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/*!
* # Indexing
*
* Many libraries represent tensors as N dimensional arrays, however there is often some semantic
* meaning to each dimension. You may have a batch of 2000 images, each 100 pixels wide and high,
* with each pixel representing 3 numbers for rgb values. This can be represented as a
* 2000 x 100 x 100 x 3 tensor, but a 4 dimensional array does not track the semantic meaning
* of each dimension and associated index.
*
* 6 months later you could come back to the code and forget which order the dimensions were
* created in, at best getting the indexes out of bounds and causing a crash in your application,
* and at worst silently reading the wrong data without realising. *Was it width then height or
* height then width?*...
*
* Easy ML moves the N dimensional array to an implementation detail, and most of its APIs work
* on the names of each dimension in a tensor instead of just the order. Instead of a
* 2000 x 100 x 100 x 3 tensor in which the last element is at [1999, 99, 99, 2], Easy ML tracks
* the names of the dimensions, so you have a
* `[("batch", 2000), ("width", 100), ("height", 100), ("rgb", 3)]` shaped tensor.
*
* This can't stop you from getting the math wrong, but confusion over which dimension
* means what is reduced. Tensors carry around their pairs of dimension name and length
* so adding a `[("batch", 2000), ("width", 100), ("height", 100), ("rgb", 3)]` shaped tensor
* to a `[("batch", 2000), ("height", 100), ("width", 100), ("rgb", 3)]` will fail unless you
* reorder one first, and you could access an element as
* `tensor.index_by(["batch", "width", "height", "rgb"]).get([1999, 0, 99, 3])` or
* `tensor.index_by(["batch", "height", "width", "rgb"]).get([1999, 99, 0, 3])` and read the same data,
* because you index into dimensions based on their name, not just the order they are stored in
* memory.
*
* Even with a name for each dimension, at some point you still need to say what order you want
* to index each dimension with, and this is where [`TensorAccess`] comes in. It
* creates a mapping from the dimension name order you want to access elements with to the order
* the dimensions are stored as.
*/
use crate::differentiation::{Index, Primitive, Record, RecordTensor};
use crate::numeric::Numeric;
use crate::tensors::dimensions;
use crate::tensors::dimensions::DimensionMappings;
use crate::tensors::views::{DataLayout, TensorMut, TensorRef};
use crate::tensors::{Dimension, Tensor};
use std::error::Error;
use std::fmt;
use std::iter::{ExactSizeIterator, FusedIterator};
use std::marker::PhantomData;
pub use crate::matrices::iterators::WithIndex;
// TODO: Iterators should use unchecked indexing once fully stress tested.
/**
* Access to the data in a Tensor with a particular order of dimension indexing. The order
* affects the shape of the TensorAccess as well as the order of indexes you supply to read
* or write values to the tensor.
*
* See the [module level documentation](crate::tensors::indexing) for more information.
*/
#[derive(Clone, Debug)]
pub struct TensorAccess<T, S, const D: usize> {
source: S,
dimension_mapping: DimensionMappings<D>,
_type: PhantomData<T>,
}
impl<T, S, const D: usize> TensorAccess<T, S, D>
where
S: TensorRef<T, D>,
{
/**
* Creates a TensorAccess which can be indexed in the order of the supplied dimensions
* to read or write values from this tensor.
*
* # Panics
*
* If the set of dimensions supplied do not match the set of dimensions in this tensor's shape.
*/
#[track_caller]
pub fn from(source: S, dimensions: [Dimension; D]) -> TensorAccess<T, S, D> {
match TensorAccess::try_from(source, dimensions) {
Err(error) => panic!("{}", error),
Ok(success) => success,
}
}
/**
* Creates a TensorAccess which can be indexed in the order of the supplied dimensions
* to read or write values from this tensor.
*
* Returns Err if the set of dimensions supplied do not match the set of dimensions in this
* tensor's shape.
*/
pub fn try_from(
source: S,
dimensions: [Dimension; D],
) -> Result<TensorAccess<T, S, D>, InvalidDimensionsError<D>> {
Ok(TensorAccess {
dimension_mapping: DimensionMappings::new(&source.view_shape(), &dimensions)
.ok_or_else(|| InvalidDimensionsError {
actual: source.view_shape(),
requested: dimensions,
})?,
source,
_type: PhantomData,
})
}
/**
* Creates a TensorAccess which is indexed in the same order as the dimensions in the view
* shape of the tensor it is created from.
*
* Hence if you create a TensorAccess directly from a Tensor by `from_source_order`
* this uses the order the dimensions were laid out in memory with.
*
* ```
* use easy_ml::tensors::Tensor;
* use easy_ml::tensors::indexing::TensorAccess;
* let tensor = Tensor::from([("x", 2), ("y", 2), ("z", 2)], vec![
* 1, 2,
* 3, 4,
*
* 5, 6,
* 7, 8
* ]);
* let xyz = tensor.index_by(["x", "y", "z"]);
* let also_xyz = TensorAccess::from_source_order(&tensor);
* let also_xyz = tensor.index();
* ```
*/
pub fn from_source_order(source: S) -> TensorAccess<T, S, D> {
TensorAccess {
dimension_mapping: DimensionMappings::no_op_mapping(),
source,
_type: PhantomData,
}
}
/**
* Creates a TensorAccess which is indexed in the same order as the linear data layout
* dimensions in the tensor it is created from, or None if the source data layout
* is not linear.
*
* Hence if you use `from_memory_order` on a source that was originally big endian like
* [Tensor] this uses the order for efficient iteration through each step in memory
* when [iterating](TensorIterator).
*/
pub fn from_memory_order(source: S) -> Option<TensorAccess<T, S, D>> {
let data_layout = match source.data_layout() {
DataLayout::Linear(order) => order,
_ => return None,
};
let shape = source.view_shape();
Some(TensorAccess::try_from(source, data_layout).unwrap_or_else(|_| panic!(
"Source implementation contained dimensions {:?} in data_layout that were not the same set as in the view_shape {:?} which breaks the contract of TensorRef",
data_layout, shape
)))
}
/**
* The shape this TensorAccess has with the dimensions mapped to the order the TensorAccess
* was created with, not necessarily the same order as in the underlying tensor.
*/
pub fn shape(&self) -> [(Dimension, usize); D] {
self.dimension_mapping
.map_shape_to_requested(&self.source.view_shape())
}
pub fn source(self) -> S {
self.source
}
// # Safety
//
// Giving out a mutable reference to our source could allow it to be changed out from under us
// and make our dimmension mapping invalid. However, since the source implements TensorRef
// interior mutability is not allowed, so we can give out shared references without breaking
// our own integrity.
pub fn source_ref(&self) -> &S {
&self.source
}
}
/**
* An error indicating failure to create a TensorAccess because the requested dimension order
* does not match the shape in the source data.
*/
#[derive(Debug, Clone, Eq, PartialEq, Ord, PartialOrd)]
pub struct InvalidDimensionsError<const D: usize> {
pub actual: [(Dimension, usize); D],
pub requested: [Dimension; D],
}
impl<const D: usize> Error for InvalidDimensionsError<D> {}
impl<const D: usize> fmt::Display for InvalidDimensionsError<D> {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
write!(
f,
"Requested dimension order: {:?} does not match the shape in the source: {:?}",
&self.actual, &self.requested
)
}
}
#[test]
fn test_sync() {
fn assert_sync<T: Sync>() {}
assert_sync::<InvalidDimensionsError<3>>();
}
#[test]
fn test_send() {
fn assert_send<T: Send>() {}
assert_send::<InvalidDimensionsError<3>>();
}
impl<T, S, const D: usize> TensorAccess<T, S, D>
where
S: TensorRef<T, D>,
{
/**
* Using the dimension ordering of the TensorAccess, gets a reference to the value at the
* index if the index is in range. Otherwise returns None.
*/
pub fn try_get_reference(&self, indexes: [usize; D]) -> Option<&T> {
self.source
.get_reference(self.dimension_mapping.map_dimensions_to_source(&indexes))
}
/**
* Using the dimension ordering of the TensorAccess, gets a reference to the value at the
* index if the index is in range, panicking if the index is out of range.
*/
// NOTE: Ideally `get_reference` would be used here for consistency, but that opens the
// minefield of TensorRef::get_reference and TensorAccess::get_ref being different signatures
// but the same name.
#[track_caller]
pub fn get_ref(&self, indexes: [usize; D]) -> &T {
match self.try_get_reference(indexes) {
Some(reference) => reference,
None => panic!(
"Unable to index with {:?}, Tensor dimensions are {:?}.",
indexes,
self.shape()
),
}
}
/**
* Using the dimension ordering of the TensorAccess, gets a reference to the value at the
* index wihout any bounds checking.
*
* # Safety
*
* Calling this method with an out-of-bounds index is *[undefined behavior]* even if the
* resulting reference is not used. Valid indexes are defined as in [TensorRef]. Note that
* the order of the indexes needed here must match with
* [`TensorAccess::shape`](TensorAccess::shape) which may not neccessarily be the same
* as the `view_shape` of the `TensorRef` implementation this TensorAccess was created from).
*
* [undefined behavior]: <https://doc.rust-lang.org/reference/behavior-considered-undefined.html>
* [TensorRef]: TensorRef
*/
// NOTE: This aliases with TensorRef::get_reference_unchecked but the TensorRef impl
// just calls this and the signatures match anyway, so there are no potential issues.
#[allow(clippy::missing_safety_doc)] // it's not missing
pub unsafe fn get_reference_unchecked(&self, indexes: [usize; D]) -> &T {
self.source
.get_reference_unchecked(self.dimension_mapping.map_dimensions_to_source(&indexes))
}
/**
* Returns an iterator over references to the data in this TensorAccess, in the order of
* the TensorAccess shape.
*/
pub fn iter_reference(&self) -> TensorReferenceIterator<T, TensorAccess<T, S, D>, D> {
TensorReferenceIterator::from(self)
}
}
impl<T, S, const D: usize> TensorAccess<T, S, D>
where
S: TensorRef<T, D>,
T: Clone,
{
/**
* Using the dimension ordering of the TensorAccess, gets a copy of the value at the
* index if the index is in range, panicking if the index is out of range.
*
* For a non panicking API see [`try_get_reference`](TensorAccess::try_get_reference)
*/
#[track_caller]
pub fn get(&self, indexes: [usize; D]) -> T {
match self.try_get_reference(indexes) {
Some(reference) => reference.clone(),
None => panic!(
"Unable to index with {:?}, Tensor dimensions are {:?}.",
indexes,
self.shape()
),
}
}
/**
* Gets a copy of the first value in this tensor.
* For 0 dimensional tensors this is the only index `[]`, for 1 dimensional tensors this
* is `[0]`, for 2 dimensional tensors `[0,0]`, etcetera.
*/
pub fn first(&self) -> T {
self.iter()
.next()
.expect("Tensors always have at least 1 element")
}
/**
* Creates and returns a new tensor with all values from the original with the
* function applied to each.
*
* Note: mapping methods are defined on [Tensor] and
* [TensorView](crate::tensors::views::TensorView) directly so you don't need to create a
* TensorAccess unless you want to do the mapping with a different dimension order.
*/
pub fn map<U>(&self, mapping_function: impl Fn(T) -> U) -> Tensor<U, D> {
let mapped = self.iter().map(mapping_function).collect();
Tensor::from(self.shape(), mapped)
}
/**
* Creates and returns a new tensor with all values from the original and
* the index of each value mapped by a function. The indexes passed to the mapping
* function always increment the rightmost index, starting at all 0s, using the dimension
* order that the TensorAccess is indexed by, not neccessarily the index order the
* original source uses.
*
* Note: mapping methods are defined on [Tensor] and
* [TensorView](crate::tensors::views::TensorView) directly so you don't need to create a
* TensorAccess unless you want to do the mapping with a different dimension order.
*/
pub fn map_with_index<U>(&self, mapping_function: impl Fn([usize; D], T) -> U) -> Tensor<U, D> {
let mapped = self
.iter()
.with_index()
.map(|(i, x)| mapping_function(i, x))
.collect();
Tensor::from(self.shape(), mapped)
}
/**
* Returns an iterator over copies of the data in this TensorAccess, in the order of
* the TensorAccess shape.
*/
pub fn iter(&self) -> TensorIterator<T, TensorAccess<T, S, D>, D> {
TensorIterator::from(self)
}
}
impl<T, S, const D: usize> TensorAccess<T, S, D>
where
S: TensorMut<T, D>,
{
/**
* Using the dimension ordering of the TensorAccess, gets a mutable reference to the value at
* the index if the index is in range. Otherwise returns None.
*/
pub fn try_get_reference_mut(&mut self, indexes: [usize; D]) -> Option<&mut T> {
self.source
.get_reference_mut(self.dimension_mapping.map_dimensions_to_source(&indexes))
}
/**
* Using the dimension ordering of the TensorAccess, gets a mutable reference to the value at
* the index if the index is in range, panicking if the index is out of range.
*/
// NOTE: Ideally `get_reference_mut` would be used here for consistency, but that opens the
// minefield of TensorMut::get_reference_mut and TensorAccess::get_ref_mut being different
// signatures but the same name.
#[track_caller]
pub fn get_ref_mut(&mut self, indexes: [usize; D]) -> &mut T {
match self.try_get_reference_mut(indexes) {
Some(reference) => reference,
// can't provide a better error because the borrow checker insists that returning
// a reference in the Some branch means our mutable borrow prevents us calling
// self.shape() and a bad error is better than cloning self.shape() on every call
None => panic!("Unable to index with {:?}", indexes),
}
}
/**
* Using the dimension ordering of the TensorAccess, gets a mutable reference to the value at
* the index wihout any bounds checking.
*
* # Safety
*
* Calling this method with an out-of-bounds index is *[undefined behavior]* even if the
* resulting reference is not used. Valid indexes are defined as in [TensorRef]. Note that
* the order of the indexes needed here must match with
* [`TensorAccess::shape`](TensorAccess::shape) which may not neccessarily be the same
* as the `view_shape` of the `TensorRef` implementation this TensorAccess was created from).
*
* [undefined behavior]: <https://doc.rust-lang.org/reference/behavior-considered-undefined.html>
* [TensorRef]: TensorRef
*/
// NOTE: This aliases with TensorRef::get_reference_unchecked_mut but the TensorMut impl
// just calls this and the signatures match anyway, so there are no potential issues.
#[allow(clippy::missing_safety_doc)] // it's not missing
pub unsafe fn get_reference_unchecked_mut(&mut self, indexes: [usize; D]) -> &mut T {
self.source
.get_reference_unchecked_mut(self.dimension_mapping.map_dimensions_to_source(&indexes))
}
/**
* Returns an iterator over mutable references to the data in this TensorAccess, in the order
* of the TensorAccess shape.
*/
pub fn iter_reference_mut(
&mut self,
) -> TensorReferenceMutIterator<T, TensorAccess<T, S, D>, D> {
TensorReferenceMutIterator::from(self)
}
}
impl<T, S, const D: usize> TensorAccess<T, S, D>
where
S: TensorMut<T, D>,
T: Clone,
{
/**
* Applies a function to all values in the tensor, modifying
* the tensor in place.
*/
pub fn map_mut(&mut self, mapping_function: impl Fn(T) -> T) {
self.iter_reference_mut()
.for_each(|x| *x = mapping_function(x.clone()));
}
/**
* Applies a function to all values and each value's index in the tensor, modifying
* the tensor in place. The indexes passed to the mapping function always increment
* the rightmost index, starting at all 0s, using the dimension order that the
* TensorAccess is indexed by, not neccessarily the index order the original source uses.
*/
pub fn map_mut_with_index(&mut self, mapping_function: impl Fn([usize; D], T) -> T) {
self.iter_reference_mut()
.with_index()
.for_each(|(i, x)| *x = mapping_function(i, x.clone()));
}
}
impl<'a, T, S, const D: usize> TensorAccess<(T, Index), &RecordTensor<'a, T, S, D>, D>
where
T: Numeric + Primitive,
S: TensorRef<(T, Index), D>,
{
/**
* Using the dimension ordering of the TensorAccess, returns a copy of the data at the index
* as a Record if the index is in range, panicking if the index is out of range.
*
* If you need to access all the data as records instead of just a specific index you should
* probably use one of the iterator APIs instead.
*
* See also: [iter_as_records](RecordTensor::iter_as_records)
*
* # Panics
*
* If the index is out of range.
*
* For a non panicking API see [try_get_as_record](TensorAccess::try_get_as_record)
*
* ```
* use easy_ml::differentiation::RecordTensor;
* use easy_ml::differentiation::WengertList;
* use easy_ml::tensors::Tensor;
*
* let list = WengertList::new();
* let X = RecordTensor::variables(
* &list,
* Tensor::from(
* [("r", 2), ("c", 3)],
* vec![
* 3.0, 4.0, 5.0,
* 1.0, 4.0, 9.0,
* ]
* )
* );
* let x = X.index_by(["c", "r"]).get_as_record([2, 0]);
* assert_eq!(x.number, 5.0);
* ```
*/
#[track_caller]
pub fn get_as_record(&self, indexes: [usize; D]) -> Record<'a, T> {
Record::from_existing(self.get(indexes), self.source.history())
}
/**
* Using the dimension ordering of the TensorAccess, returns a copy of the data at the index
* as a Record if the index is in range. Otherwise returns None.
*
* If you need to access all the data as records instead of just a specific index you should
* probably use one of the iterator APIs instead.
*
* See also: [iter_as_records](RecordTensor::iter_as_records)
*/
pub fn try_get_as_record(&self, indexes: [usize; D]) -> Option<Record<'a, T>> {
self.try_get_reference(indexes)
.map(|r| Record::from_existing(r.clone(), self.source.history()))
}
}
impl<'a, T, S, const D: usize> TensorAccess<(T, Index), RecordTensor<'a, T, S, D>, D>
where
T: Numeric + Primitive,
S: TensorRef<(T, Index), D>,
{
/**
* Using the dimension ordering of the TensorAccess, returns a copy of the data at the index
* as a Record if the index is in range, panicking if the index is out of range.
*
* If you need to access all the data as records instead of just a specific index you should
* probably use one of the iterator APIs instead.
*
* See also: [iter_as_records](RecordTensor::iter_as_records)
*
* # Panics
*
* If the index is out of range.
*
* For a non panicking API see [try_get_as_record](TensorAccess::try_get_as_record)
*/
#[track_caller]
pub fn get_as_record(&self, indexes: [usize; D]) -> Record<'a, T> {
Record::from_existing(self.get(indexes), self.source.history())
}
/**
* Using the dimension ordering of the TensorAccess, returns a copy of the data at the index
* as a Record if the index is in range. Otherwise returns None.
*
* If you need to access all the data as records instead of just a specific index you should
* probably use one of the iterator APIs instead.
*
* See also: [iter_as_records](RecordTensor::iter_as_records)
*/
pub fn try_get_as_record(&self, indexes: [usize; D]) -> Option<Record<'a, T>> {
self.try_get_reference(indexes)
.map(|r| Record::from_existing(r.clone(), self.source.history()))
}
}
impl<'a, T, S, const D: usize> TensorAccess<(T, Index), &mut RecordTensor<'a, T, S, D>, D>
where
T: Numeric + Primitive,
S: TensorRef<(T, Index), D>,
{
/**
* Using the dimension ordering of the TensorAccess, returns a copy of the data at the index
* as a Record if the index is in range, panicking if the index is out of range.
*
* If you need to access all the data as records instead of just a specific index you should
* probably use one of the iterator APIs instead.
*
* See also: [iter_as_records](RecordTensor::iter_as_records)
*
* # Panics
*
* If the index is out of range.
*
* For a non panicking API see [try_get_as_record](TensorAccess::try_get_as_record)
*/
#[track_caller]
pub fn get_as_record(&self, indexes: [usize; D]) -> Record<'a, T> {
Record::from_existing(self.get(indexes), self.source.history())
}
/**
* Using the dimension ordering of the TensorAccess, returns a copy of the data at the index
* as a Record if the index is in range. Otherwise returns None.
*
* If you need to access all the data as records instead of just a specific index you should
* probably use one of the iterator APIs instead.
*
* See also: [iter_as_records](RecordTensor::iter_as_records)
*/
pub fn try_get_as_record(&self, indexes: [usize; D]) -> Option<Record<'a, T>> {
self.try_get_reference(indexes)
.map(|r| Record::from_existing(r.clone(), self.source.history()))
}
}
// # Safety
//
// The type implementing TensorRef inside the TensorAccess must implement it correctly, so by
// delegating to it without changing anything other than the order we index it, we implement
// TensorRef correctly as well.
/**
* A TensorAccess implements TensorRef, with the dimension order and indexing matching that of the
* TensorAccess shape.
*/
unsafe impl<T, S, const D: usize> TensorRef<T, D> for TensorAccess<T, S, D>
where
S: TensorRef<T, D>,
{
fn get_reference(&self, indexes: [usize; D]) -> Option<&T> {
self.try_get_reference(indexes)
}
fn view_shape(&self) -> [(Dimension, usize); D] {
self.shape()
}
unsafe fn get_reference_unchecked(&self, indexes: [usize; D]) -> &T {
self.get_reference_unchecked(indexes)
}
fn data_layout(&self) -> DataLayout<D> {
match self.source.data_layout() {
// We might have reordered the view_shape but we didn't rearrange the memory or change
// what each dimension name refers to in memory, so the data layout remains as is.
DataLayout::Linear(order) => DataLayout::Linear(order),
DataLayout::NonLinear => DataLayout::NonLinear,
DataLayout::Other => DataLayout::Other,
}
}
}
// # Safety
//
// The type implementing TensorMut inside the TensorAccess must implement it correctly, so by
// delegating to it without changing anything other than the order we index it, we implement
// TensorMut correctly as well.
/**
* A TensorAccess implements TensorMut, with the dimension order and indexing matching that of the
* TensorAccess shape.
*/
unsafe impl<T, S, const D: usize> TensorMut<T, D> for TensorAccess<T, S, D>
where
S: TensorMut<T, D>,
{
fn get_reference_mut(&mut self, indexes: [usize; D]) -> Option<&mut T> {
self.try_get_reference_mut(indexes)
}
unsafe fn get_reference_unchecked_mut(&mut self, indexes: [usize; D]) -> &mut T {
self.get_reference_unchecked_mut(indexes)
}
}
/**
* Any tensor access of a Displayable type implements Display
*
* You can control the precision of the formatting using format arguments, i.e.
* `format!("{:.3}", tensor)`
*/
impl<T: std::fmt::Display, S, const D: usize> std::fmt::Display for TensorAccess<T, S, D>
where
T: std::fmt::Display,
S: TensorRef<T, D>,
{
fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
crate::tensors::display::format_view(&self, f)?;
writeln!(f)?;
write!(f, "Data Layout = {:?}", self.data_layout())
}
}
/**
* An iterator over all indexes in a shape.
*
* First the all 0 index is iterated, then each iteration increments the rightmost index.
* For a shape of `[("a", 2), ("b", 2), ("c", 2)]` this will yield indexes in order of: `[0,0,0]`,
* `[0,0,1]`, `[0,1,0]`, `[0,1,1]`, `[1,0,0]`, `[1,0,1]`, `[1,1,0]`, `[1,1,1]`,
*
* You don't typically need to use this directly, as tensors have iterators that iterate over
* them and return values to you (using this under the hood), but `ShapeIterator` can be useful
* if you need to hold a mutable reference to a tensor while iterating as `ShapeIterator` does
* not borrow the tensor. NB: if you do index into a tensor you're mutably borrowing using
* `ShapeIterator` directly, take care to ensure you don't accidentally reshape the tensor and
* continue to use indexes from `ShapeIterator` as they would then be invalid.
*/
#[derive(Clone, Debug)]
pub struct ShapeIterator<const D: usize> {
shape: [(Dimension, usize); D],
indexes: [usize; D],
finished: bool,
}
impl<const D: usize> ShapeIterator<D> {
/**
* Constructs a ShapeIterator for a shape.
*
* If the shape has any dimensions with a length of zero, the iterator will immediately
* return None on [`next()`](Iterator::next).
*/
pub fn from(shape: [(Dimension, usize); D]) -> ShapeIterator<D> {
// If we're given an invalid shape (shape input is not neccessarily going to meet the no
// 0 lengths contract of TensorRef because that's not actually required here), return
// a finished iterator
// Since this is an iterator over an owned shape, it's not going to become invalid later
// when we start iterating so this is the only check we need.
let starting_index_valid = shape.iter().all(|(_, l)| *l > 0);
ShapeIterator {
shape,
indexes: [0; D],
finished: !starting_index_valid,
}
}
}
impl<const D: usize> Iterator for ShapeIterator<D> {
type Item = [usize; D];
fn next(&mut self) -> Option<Self::Item> {
iter(&mut self.finished, &mut self.indexes, &self.shape)
}
fn size_hint(&self) -> (usize, Option<usize>) {
size_hint(self.finished, &self.indexes, &self.shape)
}
}
// Once we hit the end we mark ourselves as finished so we're always Fused.
impl<const D: usize> FusedIterator for ShapeIterator<D> {}
// We can always calculate the exact number of steps remaining because the shape and indexes are
// private fields that are only mutated by `next` to count up.
impl<const D: usize> ExactSizeIterator for ShapeIterator<D> {}
/// Common index order iterator logic
fn iter<const D: usize>(
finished: &mut bool,
indexes: &mut [usize; D],
shape: &[(Dimension, usize); D],
) -> Option<[usize; D]> {
if *finished {
return None;
}
let value = Some(*indexes);
if D > 0 {
// Increment index of final dimension. In the 2D case, we iterate through a row by
// incrementing through every column index.
indexes[D - 1] += 1;
for d in (1..D).rev() {
if indexes[d] == shape[d].1 {
// ran to end of this dimension with our index
// In the 2D case, we finished indexing through every column in the row,
// and it's now time to move onto the next row.
indexes[d] = 0;
indexes[d - 1] += 1;
}
}
// Check if we ran past the final index
if indexes[0] == shape[0].1 {
*finished = true;
}
} else {
*finished = true;
}
value
}
/// Common size hint logic
fn size_hint<const D: usize>(
finished: bool,
indexes: &[usize; D],
shape: &[(Dimension, usize); D],
) -> (usize, Option<usize>) {
if finished {
return (0, Some(0));
}
let remaining = if D > 0 {
let total = dimensions::elements(shape);
let strides = crate::tensors::compute_strides(shape);
let seen = crate::tensors::get_index_direct_unchecked(indexes, &strides);
total - seen
} else {
1
// If D == 0 and we're not finished we've not returned the sole index yet so there's
// exactly 1 left
};
(remaining, Some(remaining))
}
/**
* An iterator over copies of all values in a tensor.
*
* First the all 0 index is iterated, then each iteration increments the rightmost index.
* For [Tensor] or [TensorRef]s which do not reorder the underlying Tensor
* this will take a single step in memory on each iteration, akin to iterating through the
* flattened data of the tensor.
*
* If the TensorRef reorders the tensor data (e.g. [TensorAccess]) this iterator
* will still iterate the rightmost index allowing iteration through dimensions in a different
* order to how they are stored, but no longer taking a single step in memory on each
* iteration (which may be less cache friendly for the CPU).
*
* ```
* use easy_ml::tensors::Tensor;
* let tensor_0 = Tensor::from_scalar(1);
* let tensor_1 = Tensor::from([("a", 7)], vec![ 1, 2, 3, 4, 5, 6, 7 ]);
* let tensor_2 = Tensor::from([("a", 2), ("b", 3)], vec![
* // two rows, three columns
* 1, 2, 3,
* 4, 5, 6
* ]);
* let tensor_3 = Tensor::from([("a", 2), ("b", 1), ("c", 2)], vec![
* // two rows each a single column, stacked on top of each other
* 1,
* 2,
*
* 3,
* 4
* ]);
* let tensor_access_0 = tensor_0.index_by([]);
* let tensor_access_1 = tensor_1.index_by(["a"]);
* let tensor_access_2 = tensor_2.index_by(["a", "b"]);
* let tensor_access_2_rev = tensor_2.index_by(["b", "a"]);
* let tensor_access_3 = tensor_3.index_by(["a", "b", "c"]);
* let tensor_access_3_rev = tensor_3.index_by(["c", "b", "a"]);
* assert_eq!(
* tensor_0.iter().collect::<Vec<i32>>(),
* vec![1]
* );
* assert_eq!(
* tensor_access_0.iter().collect::<Vec<i32>>(),
* vec![1]
* );
* assert_eq!(
* tensor_1.iter().collect::<Vec<i32>>(),
* vec![1, 2, 3, 4, 5, 6, 7]
* );
* assert_eq!(
* tensor_access_1.iter().collect::<Vec<i32>>(),
* vec![1, 2, 3, 4, 5, 6, 7]
* );
* assert_eq!(
* tensor_2.iter().collect::<Vec<i32>>(),
* vec![1, 2, 3, 4, 5, 6]
* );
* assert_eq!(
* tensor_access_2.iter().collect::<Vec<i32>>(),
* vec![1, 2, 3, 4, 5, 6]
* );
* assert_eq!(
* tensor_access_2_rev.iter().collect::<Vec<i32>>(),
* vec![1, 4, 2, 5, 3, 6]
* );
* assert_eq!(
* tensor_3.iter().collect::<Vec<i32>>(),
* vec![1, 2, 3, 4]
* );
* assert_eq!(
* tensor_access_3.iter().collect::<Vec<i32>>(),
* vec![1, 2, 3, 4]
* );
* assert_eq!(
* tensor_access_3_rev.iter().collect::<Vec<i32>>(),
* vec![1, 3, 2, 4]
* );
* ```
*/
#[derive(Debug)]
pub struct TensorIterator<'a, T, S, const D: usize> {
shape_iterator: ShapeIterator<D>,
source: &'a S,
_type: PhantomData<T>,
}
impl<'a, T, S, const D: usize> TensorIterator<'a, T, S, D>
where
T: Clone,
S: TensorRef<T, D>,
{
pub fn from(source: &S) -> TensorIterator<T, S, D> {
TensorIterator {
shape_iterator: ShapeIterator::from(source.view_shape()),
source,
_type: PhantomData,
}
}
/**
* Constructs an iterator which also yields the indexes of each element in
* this iterator.
*/
pub fn with_index(self) -> WithIndex<Self> {
WithIndex { iterator: self }
}
}
impl<'a, T, S, const D: usize> From<TensorIterator<'a, T, S, D>>
for WithIndex<TensorIterator<'a, T, S, D>>
where
T: Clone,
S: TensorRef<T, D>,
{
fn from(iterator: TensorIterator<'a, T, S, D>) -> Self {
iterator.with_index()
}
}
impl<'a, T, S, const D: usize> Iterator for TensorIterator<'a, T, S, D>
where
T: Clone,
S: TensorRef<T, D>,
{
type Item = T;
fn next(&mut self) -> Option<Self::Item> {
// Safety: ShapeIterator only iterates over the correct indexes into our tensor's shape as
// defined by TensorRef. Since TensorRef promises no interior mutability and we hold an
// immutable reference to our tensor source, it can't be resized which ensures
// ShapeIterator can always yield valid indexes for our iteration.
self.shape_iterator
.next()
.map(|indexes| unsafe { self.source.get_reference_unchecked(indexes) }.clone())
}
fn size_hint(&self) -> (usize, Option<usize>) {
self.shape_iterator.size_hint()
}
}
impl<'a, T, S, const D: usize> FusedIterator for TensorIterator<'a, T, S, D>
where
T: Clone,
S: TensorRef<T, D>,
{
}
impl<'a, T, S, const D: usize> ExactSizeIterator for TensorIterator<'a, T, S, D>
where
T: Clone,
S: TensorRef<T, D>,
{
}
impl<'a, T, S, const D: usize> Iterator for WithIndex<TensorIterator<'a, T, S, D>>
where
T: Clone,
S: TensorRef<T, D>,
{
type Item = ([usize; D], T);
fn next(&mut self) -> Option<Self::Item> {
let index = self.iterator.shape_iterator.indexes;
self.iterator.next().map(|x| (index, x))
}
fn size_hint(&self) -> (usize, Option<usize>) {
self.iterator.size_hint()
}
}
impl<'a, T, S, const D: usize> FusedIterator for WithIndex<TensorIterator<'a, T, S, D>>
where
T: Clone,
S: TensorRef<T, D>,
{
}
impl<'a, T, S, const D: usize> ExactSizeIterator for WithIndex<TensorIterator<'a, T, S, D>>
where
T: Clone,
S: TensorRef<T, D>,
{
}
/**
* An iterator over references to all values in a tensor.
*
* First the all 0 index is iterated, then each iteration increments the rightmost index.
* For [Tensor] or [TensorRef]s which do not reorder the underlying Tensor
* this will take a single step in memory on each iteration, akin to iterating through the
* flattened data of the tensor.
*
* If the TensorRef reorders the tensor data (e.g. [TensorAccess]) this iterator
* will still iterate the rightmost index allowing iteration through dimensions in a different
* order to how they are stored, but no longer taking a single step in memory on each
* iteration (which may be less cache friendly for the CPU).
*
* ```
* use easy_ml::tensors::Tensor;
* let tensor_0 = Tensor::from_scalar(1);
* let tensor_1 = Tensor::from([("a", 7)], vec![ 1, 2, 3, 4, 5, 6, 7 ]);
* let tensor_2 = Tensor::from([("a", 2), ("b", 3)], vec![
* // two rows, three columns
* 1, 2, 3,
* 4, 5, 6
* ]);
* let tensor_3 = Tensor::from([("a", 2), ("b", 1), ("c", 2)], vec![
* // two rows each a single column, stacked on top of each other
* 1,
* 2,
*
* 3,
* 4
* ]);
* let tensor_access_0 = tensor_0.index_by([]);
* let tensor_access_1 = tensor_1.index_by(["a"]);
* let tensor_access_2 = tensor_2.index_by(["a", "b"]);
* let tensor_access_2_rev = tensor_2.index_by(["b", "a"]);
* let tensor_access_3 = tensor_3.index_by(["a", "b", "c"]);
* let tensor_access_3_rev = tensor_3.index_by(["c", "b", "a"]);
* assert_eq!(
* tensor_0.iter_reference().cloned().collect::<Vec<i32>>(),
* vec![1]
* );
* assert_eq!(
* tensor_access_0.iter_reference().cloned().collect::<Vec<i32>>(),
* vec![1]
* );
* assert_eq!(
* tensor_1.iter_reference().cloned().collect::<Vec<i32>>(),
* vec![1, 2, 3, 4, 5, 6, 7]
* );
* assert_eq!(
* tensor_access_1.iter_reference().cloned().collect::<Vec<i32>>(),
* vec![1, 2, 3, 4, 5, 6, 7]
* );
* assert_eq!(
* tensor_2.iter_reference().cloned().collect::<Vec<i32>>(),
* vec![1, 2, 3, 4, 5, 6]
* );
* assert_eq!(
* tensor_access_2.iter_reference().cloned().collect::<Vec<i32>>(),
* vec![1, 2, 3, 4, 5, 6]
* );
* assert_eq!(
* tensor_access_2_rev.iter_reference().cloned().collect::<Vec<i32>>(),
* vec![1, 4, 2, 5, 3, 6]
* );
* assert_eq!(
* tensor_3.iter_reference().cloned().collect::<Vec<i32>>(),
* vec![1, 2, 3, 4]
* );
* assert_eq!(
* tensor_access_3.iter_reference().cloned().collect::<Vec<i32>>(),
* vec![1, 2, 3, 4]
* );
* assert_eq!(
* tensor_access_3_rev.iter_reference().cloned().collect::<Vec<i32>>(),
* vec![1, 3, 2, 4]
* );
* ```
*/
#[derive(Debug)]
pub struct TensorReferenceIterator<'a, T, S, const D: usize> {
shape_iterator: ShapeIterator<D>,
source: &'a S,
_type: PhantomData<&'a T>,
}
impl<'a, T, S, const D: usize> TensorReferenceIterator<'a, T, S, D>
where
S: TensorRef<T, D>,
{
pub fn from(source: &S) -> TensorReferenceIterator<T, S, D> {
TensorReferenceIterator {
shape_iterator: ShapeIterator::from(source.view_shape()),
source,
_type: PhantomData,
}
}
/**
* Constructs an iterator which also yields the indexes of each element in
* this iterator.
*/
pub fn with_index(self) -> WithIndex<Self> {
WithIndex { iterator: self }
}
}
impl<'a, T, S, const D: usize> From<TensorReferenceIterator<'a, T, S, D>>
for WithIndex<TensorReferenceIterator<'a, T, S, D>>
where
S: TensorRef<T, D>,
{
fn from(iterator: TensorReferenceIterator<'a, T, S, D>) -> Self {
iterator.with_index()
}
}
impl<'a, T, S, const D: usize> Iterator for TensorReferenceIterator<'a, T, S, D>
where
S: TensorRef<T, D>,
{
type Item = &'a T;
fn next(&mut self) -> Option<Self::Item> {
// Safety: ShapeIterator only iterates over the correct indexes into our tensor's shape as
// defined by TensorRef. Since TensorRef promises no interior mutability and we hold an
// immutable reference to our tensor source, it can't be resized which ensures
// ShapeIterator can always yield valid indexes for our iteration.
self.shape_iterator
.next()
.map(|indexes| unsafe { self.source.get_reference_unchecked(indexes) })
}
fn size_hint(&self) -> (usize, Option<usize>) {
self.shape_iterator.size_hint()
}
}
impl<'a, T, S, const D: usize> FusedIterator for TensorReferenceIterator<'a, T, S, D> where
S: TensorRef<T, D>
{
}
impl<'a, T, S, const D: usize> ExactSizeIterator for TensorReferenceIterator<'a, T, S, D> where
S: TensorRef<T, D>
{
}
impl<'a, T, S, const D: usize> Iterator for WithIndex<TensorReferenceIterator<'a, T, S, D>>
where
S: TensorRef<T, D>,
{
type Item = ([usize; D], &'a T);
fn next(&mut self) -> Option<Self::Item> {
let index = self.iterator.shape_iterator.indexes;
self.iterator.next().map(|x| (index, x))
}
fn size_hint(&self) -> (usize, Option<usize>) {
self.iterator.size_hint()
}
}
impl<'a, T, S, const D: usize> FusedIterator for WithIndex<TensorReferenceIterator<'a, T, S, D>> where
S: TensorRef<T, D>
{
}
impl<'a, T, S, const D: usize> ExactSizeIterator for WithIndex<TensorReferenceIterator<'a, T, S, D>> where
S: TensorRef<T, D>
{
}
/**
* An iterator over mutable references to all values in a tensor.
*
* First the all 0 index is iterated, then each iteration increments the rightmost index.
* For [Tensor] or [TensorRef]s which do not reorder the underlying Tensor
* this will take a single step in memory on each iteration, akin to iterating through the
* flattened data of the tensor.
*
* If the TensorRef reorders the tensor data (e.g. [TensorAccess]) this iterator
* will still iterate the rightmost index allowing iteration through dimensions in a different
* order to how they are stored, but no longer taking a single step in memory on each
* iteration (which may be less cache friendly for the CPU).
*
* ```
* use easy_ml::tensors::Tensor;
* let mut tensor = Tensor::from([("a", 7)], vec![ 1, 2, 3, 4, 5, 6, 7 ]);
* let doubled = tensor.map(|x| 2 * x);
* // mutating a tensor in place can also be done with Tensor::map_mut and
* // Tensor::map_mut_with_index
* for elem in tensor.iter_reference_mut() {
* *elem = 2 * *elem;
* }
* assert_eq!(
* tensor,
* doubled,
* );
* ```
*/
#[derive(Debug)]
pub struct TensorReferenceMutIterator<'a, T, S, const D: usize> {
shape_iterator: ShapeIterator<D>,
source: &'a mut S,
_type: PhantomData<&'a mut T>,
}
impl<'a, T, S, const D: usize> TensorReferenceMutIterator<'a, T, S, D>
where
S: TensorMut<T, D>,
{
pub fn from(source: &mut S) -> TensorReferenceMutIterator<T, S, D> {
TensorReferenceMutIterator {
shape_iterator: ShapeIterator::from(source.view_shape()),
source,
_type: PhantomData,
}
}
/**
* Constructs an iterator which also yields the indexes of each element in
* this iterator.
*/
pub fn with_index(self) -> WithIndex<Self> {
WithIndex { iterator: self }
}
}
impl<'a, T, S, const D: usize> From<TensorReferenceMutIterator<'a, T, S, D>>
for WithIndex<TensorReferenceMutIterator<'a, T, S, D>>
where
S: TensorMut<T, D>,
{
fn from(iterator: TensorReferenceMutIterator<'a, T, S, D>) -> Self {
iterator.with_index()
}
}
impl<'a, T, S, const D: usize> Iterator for TensorReferenceMutIterator<'a, T, S, D>
where
S: TensorMut<T, D>,
{
type Item = &'a mut T;
fn next(&mut self) -> Option<Self::Item> {
self.shape_iterator.next().map(|indexes| {
unsafe {
// Safety: We are not allowed to give out overlapping mutable references,
// but since we will always increment the counter on every call to next()
// and stop when we reach the end no references will overlap.
// The compiler doesn't know this, so transmute the lifetime for it.
// Safety: ShapeIterator only iterates over the correct indexes into our
// tensor's shape as defined by TensorRef. Since TensorRef promises no interior
// mutability and we hold an exclusive reference to our tensor source, it can't
// be resized (except by us - and we don't) which ensures ShapeIterator can always
// yield valid indexes for our iteration.
std::mem::transmute(self.source.get_reference_unchecked_mut(indexes))
}
})
}
fn size_hint(&self) -> (usize, Option<usize>) {
self.shape_iterator.size_hint()
}
}
impl<'a, T, S, const D: usize> FusedIterator for TensorReferenceMutIterator<'a, T, S, D> where
S: TensorMut<T, D>
{
}
impl<'a, T, S, const D: usize> ExactSizeIterator for TensorReferenceMutIterator<'a, T, S, D> where
S: TensorMut<T, D>
{
}
impl<'a, T, S, const D: usize> Iterator for WithIndex<TensorReferenceMutIterator<'a, T, S, D>>
where
S: TensorMut<T, D>,
{
type Item = ([usize; D], &'a mut T);
fn next(&mut self) -> Option<Self::Item> {
let index = self.iterator.shape_iterator.indexes;
self.iterator.next().map(|x| (index, x))
}
fn size_hint(&self) -> (usize, Option<usize>) {
self.iterator.size_hint()
}
}
impl<'a, T, S, const D: usize> FusedIterator for WithIndex<TensorReferenceMutIterator<'a, T, S, D>> where
S: TensorMut<T, D>
{
}
impl<'a, T, S, const D: usize> ExactSizeIterator
for WithIndex<TensorReferenceMutIterator<'a, T, S, D>>
where
S: TensorMut<T, D>,
{
}
/**
* An iterator over all values in an owned tensor.
*
* This iterator does not clone the values, it returns the actual values stored in the tensor.
* There is no such method to return `T` by value from a [TensorRef]/[TensorMut], to do
* this it [replaces](std::mem::replace) the values with dummy values. Hence it can only be
* created for types that implement [Default] or [ZeroOne](crate::numeric::ZeroOne)
* from [Numeric](crate::numeric) which provide a means to create dummy values.
*
* First the all 0 index is iterated, then each iteration increments the rightmost index.
* For [Tensor] or [TensorRef]s which do not reorder the underlying Tensor
* this will take a single step in memory on each iteration, akin to iterating through the
* flattened data of the tensor.
*
* If the TensorRef reorders the tensor data (e.g. [TensorAccess]) this iterator
* will still iterate the rightmost index allowing iteration through dimensions in a different
* order to how they are stored, but no longer taking a single step in memory on each
* iteration (which may be less cache friendly for the CPU).
*
* ```
* use easy_ml::tensors::Tensor;
*
* #[derive(Debug, Default, Eq, PartialEq)]
* struct NoClone(i32);
*
* let tensor = Tensor::from([("a", 3)], vec![ NoClone(1), NoClone(2), NoClone(3) ]);
* let values = tensor.iter_owned(); // will use T::default() for dummy values
* assert_eq!(vec![ NoClone(1), NoClone(2), NoClone(3) ], values.collect::<Vec<NoClone>>());
* ```
*/
#[derive(Debug)]
pub struct TensorOwnedIterator<T, S, const D: usize> {
shape_iterator: ShapeIterator<D>,
source: S,
producer: fn() -> T,
}
impl<T, S, const D: usize> TensorOwnedIterator<T, S, D>
where
S: TensorMut<T, D>,
{
/**
* Creates the TensorOwnedIterator from a source where the default values will be provided
* by [Default::default]. This constructor is also used by the convenience
* methods on [Tensor::iter_owned](Tensor::iter_owned) and
* [TensorView::iter_owned](crate::tensors::views::TensorView::iter_owned).
*/
pub fn from(source: S) -> TensorOwnedIterator<T, S, D>
where
T: Default,
{
TensorOwnedIterator {
shape_iterator: ShapeIterator::from(source.view_shape()),
source,
producer: || T::default(),
}
}
/**
* Creates the TensorOwnedIterator from a source where the default values will be provided
* by [ZeroOne::zero](crate::numeric::ZeroOne::zero).
*/
pub fn from_numeric(source: S) -> TensorOwnedIterator<T, S, D>
where
T: crate::numeric::ZeroOne,
{
TensorOwnedIterator {
shape_iterator: ShapeIterator::from(source.view_shape()),
source,
producer: || T::zero(),
}
}
/**
* Constructs an iterator which also yields the indexes of each element in
* this iterator.
*/
pub fn with_index(self) -> WithIndex<Self> {
WithIndex { iterator: self }
}
}
impl<T, S, const D: usize> From<TensorOwnedIterator<T, S, D>>
for WithIndex<TensorOwnedIterator<T, S, D>>
where
S: TensorMut<T, D>,
{
fn from(iterator: TensorOwnedIterator<T, S, D>) -> Self {
iterator.with_index()
}
}
impl<T, S, const D: usize> Iterator for TensorOwnedIterator<T, S, D>
where
S: TensorMut<T, D>,
{
type Item = T;
fn next(&mut self) -> Option<Self::Item> {
self.shape_iterator.next().map(|indexes| {
let producer = self.producer;
let dummy = producer();
// Safety: ShapeIterator only iterates over the correct indexes into our
// tensor's shape as defined by TensorRef. Since TensorRef promises no interior
// mutability and we hold our tensor source by value, it can't be resized (except by
// us - and we don't) which ensures ShapeIterator can always yield valid indexes for
// our iteration.
let value = std::mem::replace(
unsafe { self.source.get_reference_unchecked_mut(indexes) },
dummy,
);
value
})
}
fn size_hint(&self) -> (usize, Option<usize>) {
self.shape_iterator.size_hint()
}
}
impl<T, S, const D: usize> FusedIterator for TensorOwnedIterator<T, S, D> where S: TensorMut<T, D> {}
impl<T, S, const D: usize> ExactSizeIterator for TensorOwnedIterator<T, S, D> where
S: TensorMut<T, D>
{
}
impl<T, S, const D: usize> Iterator for WithIndex<TensorOwnedIterator<T, S, D>>
where
S: TensorMut<T, D>,
{
type Item = ([usize; D], T);
fn next(&mut self) -> Option<Self::Item> {
let index = self.iterator.shape_iterator.indexes;
self.iterator.next().map(|x| (index, x))
}
fn size_hint(&self) -> (usize, Option<usize>) {
self.iterator.size_hint()
}
}
impl<T, S, const D: usize> FusedIterator for WithIndex<TensorOwnedIterator<T, S, D>> where
S: TensorMut<T, D>
{
}
impl<T, S, const D: usize> ExactSizeIterator for WithIndex<TensorOwnedIterator<T, S, D>> where
S: TensorMut<T, D>
{
}
/**
* A TensorTranspose makes the data in the tensor it is created from appear to be in a different
* order, swapping the lengths of each named dimension to match the new order but leaving the
* dimension name order unchanged.
*
* ```
* use easy_ml::tensors::Tensor;
* use easy_ml::tensors::indexing::TensorTranspose;
* use easy_ml::tensors::views::TensorView;
* let tensor = Tensor::from([("batch", 2), ("rows", 3), ("columns", 2)], vec![
* 1, 2,
* 3, 4,
* 5, 6,
*
* 7, 8,
* 9, 0,
* 1, 2
* ]);
* let transposed = TensorView::from(TensorTranspose::from(&tensor, ["batch", "columns", "rows"]));
* assert_eq!(
* transposed,
* Tensor::from([("batch", 2), ("rows", 2), ("columns", 3)], vec![
* 1, 3, 5,
* 2, 4, 6,
*
* 7, 9, 1,
* 8, 0, 2
* ])
* );
* let also_transposed = tensor.transpose_view(["batch", "columns", "rows"]);
* ```
*/
#[derive(Clone)]
pub struct TensorTranspose<T, S, const D: usize> {
access: TensorAccess<T, S, D>,
}
impl<T: fmt::Debug, S: fmt::Debug, const D: usize> fmt::Debug for TensorTranspose<T, S, D> {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
f.debug_struct("TensorTranspose")
.field("source", &self.access.source)
.field("dimension_mapping", &self.access.dimension_mapping)
.field("_type", &self.access._type)
.finish()
}
}
impl<T, S, const D: usize> TensorTranspose<T, S, D>
where
S: TensorRef<T, D>,
{
/**
* Creates a TensorTranspose which makes the data appear in the order of the
* supplied dimensions. The order of the dimension names is unchanged, although their lengths
* may swap.
*
* # Panics
*
* If the set of dimensions in the tensor does not match the set of dimensions provided. The
* order need not match.
*/
#[track_caller]
pub fn from(source: S, dimensions: [Dimension; D]) -> TensorTranspose<T, S, D> {
TensorTranspose {
access: match TensorAccess::try_from(source, dimensions) {
Err(error) => panic!("{}", error),
Ok(success) => success,
},
}
}
/**
* Creates a TensorTranspose which makes the data to appear in the order of the
* supplied dimensions. The order of the dimension names is unchanged, although their lengths
* may swap.
*
* Returns Err if the set of dimensions supplied do not match the set of dimensions in this
* tensor's shape.
*/
pub fn try_from(
source: S,
dimensions: [Dimension; D],
) -> Result<TensorTranspose<T, S, D>, InvalidDimensionsError<D>> {
TensorAccess::try_from(source, dimensions).map(|access| TensorTranspose { access })
}
/**
* The shape of this TensorTranspose appears to rearrange the data to the order of supplied
* dimensions. The actual data in the underlying tensor and the order of the dimension names
* on this TensorTranspose remains unchanged, although the lengths of the dimensions in this
* shape of may swap compared to the source's shape.
*/
pub fn shape(&self) -> [(Dimension, usize); D] {
let names = self.access.source.view_shape();
let order = self.access.shape();
std::array::from_fn(|d| (names[d].0, order[d].1))
}
pub fn source(self) -> S {
self.access.source
}
// # Safety
//
// Giving out a mutable reference to our source could allow it to be changed out from under us
// and make our dimmension mapping invalid. However, since the source implements TensorRef
// interior mutability is not allowed, so we can give out shared references without breaking
// our own integrity.
pub fn source_ref(&self) -> &S {
&self.access.source
}
}
// # Safety
//
// The TensorAccess must implement TensorRef correctly, so by delegating to it without changing
// anything other than the order of the dimension names we expose, we implement
// TensoTensorRefrMut correctly as well.
/**
* A TensorTranspose implements TensorRef, with the dimension order and indexing matching that
* of the TensorTranspose shape.
*/
unsafe impl<T, S, const D: usize> TensorRef<T, D> for TensorTranspose<T, S, D>
where
S: TensorRef<T, D>,
{
fn get_reference(&self, indexes: [usize; D]) -> Option<&T> {
// we didn't change the lengths of any dimension in our shape from the TensorAccess so we
// can delegate to the tensor access for non named indexing here
self.access.try_get_reference(indexes)
}
fn view_shape(&self) -> [(Dimension, usize); D] {
self.shape()
}
unsafe fn get_reference_unchecked(&self, indexes: [usize; D]) -> &T {
self.access.get_reference_unchecked(indexes)
}
fn data_layout(&self) -> DataLayout<D> {
let data_layout = self.access.data_layout();
match data_layout {
DataLayout::Linear(order) => DataLayout::Linear(
self.access
.dimension_mapping
.map_linear_data_layout_to_transposed(&order),
),
_ => data_layout,
}
}
}
// # Safety
//
// The TensorAccess must implement TensorMut correctly, so so by delegating to it without changing
// anything other than the order of the dimension names we expose, we implement, we implement
// TensorMut correctly as well.
/**
* A TensorTranspose implements TensorMut, with the dimension order and indexing matching that of
* the TensorTranspose shape.
*/
unsafe impl<T, S, const D: usize> TensorMut<T, D> for TensorTranspose<T, S, D>
where
S: TensorMut<T, D>,
{
fn get_reference_mut(&mut self, indexes: [usize; D]) -> Option<&mut T> {
self.access.try_get_reference_mut(indexes)
}
unsafe fn get_reference_unchecked_mut(&mut self, indexes: [usize; D]) -> &mut T {
self.access.get_reference_unchecked_mut(indexes)
}
}
/**
* Any tensor transpose of a Displayable type implements Display
*
* You can control the precision of the formatting using format arguments, i.e.
* `format!("{:.3}", tensor)`
*/
impl<T: std::fmt::Display, S, const D: usize> std::fmt::Display for TensorTranspose<T, S, D>
where
T: std::fmt::Display,
S: TensorRef<T, D>,
{
fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
crate::tensors::display::format_view(&self, f)?;
writeln!(f)?;
write!(f, "Data Layout = {:?}", self.data_layout())
}
}