Trait TensorTrait 

pub trait TensorTrait<T>: Send + Sync
where
    T: Num + Clone + Debug,
{
    // Required methods
    fn new(shape: &[usize], name: Option<&str>) -> Result<Self>
       where Self: Sized;
    fn from_fd(fd: OwnedFd, shape: &[usize], name: Option<&str>) -> Result<Self>
       where Self: Sized;
    fn clone_fd(&self) -> Result<OwnedFd>;
    fn memory(&self) -> TensorMemory;
    fn name(&self) -> String;
    fn shape(&self) -> &[usize];
    fn reshape(&mut self, shape: &[usize]) -> Result<()>;
    fn map_with(&self, access: CpuAccess) -> Result<TensorMap<T>>;
    fn buffer_identity(&self) -> &BufferIdentity;

    // Provided methods
    fn len(&self) -> usize { ... }
    fn is_empty(&self) -> bool { ... }
    fn size(&self) -> usize { ... }
    fn capacity_bytes(&self) -> usize { ... }
    fn set_logical_shape(&mut self, shape: &[usize]) -> Result<()> { ... }
    fn map(&self) -> Result<TensorMap<T>> { ... }
    fn map_read(&self) -> Result<TensorMap<T>> { ... }
    fn map_write(&self) -> Result<TensorMap<T>> { ... }
    fn map_mut(&self) -> Result<TensorMap<T>> { ... }
    fn view(&self, offset_bytes: usize, shape: &[usize]) -> Result<Self>
       where Self: Sized { ... }
}

The operations every memory backend implements.

This is the seam that lets Tensor<T> hide which backend is in play. The implementors are DmaTensor (Linux DMA-BUF), IoSurfaceTensor (macOS/iOS), AHardwareBufferTensor (Android), ShmTensor, MemTensor, and PboTensor; TensorStorage<T> dispatches to whichever is active. Backend types are not public — allocate a Tensor<T> or TensorDyn and call these methods through it.

Import the trait to get shape, size, map, clone_fd, buffer_identity, and the zero-copy sub-region view on a tensor value. Tensor::view and Tensor::batch route through TensorTrait::view, so each backend’s identity-sharing rule lives in exactly one place.

Required Methods

fn new(shape: &[usize], name: Option<&str>) -> Result<Self>

where Self: Sized,

Create a new tensor with the given shape and optional name. If no name is given, a random name will be generated.

fn from_fd(fd: OwnedFd, shape: &[usize], name: Option<&str>) -> Result<Self>

where Self: Sized,

Import an existing buffer as a tensor, taking ownership of its file descriptor. The buffer is adopted in place — no bytes are copied.

The backend is detected, not chosen: the fd already belongs to a buffer type, and this call’s job is to recognize which. On Linux that is decided by the fd’s filesystem magic, which is stable UAPI (include/uapi/linux/magic.h):

Filesystem	Magic	Resulting TensorMemory
dma_buf	DMA_BUF_MAGIC (0x444d4142)	TensorMemory::Dma
tmpfs (/dev/shm and memfd)	TMPFS_MAGIC (0x01021994)	TensorMemory::Shm
anything else	—	rejected — see Errors

Both supported types are identified positively. An unrecognized filesystem is an error, never a fallback to shared memory: the wrong branch does not fail loudly (a DMA-BUF is mmap-able, so it would import as a perfectly functional tensor that merely isn’t DMA, and a pipe would import as a zero-length one), so guessing would trade a clear error here for silent loss of zero-copy far downstream.

The device number is deliberately not consulted. dma_buf files live on an internal kernel mount whose st_dev comes from get_anon_bdev() — an IDA shared with every other anonymous pseudo-filesystem and allocated in boot order — so the minor a DMA-BUF lands on varies by kernel build and is not part of any ABI.

On non-Linux Unix (macOS/iOS/Android) there is no fd-based DMA import; the fd is always adopted as TensorMemory::Shm.

Arguments

• fd - Owned descriptor for the buffer to import. Ownership transfers to the returned tensor and the fd is closed on drop; pass clone_fd output to keep your own handle.
• shape - Logical shape to interpret the buffer with. Must describe no more elements than the buffer holds.
• name - Optional name; a random one is generated when None.

Returns

A tensor sharing the imported buffer’s memory, whose memory() reports the detected backend.

Errors

• Error::UnknownBufferType - the fd is on a filesystem that is neither dma_buf nor tmpfs, so its buffer type cannot be determined. Carries the observed fstatfs magic as a u32, normalized so it can be looked up in magic.h directly on both 32- and 64-bit targets. Typical causes: a regular file, a pipe or socket, or a MFD_HUGETLB memfd (hugetlbfs, not tmpfs). Linux only.
• Error::UnknownDeviceType - the fd’s st_dev major is non-zero, i.e. it lives on a real block device rather than an anonymous or in-memory filesystem. Linux only.
• Error::InvalidSize - shape is empty or describes zero elements.
• Error::NixError - fstat, fstatfs, or mmap failed on the descriptor.

Examples

use edgefirst_tensor::{Tensor, TensorMemory, TensorTrait};

let src = Tensor::<u8>::new(&[480, 640, 3], Some(TensorMemory::Dma), None)?;

// Round-tripping a DMA-BUF fd preserves the backend — the import is
// still zero-copy, and still eligible for GPU/NPU paths.
let imported = Tensor::<u8>::from_fd(src.clone_fd()?, src.shape(), None)?;
assert_eq!(imported.memory(), TensorMemory::Dma);

fn clone_fd(&self) -> Result<OwnedFd>

Clone the file descriptor associated with this tensor.

fn memory(&self) -> TensorMemory

Get the memory type of this tensor.

fn name(&self) -> String

Get the name of this tensor.

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

Get the shape of this tensor.

fn reshape(&mut self, shape: &[usize]) -> Result<()>

Reshape this tensor to the given shape. The total number of elements must remain the same.

fn map_with(&self, access: CpuAccess) -> Result<TensorMap<T>>

Map the tensor into memory with the given access direction and return a TensorMap for accessing the data.

access selects the platform mapping mode (read-only IOSurface lock, dma-buf sync direction, AHardwareBuffer lock usage) and the map’s mutability: a map obtained with CpuAccess::Read rejects as_mut_slice. CpuAccess::None is not a mappable direction and returns Error::InvalidArgument.

Prefer the typed wrappers map_read / map_write / map_mut.

fn buffer_identity(&self) -> &BufferIdentity

Get the buffer identity for cache keying and liveness tracking.

Provided Methods

fn len(&self) -> usize

Get the number of elements in this tensor.

fn is_empty(&self) -> bool

Check if the tensor is empty.

fn size(&self) -> usize

Get the size in bytes of this tensor.

fn capacity_bytes(&self) -> usize

Bytes of the underlying allocation (>= the current logical size()). Defaults to the logical size for storages without spare capacity.

fn set_logical_shape(&mut self, shape: &[usize]) -> Result<()>

Set the logical shape to any shape whose byte size fits the allocation capacity, without the equal-size constraint of reshape.

fn map(&self) -> Result<TensorMap<T>>

Map the tensor read-write (equivalent to map_with(CpuAccess::ReadWrite) — the historical map() behavior).

fn map_read(&self) -> Result<TensorMap<T>>

Map the tensor for CPU reading only. The returned map rejects as_mut_slice; on macOS this takes the read-only IOSurface lock (skips the unlock flush), on Linux the dma-buf read-direction sync.

fn map_write(&self) -> Result<TensorMap<T>>

Map the tensor for CPU writing (fill-only: reading through a write map may see write-combined memory — do not read the slice).

fn map_mut(&self) -> Result<TensorMap<T>>

Map the tensor read-write (alias of map with the intent spelled out).

fn view(&self, offset_bytes: usize, shape: &[usize]) -> Result<Self>

where Self: Sized,

Create a zero-copy sub-region view of this backing that shares the underlying allocation and BufferIdentity.

The window is [offset_bytes, offset_bytes + shape.product() * size_of::<T>()) measured from this tensor’s own logical start, so a sub-view of a sub-view composes by adding offsets. Sharing the parent’s identity is the contract that lets identity-keyed caches (e.g. the GL EGLImage import cache) treat offset-distinct windows as one buffer rather than unrelated allocations — view must never mint a fresh identity.

Defaults to Error::NotImplemented; every backend that supports sub-views overrides it (Mem, Shm, Linux DMA, macOS IOSurface, Pbo).

Dyn Compatibility

This trait is dyn compatible.

In older versions of Rust, dyn compatibility was called "object safety".

Implementors

impl<T> TensorTrait<T> for PboTensor<T>

where T: Num + Clone + Debug + Send + Sync,

impl<T> TensorTrait<T> for Tensor<T>

where T: Num + Clone + Debug + Send + Sync,