[ExecuTorch] Remove unneeded FFHT files

extension/llm/custom_ops/spinquant/third-party/FFHT/README.md

@@ -1,115 +1,5 @@
 # Fast Fast Hadamard Transform
 
-FFHT (Fast Fast Hadamard Transform) is a library that provides a heavily
-optimized C99 implementation of the Fast Hadamard Transform. FFHT also provides
-a thin Python wrapper that allows to perform the Fast Hadamard Transform on
-one-dimensional [NumPy](http://www.numpy.org/) arrays.
-
-The Hadamard Transform is a linear orthogonal map defined on real vectors whose
-length is a _power of two_. For the precise definition, see the
-[Uncyclopedia entry](https://en.wikipedia.org/wiki/Hadamard_transform). The
-Hadamard Transform has been recently used a lot in various machine learning
-and numerical algorithms.
-
-FFHT uses [AVX](https://en.wikipedia.org/wiki/Advanced_Vector_Extensions)
-to speed up the computation.
-
-The header file `fht.h` exports two functions: `int fht_float(float *buf, int
-log_n)` and `int fht_double(double *buf, int log_n)`. The
-only difference between them is the type of vector entries. So, in what follows,
-we describe how the version for floats `fht_float` works.
-
-The function `fht_float` takes two parameters:
-
-* `buf` is a pointer to the data on which one needs to perform the Fast
-Hadamard Transform.
-* `log_n` is the binary logarithm of the length of `buffer`.
-That is, the length is equal to `2^log_n`.
-
-The return value is -1 if the input is invalid and is zero otherwise.
-
-A header-only version of the library is provided in `fht_header_only.h`.
-
-In addition to the Fast Hadamard Transform, we provide two auxiliary programs:
-`test_float` and `test_double`, which are implemented in C99. The exhaustively
-test and benchmark the library.
-
-FFHT has been tested on 64-bit versions of Linux, OS X and Windows (the latter
-is via Cygwin).
-
-To install the Python package, run `python setup.py install`. The script
-`example.py` shows how to use FFHT from Python.
-
-## Benchmarks
-
-Below are the times for the Fast Hadamard Transform for vectors of
-various lengths. The benchmarks were run on a machine with Intel
-Core i7-6700K and 2133 MHz DDR4 RAM. We compare FFHT,
-[FFTW 3.3.6](http://fftw.org/), and
-[fht](https://github.com/nbarbey/fht) by
-[Nicolas Barbey](https://github.com/nbarbey).
-
-Let us stress that FFTW is a great versatile tool, and the authors of FFTW did
-not try to optimize the performace of the Fast Hadamard Transform. On the other
-hand, FFHT does one thing (the Fast Hadamard Transform), but does it extremely
-well.
-
-Vector size | FFHT (float) | FFHT (double) | FFTW 3.3.6 (float) | FFTW 3.3.6 (double) | fht (float) | fht (double)
-:---: | :---: | :---: | :---: | :---: | :---: | :---:
-2<sup>10</sup> | 0.31 us | 0.49 us | 4.48 us | 7.72 us | 17.4 us | 19.3 us
-2<sup>20</sup> | 0.68 ms | 1.39 ms | 8.81 ms | 17.07 ms | 29.8 ms | 35.0 ms
-2<sup>27</sup> | 0.22 s | 0.50 s | 2.08 s | 3.57 s | 6.89 s | 7.49 s
-
-## Troubleshooting
-
-For some versions of OS X the native `clang` compiler (that mimicks `gcc`) may
-not recognize the availability of AVX. A solution for this problem is to use a
-genuine `gcc` (say from [Homebrew](http://brew.sh/)) or to use `-march=corei7-avx`
-instead of `-march=native` for compiler flags.
-
-A symptom of the above happening is the undefined macros `__AVX__`.
-
-## Related Work
-
-FFHT has been created as a part of
-[FALCONN](https://github.com/falconn-lib/falconn): a library for similarity
-search over high-dimensional data. FALCONN's underlying algorithms are described
-and analyzed in the following research paper:
-
-> Alexandr Andoni, Piotr Indyk, Thijs Laarhoven, Ilya Razenshteyn and Ludwig
-> Schmidt, "Practical and Optimal LSH for Angular Distance", NIPS 2015, full
-> version available at [arXiv:1509.02897](http://arxiv.org/abs/1509.02897)
-
-This is the right paper to cite, if you use FFHT for your research projects.
-
-## Acknowledgments
-
-We thank Ruslan Savchenko for useful discussions.
-
-Thanks to:
-
-* Clement Canonne
-* Michal Forisek
-* Rati Gelashvili
-* Daniel Grier
-* Dhiraj Holden
-* Justin Holmgren
-* Aleksandar Ivanovic
-* Vladislav Isenbaev
-* Jacob Kogler
-* Ilya Kornakov
-* Anton Lapshin
-* Rio LaVigne
-* Oleg Martynov
-* Linar Mikeev
-* Cameron Musco
-* Sam Park
-* Sunoo Park
-* Amelia Perry
-* Andrew Sabisch
-* Abhishek Sarkar
-* Ruslan Savchenko
-* Vadim Semenov
-* Arman Yessenamanov
-
-for helping us with testing FFHT.
+This directory contains a fork of https://github.com/FALCONN-LIB/FFHT
+(License: https://github.com/FALCONN-LIB/FFHT/blob/master/LICENSE.md)
+focused on ARM64 NEON code generation.