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d394af7f4d
Notable changes: - StackReq methods no longer returns Result<StackReq, SizeOverflow> instead, StackReq contains the invalid state. Now, its when we create a PodBuffer that we can check/catch if the size req is invalid by catching errors when calling `PodBuffer::try_new`. Its also possible to manually check that `stack_req != StackReq::OVERFLOW` - GlobalaPodBuffer is now PodBuffer
85 lines
3.4 KiB
Markdown
85 lines
3.4 KiB
Markdown
tfhe-fft is a pure Rust high performance fast Fourier transform library
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that processes vectors of sizes that are powers of two. It was made to be used
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as a backend in Zama's [TFHE-rs](https://docs.zama.ai/tfhe-rs) library.
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This library provides two FFT modules:
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- The ordered module FFT applies a forward/inverse FFT that takes its input in standard
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order, and outputs the result in standard order. For more detail on what the FFT
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computes, check the ordered module-level documentation.
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- The unordered module FFT applies a forward FFT that takes its input in standard order,
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and outputs the result in a certain permuted order that may depend on the FFT plan. On the
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other hand, the inverse FFT takes its input in that same permuted order and outputs its result
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in standard order. This is useful for cases where the order of the coefficients in the
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Fourier domain is not important. An example is using the Fourier transform for vector
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convolution. The only operations that are performed in the Fourier domain are elementwise, and
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so the order of the coefficients does not affect the results.
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Additionally, an optional 128-bit negacyclic FFT module is provided.
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## Features
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- `std` (default): This enables runtime arch detection for accelerated SIMD
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instructions, and an FFT plan that measures the various implementations to
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choose the fastest one at runtime.
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- `fft128`: This flag provides access to the 128-bit FFT, which is accessible in the
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[`fft128`] module.
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- `avx512` (default): This enables AVX512F instructions on CPUs that support them to further
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speed up the FFT.
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- `serde`: This enables serialization and deserialization functions for the
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unordered plan. These allow for data in the Fourier domain to be serialized
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from the permuted order to the standard order, and deserialized from the
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standard order to the permuted order. This is needed since the inverse
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transform must be used with the same plan that computed/deserialized the
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forward transform (or more specifically, a plan with the same internal base
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FFT size).
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## Example
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```rust
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use tfhe_fft::c64;
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use tfhe_fft::ordered::{Method, Plan};
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use dyn_stack::{PodBuffer, PodStack};
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use num_complex::ComplexFloat;
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use std::time::Duration;
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fn main() {
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const N: usize = 4;
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let plan = Plan::new(4, Method::Measure(Duration::from_millis(10)));
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let mut scratch_memory = PodBuffer::try_new(plan.fft_scratch()).unwrap();
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let stack = PodStack::new(&mut scratch_memory);
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let data = [
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c64::new(1.0, 0.0),
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c64::new(2.0, 0.0),
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c64::new(3.0, 0.0),
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c64::new(4.0, 0.0),
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];
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let mut transformed_fwd = data;
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plan.fwd(&mut transformed_fwd, stack);
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let mut transformed_inv = transformed_fwd;
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plan.inv(&mut transformed_inv, stack);
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for (actual, expected) in transformed_inv.iter().map(|z| z / N as f64).zip(data) {
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assert!((expected - actual).abs() < 1e-9);
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}
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}
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```
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## Links
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- [Zama](https://www.zama.ai/)
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- [TFHE-rs Sources](https://github.com/zama-ai/tfhe-rs)
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## License
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This software is distributed under the BSD-3-Clause-Clear license with an
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exemption that gives rights to use our patents for research, evaluation and
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prototyping purposes, as well as for your personal projects.
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If you want to use tfhe-fft in a commercial product however, you will need to
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purchase a separate commercial licence.
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If you have any questions, please contact us at `hello@zama.ai.`
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