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//! Module handling lazy loading via iterating on slices on the original buffer.
use crate::tensor::TensorView;
use std::ops::{
Bound, Range, RangeBounds, RangeFrom, RangeFull, RangeInclusive, RangeTo, RangeToInclusive,
};
/// Error representing invalid slicing attempt
#[derive(Debug)]
pub enum InvalidSlice {
/// When the client asked for more slices than the tensors has dimensions
TooManySlices,
}
#[derive(Debug, Clone)]
/// Generic structure used to index a slice of the tensor
pub enum TensorIndexer {
//Select(usize),
/// This is a regular slice, purely indexing a chunk of the tensor
Narrow(Bound<usize>, Bound<usize>),
//IndexSelect(Tensor),
}
// impl From<usize> for TensorIndexer {
// fn from(index: usize) -> Self {
// TensorIndexer::Select(index)
// }
// }
// impl From<&[usize]> for TensorIndexer {
// fn from(index: &[usize]) -> Self {
// let tensor = index.into();
// TensorIndexer::IndexSelect(tensor)
// }
// }
//
// impl From<Vec<usize>> for TensorIndexer {
// fn from(index: Vec<usize>) -> Self {
// let tensor = Tensor::of_slice(&index);
// TensorIndexer::IndexSelect(tensor)
// }
// }
macro_rules! impl_from_range {
($range_type:ty) => {
impl From<$range_type> for TensorIndexer {
fn from(range: $range_type) -> Self {
use std::ops::Bound::*;
let start = match range.start_bound() {
Included(idx) => Included(*idx),
Excluded(idx) => Excluded(*idx),
Unbounded => Unbounded,
};
let end = match range.end_bound() {
Included(idx) => Included(*idx),
Excluded(idx) => Excluded(*idx),
Unbounded => Unbounded,
};
TensorIndexer::Narrow(start, end)
}
}
};
}
impl_from_range!(Range<usize>);
impl_from_range!(RangeFrom<usize>);
impl_from_range!(RangeFull);
impl_from_range!(RangeInclusive<usize>);
impl_from_range!(RangeTo<usize>);
impl_from_range!(RangeToInclusive<usize>);
/// Trait used to implement multiple signatures for ease of use of the slicing
/// of a tensor
pub trait IndexOp<'data, T> {
/// Returns a slicing iterator which are the chunks of data necessary to
/// reconstruct the desired tensor.
fn slice(&'data self, index: T) -> Result<SliceIterator<'data>, InvalidSlice>;
}
impl<'data, A> IndexOp<'data, A> for TensorView<'data>
where
A: Into<TensorIndexer>,
{
fn slice(&'data self, index: A) -> Result<SliceIterator<'data>, InvalidSlice> {
self.sliced_data(&[index.into()])
}
}
impl<'data, A> IndexOp<'data, (A,)> for TensorView<'data>
where
A: Into<TensorIndexer>,
{
fn slice(&'data self, index: (A,)) -> Result<SliceIterator<'data>, InvalidSlice> {
let idx_a = index.0.into();
self.sliced_data(&[idx_a])
}
}
impl<'data, A, B> IndexOp<'data, (A, B)> for TensorView<'data>
where
A: Into<TensorIndexer>,
B: Into<TensorIndexer>,
{
fn slice(&'data self, index: (A, B)) -> Result<SliceIterator<'data>, InvalidSlice> {
let idx_a = index.0.into();
let idx_b = index.1.into();
self.sliced_data(&[idx_a, idx_b])
}
}
impl<'data, A, B, C> IndexOp<'data, (A, B, C)> for TensorView<'data>
where
A: Into<TensorIndexer>,
B: Into<TensorIndexer>,
C: Into<TensorIndexer>,
{
fn slice(&'data self, index: (A, B, C)) -> Result<SliceIterator<'data>, InvalidSlice> {
let idx_a = index.0.into();
let idx_b = index.1.into();
let idx_c = index.2.into();
self.sliced_data(&[idx_a, idx_b, idx_c])
}
}
// impl<A, B, C, D> IndexOp<(A, B, C, D)> for TensorView<'data>
// where
// A: Into<TensorIndexer>,
// B: Into<TensorIndexer>,
// C: Into<TensorIndexer>,
// D: Into<TensorIndexer>,
// {
// fn slice(&self, index: (A, B, C, D)) -> TensorView<'data> {
// let idx_a = index.0.into();
// let idx_b = index.1.into();
// let idx_c = index.2.into();
// let idx_d = index.3.into();
// self.sliced_data(&[idx_a, idx_b, idx_c, idx_d])
// }
// }
//
// impl<A, B, C, D, E> IndexOp<(A, B, C, D, E)> for TensorView<'data>
// where
// A: Into<TensorIndexer>,
// B: Into<TensorIndexer>,
// C: Into<TensorIndexer>,
// D: Into<TensorIndexer>,
// E: Into<TensorIndexer>,
// {
// fn slice(&self, index: (A, B, C, D, E)) -> TensorView<'data> {
// let idx_a = index.0.into();
// let idx_b = index.1.into();
// let idx_c = index.2.into();
// let idx_d = index.3.into();
// let idx_e = index.4.into();
// self.sliced_data(&[idx_a, idx_b, idx_c, idx_d, idx_e])
// }
// }
//
// impl<A, B, C, D, E, F> IndexOp<(A, B, C, D, E, F)> for TensorView<'data>
// where
// A: Into<TensorIndexer>,
// B: Into<TensorIndexer>,
// C: Into<TensorIndexer>,
// D: Into<TensorIndexer>,
// E: Into<TensorIndexer>,
// F: Into<TensorIndexer>,
// {
// fn slice(&self, index: (A, B, C, D, E, F)) -> TensorView<'data> {
// let idx_a = index.0.into();
// let idx_b = index.1.into();
// let idx_c = index.2.into();
// let idx_d = index.3.into();
// let idx_e = index.4.into();
// let idx_f = index.5.into();
// self.sliced_data(&[idx_a, idx_b, idx_c, idx_d, idx_e, idx_f])
// }
// }
//
// impl<A, B, C, D, E, F, G> IndexOp<(A, B, C, D, E, F, G)> for TensorView<'data>
// where
// A: Into<TensorIndexer>,
// B: Into<TensorIndexer>,
// C: Into<TensorIndexer>,
// D: Into<TensorIndexer>,
// E: Into<TensorIndexer>,
// F: Into<TensorIndexer>,
// G: Into<TensorIndexer>,
// {
// fn slice(&self, index: (A, B, C, D, E, F, G)) -> TensorView<'data> {
// let idx_a = index.0.into();
// let idx_b = index.1.into();
// let idx_c = index.2.into();
// let idx_d = index.3.into();
// let idx_e = index.4.into();
// let idx_f = index.5.into();
// let idx_g = index.6.into();
// self.sliced_data(&[idx_a, idx_b, idx_c, idx_d, idx_e, idx_f, idx_g])
// }
// }
/// Iterator used to return the bits of the overall tensor buffer
/// when client asks for a slice of the original tensor.
pub struct SliceIterator<'data> {
view: &'data TensorView<'data>,
indices: Vec<(usize, usize)>,
newshape: Vec<usize>,
}
impl<'data> SliceIterator<'data> {
pub(crate) fn new(
view: &'data TensorView<'data>,
slices: &[TensorIndexer],
) -> Result<Self, InvalidSlice> {
// Make sure n. axis does not exceed n. of dimensions
let n_slice = slices.len();
let n_shape = view.shape().len();
if n_slice > n_shape {
return Err(InvalidSlice::TooManySlices);
}
let mut newshape = Vec::with_capacity(view.shape().len());
// Minimum span is the span of 1 item;
let mut span = view.dtype().size();
let mut indices = vec![];
// Everything is row major.
for (i, &shape) in view.shape().iter().enumerate().rev() {
if i >= slices.len() {
// We are not slicing yet, just increase the local span
newshape.push(shape);
} else {
let slice = &slices[i];
let (start, stop) = match slice {
TensorIndexer::Narrow(Bound::Unbounded, Bound::Unbounded) => (0, shape),
TensorIndexer::Narrow(Bound::Unbounded, Bound::Excluded(stop)) => (0, *stop),
TensorIndexer::Narrow(Bound::Unbounded, Bound::Included(stop)) => {
(0, *stop + 1)
}
TensorIndexer::Narrow(Bound::Included(s), Bound::Unbounded) => (*s, shape),
TensorIndexer::Narrow(Bound::Included(s), Bound::Excluded(stop)) => (*s, *stop),
TensorIndexer::Narrow(Bound::Included(s), Bound::Included(stop)) => {
(*s, *stop + 1)
}
TensorIndexer::Narrow(Bound::Excluded(s), Bound::Unbounded) => (*s + 1, shape),
TensorIndexer::Narrow(Bound::Excluded(s), Bound::Excluded(stop)) => {
(*s + 1, *stop)
}
TensorIndexer::Narrow(Bound::Excluded(s), Bound::Included(stop)) => {
(*s + 1, *stop + 1)
}
};
newshape.push(stop - start);
if indices.is_empty() {
if start == 0 && stop == shape {
// We haven't started to slice yet, just increase the span
} else {
let offset = start * span;
let small_span = stop * span - offset;
indices.push((offset, offset + small_span));
}
} else {
let mut newindices = vec![];
for n in start..stop {
let offset = n * span;
for (old_start, old_stop) in &indices {
newindices.push((old_start + offset, old_stop + offset));
}
}
indices = newindices;
}
}
span *= shape;
}
if indices.is_empty() {
indices.push((0, view.data().len()));
}
// Reversing so we can pop faster while iterating on the slice
let indices = indices.into_iter().rev().collect();
let newshape = newshape.into_iter().rev().collect();
Ok(Self {
view,
indices,
newshape,
})
}
/// Gives back the amount of bytes still being in the iterator
pub fn remaining_byte_len(&self) -> usize {
self.indices
.iter()
.map(|(start, stop)| (stop - start))
.sum()
}
/// Gives back the amount of bytes still being in the iterator
pub fn newshape(&self) -> Vec<usize> {
self.newshape.clone()
}
}
impl<'data> Iterator for SliceIterator<'data> {
type Item = &'data [u8];
fn next(&mut self) -> Option<Self::Item> {
// TODO We might want to move the logic from `new`
// here actually to remove the need to get all the indices
// upfront.
let (start, stop) = self.indices.pop()?;
Some(&self.view.data()[start..stop])
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::tensor::{Dtype, TensorView};
#[test]
fn test_helpers() {
let data: Vec<u8> = vec![0.0f32, 1.0, 2.0, 3.0, 4.0, 5.0]
.into_iter()
.flat_map(|f| f.to_le_bytes())
.collect();
let attn_0 = TensorView::new(Dtype::F32, vec![1, 2, 3], &data).unwrap();
let iterator = SliceIterator::new(
&attn_0,
&[TensorIndexer::Narrow(Bound::Unbounded, Bound::Unbounded)],
)
.unwrap();
assert_eq!(iterator.remaining_byte_len(), 24);
assert_eq!(iterator.newshape(), vec![1, 2, 3]);
let iterator = SliceIterator::new(
&attn_0,
&[
TensorIndexer::Narrow(Bound::Unbounded, Bound::Unbounded),
TensorIndexer::Narrow(Bound::Included(0), Bound::Excluded(1)),
],
)
.unwrap();
assert_eq!(iterator.remaining_byte_len(), 12);
assert_eq!(iterator.newshape(), vec![1, 1, 3]);
}
#[test]
fn test_dummy() {
let data: Vec<u8> = vec![0.0f32, 1.0, 2.0, 3.0, 4.0, 5.0]
.into_iter()
.flat_map(|f| f.to_le_bytes())
.collect();
let attn_0 = TensorView::new(Dtype::F32, vec![1, 2, 3], &data).unwrap();
let mut iterator = SliceIterator::new(
&attn_0,
&[TensorIndexer::Narrow(Bound::Unbounded, Bound::Unbounded)],
)
.unwrap();
assert_eq!(iterator.next(), Some(&data[0..24]));
assert_eq!(iterator.next(), None);
let mut iterator = SliceIterator::new(
&attn_0,
&[
TensorIndexer::Narrow(Bound::Unbounded, Bound::Unbounded),
TensorIndexer::Narrow(Bound::Unbounded, Bound::Unbounded),
],
)
.unwrap();
assert_eq!(iterator.next(), Some(&data[0..24]));
assert_eq!(iterator.next(), None);
let mut iterator = SliceIterator::new(
&attn_0,
&[
TensorIndexer::Narrow(Bound::Unbounded, Bound::Unbounded),
TensorIndexer::Narrow(Bound::Unbounded, Bound::Unbounded),
],
)
.unwrap();
assert_eq!(iterator.next(), Some(&data[0..24]));
assert_eq!(iterator.next(), None);
let mut iterator = SliceIterator::new(
&attn_0,
&[
TensorIndexer::Narrow(Bound::Unbounded, Bound::Unbounded),
TensorIndexer::Narrow(Bound::Unbounded, Bound::Unbounded),
TensorIndexer::Narrow(Bound::Unbounded, Bound::Unbounded),
],
)
.unwrap();
assert_eq!(iterator.next(), Some(&data[0..24]));
assert_eq!(iterator.next(), None);
assert!(SliceIterator::new(
&attn_0,
&[
TensorIndexer::Narrow(Bound::Unbounded, Bound::Unbounded),
TensorIndexer::Narrow(Bound::Unbounded, Bound::Unbounded),
TensorIndexer::Narrow(Bound::Unbounded, Bound::Unbounded),
TensorIndexer::Narrow(Bound::Unbounded, Bound::Unbounded),
],
)
.is_err(),);
}
#[test]
fn test_slice_variety() {
let data: Vec<u8> = vec![0.0f32, 1.0, 2.0, 3.0, 4.0, 5.0]
.into_iter()
.flat_map(|f| f.to_le_bytes())
.collect();
let attn_0 = TensorView::new(Dtype::F32, vec![1, 2, 3], &data).unwrap();
let mut iterator = SliceIterator::new(
&attn_0,
&[TensorIndexer::Narrow(
Bound::Included(0),
Bound::Excluded(1),
)],
)
.unwrap();
assert_eq!(iterator.next(), Some(&data[0..24]));
assert_eq!(iterator.next(), None);
let mut iterator = SliceIterator::new(
&attn_0,
&[
TensorIndexer::Narrow(Bound::Unbounded, Bound::Unbounded),
TensorIndexer::Narrow(Bound::Included(0), Bound::Excluded(1)),
],
)
.unwrap();
assert_eq!(iterator.next(), Some(&data[0..12]));
assert_eq!(iterator.next(), None);
let mut iterator = SliceIterator::new(
&attn_0,
&[
TensorIndexer::Narrow(Bound::Unbounded, Bound::Unbounded),
TensorIndexer::Narrow(Bound::Unbounded, Bound::Unbounded),
TensorIndexer::Narrow(Bound::Included(0), Bound::Excluded(1)),
],
)
.unwrap();
assert_eq!(iterator.next(), Some(&data[0..4]));
assert_eq!(iterator.next(), Some(&data[12..16]));
assert_eq!(iterator.next(), None);
let mut iterator = SliceIterator::new(
&attn_0,
&[
TensorIndexer::Narrow(Bound::Unbounded, Bound::Unbounded),
TensorIndexer::Narrow(Bound::Included(1), Bound::Excluded(2)),
TensorIndexer::Narrow(Bound::Included(0), Bound::Excluded(1)),
],
)
.unwrap();
assert_eq!(iterator.next(), Some(&data[12..16]));
assert_eq!(iterator.next(), None);
}
#[test]
fn test_slice_variety2() {
let data: Vec<u8> = vec![0.0f32, 1.0, 2.0, 3.0, 4.0, 5.0]
.into_iter()
.flat_map(|f| f.to_le_bytes())
.collect();
let attn_0 = TensorView::new(Dtype::F32, vec![2, 3], &data).unwrap();
let mut iterator = SliceIterator::new(
&attn_0,
&[
TensorIndexer::Narrow(Bound::Unbounded, Bound::Unbounded),
TensorIndexer::Narrow(Bound::Included(1), Bound::Excluded(3)),
],
)
.unwrap();
assert_eq!(iterator.next(), Some(&data[4..12]));
assert_eq!(iterator.next(), Some(&data[16..24]));
assert_eq!(iterator.next(), None);
}
}