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use std::any::TypeId;
use std::sync::Arc;
use num_complex::Complex;
use num_integer::div_ceil;
use num_traits::Zero;
use crate::common::{fft_error_inplace, fft_error_outofplace};
use crate::{array_utils, twiddles, FftDirection};
use crate::{Direction, Fft, FftNum, Length};
use super::CommonSimdData;
use super::{
avx_vector::{AvxArray, AvxArrayMut, AvxVector, AvxVector128, AvxVector256},
AvxNum,
};
/// Implementation of Bluestein's Algorithm
///
/// This algorithm computes an arbitrary-sized FFT in O(nlogn) time. It does this by converting this size n FFT into a
/// size M where M >= 2N - 1. The most obvious choice for M is a power of two, although that isn't a requirement.
///
/// It requires a large scratch space, so it's probably inconvenient to use as an inner FFT to other algorithms.
///
/// Bluestein's Algorithm is relatively expensive compared to other FFT algorithms. Benchmarking shows that it is up to
/// an order of magnitude slower than similar composite sizes.
pub struct BluesteinsAvx<A: AvxNum, T> {
inner_fft_multiplier: Box<[A::VectorType]>,
common_data: CommonSimdData<T, A::VectorType>,
_phantom: std::marker::PhantomData<T>,
}
boilerplate_avx_fft_commondata!(BluesteinsAvx);
impl<A: AvxNum, T: FftNum> BluesteinsAvx<A, T> {
/// Pairwise multiply the complex numbers in `left` with the complex numbers in `right`.
/// This is exactly the same as `mul_complex` in `AvxVector`, but this implementation also conjugates the `left` input before multiplying
#[inline(always)]
unsafe fn mul_complex_conjugated<V: AvxVector>(left: V, right: V) -> V {
// Extract the real and imaginary components from left into 2 separate registers
let (left_real, left_imag) = V::duplicate_complex_components(left);
// create a shuffled version of right where the imaginary values are swapped with the reals
let right_shuffled = V::swap_complex_components(right);
// multiply our duplicated imaginary left vector by our shuffled right vector. that will give us the right side of the traditional complex multiplication formula
let output_right = V::mul(left_imag, right_shuffled);
// use a FMA instruction to multiply together left side of the complex multiplication formula, then alternatingly add and subtract the left side from the right
// By using subadd instead of addsub, we can conjugate the left side for free.
V::fmsubadd(left_real, right, output_right)
}
/// Preallocates necessary arrays and precomputes necessary data to efficiently compute the FFT
/// Returns Ok() if this machine has the required instruction sets, Err() if some instruction sets are missing
#[inline]
pub fn new(len: usize, inner_fft: Arc<dyn Fft<T>>) -> Result<Self, ()> {
// Internal sanity check: Make sure that A == T.
// This struct has two generic parameters A and T, but they must always be the same, and are only kept separate to help work around the lack of specialization.
// It would be cool if we could do this as a static_assert instead
let id_a = TypeId::of::<A>();
let id_t = TypeId::of::<T>();
assert_eq!(id_a, id_t);
let has_avx = is_x86_feature_detected!("avx");
let has_fma = is_x86_feature_detected!("fma");
if has_avx && has_fma {
// Safety: new_with_avx requires the "avx" feature set. Since we know it's present, we're safe
Ok(unsafe { Self::new_with_avx(len, inner_fft) })
} else {
Err(())
}
}
#[target_feature(enable = "avx")]
unsafe fn new_with_avx(len: usize, inner_fft: Arc<dyn Fft<T>>) -> Self {
let inner_fft_len = inner_fft.len();
assert!(len * 2 - 1 <= inner_fft_len, "Bluestein's algorithm requires inner_fft.len() >= self.len() * 2 - 1. Expected >= {}, got {}", len * 2 - 1, inner_fft_len);
assert_eq!(inner_fft_len % A::VectorType::COMPLEX_PER_VECTOR, 0, "BluesteinsAvx requires its inner_fft.len() to be a multiple of {} (IE the number of complex numbers in a single vector) inner_fft.len() = {}", A::VectorType::COMPLEX_PER_VECTOR, inner_fft_len);
// when computing FFTs, we're going to run our inner multiply pairwise by some precomputed data, then run an inverse inner FFT. We need to precompute that inner data here
let inner_fft_scale = A::one() / A::from_usize(inner_fft_len).unwrap();
let direction = inner_fft.fft_direction();
// Compute twiddle factors that we'll run our inner FFT on
let mut inner_fft_input = vec![Complex::zero(); inner_fft_len];
twiddles::fill_bluesteins_twiddles(
&mut inner_fft_input[..len],
direction.opposite_direction(),
);
// Scale the computed twiddles and copy them to the end of the array
inner_fft_input[0] = inner_fft_input[0] * inner_fft_scale;
for i in 1..len {
let twiddle = inner_fft_input[i] * inner_fft_scale;
inner_fft_input[i] = twiddle;
inner_fft_input[inner_fft_len - i] = twiddle;
}
//Compute the inner fft
let mut inner_fft_scratch = vec![Complex::zero(); inner_fft.get_inplace_scratch_len()];
{
// Specialization workaround: See the comments in FftPlannerAvx::new() for why these calls to array_utils::workaround_transmute are necessary
let transmuted_input: &mut [Complex<T>] =
array_utils::workaround_transmute_mut(&mut inner_fft_input);
inner_fft.process_with_scratch(transmuted_input, &mut inner_fft_scratch);
}
// When computing the FFT, we'll want this array to be pre-conjugated, so conjugate it now
let conjugation_mask =
AvxVector256::broadcast_complex_elements(Complex::new(A::zero(), -A::zero()));
let inner_fft_multiplier = inner_fft_input
.chunks_exact(A::VectorType::COMPLEX_PER_VECTOR)
.map(|chunk| {
let chunk_vector = chunk.load_complex(0);
AvxVector::xor(chunk_vector, conjugation_mask) // compute our conjugation by xoring our data with a precomputed mask
})
.collect::<Vec<_>>()
.into_boxed_slice();
// also compute some more mundane twiddle factors to start and end with.
let chunk_count = div_ceil(len, A::VectorType::COMPLEX_PER_VECTOR);
let twiddle_count = chunk_count * A::VectorType::COMPLEX_PER_VECTOR;
let mut twiddles_scalar: Vec<Complex<A>> = vec![Complex::zero(); twiddle_count];
twiddles::fill_bluesteins_twiddles(&mut twiddles_scalar[..len], direction);
// We have the twiddles in scalar format, last step is to copy them over to AVX vectors
let twiddles: Vec<_> = twiddles_scalar
.chunks_exact(A::VectorType::COMPLEX_PER_VECTOR)
.map(|chunk| chunk.load_complex(0))
.collect();
let required_scratch = inner_fft_input.len() + inner_fft_scratch.len();
Self {
inner_fft_multiplier,
common_data: CommonSimdData {
inner_fft,
twiddles: twiddles.into_boxed_slice(),
len,
inplace_scratch_len: required_scratch,
outofplace_scratch_len: required_scratch,
direction,
},
_phantom: std::marker::PhantomData,
}
}
// Do the necessary setup for bluestein's algorithm: copy the data to the inner buffers, apply some twiddle factors, zero out the rest of the inner buffer
#[target_feature(enable = "avx", enable = "fma")]
unsafe fn prepare_bluesteins(&self, input: &[Complex<A>], inner_fft_buffer: &mut [Complex<A>]) {
let chunk_count = self.common_data.twiddles.len() - 1;
let remainder = self.len() - chunk_count * A::VectorType::COMPLEX_PER_VECTOR;
// Copy the buffer into our inner FFT input, applying twiddle factors as we go. the buffer will only fill part of the FFT input, so zero fill the rest
for (i, twiddle) in self.common_data.twiddles[..chunk_count].iter().enumerate() {
let index = i * A::VectorType::COMPLEX_PER_VECTOR;
let input_vector = input.load_complex(index);
let product_vector = AvxVector::mul_complex(input_vector, *twiddle);
inner_fft_buffer.store_complex(product_vector, index);
}
// the buffer will almost certainly have a remainder. it's so likely, in fact, that we're just going to apply a remainder unconditionally
// it uses a couple more instructions in the rare case when our FFT size is a multiple of 4, but saves instructions when it's not
{
let remainder_twiddle = self.common_data.twiddles[chunk_count];
let remainder_index = chunk_count * A::VectorType::COMPLEX_PER_VECTOR;
let remainder_data = match remainder {
1 => input.load_partial1_complex(remainder_index).zero_extend(),
2 => {
if A::VectorType::COMPLEX_PER_VECTOR == 2 {
input.load_complex(remainder_index)
} else {
input.load_partial2_complex(remainder_index).zero_extend()
}
}
3 => input.load_partial3_complex(remainder_index),
4 => input.load_complex(remainder_index),
_ => unreachable!(),
};
let twiddled_remainder = AvxVector::mul_complex(remainder_twiddle, remainder_data);
inner_fft_buffer.store_complex(twiddled_remainder, remainder_index);
}
// zero fill the rest of the `inner` array
let zerofill_start = chunk_count + 1;
for i in zerofill_start..(inner_fft_buffer.len() / A::VectorType::COMPLEX_PER_VECTOR) {
let index = i * A::VectorType::COMPLEX_PER_VECTOR;
inner_fft_buffer.store_complex(AvxVector::zero(), index);
}
}
// Do the necessary finalization for bluestein's algorithm: Conjugate the inner FFT buffer, apply some twiddle factors, zero out the rest of the inner buffer
#[target_feature(enable = "avx", enable = "fma")]
unsafe fn finalize_bluesteins(
&self,
inner_fft_buffer: &[Complex<A>],
output: &mut [Complex<A>],
) {
let chunk_count = self.common_data.twiddles.len() - 1;
let remainder = self.len() - chunk_count * A::VectorType::COMPLEX_PER_VECTOR;
// copy our data to the output, applying twiddle factors again as we go. Also conjugate inner_fft_buffer to complete the inverse FFT
for (i, twiddle) in self.common_data.twiddles[..chunk_count].iter().enumerate() {
let index = i * A::VectorType::COMPLEX_PER_VECTOR;
let inner_vector = inner_fft_buffer.load_complex(index);
let product_vector = Self::mul_complex_conjugated(inner_vector, *twiddle);
output.store_complex(product_vector, index);
}
// again, unconditionally apply a remainder
{
let remainder_twiddle = self.common_data.twiddles[chunk_count];
let remainder_index = chunk_count * A::VectorType::COMPLEX_PER_VECTOR;
let inner_vector = inner_fft_buffer.load_complex(remainder_index);
let product_vector = Self::mul_complex_conjugated(inner_vector, remainder_twiddle);
match remainder {
1 => output.store_partial1_complex(product_vector.lo(), remainder_index),
2 => {
if A::VectorType::COMPLEX_PER_VECTOR == 2 {
output.store_complex(product_vector, remainder_index)
} else {
output.store_partial2_complex(product_vector.lo(), remainder_index)
}
}
3 => output.store_partial3_complex(product_vector, remainder_index),
4 => output.store_complex(product_vector, remainder_index),
_ => unreachable!(),
};
}
}
// compute buffer[i] = buffer[i].conj() * multiplier[i] pairwise complex multiplication for each element.
#[target_feature(enable = "avx", enable = "fma")]
unsafe fn pairwise_complex_multiply_conjugated(
buffer: &mut [Complex<A>],
multiplier: &[A::VectorType],
) {
for (i, right) in multiplier.iter().enumerate() {
let left = buffer.load_complex(i * A::VectorType::COMPLEX_PER_VECTOR);
// Do a complex multiplication between `left` and `right`
let product = Self::mul_complex_conjugated(left, *right);
// Store the result
buffer.store_complex(product, i * A::VectorType::COMPLEX_PER_VECTOR);
}
}
fn perform_fft_inplace(&self, buffer: &mut [Complex<T>], scratch: &mut [Complex<T>]) {
let (inner_input, inner_scratch) = scratch
.split_at_mut(self.inner_fft_multiplier.len() * A::VectorType::COMPLEX_PER_VECTOR);
// do the necessary setup for bluestein's algorithm: copy the data to the inner buffers, apply some twiddle factors, zero out the rest of the inner buffer
unsafe {
// Specialization workaround: See the comments in FftPlannerAvx::new() for why these calls to array_utils::workaround_transmute are necessary
let transmuted_buffer: &mut [Complex<A>] =
array_utils::workaround_transmute_mut(buffer);
let transmuted_inner_input: &mut [Complex<A>] =
array_utils::workaround_transmute_mut(inner_input);
self.prepare_bluesteins(transmuted_buffer, transmuted_inner_input);
}
// run our inner forward FFT
self.common_data
.inner_fft
.process_with_scratch(inner_input, inner_scratch);
// Multiply our inner FFT output by our precomputed data. Then, conjugate the result to set up for an inverse FFT.
// We can conjugate the result of multiplication by conjugating both inputs. We pre-conjugated the multiplier array,
// so we just need to conjugate inner_input, which the pairwise_complex_multiply_conjugated function will handle
unsafe {
// Specialization workaround: See the comments in FftPlannerAvx::new() for why these calls to array_utils::workaround_transmute are necessary
let transmuted_inner_input: &mut [Complex<A>] =
array_utils::workaround_transmute_mut(inner_input);
Self::pairwise_complex_multiply_conjugated(
transmuted_inner_input,
&self.inner_fft_multiplier,
)
};
// inverse FFT. we're computing a forward but we're massaging it into an inverse by conjugating the inputs and outputs
self.common_data
.inner_fft
.process_with_scratch(inner_input, inner_scratch);
// finalize the result
unsafe {
// Specialization workaround: See the comments in FftPlannerAvx::new() for why these calls to array_utils::workaround_transmute are necessary
let transmuted_buffer: &mut [Complex<A>] =
array_utils::workaround_transmute_mut(buffer);
let transmuted_inner_input: &mut [Complex<A>] =
array_utils::workaround_transmute_mut(inner_input);
self.finalize_bluesteins(transmuted_inner_input, transmuted_buffer);
}
}
fn perform_fft_out_of_place(
&self,
input: &[Complex<T>],
output: &mut [Complex<T>],
scratch: &mut [Complex<T>],
) {
let (inner_input, inner_scratch) = scratch
.split_at_mut(self.inner_fft_multiplier.len() * A::VectorType::COMPLEX_PER_VECTOR);
// do the necessary setup for bluestein's algorithm: copy the data to the inner buffers, apply some twiddle factors, zero out the rest of the inner buffer
unsafe {
// Specialization workaround: See the comments in FftPlannerAvx::new() for why these calls to array_utils::workaround_transmute are necessary
let transmuted_input: &[Complex<A>] = array_utils::workaround_transmute(input);
let transmuted_inner_input: &mut [Complex<A>] =
array_utils::workaround_transmute_mut(inner_input);
self.prepare_bluesteins(transmuted_input, transmuted_inner_input)
}
// run our inner forward FFT
self.common_data
.inner_fft
.process_with_scratch(inner_input, inner_scratch);
// Multiply our inner FFT output by our precomputed data. Then, conjugate the result to set up for an inverse FFT.
// We can conjugate the result of multiplication by conjugating both inputs. We pre-conjugated the multiplier array,
// so we just need to conjugate inner_input, which the pairwise_complex_multiply_conjugated function will handle
unsafe {
// Specialization workaround: See the comments in FftPlannerAvx::new() for why these calls to array_utils::workaround_transmute are necessary
let transmuted_inner_input: &mut [Complex<A>] =
array_utils::workaround_transmute_mut(inner_input);
Self::pairwise_complex_multiply_conjugated(
transmuted_inner_input,
&self.inner_fft_multiplier,
)
};
// inverse FFT. we're computing a forward but we're massaging it into an inverse by conjugating the inputs and outputs
self.common_data
.inner_fft
.process_with_scratch(inner_input, inner_scratch);
// finalize the result
unsafe {
// Specialization workaround: See the comments in FftPlannerAvx::new() for why these calls to array_utils::workaround_transmute are necessary
let transmuted_output: &mut [Complex<A>] =
array_utils::workaround_transmute_mut(output);
let transmuted_inner_input: &mut [Complex<A>] =
array_utils::workaround_transmute_mut(inner_input);
self.finalize_bluesteins(transmuted_inner_input, transmuted_output)
}
}
}
#[cfg(test)]
mod unit_tests {
use num_traits::Float;
use rand::distributions::uniform::SampleUniform;
use super::*;
use crate::algorithm::Dft;
use crate::test_utils::check_fft_algorithm;
use std::sync::Arc;
#[test]
fn test_bluesteins_avx_f32() {
for len in 2..16 {
// for this len, compute the range of inner FFT lengths we'll use.
// Bluesteins AVX f32 requires a multiple of 4 for the inner FFT, so we need to go up to the next multiple of 4 from the minimum
let minimum_inner: usize = len * 2 - 1;
let remainder = minimum_inner % 4;
// remainder will never be 0, because "n * 2 - 1" is guaranteed to be odd. so we can just subtract the remainder and add 4.
let next_multiple_of_4 = minimum_inner - remainder + 4;
let maximum_inner = minimum_inner.checked_next_power_of_two().unwrap() + 1;
// start at the next multiple of 4, and increment by 4 unti lwe get to the next power of 2.
for inner_len in (next_multiple_of_4..maximum_inner).step_by(4) {
test_bluesteins_avx_with_length::<f32>(len, inner_len, FftDirection::Forward);
test_bluesteins_avx_with_length::<f32>(len, inner_len, FftDirection::Inverse);
}
}
}
#[test]
fn test_bluesteins_avx_f64() {
for len in 2..16 {
// for this len, compute the range of inner FFT lengths we'll use.
// Bluesteins AVX f64 requires a multiple of 2 for the inner FFT, so we need to go up to the next multiple of 2 from the minimum
let minimum_inner: usize = len * 2 - 1;
let remainder = minimum_inner % 2;
let next_multiple_of_2 = minimum_inner + remainder;
let maximum_inner = minimum_inner.checked_next_power_of_two().unwrap() + 1;
// start at the next multiple of 2, and increment by 2 unti lwe get to the next power of 2.
for inner_len in (next_multiple_of_2..maximum_inner).step_by(2) {
test_bluesteins_avx_with_length::<f64>(len, inner_len, FftDirection::Forward);
test_bluesteins_avx_with_length::<f64>(len, inner_len, FftDirection::Inverse);
}
}
}
fn test_bluesteins_avx_with_length<T: AvxNum + Float + SampleUniform>(
len: usize,
inner_len: usize,
direction: FftDirection,
) {
let inner_fft = Arc::new(Dft::new(inner_len, direction));
let fft: BluesteinsAvx<T, T> = BluesteinsAvx::new(len, inner_fft).expect(
"Can't run test because this machine doesn't have the required instruction sets",
);
check_fft_algorithm::<T>(&fft, len, direction);
}
}
Sindbad File Manager Version 1.0, Coded By Sindbad EG ~ The Terrorists