Struct Tensor

Source

pub struct Tensor { /* private fields */ }

Expand description

A symbolic tensor in a traced graph.

Lazy by construction. Despite the eager-looking operator syntax, a Tensor is a deferred handle: &a + &b, .relu(), .matmul(..) etc. only append IR nodes — no host or device buffers are allocated and nothing executes. The graph is compiled (fused + memory-planned) and run only when you force it with to_vec / realize (both require the eval feature). This is what lets the whole expression fuse instead of materializing every intermediate.

Zero-copy clone & slice. clone() just bumps a refcount on the shared graph handle — no data is duplicated. slice/narrow/reshape/select add a view node into the same graph (referencing the parent by id), so they move zero payload bytes either. Use shares_graph to observe storage sharing and storage_bytes to confirm no data was copied. (Combining two independently-constructed tensors is the one case that copies — the right operand’s nodes are merged in once; see [crate::handle].)

Struct Tensor Copy item path

Implementations§

impl Tensor

pub fn from_vec(data: impl Into<Vec<f32>>, shape: impl AsRef<[usize]>) -> Self

pub fn zeros(shape: impl AsRef<[usize]>) -> Self

pub fn ones(shape: impl AsRef<[usize]>) -> Self

pub fn full(shape: impl AsRef<[usize]>, value: f32) -> Self

pub fn eye(n: usize) -> Self

pub fn arange(end: i64) -> Self

pub fn arange_step(start: i64, end: i64, step: i64) -> Self

pub fn from_f64(data: impl AsRef<[f64]>, shape: impl AsRef<[usize]>) -> Self

pub fn from_i64(data: impl AsRef<[i64]>, shape: impl AsRef<[usize]>) -> Self

pub fn index_vec(data: impl AsRef<[i64]>) -> Self

pub fn rand(shape: impl AsRef<[usize]>, seed: u64) -> Self

pub fn rand_range( shape: impl AsRef<[usize]>, seed: u64, lo: f32, hi: f32, ) -> Self

pub fn randn(shape: impl AsRef<[usize]>, seed: u64) -> Self

pub fn randn_std( shape: impl AsRef<[usize]>, seed: u64, mean: f32, std: f32, ) -> Self

impl Tensor

pub fn id(&self) -> NodeId

pub fn shape(&self) -> Shape

pub fn dtype(&self) -> DType

pub fn rank(&self) -> usize

pub fn dims(&self) -> Vec<usize>

pub fn numel(&self) -> usize

pub fn is_static_shape(&self) -> bool

pub fn shares_graph(&self, other: &Tensor) -> bool

pub fn storage_bytes(&self) -> usize

pub fn slice(&self, spec: SliceSpec) -> Self

pub fn mm(&self, rhs: &Tensor) -> Self

pub fn matmul(&self, rhs: &Tensor) -> Self

pub fn scaled_matmul( &self, rhs: &Tensor, fmt: ScaledFormat, layout: ScaleLayout, ) -> Self

pub fn softmax(&self, axis: i32) -> Self

pub fn reshape(&self, new_shape: impl Into<Vec<i64>>) -> Self

pub fn transpose(&self, perm: impl Into<Vec<usize>>) -> Self

pub fn t(&self) -> Self

pub fn cast(&self, to: DType) -> Self

pub fn narrow(&self, axis: usize, start: usize, len: usize) -> Self

pub fn broadcast_to(&self, shape: impl Into<Vec<i64>>) -> Self

pub fn flatten(&self) -> Self

pub fn unsqueeze(&self, axis: usize) -> Self

pub fn squeeze(&self, axis: usize) -> Self

pub fn squeeze_all(&self) -> Self

pub fn split(&self, axis: usize, sizes: &[usize]) -> Vec<Tensor>

pub fn chunk(&self, axis: usize, n: usize) -> Vec<Tensor>

pub fn tile(&self, axis: usize, reps: usize) -> Tensor

pub fn flip(&self, axis: usize) -> Tensor

pub fn roll(&self, axis: usize, shift: usize) -> Tensor

pub fn pad( &self, axis: usize, before: usize, after: usize, value: f32, ) -> Tensor

pub fn gather(&self, indices: &Tensor, axis: usize) -> Self

pub fn index_select(&self, axis: usize, indices: &Tensor) -> Self

pub fn select(&self, axis: usize, index: i64) -> Self

pub fn stop_gradient(&self) -> Self

pub fn argmax(&self, axis: usize, keep_dim: bool) -> Self

pub fn argmin(&self, axis: usize, keep_dim: bool) -> Self

pub fn inv(&self) -> Self

pub fn solve(&self, b: &Tensor) -> Self

pub fn cumsum(&self, axis: i32, exclusive: bool) -> Self

pub fn var(&self, axes: impl Into<Vec<usize>>, keep_dim: bool) -> Self

pub fn std(&self, axes: impl Into<Vec<usize>>, keep_dim: bool) -> Self

pub fn norm(&self, axes: impl Into<Vec<usize>>, keep_dim: bool) -> Self

pub fn logsumexp(&self, axis: usize, keep_dim: bool) -> Self

pub fn fft(&self) -> Self

pub fn ifft(&self) -> Self

pub fn fft_axis(&self, axis: usize, inverse: bool) -> Self

pub fn layer_norm(&self, gamma: &Tensor, beta: &Tensor, eps: f32) -> Self

pub fn rms_norm(&self, gamma: &Tensor, beta: &Tensor, eps: f32) -> Self

pub fn conv2d( &self, weight: &Tensor, kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2], dilation: [usize; 2], groups: usize, ) -> Self

pub fn attention( &self, k: &Tensor, v: &Tensor, num_heads: usize, head_dim: usize, mask_kind: MaskKind, ) -> Self

pub fn rope(&self, cos: &Tensor, sin: &Tensor, head_dim: usize) -> Self

pub fn where_(&self, on_true: &Tensor, on_false: &Tensor) -> Self

pub fn masked_fill(&self, mask: &Tensor, value: f64) -> Self

pub fn maximum(&self, rhs: &Tensor) -> Self

pub fn minimum(&self, rhs: &Tensor) -> Self

pub fn clamp(&self, min: f64, max: f64) -> Self

pub fn clamp_min(&self, min: f64) -> Self

pub fn clamp_max(&self, max: f64) -> Self

pub fn pow(&self, rhs: &Tensor) -> Self

pub fn pow_scalar(&self, exp: f64) -> Self

impl Tensor

pub fn relu(&self) -> Self

Struct Tensor