilers | |
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| over 7 years ago | 3 months ago | |
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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
What are some alternatives?
FunScript - F# to JavaScript compiler with JQuery etc. mappings through a TypeScript type provider
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
Bridge.NET - :spades: C# to JavaScript compiler. Write modern mobile and web apps in C#. Run anywhere with Bridge.NET.
alpa - Training and serving large-scale neural networks with auto parallelization.
ClojureCLR - A port of Clojure to the CLR, part of the Clojure project
Distributed-Systems-Guide - Distributed Systems Guide
JSIL - CIL to Javascript Compiler
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
F# - Please file issues or pull requests here: https://github.com/dotnet/fsharp
tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators
VisualFSharp - The F# compiler, F# core library, F# language service, and F# tooling integration for Visual Studio
awesome-machine-learning-in-compilers - Must read research papers and links to tools and datasets that are related to using machine learning for compilers and systems optimisation