rustfft/avx/

avx_mixed_radix.rs

use std::any::TypeId;
use std::sync::Arc;

use num_complex::Complex;
use num_integer::div_ceil;

use crate::array_utils;
use crate::{Direction, Fft, FftDirection, FftNum, Length};

use super::{AvxNum, CommonSimdData};

use super::avx_vector;
use super::avx_vector::{AvxArray, AvxArrayMut, AvxVector, AvxVector128, AvxVector256, Rotation90};

macro_rules! boilerplate_mixedradix {
    () => {
        /// 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(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(inner_fft) })
            } else {
                Err(())
            }
        }

        #[target_feature(enable = "avx", enable = "fma")]
        unsafe fn perform_fft_inplace(
            &self,
            buffer: &mut [Complex<T>],
            scratch: &mut [Complex<T>],
        ) {
            // Perform the column FFTs
            // Safety: self.perform_column_butterflies() requres the "avx" and "fma" instruction sets, and we return Err() in our constructor if the instructions aren't available
            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);

                self.perform_column_butterflies(transmuted_buffer)
            }

            // process the row FFTs
            let (scratch, inner_scratch) = scratch.split_at_mut(self.len());
            self.common_data.inner_fft.process_outofplace_with_scratch(
                buffer,
                scratch,
                inner_scratch,
            );

            // Transpose
            // Safety: self.transpose() requres the "avx" instruction set, and we return Err() in our constructor if the instructions aren't available
            unsafe {
                // Specialization workaround: See the comments in FftPlannerAvx::new() for why these calls to array_utils::workaround_transmute are necessary
                let transmuted_scratch: &mut [Complex<A>] =
                    array_utils::workaround_transmute_mut(scratch);
                let transmuted_buffer: &mut [Complex<A>] =
                    array_utils::workaround_transmute_mut(buffer);

                self.transpose(transmuted_scratch, transmuted_buffer)
            }
        }

        #[target_feature(enable = "avx", enable = "fma")]
        unsafe fn perform_fft_immut(
            &self,
            input: &[Complex<T>],
            output: &mut [Complex<T>],
            scratch: &mut [Complex<T>],
        ) {
            // Perform the column FFTs
            let (scratch, inner_scratch) = scratch.split_at_mut(input.len());
            {
                // 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_output: &mut [Complex<A>] =
                    array_utils::workaround_transmute_mut(scratch);

                self.perform_column_butterflies_immut(transmuted_input, transmuted_output);
            }

            // process the row FFTs. If extra scratch was provided, pass it in. Otherwise, use the output.
            self.common_data
                .inner_fft
                .process_with_scratch(scratch, inner_scratch);

            // Transpose
            {
                // Specialization workaround: See the comments in FftPlannerAvx::new() for why these calls to array_utils::workaround_transmute are necessary
                let transmuted_input: &mut [Complex<A>] =
                    array_utils::workaround_transmute_mut(scratch);
                let transmuted_output: &mut [Complex<A>] =
                    array_utils::workaround_transmute_mut(output);

                self.transpose(transmuted_input, transmuted_output)
            }
        }

        #[target_feature(enable = "avx", enable = "fma")]
        unsafe fn perform_fft_out_of_place(
            &self,
            input: &mut [Complex<T>],
            output: &mut [Complex<T>],
            scratch: &mut [Complex<T>],
        ) {
            // Perform the column FFTs
            {
                // Specialization workaround: See the comments in FftPlannerAvx::new() for why these calls to array_utils::workaround_transmute are necessary
                let transmuted_input: &mut [Complex<A>] =
                    array_utils::workaround_transmute_mut(input);
                self.perform_column_butterflies(transmuted_input);
            }

            // process the row FFTs. If extra scratch was provided, pass it in. Otherwise, use the output.
            let inner_scratch = if scratch.len() > 0 {
                scratch
            } else {
                &mut output[..]
            };
            self.common_data
                .inner_fft
                .process_with_scratch(input, inner_scratch);

            // Transpose
            {
                // Specialization workaround: See the comments in FftPlannerAvx::new() for why these calls to array_utils::workaround_transmute are necessary
                let transmuted_input: &mut [Complex<A>] =
                    array_utils::workaround_transmute_mut(input);
                let transmuted_output: &mut [Complex<A>] =
                    array_utils::workaround_transmute_mut(output);

                self.transpose(transmuted_input, transmuted_output)
            }
        }
    };
}

macro_rules! mixedradix_gen_data {
    ($row_count: expr, $inner_fft:expr) => {{
        // Important constants
        const ROW_COUNT : usize = $row_count;
        const TWIDDLES_PER_COLUMN : usize = ROW_COUNT - 1;

        // derive some info from our inner FFT
        let direction = $inner_fft.fft_direction();
        let len_per_row = $inner_fft.len();
        let len = len_per_row * ROW_COUNT;

        // We're going to process each row of the FFT one AVX register at a time. We need to know how many AVX registers each row can fit,
        // and if the last register in each row going to have partial data (ie a remainder)
        let quotient = len_per_row / A::VectorType::COMPLEX_PER_VECTOR;
        let remainder = len_per_row % A::VectorType::COMPLEX_PER_VECTOR;

        // Compute our twiddle factors, and arrange them so that we can access them one column of AVX vectors at a time
        let num_twiddle_columns = quotient + div_ceil(remainder, A::VectorType::COMPLEX_PER_VECTOR);
        let mut twiddles = Vec::with_capacity(num_twiddle_columns * TWIDDLES_PER_COLUMN);
        for x in 0..num_twiddle_columns {
            for y in 1..ROW_COUNT {
                twiddles.push(AvxVector::make_mixedradix_twiddle_chunk(x * A::VectorType::COMPLEX_PER_VECTOR, y, len, direction));
            }
        }

        let inner_outofplace_scratch = $inner_fft.get_outofplace_scratch_len();
        let inner_inplace_scratch = $inner_fft.get_inplace_scratch_len();
        let immut_scratch_len = len + $inner_fft.get_inplace_scratch_len();

        CommonSimdData {
            twiddles: twiddles.into_boxed_slice(),
            inplace_scratch_len: len + inner_outofplace_scratch,
            outofplace_scratch_len: if inner_inplace_scratch > len { inner_inplace_scratch } else { 0 },
            immut_scratch_len,
            inner_fft: $inner_fft,
            len,
            direction,
        }
    }}
}

macro_rules! mixedradix_column_butterflies {
    ($row_count: expr, $butterfly_fn: expr, $butterfly_fn_lo: expr) => {
        #[target_feature(enable = "avx", enable = "fma")]
        unsafe fn perform_column_butterflies_immut(
            &self,
            input: impl AvxArray<A>,
            mut buffer: impl AvxArrayMut<A>,
        ) {
            // How many rows this FFT has, ie 2 for 2xn, 4 for 4xn, etc
            const ROW_COUNT: usize = $row_count;
            const TWIDDLES_PER_COLUMN: usize = ROW_COUNT - 1;

            let len_per_row = self.len() / ROW_COUNT;
            let chunk_count = len_per_row / A::VectorType::COMPLEX_PER_VECTOR;

            // process the column FFTs
            for (c, twiddle_chunk) in self
                .common_data
                .twiddles
                .chunks_exact(TWIDDLES_PER_COLUMN)
                .take(chunk_count)
                .enumerate()
            {
                let index_base = c * A::VectorType::COMPLEX_PER_VECTOR;

                // Load columns from the input into registers
                let mut columns = [AvxVector::zero(); ROW_COUNT];
                for i in 0..ROW_COUNT {
                    columns[i] = input.load_complex(index_base + len_per_row * i);
                }

                // apply our butterfly function down the columns
                let output = $butterfly_fn(columns, self);

                // always write the first row directly back without twiddles
                buffer.store_complex(output[0], index_base);

                // for every other row, apply twiddle factors and then write back to memory
                for i in 1..ROW_COUNT {
                    let twiddle = twiddle_chunk[i - 1];
                    let output = AvxVector::mul_complex(twiddle, output[i]);
                    buffer.store_complex(output, index_base + len_per_row * i);
                }
            }

            // finally, we might have a remainder chunk
            // Normally, we can fit COMPLEX_PER_VECTOR complex numbers into an AVX register, but we only have `partial_remainder` columns left, so we need special logic to handle these final columns
            let partial_remainder = len_per_row % A::VectorType::COMPLEX_PER_VECTOR;
            if partial_remainder > 0 {
                let partial_remainder_base = chunk_count * A::VectorType::COMPLEX_PER_VECTOR;
                let partial_remainder_twiddle_base =
                    self.common_data.twiddles.len() - TWIDDLES_PER_COLUMN;
                let final_twiddle_chunk =
                    &self.common_data.twiddles[partial_remainder_twiddle_base..];

                if partial_remainder > 2 {
                    // Load 3 columns into full AVX vectors to process our remainder
                    let mut columns = [AvxVector::zero(); ROW_COUNT];
                    for i in 0..ROW_COUNT {
                        columns[i] =
                            input.load_partial3_complex(partial_remainder_base + len_per_row * i);
                    }

                    // apply our butterfly function down the columns
                    let mid = $butterfly_fn(columns, self);

                    // always write the first row without twiddles
                    buffer.store_partial3_complex(mid[0], partial_remainder_base);

                    // for the remaining rows, apply twiddle factors and then write back to memory
                    for i in 1..ROW_COUNT {
                        let twiddle = final_twiddle_chunk[i - 1];
                        let output = AvxVector::mul_complex(twiddle, mid[i]);
                        buffer.store_partial3_complex(
                            output,
                            partial_remainder_base + len_per_row * i,
                        );
                    }
                } else {
                    // Load 1 or 2 columns into half vectors to process our remainder. Thankfully, the compiler is smart enough to eliminate this branch on f64, since the partial remainder can only possibly be 1
                    let mut columns = [AvxVector::zero(); ROW_COUNT];
                    if partial_remainder == 1 {
                        for i in 0..ROW_COUNT {
                            columns[i] = input
                                .load_partial1_complex(partial_remainder_base + len_per_row * i);
                        }
                    } else {
                        for i in 0..ROW_COUNT {
                            columns[i] = input
                                .load_partial2_complex(partial_remainder_base + len_per_row * i);
                        }
                    }

                    // apply our butterfly function down the columns
                    let mut mid = $butterfly_fn_lo(columns, self);

                    // apply twiddle factors
                    for i in 1..ROW_COUNT {
                        mid[i] = AvxVector::mul_complex(final_twiddle_chunk[i - 1].lo(), mid[i]);
                    }

                    // store output
                    if partial_remainder == 1 {
                        for i in 0..ROW_COUNT {
                            buffer.store_partial1_complex(
                                mid[i],
                                partial_remainder_base + len_per_row * i,
                            );
                        }
                    } else {
                        for i in 0..ROW_COUNT {
                            buffer.store_partial2_complex(
                                mid[i],
                                partial_remainder_base + len_per_row * i,
                            );
                        }
                    }
                }
            }
        }
        #[target_feature(enable = "avx", enable = "fma")]
        unsafe fn perform_column_butterflies(&self, mut buffer: impl AvxArrayMut<A>) {
            // How many rows this FFT has, ie 2 for 2xn, 4 for 4xn, etc
            const ROW_COUNT: usize = $row_count;
            const TWIDDLES_PER_COLUMN: usize = ROW_COUNT - 1;

            let len_per_row = self.len() / ROW_COUNT;
            let chunk_count = len_per_row / A::VectorType::COMPLEX_PER_VECTOR;

            // process the column FFTs
            for (c, twiddle_chunk) in self
                .common_data
                .twiddles
                .chunks_exact(TWIDDLES_PER_COLUMN)
                .take(chunk_count)
                .enumerate()
            {
                let index_base = c * A::VectorType::COMPLEX_PER_VECTOR;

                // Load columns from the buffer into registers
                let mut columns = [AvxVector::zero(); ROW_COUNT];
                for i in 0..ROW_COUNT {
                    columns[i] = buffer.load_complex(index_base + len_per_row * i);
                }

                // apply our butterfly function down the columns
                let output = $butterfly_fn(columns, self);

                // always write the first row directly back without twiddles
                buffer.store_complex(output[0], index_base);

                // for every other row, apply twiddle factors and then write back to memory
                for i in 1..ROW_COUNT {
                    let twiddle = twiddle_chunk[i - 1];
                    let output = AvxVector::mul_complex(twiddle, output[i]);
                    buffer.store_complex(output, index_base + len_per_row * i);
                }
            }

            // finally, we might have a remainder chunk
            // Normally, we can fit COMPLEX_PER_VECTOR complex numbers into an AVX register, but we only have `partial_remainder` columns left, so we need special logic to handle these final columns
            let partial_remainder = len_per_row % A::VectorType::COMPLEX_PER_VECTOR;
            if partial_remainder > 0 {
                let partial_remainder_base = chunk_count * A::VectorType::COMPLEX_PER_VECTOR;
                let partial_remainder_twiddle_base =
                    self.common_data.twiddles.len() - TWIDDLES_PER_COLUMN;
                let final_twiddle_chunk =
                    &self.common_data.twiddles[partial_remainder_twiddle_base..];

                if partial_remainder > 2 {
                    // Load 3 columns into full AVX vectors to process our remainder
                    let mut columns = [AvxVector::zero(); ROW_COUNT];
                    for i in 0..ROW_COUNT {
                        columns[i] =
                            buffer.load_partial3_complex(partial_remainder_base + len_per_row * i);
                    }

                    // apply our butterfly function down the columns
                    let mid = $butterfly_fn(columns, self);

                    // always write the first row without twiddles
                    buffer.store_partial3_complex(mid[0], partial_remainder_base);

                    // for the remaining rows, apply twiddle factors and then write back to memory
                    for i in 1..ROW_COUNT {
                        let twiddle = final_twiddle_chunk[i - 1];
                        let output = AvxVector::mul_complex(twiddle, mid[i]);
                        buffer.store_partial3_complex(
                            output,
                            partial_remainder_base + len_per_row * i,
                        );
                    }
                } else {
                    // Load 1 or 2 columns into half vectors to process our remainder. Thankfully, the compiler is smart enough to eliminate this branch on f64, since the partial remainder can only possibly be 1
                    let mut columns = [AvxVector::zero(); ROW_COUNT];
                    if partial_remainder == 1 {
                        for i in 0..ROW_COUNT {
                            columns[i] = buffer
                                .load_partial1_complex(partial_remainder_base + len_per_row * i);
                        }
                    } else {
                        for i in 0..ROW_COUNT {
                            columns[i] = buffer
                                .load_partial2_complex(partial_remainder_base + len_per_row * i);
                        }
                    }

                    // apply our butterfly function down the columns
                    let mut mid = $butterfly_fn_lo(columns, self);

                    // apply twiddle factors
                    for i in 1..ROW_COUNT {
                        mid[i] = AvxVector::mul_complex(final_twiddle_chunk[i - 1].lo(), mid[i]);
                    }

                    // store output
                    if partial_remainder == 1 {
                        for i in 0..ROW_COUNT {
                            buffer.store_partial1_complex(
                                mid[i],
                                partial_remainder_base + len_per_row * i,
                            );
                        }
                    } else {
                        for i in 0..ROW_COUNT {
                            buffer.store_partial2_complex(
                                mid[i],
                                partial_remainder_base + len_per_row * i,
                            );
                        }
                    }
                }
            }
        }
    };
}

macro_rules! mixedradix_transpose{
    ($row_count: expr, $transpose_fn: path, $transpose_fn_lo: path, $($unroll_workaround_index:expr);*, $($remainder3_unroll_workaround_index:expr);*) => (

    // Transpose the input (treated as a nxc array) into the output (as a cxn array)
    #[target_feature(enable = "avx")]
    unsafe fn transpose(&self, input: &[Complex<A>], mut output: &mut [Complex<A>]) {
        const ROW_COUNT : usize = $row_count;

        let len_per_row = self.len() / ROW_COUNT;
        let chunk_count = len_per_row / A::VectorType::COMPLEX_PER_VECTOR;

        // transpose the scratch as a nx2 array into the buffer as an 2xn array
        for c in 0..chunk_count {
            let input_index_base = c*A::VectorType::COMPLEX_PER_VECTOR;
            let output_index_base = input_index_base * ROW_COUNT;

            // Load rows from the input into registers
            let mut rows : [A::VectorType; ROW_COUNT] = [AvxVector::zero(); ROW_COUNT];
            for i in 0..ROW_COUNT {
                rows[i] = input.load_complex(input_index_base + len_per_row*i);
            }

            // transpose the rows to the columns
            let transposed = $transpose_fn(rows);

            // store the transposed rows contiguously
            // IE, unlike the way we loaded the data, which was to load it strided across each of our rows
            // we will not output it strided, but instead writing it out as a contiguous block

            // we are using a macro hack to manually unroll the loop, to work around this rustc bug:
            // https://github.com/rust-lang/rust/issues/71025

            // if we don't manually unroll the loop, the compiler will insert unnecessary writes+reads to the stack which tank performance
            // once the compiler bug is fixed, this can be replaced by a "for i in 0..ROW_COUNT" loop
            $(
                output.store_complex(transposed[$unroll_workaround_index], output_index_base + A::VectorType::COMPLEX_PER_VECTOR * $unroll_workaround_index);
            )*
        }

        // transpose the remainder
        let input_index_base = chunk_count * A::VectorType::COMPLEX_PER_VECTOR;
        let output_index_base = input_index_base * ROW_COUNT;

        let partial_remainder = len_per_row % A::VectorType::COMPLEX_PER_VECTOR;
        if partial_remainder == 1 {
            // If the partial remainder is 1, there's no transposing to do - just gather from across the rows and store contiguously
            for i in 0..ROW_COUNT {
                let input_cell = input.get_unchecked(input_index_base + len_per_row*i);
                let output_cell = output.get_unchecked_mut(output_index_base + i);
                *output_cell = *input_cell;
            }
        } else if partial_remainder == 2 {
            // If the partial remainder is 2, use the provided transpose_lo function to do a transpose on half-vectors
            let mut rows = [AvxVector::zero(); ROW_COUNT];
            for i in 0..ROW_COUNT {
                rows[i] = input.load_partial2_complex(input_index_base + len_per_row*i);
            }

            let transposed = $transpose_fn_lo(rows);

            // use the same macro hack as above to unroll the loop
            $(
                output.store_partial2_complex(transposed[$unroll_workaround_index], output_index_base + <A::VectorType as AvxVector256>::HalfVector::COMPLEX_PER_VECTOR * $unroll_workaround_index);
            )*
        }
        else if partial_remainder == 3 {
            // If the partial remainder is 3, we have to load full vectors, use the full transpose, and then write out a variable number of outputs
            let mut rows = [AvxVector::zero(); ROW_COUNT];
            for i in 0..ROW_COUNT {
                rows[i] = input.load_partial3_complex(input_index_base + len_per_row*i);
            }

            // transpose the rows to the columns
            let transposed = $transpose_fn(rows);

            // We're going to write constant number of full vectors, and then some constant-sized partial vector
            // Sadly, because of rust limitations, we can't make full_vector_count a const, so we have to cross our fingers that the compiler optimizes it to a constant
            let element_count = 3*ROW_COUNT;
            let full_vector_count = element_count / A::VectorType::COMPLEX_PER_VECTOR;
            let final_remainder_count = element_count % A::VectorType::COMPLEX_PER_VECTOR;

            // write out our full vectors
            // we are using a macro hack to manually unroll the loop, to work around this rustc bug:
            // https://github.com/rust-lang/rust/issues/71025

            // if we don't manually unroll the loop, the compiler will insert unnecessary writes+reads to the stack which tank performance
            // once the compiler bug is fixed, this can be replaced by a "for i in 0..full_vector_count" loop
            $(
                output.store_complex(transposed[$remainder3_unroll_workaround_index], output_index_base + A::VectorType::COMPLEX_PER_VECTOR * $remainder3_unroll_workaround_index);
            )*

            // write out our partial vector. again, this is a compile-time constant, even if we can't represent that within rust yet
            match final_remainder_count {
                0 => {},
                1 => output.store_partial1_complex(transposed[full_vector_count].lo(), output_index_base + full_vector_count * A::VectorType::COMPLEX_PER_VECTOR),
                2 => output.store_partial2_complex(transposed[full_vector_count].lo(), output_index_base + full_vector_count * A::VectorType::COMPLEX_PER_VECTOR),
                3 => output.store_partial3_complex(transposed[full_vector_count], output_index_base + full_vector_count * A::VectorType::COMPLEX_PER_VECTOR),
                _ => unreachable!(),
            }
        }
    }
)}

pub struct MixedRadix2xnAvx<A: AvxNum, T> {
    common_data: CommonSimdData<T, A::VectorType>,
    _phantom: std::marker::PhantomData<T>,
}
boilerplate_avx_fft_commondata!(MixedRadix2xnAvx);

impl<A: AvxNum, T: FftNum> MixedRadix2xnAvx<A, T> {
    #[target_feature(enable = "avx")]
    unsafe fn new_with_avx(inner_fft: Arc<dyn Fft<T>>) -> Self {
        Self {
            common_data: mixedradix_gen_data!(2, inner_fft),
            _phantom: std::marker::PhantomData,
        }
    }
    mixedradix_column_butterflies!(
        2,
        |columns, _: _| AvxVector::column_butterfly2(columns),
        |columns, _: _| AvxVector::column_butterfly2(columns)
    );
    mixedradix_transpose!(2,
        AvxVector::transpose2_packed,
        AvxVector::transpose2_packed,
        0;1, 0
    );
    boilerplate_mixedradix!();
}

pub struct MixedRadix3xnAvx<A: AvxNum, T> {
    twiddles_butterfly3: A::VectorType,
    common_data: CommonSimdData<T, A::VectorType>,
    _phantom: std::marker::PhantomData<T>,
}
boilerplate_avx_fft_commondata!(MixedRadix3xnAvx);

impl<A: AvxNum, T: FftNum> MixedRadix3xnAvx<A, T> {
    #[target_feature(enable = "avx")]
    unsafe fn new_with_avx(inner_fft: Arc<dyn Fft<T>>) -> Self {
        Self {
            twiddles_butterfly3: AvxVector::broadcast_twiddle(1, 3, inner_fft.fft_direction()),
            common_data: mixedradix_gen_data!(3, inner_fft),
            _phantom: std::marker::PhantomData,
        }
    }
    mixedradix_column_butterflies!(
        3,
        |columns, this: &Self| AvxVector::column_butterfly3(columns, this.twiddles_butterfly3),
        |columns, this: &Self| AvxVector::column_butterfly3(columns, this.twiddles_butterfly3.lo())
    );
    mixedradix_transpose!(3,
        AvxVector::transpose3_packed,
        AvxVector::transpose3_packed,
        0;1;2, 0;1
    );
    boilerplate_mixedradix!();
}

pub struct MixedRadix4xnAvx<A: AvxNum, T> {
    twiddles_butterfly4: Rotation90<A::VectorType>,
    common_data: CommonSimdData<T, A::VectorType>,
    _phantom: std::marker::PhantomData<T>,
}
boilerplate_avx_fft_commondata!(MixedRadix4xnAvx);

impl<A: AvxNum, T: FftNum> MixedRadix4xnAvx<A, T> {
    #[target_feature(enable = "avx")]
    unsafe fn new_with_avx(inner_fft: Arc<dyn Fft<T>>) -> Self {
        Self {
            twiddles_butterfly4: AvxVector::make_rotation90(inner_fft.fft_direction()),
            common_data: mixedradix_gen_data!(4, inner_fft),
            _phantom: std::marker::PhantomData,
        }
    }
    mixedradix_column_butterflies!(
        4,
        |columns, this: &Self| AvxVector::column_butterfly4(columns, this.twiddles_butterfly4),
        |columns, this: &Self| AvxVector::column_butterfly4(columns, this.twiddles_butterfly4.lo())
    );
    mixedradix_transpose!(4,
        AvxVector::transpose4_packed,
        AvxVector::transpose4_packed,
        0;1;2;3, 0;1;2
    );
    boilerplate_mixedradix!();
}

pub struct MixedRadix5xnAvx<A: AvxNum, T> {
    twiddles_butterfly5: [A::VectorType; 2],
    common_data: CommonSimdData<T, A::VectorType>,
    _phantom: std::marker::PhantomData<T>,
}
boilerplate_avx_fft_commondata!(MixedRadix5xnAvx);

impl<A: AvxNum, T: FftNum> MixedRadix5xnAvx<A, T> {
    #[target_feature(enable = "avx")]
    unsafe fn new_with_avx(inner_fft: Arc<dyn Fft<T>>) -> Self {
        Self {
            twiddles_butterfly5: [
                AvxVector::broadcast_twiddle(1, 5, inner_fft.fft_direction()),
                AvxVector::broadcast_twiddle(2, 5, inner_fft.fft_direction()),
            ],
            common_data: mixedradix_gen_data!(5, inner_fft),
            _phantom: std::marker::PhantomData,
        }
    }
    mixedradix_column_butterflies!(
        5,
        |columns, this: &Self| AvxVector::column_butterfly5(columns, this.twiddles_butterfly5),
        |columns, this: &Self| AvxVector::column_butterfly5(
            columns,
            [
                this.twiddles_butterfly5[0].lo(),
                this.twiddles_butterfly5[1].lo()
            ]
        )
    );
    mixedradix_transpose!(5,
        AvxVector::transpose5_packed,
        AvxVector::transpose5_packed,
        0;1;2;3;4, 0;1;2
    );
    boilerplate_mixedradix!();
}

pub struct MixedRadix6xnAvx<A: AvxNum, T> {
    twiddles_butterfly3: A::VectorType,
    common_data: CommonSimdData<T, A::VectorType>,
    _phantom: std::marker::PhantomData<T>,
}
boilerplate_avx_fft_commondata!(MixedRadix6xnAvx);

impl<A: AvxNum, T: FftNum> MixedRadix6xnAvx<A, T> {
    #[target_feature(enable = "avx")]
    unsafe fn new_with_avx(inner_fft: Arc<dyn Fft<T>>) -> Self {
        Self {
            twiddles_butterfly3: AvxVector::broadcast_twiddle(1, 3, inner_fft.fft_direction()),
            common_data: mixedradix_gen_data!(6, inner_fft),
            _phantom: std::marker::PhantomData,
        }
    }
    mixedradix_column_butterflies!(
        6,
        |columns, this: &Self| AvxVector256::column_butterfly6(columns, this.twiddles_butterfly3),
        |columns, this: &Self| AvxVector128::column_butterfly6(columns, this.twiddles_butterfly3)
    );
    mixedradix_transpose!(6,
        AvxVector::transpose6_packed,
        AvxVector::transpose6_packed,
        0;1;2;3;4;5, 0;1;2;3
    );
    boilerplate_mixedradix!();
}

pub struct MixedRadix7xnAvx<A: AvxNum, T> {
    twiddles_butterfly7: [A::VectorType; 3],
    common_data: CommonSimdData<T, A::VectorType>,
    _phantom: std::marker::PhantomData<T>,
}
boilerplate_avx_fft_commondata!(MixedRadix7xnAvx);

impl<A: AvxNum, T: FftNum> MixedRadix7xnAvx<A, T> {
    #[target_feature(enable = "avx")]
    unsafe fn new_with_avx(inner_fft: Arc<dyn Fft<T>>) -> Self {
        Self {
            twiddles_butterfly7: [
                AvxVector::broadcast_twiddle(1, 7, inner_fft.fft_direction()),
                AvxVector::broadcast_twiddle(2, 7, inner_fft.fft_direction()),
                AvxVector::broadcast_twiddle(3, 7, inner_fft.fft_direction()),
            ],
            common_data: mixedradix_gen_data!(7, inner_fft),
            _phantom: std::marker::PhantomData,
        }
    }
    mixedradix_column_butterflies!(
        7,
        |columns, this: &Self| AvxVector::column_butterfly7(columns, this.twiddles_butterfly7),
        |columns, this: &Self| AvxVector::column_butterfly7(
            columns,
            [
                this.twiddles_butterfly7[0].lo(),
                this.twiddles_butterfly7[1].lo(),
                this.twiddles_butterfly7[2].lo()
            ]
        )
    );
    mixedradix_transpose!(7,
        AvxVector::transpose7_packed,
        AvxVector::transpose7_packed,
        0;1;2;3;4;5;6, 0;1;2;3;4
    );
    boilerplate_mixedradix!();
}

pub struct MixedRadix8xnAvx<A: AvxNum, T> {
    twiddles_butterfly4: Rotation90<A::VectorType>,
    common_data: CommonSimdData<T, A::VectorType>,
    _phantom: std::marker::PhantomData<T>,
}
boilerplate_avx_fft_commondata!(MixedRadix8xnAvx);

impl<A: AvxNum, T: FftNum> MixedRadix8xnAvx<A, T> {
    #[target_feature(enable = "avx")]
    unsafe fn new_with_avx(inner_fft: Arc<dyn Fft<T>>) -> Self {
        Self {
            twiddles_butterfly4: AvxVector::make_rotation90(inner_fft.fft_direction()),
            common_data: mixedradix_gen_data!(8, inner_fft),
            _phantom: std::marker::PhantomData,
        }
    }

    mixedradix_column_butterflies!(
        8,
        |columns, this: &Self| AvxVector::column_butterfly8(columns, this.twiddles_butterfly4),
        |columns, this: &Self| AvxVector::column_butterfly8(columns, this.twiddles_butterfly4.lo())
    );
    mixedradix_transpose!(8,
        AvxVector::transpose8_packed,
        AvxVector::transpose8_packed,
        0;1;2;3;4;5;6;7, 0;1;2;3;4;5
    );
    boilerplate_mixedradix!();
}

pub struct MixedRadix9xnAvx<A: AvxNum, T> {
    twiddles_butterfly9: [A::VectorType; 3],
    twiddles_butterfly9_lo: [A::VectorType; 2],
    twiddles_butterfly3: A::VectorType,
    common_data: CommonSimdData<T, A::VectorType>,
    _phantom: std::marker::PhantomData<T>,
}
boilerplate_avx_fft_commondata!(MixedRadix9xnAvx);

impl<A: AvxNum, T: FftNum> MixedRadix9xnAvx<A, T> {
    #[target_feature(enable = "avx")]
    unsafe fn new_with_avx(inner_fft: Arc<dyn Fft<T>>) -> Self {
        let inverse = inner_fft.fft_direction();

        let twiddle1 = AvxVector::broadcast_twiddle(1, 9, inner_fft.fft_direction());
        let twiddle2 = AvxVector::broadcast_twiddle(2, 9, inner_fft.fft_direction());
        let twiddle4 = AvxVector::broadcast_twiddle(4, 9, inner_fft.fft_direction());

        Self {
            twiddles_butterfly9: [
                AvxVector::broadcast_twiddle(1, 9, inverse),
                AvxVector::broadcast_twiddle(2, 9, inverse),
                AvxVector::broadcast_twiddle(4, 9, inverse),
            ],
            twiddles_butterfly9_lo: [
                AvxVector256::merge(twiddle1, twiddle2),
                AvxVector256::merge(twiddle2, twiddle4),
            ],
            twiddles_butterfly3: AvxVector::broadcast_twiddle(1, 3, inner_fft.fft_direction()),
            common_data: mixedradix_gen_data!(9, inner_fft),
            _phantom: std::marker::PhantomData,
        }
    }

    mixedradix_column_butterflies!(
        9,
        |columns, this: &Self| AvxVector256::column_butterfly9(
            columns,
            this.twiddles_butterfly9,
            this.twiddles_butterfly3
        ),
        |columns, this: &Self| AvxVector128::column_butterfly9(
            columns,
            this.twiddles_butterfly9_lo,
            this.twiddles_butterfly3
        )
    );
    mixedradix_transpose!(9,
        AvxVector::transpose9_packed,
        AvxVector::transpose9_packed,
        0;1;2;3;4;5;6;7;8, 0;1;2;3;4;5
    );
    boilerplate_mixedradix!();
}

pub struct MixedRadix11xnAvx<A: AvxNum, T> {
    twiddles_butterfly11: [A::VectorType; 5],
    common_data: CommonSimdData<T, A::VectorType>,
    _phantom: std::marker::PhantomData<T>,
}
boilerplate_avx_fft_commondata!(MixedRadix11xnAvx);

impl<A: AvxNum, T: FftNum> MixedRadix11xnAvx<A, T> {
    #[target_feature(enable = "avx")]
    unsafe fn new_with_avx(inner_fft: Arc<dyn Fft<T>>) -> Self {
        Self {
            twiddles_butterfly11: [
                AvxVector::broadcast_twiddle(1, 11, inner_fft.fft_direction()),
                AvxVector::broadcast_twiddle(2, 11, inner_fft.fft_direction()),
                AvxVector::broadcast_twiddle(3, 11, inner_fft.fft_direction()),
                AvxVector::broadcast_twiddle(4, 11, inner_fft.fft_direction()),
                AvxVector::broadcast_twiddle(5, 11, inner_fft.fft_direction()),
            ],
            common_data: mixedradix_gen_data!(11, inner_fft),
            _phantom: std::marker::PhantomData,
        }
    }
    mixedradix_column_butterflies!(
        11,
        |columns, this: &Self| AvxVector::column_butterfly11(columns, this.twiddles_butterfly11),
        |columns, this: &Self| AvxVector::column_butterfly11(
            columns,
            [
                this.twiddles_butterfly11[0].lo(),
                this.twiddles_butterfly11[1].lo(),
                this.twiddles_butterfly11[2].lo(),
                this.twiddles_butterfly11[3].lo(),
                this.twiddles_butterfly11[4].lo()
            ]
        )
    );
    mixedradix_transpose!(11,
        AvxVector::transpose11_packed,
        AvxVector::transpose11_packed,
        0;1;2;3;4;5;6;7;8;9;10, 0;1;2;3;4;5;6;7
    );
    boilerplate_mixedradix!();
}

pub struct MixedRadix12xnAvx<A: AvxNum, T> {
    twiddles_butterfly4: Rotation90<A::VectorType>,
    twiddles_butterfly3: A::VectorType,
    common_data: CommonSimdData<T, A::VectorType>,
    _phantom: std::marker::PhantomData<T>,
}
boilerplate_avx_fft_commondata!(MixedRadix12xnAvx);

impl<A: AvxNum, T: FftNum> MixedRadix12xnAvx<A, T> {
    #[target_feature(enable = "avx")]
    unsafe fn new_with_avx(inner_fft: Arc<dyn Fft<T>>) -> Self {
        let inverse = inner_fft.fft_direction();
        Self {
            twiddles_butterfly4: AvxVector::make_rotation90(inverse),
            twiddles_butterfly3: AvxVector::broadcast_twiddle(1, 3, inverse),
            common_data: mixedradix_gen_data!(12, inner_fft),
            _phantom: std::marker::PhantomData,
        }
    }

    mixedradix_column_butterflies!(
        12,
        |columns, this: &Self| AvxVector256::column_butterfly12(
            columns,
            this.twiddles_butterfly3,
            this.twiddles_butterfly4
        ),
        |columns, this: &Self| AvxVector128::column_butterfly12(
            columns,
            this.twiddles_butterfly3,
            this.twiddles_butterfly4
        )
    );
    mixedradix_transpose!(12,
        AvxVector::transpose12_packed,
        AvxVector::transpose12_packed,
        0;1;2;3;4;5;6;7;8;9;10;11, 0;1;2;3;4;5;6;7;8
    );
    boilerplate_mixedradix!();
}

pub struct MixedRadix16xnAvx<A: AvxNum, T> {
    twiddles_butterfly4: Rotation90<A::VectorType>,
    twiddles_butterfly16: [A::VectorType; 2],
    common_data: CommonSimdData<T, A::VectorType>,
    _phantom: std::marker::PhantomData<T>,
}
boilerplate_avx_fft_commondata!(MixedRadix16xnAvx);

impl<A: AvxNum, T: FftNum> MixedRadix16xnAvx<A, T> {
    #[target_feature(enable = "avx")]
    unsafe fn new_with_avx(inner_fft: Arc<dyn Fft<T>>) -> Self {
        let inverse = inner_fft.fft_direction();
        Self {
            twiddles_butterfly4: AvxVector::make_rotation90(inner_fft.fft_direction()),
            twiddles_butterfly16: [
                AvxVector::broadcast_twiddle(1, 16, inverse),
                AvxVector::broadcast_twiddle(3, 16, inverse),
            ],
            common_data: mixedradix_gen_data!(16, inner_fft),
            _phantom: std::marker::PhantomData,
        }
    }

    #[target_feature(enable = "avx", enable = "fma")]
    unsafe fn perform_column_butterflies(&self, mut buffer: impl AvxArrayMut<A>) {
        // How many rows this FFT has, ie 2 for 2xn, 4 for 4xn, etc
        const ROW_COUNT: usize = 16;
        const TWIDDLES_PER_COLUMN: usize = ROW_COUNT - 1;

        let len_per_row = self.len() / ROW_COUNT;
        let chunk_count = len_per_row / A::VectorType::COMPLEX_PER_VECTOR;

        // process the column FFTs
        for (c, twiddle_chunk) in self
            .common_data
            .twiddles
            .chunks_exact(TWIDDLES_PER_COLUMN)
            .take(chunk_count)
            .enumerate()
        {
            let index_base = c * A::VectorType::COMPLEX_PER_VECTOR;

            column_butterfly16_loadfn!(
                |index| buffer.load_complex(index_base + len_per_row * index),
                |mut data, index| {
                    if index > 0 {
                        data = AvxVector::mul_complex(data, twiddle_chunk[index - 1]);
                    }
                    buffer.store_complex(data, index_base + len_per_row * index)
                },
                self.twiddles_butterfly16,
                self.twiddles_butterfly4
            );
        }

        // finally, we might have a single partial chunk.
        // Normally, we can fit 4 complex numbers into an AVX register, but we only have `partial_remainder` columns left, so we need special logic to handle these final columns
        let partial_remainder = len_per_row % A::VectorType::COMPLEX_PER_VECTOR;
        if partial_remainder > 0 {
            let partial_remainder_base = chunk_count * A::VectorType::COMPLEX_PER_VECTOR;
            let partial_remainder_twiddle_base =
                self.common_data.twiddles.len() - TWIDDLES_PER_COLUMN;
            let final_twiddle_chunk = &self.common_data.twiddles[partial_remainder_twiddle_base..];

            match partial_remainder {
                1 => {
                    column_butterfly16_loadfn!(
                        |index| buffer
                            .load_partial1_complex(partial_remainder_base + len_per_row * index),
                        |mut data, index| {
                            if index > 0 {
                                let twiddle: A::VectorType = final_twiddle_chunk[index - 1];
                                data = AvxVector::mul_complex(data, twiddle.lo());
                            }
                            buffer.store_partial1_complex(
                                data,
                                partial_remainder_base + len_per_row * index,
                            )
                        },
                        [
                            self.twiddles_butterfly16[0].lo(),
                            self.twiddles_butterfly16[1].lo()
                        ],
                        self.twiddles_butterfly4.lo()
                    );
                }
                2 => {
                    column_butterfly16_loadfn!(
                        |index| buffer
                            .load_partial2_complex(partial_remainder_base + len_per_row * index),
                        |mut data, index| {
                            if index > 0 {
                                let twiddle: A::VectorType = final_twiddle_chunk[index - 1];
                                data = AvxVector::mul_complex(data, twiddle.lo());
                            }
                            buffer.store_partial2_complex(
                                data,
                                partial_remainder_base + len_per_row * index,
                            )
                        },
                        [
                            self.twiddles_butterfly16[0].lo(),
                            self.twiddles_butterfly16[1].lo()
                        ],
                        self.twiddles_butterfly4.lo()
                    );
                }
                3 => {
                    column_butterfly16_loadfn!(
                        |index| buffer
                            .load_partial3_complex(partial_remainder_base + len_per_row * index),
                        |mut data, index| {
                            if index > 0 {
                                data = AvxVector::mul_complex(data, final_twiddle_chunk[index - 1]);
                            }
                            buffer.store_partial3_complex(
                                data,
                                partial_remainder_base + len_per_row * index,
                            )
                        },
                        self.twiddles_butterfly16,
                        self.twiddles_butterfly4
                    );
                }
                _ => unreachable!(),
            }
        }
    }
    #[target_feature(enable = "avx", enable = "fma")]
    unsafe fn perform_column_butterflies_immut(
        &self,
        input: impl AvxArray<A>,
        mut buffer: impl AvxArrayMut<A>,
    ) {
        // How many rows this FFT has, ie 2 for 2xn, 4 for 4xn, etc
        const ROW_COUNT: usize = 16;
        const TWIDDLES_PER_COLUMN: usize = ROW_COUNT - 1;

        let len_per_row = self.len() / ROW_COUNT;
        let chunk_count = len_per_row / A::VectorType::COMPLEX_PER_VECTOR;

        // process the column FFTs
        for (c, twiddle_chunk) in self
            .common_data
            .twiddles
            .chunks_exact(TWIDDLES_PER_COLUMN)
            .take(chunk_count)
            .enumerate()
        {
            let index_base = c * A::VectorType::COMPLEX_PER_VECTOR;

            column_butterfly16_loadfn!(
                |index| input.load_complex(index_base + len_per_row * index),
                |mut data, index| {
                    if index > 0 {
                        data = AvxVector::mul_complex(data, twiddle_chunk[index - 1]);
                    }
                    buffer.store_complex(data, index_base + len_per_row * index)
                },
                self.twiddles_butterfly16,
                self.twiddles_butterfly4
            );
        }

        // finally, we might have a single partial chunk.
        // Normally, we can fit 4 complex numbers into an AVX register, but we only have `partial_remainder` columns left, so we need special logic to handle these final columns
        let partial_remainder = len_per_row % A::VectorType::COMPLEX_PER_VECTOR;
        if partial_remainder > 0 {
            let partial_remainder_base = chunk_count * A::VectorType::COMPLEX_PER_VECTOR;
            let partial_remainder_twiddle_base =
                self.common_data.twiddles.len() - TWIDDLES_PER_COLUMN;
            let final_twiddle_chunk = &self.common_data.twiddles[partial_remainder_twiddle_base..];

            match partial_remainder {
                1 => {
                    for c in 0..self.len() / len_per_row {
                        let cs = c * len_per_row + len_per_row - partial_remainder;
                        buffer.store_partial1_complex(input.load_partial1_complex(cs), cs);
                    }
                    column_butterfly16_loadfn!(
                        |index| buffer
                            .load_partial1_complex(partial_remainder_base + len_per_row * index),
                        |mut data, index| {
                            if index > 0 {
                                let twiddle: A::VectorType = final_twiddle_chunk[index - 1];
                                data = AvxVector::mul_complex(data, twiddle.lo());
                            }
                            buffer.store_partial1_complex(
                                data,
                                partial_remainder_base + len_per_row * index,
                            )
                        },
                        [
                            self.twiddles_butterfly16[0].lo(),
                            self.twiddles_butterfly16[1].lo()
                        ],
                        self.twiddles_butterfly4.lo()
                    );
                }
                2 => {
                    for c in 0..self.len() / len_per_row {
                        let cs = c * len_per_row + len_per_row - partial_remainder;
                        buffer.store_partial2_complex(input.load_partial2_complex(cs), cs);
                    }
                    column_butterfly16_loadfn!(
                        |index| buffer
                            .load_partial2_complex(partial_remainder_base + len_per_row * index),
                        |mut data, index| {
                            if index > 0 {
                                let twiddle: A::VectorType = final_twiddle_chunk[index - 1];
                                data = AvxVector::mul_complex(data, twiddle.lo());
                            }
                            buffer.store_partial2_complex(
                                data,
                                partial_remainder_base + len_per_row * index,
                            )
                        },
                        [
                            self.twiddles_butterfly16[0].lo(),
                            self.twiddles_butterfly16[1].lo()
                        ],
                        self.twiddles_butterfly4.lo()
                    );
                }
                3 => {
                    for c in 0..self.len() / len_per_row {
                        let cs = c * len_per_row + len_per_row - partial_remainder;
                        buffer.store_partial3_complex(input.load_partial3_complex(cs), cs);
                    }
                    column_butterfly16_loadfn!(
                        |index| buffer
                            .load_partial3_complex(partial_remainder_base + len_per_row * index),
                        |mut data, index| {
                            if index > 0 {
                                data = AvxVector::mul_complex(data, final_twiddle_chunk[index - 1]);
                            }
                            buffer.store_partial3_complex(
                                data,
                                partial_remainder_base + len_per_row * index,
                            )
                        },
                        self.twiddles_butterfly16,
                        self.twiddles_butterfly4
                    );
                }
                _ => unreachable!(),
            }
        }
    }
    mixedradix_transpose!(16,
        AvxVector::transpose16_packed,
        AvxVector::transpose16_packed,
        0;1;2;3;4;5;6;7;8;9;10;11;12;13;14;15, 0;1;2;3;4;5;6;7;8;9;10;11
    );
    boilerplate_mixedradix!();
}

#[cfg(test)]
mod unit_tests {
    use super::*;
    use crate::algorithm::*;
    use crate::test_utils::check_fft_algorithm;
    use std::sync::Arc;

    macro_rules! test_avx_mixed_radix {
        ($f32_test_name:ident, $f64_test_name:ident, $struct_name:ident, $inner_count:expr) => (
            #[test]
            fn $f32_test_name() {
                for inner_fft_len in 1..32 {
                    let len = inner_fft_len * $inner_count;

                    let inner_fft_forward = Arc::new(Dft::new(inner_fft_len, FftDirection::Forward)) as Arc<dyn Fft<f32>>;
                    let fft_forward = $struct_name::<f32, f32>::new(inner_fft_forward).expect("Can't run test because this machine doesn't have the required instruction sets");
                    check_fft_algorithm(&fft_forward, len, FftDirection::Forward);

                    let inner_fft_inverse = Arc::new(Dft::new(inner_fft_len, FftDirection::Inverse)) as Arc<dyn Fft<f32>>;
                    let fft_inverse = $struct_name::<f32, f32>::new(inner_fft_inverse).expect("Can't run test because this machine doesn't have the required instruction sets");
                    check_fft_algorithm(&fft_inverse, len, FftDirection::Inverse);
                }
            }
            #[test]
            fn $f64_test_name() {
                for inner_fft_len in 1..32 {
                    let len = inner_fft_len * $inner_count;

                    let inner_fft_forward = Arc::new(Dft::new(inner_fft_len, FftDirection::Forward)) as Arc<dyn Fft<f64>>;
                    let fft_forward = $struct_name::<f64, f64>::new(inner_fft_forward).expect("Can't run test because this machine doesn't have the required instruction sets");
                    check_fft_algorithm(&fft_forward, len, FftDirection::Forward);

                    let inner_fft_inverse = Arc::new(Dft::new(inner_fft_len, FftDirection::Inverse)) as Arc<dyn Fft<f64>>;
                    let fft_inverse = $struct_name::<f64, f64>::new(inner_fft_inverse).expect("Can't run test because this machine doesn't have the required instruction sets");
                    check_fft_algorithm(&fft_inverse, len, FftDirection::Inverse);
                }
            }
        )
    }

    test_avx_mixed_radix!(
        test_mixedradix_2xn_avx_f32,
        test_mixedradix_2xn_avx_f64,
        MixedRadix2xnAvx,
        2
    );
    test_avx_mixed_radix!(
        test_mixedradix_3xn_avx_f32,
        test_mixedradix_3xn_avx_f64,
        MixedRadix3xnAvx,
        3
    );
    test_avx_mixed_radix!(
        test_mixedradix_4xn_avx_f32,
        test_mixedradix_4xn_avx_f64,
        MixedRadix4xnAvx,
        4
    );
    test_avx_mixed_radix!(
        test_mixedradix_5xn_avx_f32,
        test_mixedradix_5xn_avx_f64,
        MixedRadix5xnAvx,
        5
    );
    test_avx_mixed_radix!(
        test_mixedradix_6xn_avx_f32,
        test_mixedradix_6xn_avx_f64,
        MixedRadix6xnAvx,
        6
    );
    test_avx_mixed_radix!(
        test_mixedradix_7xn_avx_f32,
        test_mixedradix_7xn_avx_f64,
        MixedRadix7xnAvx,
        7
    );
    test_avx_mixed_radix!(
        test_mixedradix_8xn_avx_f32,
        test_mixedradix_8xn_avx_f64,
        MixedRadix8xnAvx,
        8
    );
    test_avx_mixed_radix!(
        test_mixedradix_9xn_avx_f32,
        test_mixedradix_9xn_avx_f64,
        MixedRadix9xnAvx,
        9
    );
    test_avx_mixed_radix!(
        test_mixedradix_11xn_avx_f32,
        test_mixedradix_11xn_avx_f64,
        MixedRadix11xnAvx,
        11
    );
    test_avx_mixed_radix!(
        test_mixedradix_12xn_avx_f32,
        test_mixedradix_12xn_avx_f64,
        MixedRadix12xnAvx,
        12
    );
    test_avx_mixed_radix!(
        test_mixedradix_16xn_avx_f32,
        test_mixedradix_16xn_avx_f64,
        MixedRadix16xnAvx,
        16
    );
}