Struct TypedTensor 

pub struct TypedTensor<T, R = DynRank, D = Dynamic>
where
    R: TensorRank,
    D: Representation,
{ /* private fields */ }

Owned compact column-major typed tensor.

T is the element type, R the rank metadata (default DynRank) and D the owned representation (default Dynamic). Shape and placement live on the tensor itself, once, never inside a storage arm.

No bound is imposed on T by the type: plain host adoption only needs the constructor’s own requirements, and T: Clone is required only by the copying constructors. A preset TensorScalar appears only where a dtype identity or a numerical capability is actually used, never for plain host ownership.

Owned tensors are compact column-major. Arbitrary strides and metadata-only layout changes are represented by TypedTensorView and TypedTensorViewMut.

Examples

use tenferro_tensor::{DynRank, Host, Rank, Tensor, TypedTensor};

let t = TypedTensor::<f64>::from_vec_col_major(vec![2, 2], vec![1.0, 2.0, 3.0, 4.0]).unwrap();
assert_eq!(t.shape(), &[2, 2]);

let static_rank = TypedTensor::<f64, Rank<2>>::from_vec_col_major([2, 2], vec![1.0; 4]).unwrap();
assert_eq!(static_rank.rank(), 2);

let host: TypedTensor<i32, DynRank, Host> =
    TypedTensor::from_host_vec_col_major(vec![2], vec![1, 2]).unwrap();
assert_eq!(host.get(&[1])?, &2);

let dynamic = Tensor::from_vec_col_major(vec![2, 2], vec![1.0_f64; 4]).unwrap();
assert_eq!(dynamic.shape(), &[2, 2]);

Implementations

impl<T, R> TypedTensor<T, R>

where T: TensorScalar, R: TensorRank,

pub fn iter(&self) -> Result<Iter<'_, T>, Error>

Iterate over the contiguous column-major host buffer.

Examples

use tenferro_tensor::TypedTensor;

let t = TypedTensor::<f64>::from_vec_col_major(vec![2], vec![1.0, 2.0]).unwrap();
let sum: f64 = t.iter()?.copied().sum();
assert_eq!(sum, 3.0);

Errors

Returns crate::Error::RuntimeState when the tensor is backed by a device buffer and has not been downloaded to host memory.

pub fn iter_mut(&mut self) -> Result<IterMut<'_, T>, Error>

Mutably iterate over the contiguous column-major host buffer.

Examples

use tenferro_tensor::TypedTensor;

let mut t = TypedTensor::<f64>::from_vec_col_major(vec![2], vec![1.0, 2.0]).unwrap();
for value in t.iter_mut()? {
    *value *= 2.0;
}
assert_eq!(t.as_slice()?, &[2.0, 4.0]);

Errors

Returns crate::Error::RuntimeState when the tensor is backed by a device buffer and has not been downloaded to host memory.

pub fn linear_offset2(&self, i: usize, j: usize) -> Result<usize, Error>

Compute the linear physical-buffer offset for a rank-2 logical index.

Examples

use tenferro_tensor::TypedTensor;

let t = TypedTensor::<f64>::from_vec_col_major(vec![2, 3], vec![0.0; 6]).unwrap();
assert_eq!(t.linear_offset2(1, 2)?, 5);

Errors

Returns crate::Error::Validation containing tenferro_tensor_core::ValidationError::RankMismatch when the tensor rank is not two, tenferro_tensor_core::ValidationError::InvalidArgument when i or j is outside its axis extent, or tenferro_tensor_core::ValidationError::IntegerOverflow when checked offset arithmetic overflows.

pub fn linear_offset3( &self, i: usize, j: usize, k: usize, ) -> Result<usize, Error>

Compute the linear physical-buffer offset for a rank-3 logical index.

Examples

use tenferro_tensor::TypedTensor;

let t = TypedTensor::<f64>::from_vec_col_major(vec![2, 3, 2], vec![0.0; 12]).unwrap();
assert_eq!(t.linear_offset3(1, 2, 1)?, 11);

Errors

Returns crate::Error::Validation containing tenferro_tensor_core::ValidationError::RankMismatch when the tensor rank is not three, tenferro_tensor_core::ValidationError::InvalidArgument when i, j, or k is outside its axis extent, or tenferro_tensor_core::ValidationError::IntegerOverflow when checked offset arithmetic overflows.

pub fn get2(&self, i: usize, j: usize) -> Result<&T, Error>

Borrow a single element by rank-2 logical index.

Examples

use tenferro_tensor::TypedTensor;

let t = TypedTensor::<f64>::from_vec_col_major(vec![2, 2], vec![1.0, 2.0, 3.0, 4.0]).unwrap();
assert_eq!(t.get2(1, 0)?, &2.0);

Errors

Returns crate::Error::Validation containing tenferro_tensor_core::ValidationError::RankMismatch when the tensor rank is not two, tenferro_tensor_core::ValidationError::InvalidArgument when i or j is outside its axis extent, or tenferro_tensor_core::ValidationError::IntegerOverflow when checked offset arithmetic overflows. It returns crate::Error::RuntimeState when the tensor is backed by a device buffer, or tenferro_tensor_core::ValidationError::InvalidArgument if the computed offset is outside the host buffer.

pub fn get3(&self, i: usize, j: usize, k: usize) -> Result<&T, Error>

Borrow a single element by rank-3 logical index.

Examples

use tenferro_tensor::TypedTensor;

let t = TypedTensor::<f64>::from_vec_col_major(vec![1, 1, 2], vec![3.0, 4.0]).unwrap();
assert_eq!(t.get3(0, 0, 1)?, &4.0);

Errors

Returns crate::Error::Validation containing tenferro_tensor_core::ValidationError::RankMismatch when the tensor rank is not three, tenferro_tensor_core::ValidationError::InvalidArgument when i, j, or k is outside its axis extent, or tenferro_tensor_core::ValidationError::IntegerOverflow when checked offset arithmetic overflows. It returns crate::Error::RuntimeState when the tensor is backed by a device buffer, or tenferro_tensor_core::ValidationError::InvalidArgument if the computed offset is outside the host buffer.

pub unsafe fn get_unchecked(&self, indices: &[usize]) -> Result<&T, Error>

Borrow a single element by multi-index without release-mode bounds checks.

Debug builds still validate the rank and bounds.

Safety

indices must have the same rank as this tensor and every index must be in bounds.

Examples

use tenferro_tensor::TypedTensor;

let t = TypedTensor::<f64>::from_vec_col_major(vec![2], vec![1.0, 2.0]).unwrap();
assert_eq!(unsafe { *t.get_unchecked(&[1])? }, 2.0);

Errors

Returns crate::Error::RuntimeState when the tensor is backed by a device buffer and has not been downloaded to host memory.

Panics

May panic if the unsafe rank/bounds precondition is violated and the checked linear-offset calculation overflows.

pub fn get_mut2(&mut self, i: usize, j: usize) -> Result<&mut T, Error>

Mutably borrow a single element by rank-2 logical index.

Examples

use tenferro_tensor::TypedTensor;

let mut t = TypedTensor::<f64>::from_vec_col_major(vec![2, 2], vec![1.0, 2.0, 3.0, 4.0]).unwrap();
*t.get_mut2(1, 0)? = 5.0;
assert_eq!(t.as_slice()?, &[1.0, 5.0, 3.0, 4.0]);

Errors

Returns crate::Error::Validation containing tenferro_tensor_core::ValidationError::RankMismatch when the tensor rank is not two, tenferro_tensor_core::ValidationError::InvalidArgument when i or j is outside its axis extent, or tenferro_tensor_core::ValidationError::IntegerOverflow when checked offset arithmetic overflows. It returns crate::Error::RuntimeState when the tensor is backed by a device buffer, or tenferro_tensor_core::ValidationError::InvalidArgument if the computed offset is outside the host buffer.

pub fn get_mut3( &mut self, i: usize, j: usize, k: usize, ) -> Result<&mut T, Error>

Mutably borrow a single element by rank-3 logical index.

Examples

use tenferro_tensor::TypedTensor;

let mut t = TypedTensor::<f64>::from_vec_col_major(vec![1, 1, 2], vec![3.0, 4.0]).unwrap();
*t.get_mut3(0, 0, 1)? = 5.0;
assert_eq!(t.as_slice()?, &[3.0, 5.0]);

Errors

Returns crate::Error::Validation containing tenferro_tensor_core::ValidationError::RankMismatch when the tensor rank is not three, tenferro_tensor_core::ValidationError::InvalidArgument when i, j, or k is outside its axis extent, or tenferro_tensor_core::ValidationError::IntegerOverflow when checked offset arithmetic overflows. It returns crate::Error::RuntimeState when the tensor is backed by a device buffer, or tenferro_tensor_core::ValidationError::InvalidArgument if the computed offset is outside the host buffer.

pub unsafe fn get_unchecked_mut( &mut self, indices: &[usize], ) -> Result<&mut T, Error>

Mutably borrow a single element by multi-index without release-mode bounds checks.

Debug builds still validate the rank and bounds.

Safety

indices must have the same rank as this tensor and every index must be in bounds.

Examples

use tenferro_tensor::TypedTensor;

let mut t = TypedTensor::<f64>::from_vec_col_major(vec![1], vec![1.0]).unwrap();
unsafe {
    *t.get_unchecked_mut(&[0])? = 2.0;
}
assert_eq!(t.as_slice()?, &[2.0]);

Errors

Returns crate::Error::RuntimeState when the tensor is backed by a device buffer and has not been downloaded to host memory.

Panics

May panic if the unsafe rank/bounds precondition is violated and the checked linear-offset calculation overflows.

impl<T, const N: usize> TypedTensor<T, Rank<N>>

where T: TensorScalar,

pub fn host_col_major_view(&self) -> Result<ColMajorView<'_, T, N>, Error>

Validate and borrow this owned tensor as a compact column-major host view.

Examples

use tenferro_tensor::{Rank, TypedTensor};
let tensor = TypedTensor::<i32, Rank<2>>::from_vec_col_major([2, 1], vec![1, 2])?;
assert_eq!(tensor.host_col_major_view()?.get([1, 0]), Some(&2));

Errors

Returns crate::Error::RuntimeState when storage is backend-owned, or crate::Error::Validation when compact layout, shape arithmetic, or the logical host range is invalid.

pub fn host_col_major_view_mut( &mut self, ) -> Result<ColMajorViewMut<'_, T, N>, Error>

Validate and mutably borrow this owned tensor as a compact column-major host view.

Examples

use tenferro_tensor::{Rank, TypedTensor};
let mut tensor = TypedTensor::<i32, Rank<1>>::from_vec_col_major([1], vec![1])?;
if let Some(value) = tensor.host_col_major_view_mut()?.get_mut([0]) { *value = 3; }
assert_eq!(tensor.as_slice()?, &[3]);

Errors

Returns crate::Error::RuntimeState when storage is backend-owned, or crate::Error::Validation when compact layout, shape arithmetic, or the logical host range is invalid.

impl<T, const N: usize> TypedTensor<T, Rank<N>, Host>

where T: TensorScalar,

pub fn host_col_major_view(&self) -> Result<ColMajorView<'_, T, N>, Error>

Validate and borrow this host-marked tensor as a compact column-major host view. The Host marker is kept: no erasure to Dynamic is needed.

Examples

use tenferro_tensor::{Host, Rank, TypedTensor};
let tensor =
    TypedTensor::<i32, Rank<2>, Host>::from_host_vec_col_major([2, 1], vec![1, 2])?;
assert_eq!(tensor.host_col_major_view()?.get([1, 0]), Some(&2));

Errors

Returns crate::Error::Validation when compact layout, shape arithmetic, or the logical host range is invalid.

pub fn host_col_major_view_mut( &mut self, ) -> Result<ColMajorViewMut<'_, T, N>, Error>

Validate and mutably borrow this host-marked tensor as a compact column-major host view.

Examples

use tenferro_tensor::{Host, Rank, TypedTensor};
let mut tensor = TypedTensor::<i32, Rank<1>, Host>::from_host_vec_col_major([1], vec![1])?;
if let Some(value) = tensor.host_col_major_view_mut()?.get_mut([0]) { *value = 3; }
assert_eq!(tensor.as_slice(), &[3]);

Errors

Returns crate::Error::Validation when compact layout, shape arithmetic, or the logical host range is invalid.

impl<T, R> TypedTensor<T, R, Host>

where R: TensorRank,

pub fn from_host_vec_col_major( shape: impl IntoRankShape<R>, data: Vec<T>, ) -> Result<TypedTensor<T, R, Host>, Error>

Adopt a column-major host Vec<T> as a statically host-owned tensor.

The payload is the caller’s vector itself; T needs no Copy, TensorScalar or arithmetic bound, and no group, session or device is involved.

Examples

use tenferro_tensor::{Host, Rank, TypedTensor};
struct Custom(String);
let tensor = TypedTensor::<Custom, Rank<2>, Host>::from_host_vec_col_major(
    [1, 2], vec![Custom("a".into()), Custom("b".into())],
)?;
assert_eq!(tensor[&[0, 1]].0.as_str(), "b");

Errors

Returns crate::Error::Validation for a rank mismatch, shape/data length mismatch or shape/stride arithmetic overflow.

pub fn from_host_vec_row_major( shape: impl IntoRankShape<R>, data: Vec<T>, ) -> Result<TypedTensor<T, R, Host>, Error>

where T: Clone,

Explicitly import row-major host values into column-major storage.

Clones each input element once.

Examples

use tenferro_tensor::{Host, Rank, TypedTensor};
let tensor = TypedTensor::<i32, Rank<2>, Host>::from_host_vec_row_major(
    [2, 3], vec![1, 2, 3, 4, 5, 6],
)?;
assert_eq!(tensor[&[1, 0]], 4);
assert_eq!(tensor[&[0, 2]], 3);

Errors

Returns crate::Error::Validation for a rank, shape-length or stride overflow, or when the shape product disagrees with the input length.

pub fn as_slice(&self) -> &[T]

Borrow the owned host elements.

Examples

use tenferro_tensor::{DynRank, Host, TypedTensor};
let tensor = TypedTensor::<i32, DynRank, Host>::from_host_vec_col_major(vec![2], vec![1, 2])?;
assert_eq!(tensor.as_slice(), &[1, 2]);

pub fn host_data(&self) -> &[T]

Alias of Self::as_slice.

Examples

use tenferro_tensor::{DynRank, Host, TypedTensor};
let tensor = TypedTensor::<i32, DynRank, Host>::from_host_vec_col_major(vec![2], vec![1, 2])?;
assert_eq!(tensor.host_data(), &[1, 2]);

pub fn host_data_mut(&mut self) -> &mut [T]

Exclusively borrow the owned host elements.

Examples

use tenferro_tensor::{DynRank, Host, TypedTensor};
let mut tensor = TypedTensor::<i32, DynRank, Host>::from_host_vec_col_major(vec![2], vec![1, 2])?;
tensor.host_data_mut()[0] = 5;
assert_eq!(tensor.as_slice(), &[5, 2]);

pub fn get(&self, indices: &[usize]) -> Result<&T, Error>

Borrow one element by checked column-major multi-index.

Examples

use tenferro_tensor::{DynRank, Host, TypedTensor};
let tensor = TypedTensor::<i32, DynRank, Host>::from_host_vec_col_major(vec![2, 2], vec![1, 2, 3, 4])?;
assert_eq!(tensor.get(&[1, 1])?, &4);
assert!(tensor.get(&[2, 0]).is_err());

Errors

Returns crate::Error::Validation for a wrong rank, an out-of-range coordinate or offset arithmetic overflow.

pub fn get_mut(&mut self, indices: &[usize]) -> Result<&mut T, Error>

Exclusively borrow one element by checked column-major multi-index.

Examples

use tenferro_tensor::{DynRank, Host, TypedTensor};
let mut tensor = TypedTensor::<i32, DynRank, Host>::from_host_vec_col_major(vec![2], vec![1, 2])?;
*tensor.get_mut(&[1])? = 9;
assert_eq!(tensor.as_slice(), &[1, 9]);

Errors

Returns crate::Error::Validation for a wrong rank, an out-of-range coordinate or offset arithmetic overflow.

pub fn into_host_vec(self) -> Vec<T>

Consume this tensor and return the original host vector.

Examples

use tenferro_tensor::{DynRank, Host, TypedTensor};
let tensor = TypedTensor::<i32, DynRank, Host>::from_host_vec_col_major(vec![2], vec![1, 2])?;
assert_eq!(tensor.into_host_vec(), vec![1, 2]);

pub fn into_vec_col_major(self) -> (Vec<usize>, Vec<T>)

Consume this tensor and return its shape and column-major host vector.

Examples

use tenferro_tensor::{DynRank, Host, TypedTensor};
let tensor = TypedTensor::<i32, DynRank, Host>::from_host_vec_col_major(vec![2, 1], vec![1, 2])?;
let (shape, data) = tensor.into_vec_col_major();
assert_eq!(shape, vec![2, 1]);
assert_eq!(data, vec![1, 2]);

pub fn duplicate(&self) -> TypedTensor<T, R, Host>

where T: Clone,

Make an explicit independent host copy with the same placement.

Examples

use tenferro_tensor::{DynRank, Host, TypedTensor};
let tensor = TypedTensor::<String, DynRank, Host>::from_host_vec_col_major(vec![1], vec!["a".into()])?;
let copy = tensor.clone();
assert_eq!(copy[&[0]], "a");

pub fn into_dynamic(self) -> TypedTensor<T, R>

Move this host owner into the runtime union without copying or regrouping.

Examples

use tenferro_tensor::{Dynamic, DynRank, Host, TypedTensor};
let host = TypedTensor::<i32, DynRank, Host>::from_host_vec_col_major(vec![1], vec![7])?;
let dynamic: TypedTensor<i32, DynRank, Dynamic> = host.into_dynamic();
assert_eq!(dynamic.host_data()?, &[7]);

pub fn as_view(&self) -> TypedTensorView<'_, T, R, Host>

Borrow this host owner as a typed view.

Examples

use tenferro_tensor::{DynRank, Host, TypedTensor};
let tensor = TypedTensor::<i32, DynRank, Host>::from_host_vec_col_major(vec![2], vec![1, 2])?;
let view = tensor.as_view();
assert_eq!(view.shape(), &[2]);
assert_eq!(view.as_host_slice(), &[1, 2]);

pub fn as_view_mut(&mut self) -> TypedTensorViewMut<'_, T, R, Host>

Exclusively borrow this host owner as a mutable typed view.

Examples

use tenferro_tensor::{DynRank, Host, TypedTensor};
let mut tensor = TypedTensor::<i32, DynRank, Host>::from_host_vec_col_major(vec![2], vec![1, 2])?;
tensor.as_view_mut().as_host_slice_mut()[1] = 4;
assert_eq!(tensor.as_slice(), &[1, 4]);

impl<T, R> TypedTensor<T, R, Host>

where R: TensorRank,

pub fn map_read(&self) -> HostReadGuard<'_, T>

Borrow the owned host elements through one owning read mapping.

The guard retains the exclusive borrow of this owner, so no other access can overlap it while the mapping is alive.

Examples

use tenferro_tensor::{DynRank, Host, TypedTensor};
let tensor = TypedTensor::<i32, DynRank, Host>::from_host_vec_col_major(vec![2], vec![1, 2])?;
let guard = tensor.map_read();
assert_eq!(&guard[..], &[1, 2]);

pub fn map_write(&mut self) -> HostWriteGuard<'_, T>

where T: Clone,

Exclusively borrow the owned host elements through one owning write mapping.

Publish data with HostWriteGuard::copy_from_slice; the mapping is released when the guard is dropped.

Examples

use tenferro_tensor::{DynRank, Host, TypedTensor};
let mut tensor = TypedTensor::<i32, DynRank, Host>::from_host_vec_col_major(vec![2], vec![1, 2])?;
tensor.map_write().copy_from_slice(&[3, 4]).unwrap();
assert_eq!(tensor.as_slice(), &[3, 4]);

impl<T, R> TypedTensor<T, R, Host>

where T: TensorScalar, R: TensorRank,

pub fn promote(self) -> Result<TypedTensor<T, R, Gpu>, Error>

Promote this plain host owner into a group-backed owner.

The payload is adopted as a provider root: no element is copied and a pooled return target survives the promotion.

Examples

use tenferro_tensor::{DynRank, Host, TypedTensor};
let host = TypedTensor::<f64, DynRank, Host>::from_host_vec_col_major(vec![2], vec![1.0, 2.0])?;
let gpu = host.promote()?;
assert_eq!(gpu.host_data()?, &[1.0, 2.0]);

Errors

Returns crate::Error::RuntimeState when the new allocation group rejects the promoted root or its recycler.

impl<T, R> TypedTensor<T, R>

where R: TensorRank,

pub fn map_read(&self) -> Result<HostReadGuard<'_, T>, Error>

Borrow host elements through one owning read mapping.

Examples

use tenferro_tensor::TypedTensor;
let tensor = TypedTensor::<i32>::from_vec_col_major(vec![2], vec![1, 2])?;
let guard = tensor.map_read()?;
assert_eq!(&guard[..], &[1, 2]);

Errors

Returns crate::Error::HostAccess with HostAccessError::Unsupported when the group’s allocation is not host-accessible.

pub fn map_write(&mut self) -> Result<HostWriteGuard<'_, T>, Error>

where T: Clone,

Exclusively borrow host elements through one owning write mapping.

Publish data with HostWriteGuard::copy_from_slice.

Examples

use tenferro_tensor::{Error, TypedTensor};
let mut tensor = TypedTensor::<i32>::from_vec_col_major(vec![2], vec![1, 2])?;
tensor
    .map_write()?
    .copy_from_slice(&[3, 4])
    .map_err(|err| Error::host_access("map_write", err))?;
assert_eq!(tensor.host_data()?, &[3, 4]);

Errors

Returns crate::Error::HostAccess with HostAccessError::Unsupported when the group’s allocation is not host-writable.

impl<T, R> TypedTensor<T, R, Gpu>

where R: TensorRank,

pub fn from_backend_buffer_col_major( shape: impl IntoRankShape<R>, buffer: StorageBuffer<T>, placement: Placement, ) -> Result<TypedTensor<T, R, Gpu>, Error>

where T: TensorScalar + Send + Sync + 'static,

Adopt a backend-owned buffer as a statically group-backed owner.

A host buffer belongs to the Host representation; pass it to TypedTensor::<T, R, Host> instead of silently regrouping it here.

Examples

use tenferro_tensor::{BackendStorageHandle, Placement, StorageBuffer, TypedTensor};
let handle = BackendStorageHandle::<f32>::new_with_len(1, 2);
let gpu = TypedTensor::<f32, tenferro_tensor::DynRank, tenferro_tensor::Gpu>::from_backend_buffer_col_major(
    vec![2],
    StorageBuffer::Backend(Box::new(handle)),
    Placement::default(),
)?;
assert!(gpu.is_backend_buffer());

Errors

Returns crate::Error::Validation when the compact shape disagrees with the buffer length, crate::Error::RuntimeState for a host buffer, or a runtime-state error when the allocation group cannot be built.

pub fn into_dynamic(self) -> TypedTensor<T, R>

Move this group-backed owner into the runtime union without rewriting it.

Examples

use tenferro_tensor::{Dynamic, DynRank, Host, TypedTensor};
let gpu = TypedTensor::<f64, DynRank, Host>::from_host_vec_col_major(vec![1], vec![7.0])?.promote()?;
let dynamic: TypedTensor<f64, DynRank, Dynamic> = gpu.into_dynamic();
assert_eq!(dynamic.host_data()?, &[7.0]);

pub fn is_backend_buffer(&self) -> bool

Whether this group’s descriptor names a non-CPU provider.

Examples

use tenferro_tensor::{DynRank, Host, TypedTensor};
let gpu = TypedTensor::<f64, DynRank, Host>::from_host_vec_col_major(vec![1], vec![7.0])?.promote()?;
// A promoted host payload stays on the CPU provider.
assert!(!gpu.is_backend_buffer());

pub fn host_data(&self) -> Result<&[T], Error>

Borrow host elements when the group’s allocation is host-accessible.

Examples

use tenferro_tensor::{DynRank, Host, TypedTensor};
let gpu = TypedTensor::<f64, DynRank, Host>::from_host_vec_col_major(vec![2], vec![1.0, 2.0])?.promote()?;
assert_eq!(gpu.host_data()?, &[1.0, 2.0]);

Errors

Returns crate::Error::RuntimeState for a device-only allocation.

pub fn host_data_mut(&mut self) -> Result<&mut [T], Error>

Exclusively borrow host elements when the group’s allocation is host-accessible.

Examples

use tenferro_tensor::{DynRank, Host, TypedTensor};
let mut gpu = TypedTensor::<f64, DynRank, Host>::from_host_vec_col_major(vec![2], vec![1.0, 2.0])?.promote()?;
gpu.host_data_mut()?[0] = 3.0;
assert_eq!(gpu.host_data()?, &[3.0, 2.0]);

Errors

Returns crate::Error::RuntimeState for a device-only allocation.

impl<T, R> TypedTensor<T, R>

where R: TensorRank,

pub fn into_host( self, ) -> Result<TypedTensor<T, R, Host>, ReinterpretError<TypedTensor<T, R>>>

Checked narrowing to the statically host-owned representation.

Fails without consuming ownership of the source tensor.

Examples

use tenferro_tensor::TypedTensor;
let dynamic = TypedTensor::<i32>::from_vec_col_major(vec![1], vec![7])?;
let host = dynamic.into_host().unwrap();
assert_eq!(host.as_slice(), &[7]);

Errors

Returns ReinterpretError carrying the unchanged tensor when it is group-backed rather than plain host storage.

pub fn into_gpu( self, ) -> Result<TypedTensor<T, R, Gpu>, ReinterpretError<TypedTensor<T, R>>>

Checked narrowing to the group-backed representation.

Fails without consuming ownership of the source tensor.

Examples

use tenferro_tensor::{DynRank, Gpu, Host, TypedTensor};
let host = TypedTensor::<f64, DynRank, Host>::from_host_vec_col_major(vec![1], vec![2.0])?;
let dynamic = host.promote()?.into_dynamic();
let gpu: TypedTensor<f64, DynRank, Gpu> = dynamic.into_gpu().map_err(|f| f.into_parts().1)?;
assert_eq!(gpu.host_data()?, &[2.0]);
let plain = TypedTensor::<f64>::from_vec_col_major(vec![1], vec![2.0])?;
assert!(plain.into_gpu().is_err());

Errors

Returns ReinterpretError carrying the unchanged tensor when it is a plain host owner rather than a group-backed one.

impl<T, R, D> TypedTensor<T, R, D>

where R: TensorRank, D: Representation,

pub fn n_elements(&self) -> usize

Number of elements in the tensor.

Examples

use tenferro_tensor::TypedTensor;

let t = TypedTensor::<f64>::from_vec_col_major(vec![2, 3], vec![0.0; 6]).unwrap();
assert_eq!(t.n_elements(), 6);

pub fn shape(&self) -> &[usize]

Tensor shape.

Examples

use tenferro_tensor::TypedTensor;

let t = TypedTensor::<f64>::from_vec_col_major(vec![2], vec![1.0, 2.0]).unwrap();
assert_eq!(t.shape(), &[2]);

pub fn rank(&self) -> usize

Tensor rank.

Examples

use tenferro_tensor::TypedTensor;

let t = TypedTensor::<f64>::from_vec_col_major(vec![2, 3], vec![0.0; 6]).unwrap();
assert_eq!(t.rank(), 2);

pub fn layout(&self) -> TensorLayout<R>

Tensor layout metadata.

Owned typed tensors are always compact column-major layouts.

Examples

use tenferro_tensor::TypedTensor;

let t = TypedTensor::<f64>::from_vec_col_major(vec![2, 3], vec![0.0; 6]).unwrap();
assert_eq!(t.layout().strides(), &[1, 2]);

pub fn placement(&self) -> &Placement

Return placement metadata for this tensor.

Examples

use tenferro_tensor::{MemoryKind, TypedTensor};

let t = TypedTensor::<f64>::from_vec_col_major(vec![1], vec![1.0]).unwrap();
assert_eq!(t.placement().memory_kind, MemoryKind::UnpinnedHost);

pub fn set_placement(&mut self, placement: Placement)

Replace placement metadata without changing the storage buffer.

Examples

use tenferro_tensor::{MemoryKind, Placement, TypedTensor};

let mut t = TypedTensor::<f64>::from_vec_col_major(vec![1], vec![1.0]).unwrap();
t.set_placement(Placement {
    memory_kind: MemoryKind::PinnedHost,
    device: None,
    cpu_affinity: None,
});
assert_eq!(t.placement().memory_kind, MemoryKind::PinnedHost);

pub fn set_cpu_affinity(&mut self, cpu_affinity: Option<CpuDomainId>)

Replace only CPU routing/locality metadata without changing storage.

Device, memory kind, backend allocation domain, and allocation identity remain unchanged.

Examples

use tenferro_tensor::{CpuDomainId, TypedTensor};

let mut tensor = TypedTensor::<f64>::from_vec_col_major(vec![1], vec![1.0])?;
tensor.set_cpu_affinity(Some(CpuDomainId::new(4)));
assert_eq!(tensor.placement().cpu_affinity, Some(CpuDomainId::new(4)));

pub fn linear_offset(&self, indices: &[usize]) -> Result<usize, Error>

Compute the checked compact column-major offset of an element.

Examples

use tenferro_tensor::TypedTensor;
let tensor = TypedTensor::<String>::from_vec_col_major([2, 3], vec![String::new(); 6])?;
assert_eq!(tensor.linear_offset(&[1, 2])?, 5);

Errors

Wrong rank, out-of-range coordinates or arithmetic overflow return crate::Error::Validation.

pub fn into_layout(self) -> TensorLayout<R>

Consume this tensor and return its layout metadata.

Examples

use tenferro_tensor::TypedTensor;

let t = TypedTensor::<f64>::from_vec_col_major(vec![2], vec![1.0, 2.0]).unwrap();
assert!(t.into_layout().is_compact_col_major().unwrap());

pub fn layout_linear_offset(&self, indices: &[usize]) -> Result<usize, Error>

Compute the physical element offset for a logical index.

Examples

use tenferro_tensor::TypedTensor;

let t = TypedTensor::<f64>::zeros(vec![2, 3]).unwrap();
assert_eq!(t.layout_linear_offset(&[1, 2])?, 5);

Errors

Returns crate::Error::Validation with tenferro_tensor_core::ValidationError::RankMismatch when indices has the wrong rank, tenferro_tensor_core::ValidationError::InvalidArgument when an index is outside its axis extent, or tenferro_tensor_core::ValidationError::IntegerOverflow when offset arithmetic overflows.

pub fn is_col_major_contiguous(&self) -> Result<bool, Error>

Return whether this owned tensor is compact column-major.

Examples

use tenferro_tensor::TypedTensor;

let t = TypedTensor::<f64>::zeros(vec![2]).unwrap();
assert!(t.is_col_major_contiguous()?);

Errors

Returns crate::Error::Validation with tenferro_tensor_core::ValidationError::IntegerOverflow when compactness arithmetic overflows.

pub fn layout_summary(&self) -> String

Return a compact string summary of this tensor’s layout metadata.

Examples

use tenferro_tensor::TypedTensor;

let t = TypedTensor::<f64>::zeros(vec![2]).unwrap();
assert!(t.layout_summary().contains("shape=[2]"));

pub fn assert_col_major_contiguous(&self) -> Result<(), Error>

Assert this tensor is compact column-major.

Examples

use tenferro_tensor::TypedTensor;

let t = TypedTensor::<f64>::zeros(vec![2]).unwrap();
t.assert_col_major_contiguous()?;

Errors

Returns crate::Error::Validation with tenferro_tensor_core::ValidationError::IntegerOverflow when compactness arithmetic overflows, or tenferro_tensor_core::ValidationError::InvalidArgument when the tensor is not compact column-major.

impl<T, R> TypedTensor<T, R>

where T: TensorScalar + Zero, R: TensorRank,

pub fn zeros(shape: impl IntoRankShape<R>) -> Result<TypedTensor<T, R>, Error>

Allocate a zero-filled tensor.

Examples

use tenferro_tensor::TypedTensor;

let t = TypedTensor::<f64>::zeros(vec![2, 3]).unwrap();
assert_eq!(t.n_elements(), 6);

Errors

Returns crate::Error::Validation with tenferro_tensor_core::ValidationError::IntegerOverflow when shape product or compact-stride arithmetic overflows. A vector or slice with a different static rank returns tenferro_tensor_core::ValidationError::RankMismatch.

impl<T, R> TypedTensor<T, R>

where T: TensorScalar + One + Zero, R: TensorRank,

pub fn ones(shape: impl IntoRankShape<R>) -> Result<TypedTensor<T, R>, Error>

Allocate a one-filled tensor.

Examples

use tenferro_tensor::TypedTensor;

let t = TypedTensor::<f64>::ones(vec![2]).unwrap();
assert_eq!(t.host_data().unwrap(), &[1.0, 1.0]);

Errors

Returns crate::Error::Validation with tenferro_tensor_core::ValidationError::IntegerOverflow when shape product or compact-stride arithmetic overflows. A vector or slice with a different static rank returns tenferro_tensor_core::ValidationError::RankMismatch.

impl<T, R> TypedTensor<T, R>

where R: TensorRank,

pub fn from_vec_col_major( shape: impl IntoRankShape<R>, data: Vec<T>, ) -> Result<TypedTensor<T, R>, Error>

Adopt a column-major host Vec<T> with no scalar, copy or thread-safety bound.

Examples

use tenferro_tensor::{Rank, TypedTensor};
struct Custom(String);
let tensor = TypedTensor::<Custom, Rank<2>>::from_vec_col_major(
    [1, 2], vec![Custom("a".into()), Custom("b".into())],
)?;
assert_eq!(tensor.get(&[0, 1])?.0.as_str(), "b");

Errors

Returns crate::Error::Validation for a rank mismatch, shape/data length mismatch or shape/stride arithmetic overflow.

pub fn from_vec_row_major( shape: impl IntoRankShape<R>, data: Vec<T>, ) -> Result<TypedTensor<T, R>, Error>

where T: Clone,

Explicitly import row-major host values into column-major storage.

Clones each input element once; no backend or scalar registration is used.

Examples

use tenferro_tensor::{Rank, TypedTensor};
let tensor = TypedTensor::<i32, Rank<2>>::from_vec_row_major(
    [2, 3], vec![1, 2, 3, 4, 5, 6],
)?;
assert_eq!(tensor.get(&[1, 0])?, &4);
assert_eq!(tensor.get(&[0, 2])?, &3);

Errors

Returns crate::Error::Validation for a rank, shape-length or stride overflow, or when the shape product disagrees with the input length.

pub fn into_host_vec( self, ) -> Result<Vec<T>, ReinterpretError<TypedTensor<T, R>>>

where T: 'static,

Consume this compact tensor and return the original host Vec<T>.

Examples

use tenferro_tensor::TypedTensor;
let tensor = TypedTensor::<i32>::from_vec_col_major(vec![2], vec![1, 2])?;
let data = tensor.into_host_vec().map_err(|failure| failure.into_parts().1)?;
assert_eq!(data, vec![1, 2]);

Errors

Returns ReinterpretError carrying the unchanged tensor when the storage is device-only or the managed root cannot export a host vector.

pub fn into_vec_col_major( self, ) -> Result<(Vec<usize>, Vec<T>), ReinterpretError<TypedTensor<T, R>>>

where T: 'static,

Consume the original host vector along with its column-major shape.

Examples

use tenferro_tensor::TypedTensor;
let tensor = TypedTensor::<i32>::from_vec_col_major(vec![2, 1], vec![1, 2])?;
let (shape, data) = tensor.into_vec_col_major().map_err(|failure| failure.into_parts().1)?;
assert_eq!(shape, vec![2, 1]);
assert_eq!(data, vec![1, 2]);

Errors

Returns ReinterpretError carrying the unchanged tensor when the storage is device-only or the managed root cannot export a host vector.

pub fn host_data(&self) -> Result<&[T], Error>

Borrow the plain host values (or an existing managed host root).

Examples

use tenferro_tensor::TypedTensor;
let tensor = TypedTensor::<f64>::from_vec_col_major(vec![2], vec![1.0, 2.0])?;
assert_eq!(tensor.host_data()?, &[1.0, 2.0]);

Errors

Returns crate::Error::RuntimeState for device-only storage.

pub fn as_slice(&self) -> Result<&[T], Error>

Borrow compact host storage as a flat column-major slice without copying.

Examples

use tenferro_tensor::TypedTensor;
let tensor = TypedTensor::<String>::from_vec_col_major([2], vec!["a".into(), "b".into()])?;
assert_eq!(tensor.as_slice()?, &["a", "b"]);

Errors

Device-only storage returns crate::Error::RuntimeState.

pub fn host_data_mut(&mut self) -> Result<&mut [T], Error>

Mutably borrow the plain host values (or an existing managed host root).

Examples

use tenferro_tensor::TypedTensor;
let mut tensor = TypedTensor::<f64>::from_vec_col_major(vec![2], vec![1.0, 2.0])?;
tensor.host_data_mut()?[0] = 5.0;
assert_eq!(tensor.host_data()?, &[5.0, 2.0]);

Errors

Returns crate::Error::RuntimeState for device-only storage.

pub fn get(&self, indices: &[usize]) -> Result<&T, Error>

Borrow an element by checked column-major multi-index.

Examples

use tenferro_tensor::TypedTensor;
let tensor = TypedTensor::<String>::from_vec_col_major([2], vec!["a".into(), "b".into()])?;
assert_eq!(tensor.get(&[1])?, "b");

Errors

Invalid rank, coordinates or offset return crate::Error::Validation; device-only storage returns crate::Error::RuntimeState.

pub fn get_mut(&mut self, indices: &[usize]) -> Result<&mut T, Error>

Exclusively borrow an element by checked column-major multi-index.

Examples

use tenferro_tensor::TypedTensor;
let mut tensor = TypedTensor::<String>::from_vec_col_major([1], vec!["a".into()])?;
*tensor.get_mut(&[0])? = "b".into();
assert_eq!(tensor.get(&[0])?, "b");

Errors

Invalid rank, coordinates or offset return crate::Error::Validation; device-only storage returns crate::Error::RuntimeState.

pub fn duplicate(&self) -> Result<TypedTensor<T, R>, Error>

where T: Clone,

Make an explicit independent host copy with the same shape and placement.

The dynamic TypedTensor does not implement Clone (only the host-only representation does): the copy reads host data, rejects device-only storage, and creates a new owner, so it is fallible. See Tensor::duplicate for the sharing alternatives.

Examples

use tenferro_tensor::TypedTensor;
let tensor = TypedTensor::<String>::from_vec_col_major([1], vec!["a".into()])?;
assert_eq!(tensor.duplicate()?.get(&[0])?, "a");

Errors

Device-only storage returns crate::Error::RuntimeState; invalid host metadata returns crate::Error::Validation.

pub fn from_buffer_col_major( shape: impl IntoRankShape<R>, buffer: StorageBuffer<T>, placement: Placement, ) -> Result<TypedTensor<T, R>, Error>

where T: TensorScalar + Send + Sync + 'static,

Create a tensor from an existing buffer and compact column-major layout.

This preserves the owned tensor invariant that layout metadata is compact column-major, including for backend-owned buffers.

Examples

use tenferro_tensor::{StorageBuffer, Placement, TypedTensor};

let tensor = TypedTensor::<f64>::from_buffer_col_major(
    vec![2],
    StorageBuffer::Host(vec![1.0, 2.0]),
    Placement {
        memory_kind: tenferro_tensor::MemoryKind::UnpinnedHost,
        device: None,
        cpu_affinity: None,
    },
)
.unwrap();
assert_eq!(tensor.shape(), &[2]);

Errors

Returns crate::Error::Validation with tenferro_tensor_core::ValidationError::ShapeDataLengthMismatch when the shape product differs from the buffer length, tenferro_tensor_core::ValidationError::IntegerOverflow when shape or stride arithmetic overflows, or tenferro_tensor_core::ValidationError::RankMismatch when a supplied rank-specific shape cannot be represented.

pub fn try_into_rank<const N: usize>( self, ) -> Result<TypedTensor<T, Rank<N>>, ReinterpretError<TypedTensor<T, R>>>

Convert this tensor into static rank metadata after validating its rank.

The buffer and placement are preserved. This method changes only the compile-time rank marker on the owned compact column-major tensor.

Examples

use tenferro_tensor::{Rank, TypedTensor};

let tensor = TypedTensor::<f64>::from_vec_col_major(vec![2, 3], vec![1.0; 6]).unwrap();
let Ok(ranked) = tensor.try_into_rank::<2>() else {
    panic!("a rank-2 tensor converts to two axes")
};
assert_eq!(ranked.shape(), &[2, 3]);

Errors

Returns ReinterpretError carrying the unchanged tensor when the typed rank does not match the existing shape, with crate::Error::Validation and tenferro_tensor_core::ValidationError::RankMismatch as the cause, or when the compact rank layout overflows.

pub fn buffer(&self) -> &StorageBuffer<T>

where T: 'static,

Return the storage backing this tensor.

This is an explicit storage-inspection API for backend glue and tests. Host value inspection should prefer TypedTensor::host_data when the caller requires host storage.

Panics

Panics only if the typed descriptor and its single group owner are internally inconsistent.

Examples

use tenferro_tensor::{StorageBuffer, TypedTensor};

let t = TypedTensor::<f64>::from_vec_col_major(vec![2], vec![1.0, 2.0]).unwrap();
assert!(matches!(t.buffer(), StorageBuffer::Host(_)));

pub fn allocation_domain(&self) -> Option<AllocationDomainId>

where T: 'static,

Return the shared-allocation domain carried by the backend buffer.

Examples

use tenferro_tensor::TypedTensor;

let tensor = TypedTensor::<f32>::from_vec_col_major(vec![1], vec![1.0])?;
assert_eq!(tensor.allocation_domain(), None);

pub fn allocation_id(&self) -> Option<AllocationId>

where T: 'static,

Return the stable physical backend allocation identity.

Examples

use tenferro_tensor::TypedTensor;

let tensor = TypedTensor::<f32>::from_vec_col_major(vec![1], vec![1.0])?;
assert_eq!(tensor.allocation_id(), None);

pub fn as_view(&self) -> TypedTensorView<'_, T, R>

where T: 'static,

Borrow this tensor as a typed view preserving rank and layout metadata.

Panics

Panics only if the typed descriptor and its single group owner are internally inconsistent.

Examples

use tenferro_tensor::{Rank, TypedTensor};

let tensor = TypedTensor::<f64, Rank<2>>::from_vec_col_major([2, 2], vec![1.0; 4]).unwrap();
let view = tensor.as_view();
assert_eq!(view.strides(), &[1, 2]);

pub fn as_view_mut(&mut self) -> TypedTensorViewMut<'_, T, R>

where T: TensorScalar + 'static,

Mutably borrow this tensor as a typed view preserving rank and layout metadata.

Panics

Panics only if the typed descriptor and its single group owner are internally inconsistent.

Examples

use tenferro_tensor::TypedTensor;

let mut tensor = TypedTensor::<i32>::from_vec_col_major(vec![1], vec![1]).unwrap();
*tensor.as_view_mut().get_mut(&[0]).unwrap() = 2;
assert_eq!(tensor.as_slice().unwrap(), &[2]);

pub fn backend_region_view( &self, shape: Vec<usize>, strides: Vec<isize>, offset: isize, ) -> Result<TypedTensorView<'_, T>, Error>

where T: TensorScalar + 'static,

Borrow a read-only strided region view over this tensor’s backend (device) buffer from explicit layout metadata.

This is a metadata-only view: no data is copied or transferred. The layout’s reachable element span is validated against the backend buffer’s physical length. Host-backed tensors are rejected with an explicit backend error; host regions are expressed with TypedTensorView::from_slice over host storage instead.

Examples

use tenferro_tensor::TypedTensor;

// Host tensors are rejected: this constructor is for backend buffers.
let host = TypedTensor::<f64>::from_vec_col_major(vec![4], vec![0.0; 4]).unwrap();
let err = host.backend_region_view(vec![2, 2], vec![1, 2], 0).unwrap_err();
assert!(err.to_string().contains("backend"));

Errors

Returns crate::Error::RuntimeState when this tensor is host-backed; backend region views require a backend buffer. It returns crate::Error::Validation with tenferro_tensor_core::ValidationError::RankMismatch for incompatible shape/stride ranks, tenferro_tensor_core::ValidationError::ViewOutOfBounds when the region exceeds the backend buffer, or tenferro_tensor_core::ValidationError::IntegerOverflow for layout arithmetic overflow.

pub fn backend_region_view_mut( &mut self, shape: Vec<usize>, strides: Vec<isize>, offset: isize, ) -> Result<TypedTensorViewMut<'_, T>, Error>

where T: TensorScalar + 'static,

Borrow a mutable strided region view over this tensor’s backend (device) buffer from explicit layout metadata.

This is the mutable counterpart of TypedTensor::backend_region_view. The layout’s reachable element span is validated against the backend buffer’s physical length, and layouts whose logical elements alias the same physical element are rejected. Host-backed tensors are rejected with an explicit backend error; mutable host regions must go through TypedTensorViewMut::try_multi_slice_mut or host constructors.

The returned view borrows the tensor’s backend owner exclusively for its lifetime. This keeps write authority tied to the owner; a second mutable region view must be created only after the first borrow ends.

Examples

use tenferro_tensor::TypedTensor;

// Host tensors are rejected: this constructor is for backend buffers.
let mut host = TypedTensor::<f64>::from_vec_col_major(vec![4], vec![0.0; 4]).unwrap();
let err = host.backend_region_view_mut(vec![2, 2], vec![1, 2], 0).unwrap_err();
assert!(err.to_string().contains("backend"));

Errors

Returns crate::Error::RuntimeState when this tensor is host-backed; mutable backend region views require a backend buffer. It returns crate::Error::Validation with tenferro_tensor_core::ValidationError::RankMismatch for incompatible shape/stride ranks, tenferro_tensor_core::ValidationError::ViewOutOfBounds when the region exceeds the backend buffer, tenferro_tensor_core::ValidationError::OverlappingMutableLayout when logical elements alias, or tenferro_tensor_core::ValidationError::IntegerOverflow for layout arithmetic overflow.

pub fn into_parts( self, ) -> Result<(StorageBuffer<T>, TensorLayout<R>, Placement), ReinterpretError<TypedTensor<T, R>>>

where T: TensorScalar,

Consume this tensor and return its storage, layout, and placement.

Examples

use tenferro_tensor::{StorageBuffer, TypedTensor};

let t = TypedTensor::<f64>::from_vec_col_major(vec![2], vec![1.0, 2.0])?;
let Ok((buffer, layout, placement)) = t.into_parts() else {
    panic!("a plain host owner extracts")
};
assert!(matches!(buffer, StorageBuffer::Host(_)));
assert_eq!(layout.shape(), &[2]);
assert!(placement.device.is_none());

Errors

Returns ReinterpretError carrying the unchanged tensor when it uses backend storage; download it before extracting host storage.

impl<R> TypedTensor<Complex<f32>, R>

where R: TensorRank,

pub fn as_real_view(&self) -> Result<TypedTensorView<'_, f32>, Error>

Borrow this tensor as an interleaved f32 view without copying.

Errors

Returns crate::Error::Unsupported for backend reinterpretation that is not supported, or ValidationError::ViewOutOfBounds for an invalid tensor layout.

pub fn as_real_view_mut(&mut self) -> Result<TypedTensorViewMut<'_, f32>, Error>

Borrow this tensor mutably as an interleaved f32 view without copying.

Errors

Returns crate::Error::Unsupported for backend reinterpretation that is not supported, ValidationError::OverlappingMutableLayout for a non-injective layout, or ValidationError::ViewOutOfBounds for invalid representation metadata.

pub fn into_real( self, ) -> Result<TypedTensor<f32>, ReinterpretError<TypedTensor<Complex<f32>, R>>>

Consume this tensor and reinterpret its owner as f32 without copying.

A failed operation returns the unchanged owner through ReinterpretError::into_owner.

Errors

Returns ReinterpretError::error containing ValidationError::InvalidArgument or ValidationError::ViewOutOfBounds while retaining the unchanged owner.

impl<R> TypedTensor<Complex<f64>, R>

where R: TensorRank,

pub fn as_real_view(&self) -> Result<TypedTensorView<'_, f64>, Error>

Borrow this tensor as an interleaved f64 view without copying.

Errors

Returns crate::Error::Unsupported for backend reinterpretation that is not supported, or ValidationError::ViewOutOfBounds for an invalid tensor layout.

pub fn as_real_view_mut(&mut self) -> Result<TypedTensorViewMut<'_, f64>, Error>

Borrow this tensor mutably as an interleaved f64 view without copying.

Errors

Returns crate::Error::Unsupported for backend reinterpretation that is not supported, ValidationError::OverlappingMutableLayout for a non-injective layout, or ValidationError::ViewOutOfBounds for invalid representation metadata.

pub fn into_real( self, ) -> Result<TypedTensor<f64>, ReinterpretError<TypedTensor<Complex<f64>, R>>>

Consume this tensor and reinterpret its owner as f64 without copying.

A failed operation returns the unchanged owner through ReinterpretError::into_owner.

Errors

Returns ReinterpretError::error containing ValidationError::InvalidArgument or ValidationError::ViewOutOfBounds while retaining the unchanged owner.

impl<R> TypedTensor<f32, R>

where R: TensorRank,

pub fn as_complex_view( &self, ) -> Result<TypedTensorView<'_, Complex<f32>>, Error>

Borrow this tensor as a complex view without copying.

Errors

Returns crate::Error::Unsupported for backend reinterpretation that is not supported, or ValidationError::ViewOutOfBounds for an invalid tensor layout.

pub fn as_complex_view_mut( &mut self, ) -> Result<TypedTensorViewMut<'_, Complex<f32>>, Error>

Borrow this tensor mutably as a complex view without copying.

Errors

Returns crate::Error::Unsupported for backend reinterpretation that is not supported, ValidationError::OverlappingMutableLayout for a non-injective layout, or ValidationError::ViewOutOfBounds for invalid representation metadata.

pub fn into_complex( self, ) -> Result<TypedTensor<Complex<f32>>, ReinterpretError<TypedTensor<f32, R>>>

Consume this tensor and reinterpret its owner as Complex32 without copying.

The compact source must have an even physical element count. A failed operation returns the unchanged owner through ReinterpretError::into_owner.

Errors

Returns ReinterpretError::error containing ValidationError::InvalidArgument or ValidationError::ViewOutOfBounds while retaining the unchanged owner.

impl<R> TypedTensor<f64, R>

where R: TensorRank,

pub fn as_complex_view( &self, ) -> Result<TypedTensorView<'_, Complex<f64>>, Error>

Borrow this tensor as a complex view without copying.

Errors

Returns crate::Error::Unsupported for backend reinterpretation that is not supported, or ValidationError::ViewOutOfBounds for an invalid tensor layout.

pub fn as_complex_view_mut( &mut self, ) -> Result<TypedTensorViewMut<'_, Complex<f64>>, Error>

Borrow this tensor mutably as a complex view without copying.

Errors

Returns crate::Error::Unsupported for backend reinterpretation that is not supported, ValidationError::OverlappingMutableLayout for a non-injective layout, or ValidationError::ViewOutOfBounds for invalid representation metadata.

pub fn into_complex( self, ) -> Result<TypedTensor<Complex<f64>>, ReinterpretError<TypedTensor<f64, R>>>

Consume this tensor and reinterpret its owner as Complex64 without copying.

The compact source must have an even physical element count. A failed operation returns the unchanged owner through ReinterpretError::into_owner.

Errors

Returns ReinterpretError::error containing ValidationError::InvalidArgument or ValidationError::ViewOutOfBounds while retaining the unchanged owner.

Trait Implementations

impl<T, R> Clone for TypedTensor<T, R, Host>

where T: Clone, R: TensorRank,

fn clone(&self) -> TypedTensor<T, R, Host>

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<T, R, D> Debug for TypedTensor<T, R, D>

where R: TensorRank, D: Representation,

fn fmt(&self, formatter: &mut Formatter<'_>) -> Result<(), Error>

Formats the value using the given formatter.

impl From<TypedTensor<Complex<f32>>> for Tensor

Wrap a Complex32 TypedTensor into the corresponding Tensor variant.

Examples

use num_complex::Complex32;
use tenferro_tensor::{Tensor, TypedTensor};

let typed = TypedTensor::from_vec_col_major(
    vec![1],
    vec![Complex32::new(1.0, 2.0)],
).unwrap();
let tensor: Tensor = typed.into();
assert_eq!(tensor.shape(), &[1]);

fn from(t: TypedTensor<Complex<f32>>) -> Tensor

Converts to this type from the input type.

impl From<TypedTensor<Complex<f64>>> for Tensor

Wrap a Complex64 TypedTensor into the corresponding Tensor variant.

Examples

use num_complex::Complex64;
use tenferro_tensor::{Tensor, TypedTensor};

let typed = TypedTensor::from_vec_col_major(
    vec![1],
    vec![Complex64::new(1.0, 2.0)],
).unwrap();
let tensor: Tensor = typed.into();
assert_eq!(tensor.shape(), &[1]);

fn from(t: TypedTensor<Complex<f64>>) -> Tensor

Converts to this type from the input type.

impl From<TypedTensor<bool>> for Tensor

Wrap a bool TypedTensor into the corresponding Tensor variant.

Examples

use tenferro_tensor::{DType, Tensor, TypedTensor};

let typed = TypedTensor::from_vec_col_major(vec![2], vec![true, false]).unwrap();
let tensor: Tensor = typed.into();
assert_eq!(tensor.dtype(), DType::Bool);
assert_eq!(tensor.shape(), &[2]);

fn from(t: TypedTensor<bool>) -> Tensor

Converts to this type from the input type.

impl From<TypedTensor<f32>> for Tensor

Wrap an f32 TypedTensor into the corresponding Tensor variant.

Examples

use tenferro_tensor::{Tensor, TypedTensor};

let typed = TypedTensor::from_vec_col_major(vec![2], vec![1.0_f32, 2.0]).unwrap();
let tensor: Tensor = typed.into();
assert_eq!(tensor.shape(), &[2]);

fn from(t: TypedTensor<f32>) -> Tensor

Converts to this type from the input type.

impl From<TypedTensor<f64>> for Tensor

Wrap an f64 TypedTensor into the corresponding Tensor variant.

Examples

use tenferro_tensor::{Tensor, TypedTensor};

let typed = TypedTensor::from_vec_col_major(vec![2], vec![1.0_f64, 2.0]).unwrap();
let tensor: Tensor = typed.into();
assert_eq!(tensor.shape(), &[2]);

fn from(t: TypedTensor<f64>) -> Tensor

Converts to this type from the input type.

impl From<TypedTensor<i32>> for Tensor

Wrap an i32 TypedTensor into the corresponding Tensor variant.

Examples

use tenferro_tensor::{DType, Tensor, TypedTensor};

let typed = TypedTensor::from_vec_col_major(vec![2], vec![1_i32, 2]).unwrap();
let tensor: Tensor = typed.into();
assert_eq!(tensor.dtype(), DType::I32);
assert_eq!(tensor.shape(), &[2]);

fn from(t: TypedTensor<i32>) -> Tensor

Converts to this type from the input type.

impl From<TypedTensor<i64>> for Tensor

Wrap an i64 TypedTensor into the corresponding Tensor variant.

Examples

use tenferro_tensor::{DType, Tensor, TypedTensor};

let typed = TypedTensor::from_vec_col_major(vec![2], vec![1_i64, 2]).unwrap();
let tensor: Tensor = typed.into();
assert_eq!(tensor.dtype(), DType::I64);
assert_eq!(tensor.shape(), &[2]);

fn from(t: TypedTensor<i64>) -> Tensor

Converts to this type from the input type.

impl<T, R> Index<&[usize]> for TypedTensor<T, R, Host>

where R: TensorRank,

fn index(&self, indices: &[usize]) -> &T

Panics

Panics when the index has the wrong rank or is out of bounds, like any other slice indexing. Use TypedTensor::get for the checked form.

type Output = T

The returned type after indexing.

impl<T, R> IndexMut<&[usize]> for TypedTensor<T, R, Host>

where R: TensorRank,

fn index_mut(&mut self, indices: &[usize]) -> &mut T

Panics

Panics when the index has the wrong rank or is out of bounds, like any other slice indexing. Use TypedTensor::get_mut for the checked form.

impl TypedTensorMaskSessionOpsExt for TypedTensor<bool>

fn where_select<U: TensorScalar>( &self, on_true: &TypedTensor<U>, on_false: &TypedTensor<U>, session: &mut dyn BackendSession, ) -> Result<TypedTensor<U>>

Select typed values using this bool tensor as condition.

impl<T: TensorScalar> TypedTensorSessionOpsExt<T> for TypedTensor<T>

fn add( &self, rhs: &TypedTensor<T>, session: &mut dyn BackendSession, ) -> Result<TypedTensor<T>>

Elementwise addition with NumPy-style broadcasting inside a session.

fn mul( &self, rhs: &TypedTensor<T>, session: &mut dyn BackendSession, ) -> Result<TypedTensor<T>>

Elementwise multiplication with NumPy-style broadcasting inside a session.

fn exp(&self, session: &mut dyn BackendSession) -> Result<TypedTensor<T>>

Elementwise exponential inside a session.

fn reduce_sum( &self, axes: Option<&[usize]>, session: &mut dyn BackendSession, ) -> Result<TypedTensor<T>>

Sum over the selected axes inside a session. None reduces every axis and Some(&[]) keeps the input shape.

fn sub( &self, rhs: &TypedTensor<T>, session: &mut dyn BackendSession, ) -> Result<TypedTensor<T>>

Elementwise subtraction with NumPy-style broadcasting inside a session.

fn div( &self, rhs: &TypedTensor<T>, session: &mut dyn BackendSession, ) -> Result<TypedTensor<T>>

Elementwise division with NumPy-style broadcasting inside a session.

fn rem( &self, rhs: &TypedTensor<T>, session: &mut dyn BackendSession, ) -> Result<TypedTensor<T>>

Elementwise remainder with NumPy-style broadcasting inside a session.

fn pow( &self, rhs: &TypedTensor<T>, session: &mut dyn BackendSession, ) -> Result<TypedTensor<T>>

Elementwise power with NumPy-style broadcasting inside a session.

fn maximum( &self, rhs: &TypedTensor<T>, session: &mut dyn BackendSession, ) -> Result<TypedTensor<T>>

Elementwise maximum with NumPy-style broadcasting inside a session.

fn minimum( &self, rhs: &TypedTensor<T>, session: &mut dyn BackendSession, ) -> Result<TypedTensor<T>>

Elementwise minimum with NumPy-style broadcasting inside a session.

fn neg(&self, session: &mut dyn BackendSession) -> Result<TypedTensor<T>>

Elementwise negation inside a session.

fn abs(&self, session: &mut dyn BackendSession) -> Result<TypedTensor<T::Real>>

Elementwise absolute value inside a session.

fn sign(&self, session: &mut dyn BackendSession) -> Result<TypedTensor<T>>

Elementwise sign inside a session.

fn conj(&self, session: &mut dyn BackendSession) -> Result<TypedTensor<T>>

Elementwise complex conjugate inside a session.

fn log(&self, session: &mut dyn BackendSession) -> Result<TypedTensor<T>>

Elementwise natural logarithm inside a session.

fn expm1(&self, session: &mut dyn BackendSession) -> Result<TypedTensor<T>>

Elementwise exp(x) - 1 inside a session.

fn log1p(&self, session: &mut dyn BackendSession) -> Result<TypedTensor<T>>

Elementwise log(1 + x) inside a session.

fn erf(&self, session: &mut dyn BackendSession) -> Result<TypedTensor<T>>

Elementwise error function erf(x) inside a session, for real f32/f64.

fn sin(&self, session: &mut dyn BackendSession) -> Result<TypedTensor<T>>

Elementwise sine inside a session.

fn cos(&self, session: &mut dyn BackendSession) -> Result<TypedTensor<T>>

Elementwise cosine inside a session.

fn tanh(&self, session: &mut dyn BackendSession) -> Result<TypedTensor<T>>

Elementwise hyperbolic tangent inside a session.

fn sqrt(&self, session: &mut dyn BackendSession) -> Result<TypedTensor<T>>

Elementwise square root inside a session.

fn rsqrt(&self, session: &mut dyn BackendSession) -> Result<TypedTensor<T>>

Elementwise reciprocal square root inside a session.

fn compare( &self, rhs: &TypedTensor<T>, dir: CompareDir, session: &mut dyn BackendSession, ) -> Result<TypedTensor<bool>>

Elementwise comparison with NumPy-style broadcasting inside a session.

fn clamp( &self, lower: &TypedTensor<T>, upper: &TypedTensor<T>, session: &mut dyn BackendSession, ) -> Result<TypedTensor<T>>

Clamp values elementwise between lower and upper bounds inside a session.

fn matmul( &self, rhs: &TypedTensor<T>, session: &mut dyn BackendSession, ) -> Result<TypedTensor<T>>

Rank-2 matrix multiplication inside a session.

fn reshape( &self, shape: &[usize], session: &mut dyn BackendSession, ) -> Result<TypedTensor<T>>

Reshape through the backend structural operation inside a session.

fn transpose( &self, perm: &[usize], session: &mut dyn BackendSession, ) -> Result<TypedTensor<T>>

Permute axes through the backend structural operation inside a session.

fn broadcast_in_dim( &self, shape: &[usize], dims: &[usize], session: &mut dyn BackendSession, ) -> Result<TypedTensor<T>>

Broadcast into a larger shape inside a session.

fn reduce_max( &self, axes: Option<&[usize]>, session: &mut dyn BackendSession, ) -> Result<TypedTensor<T>>

Take the maximum over the selected axes inside a session. None reduces every axis and Some(&[]) keeps the input shape.

fn reduce_min( &self, axes: Option<&[usize]>, session: &mut dyn BackendSession, ) -> Result<TypedTensor<T>>

Take the minimum over the selected axes inside a session. None reduces every axis and Some(&[]) keeps the input shape.

fn reduce_prod( &self, axes: Option<&[usize]>, session: &mut dyn BackendSession, ) -> Result<TypedTensor<T>>

Multiply over the selected axes inside a session. None reduces every axis and Some(&[]) keeps the input shape.

fn reduce_sum_squares( &self, axes: Option<&[usize]>, session: &mut dyn BackendSession, ) -> Result<TypedTensor<T>>

Sum elementwise squares over the selected axes inside a session (f32/f64). None reduces every axis and Some(&[]) keeps the input shape.

fn dot_general( &self, rhs: &TypedTensor<T>, config: DotGeneralConfig, session: &mut dyn BackendSession, ) -> Result<TypedTensor<T>>

Contract this tensor with rhs inside a session (StableHLO dot_general).

fn dot_general_with_conj( &self, rhs: &TypedTensor<T>, config: DotGeneralConfig, lhs_conj: bool, rhs_conj: bool, session: &mut dyn BackendSession, ) -> Result<TypedTensor<T>>

Contract with optional conjugation of either operand inside a session.

fn scale_real( &self, factor: f64, session: &mut dyn BackendSession, ) -> Result<TypedTensor<T>>

Multiply by a real scalar inside a session, with the eager scale_real dtype rules.

fn scale_complex( &self, factor: Complex64, session: &mut dyn BackendSession, ) -> Result<TypedTensor<T>>

Multiply a complex tensor by a complex scalar inside a session.

fn sigmoid(&self, session: &mut dyn BackendSession) -> Result<TypedTensor<T>>

Logistic sigmoid 1 / (1 + exp(-x)) inside a session, overflow-free.

fn silu(&self, session: &mut dyn BackendSession) -> Result<TypedTensor<T>>

SiLU (swish) x * sigmoid(x) inside a session.

fn softplus(&self, session: &mut dyn BackendSession) -> Result<TypedTensor<T>>

Softplus log(1 + exp(x)) inside a session, in the stable form max(x, 0) + log1p(exp(-|x|)).

fn gelu(&self, session: &mut dyn BackendSession) -> Result<TypedTensor<T>>

Exact GELU x/2 * (1 + erf(x / sqrt(2))) inside a session.

fn gelu_tanh(&self, session: &mut dyn BackendSession) -> Result<TypedTensor<T>>

GELU tanh approximation inside a session (PyTorch approximate="tanh").

fn reduce_mean( &self, axes: Option<&[usize]>, session: &mut dyn BackendSession, ) -> Result<TypedTensor<T>>

Arithmetic mean over axes inside a session (None reduces every axis).

fn softmax( &self, axis: usize, session: &mut dyn BackendSession, ) -> Result<TypedTensor<T>>

Max-subtracted softmax along axis inside a session.

fn log_softmax( &self, axis: usize, session: &mut dyn BackendSession, ) -> Result<TypedTensor<T>>

Max-subtracted log-softmax along axis inside a session.

fn masked_softmax( &self, mask: &TypedTensor<bool>, axis: usize, session: &mut dyn BackendSession, ) -> Result<TypedTensor<T>>

Softmax along axis over the entries where the Bool mask is true.

fn masked_log_softmax( &self, mask: &TypedTensor<bool>, axis: usize, session: &mut dyn BackendSession, ) -> Result<TypedTensor<T>>

Log-softmax along axis over the entries where the Bool mask is true.

fn layer_norm( &self, axis: usize, weight: Option<&TypedTensor<T>>, bias: Option<&TypedTensor<T>>, eps: f64, session: &mut dyn BackendSession, ) -> Result<TypedTensor<T>>

Layer normalization along axis with optional affine weight / bias, inside a session.

fn rms_norm( &self, axis: usize, weight: Option<&TypedTensor<T>>, bias: Option<&TypedTensor<T>>, eps: f64, session: &mut dyn BackendSession, ) -> Result<TypedTensor<T>>

RMS normalization along axis with optional affine weight / bias, inside a session.