Trait TensorSessionOpsExt 

pub trait TensorSessionOpsExt {
    // Required methods
    fn add(&self, rhs: &Tensor, session: &mut dyn Engine) -> Result<Tensor>;
    fn mul(&self, rhs: &Tensor, session: &mut dyn Engine) -> Result<Tensor>;
    fn exp(&self, session: &mut dyn Engine) -> Result<Tensor>;
    fn reduce_sum(
        &self,
        axes: Option<&[usize]>,
        session: &mut dyn Engine,
    ) -> Result<Tensor>;
    fn convert(&self, to: DType, session: &mut dyn Engine) -> Result<Tensor>;
    fn cast(&self, to: DType, session: &mut dyn Engine) -> Result<Tensor>;
    fn sub(&self, rhs: &Tensor, session: &mut dyn Engine) -> Result<Tensor>;
    fn div(&self, rhs: &Tensor, session: &mut dyn Engine) -> Result<Tensor>;
    fn rem(&self, rhs: &Tensor, session: &mut dyn Engine) -> Result<Tensor>;
    fn pow(&self, rhs: &Tensor, session: &mut dyn Engine) -> Result<Tensor>;
    fn maximum(&self, rhs: &Tensor, session: &mut dyn Engine) -> Result<Tensor>;
    fn minimum(&self, rhs: &Tensor, session: &mut dyn Engine) -> Result<Tensor>;
    fn neg(&self, session: &mut dyn Engine) -> Result<Tensor>;
    fn abs(&self, session: &mut dyn Engine) -> Result<Tensor>;
    fn sign(&self, session: &mut dyn Engine) -> Result<Tensor>;
    fn conj(&self, session: &mut dyn Engine) -> Result<Tensor>;
    fn log(&self, session: &mut dyn Engine) -> Result<Tensor>;
    fn expm1(&self, session: &mut dyn Engine) -> Result<Tensor>;
    fn log1p(&self, session: &mut dyn Engine) -> Result<Tensor>;
    fn erf(&self, session: &mut dyn Engine) -> Result<Tensor>;
    fn sin(&self, session: &mut dyn Engine) -> Result<Tensor>;
    fn cos(&self, session: &mut dyn Engine) -> Result<Tensor>;
    fn tanh(&self, session: &mut dyn Engine) -> Result<Tensor>;
    fn sqrt(&self, session: &mut dyn Engine) -> Result<Tensor>;
    fn rsqrt(&self, session: &mut dyn Engine) -> Result<Tensor>;
    fn compare(
        &self,
        rhs: &Tensor,
        dir: CompareDir,
        session: &mut dyn Engine,
    ) -> Result<Tensor>;
    fn where_select(
        &self,
        on_true: &Tensor,
        on_false: &Tensor,
        session: &mut dyn Engine,
    ) -> Result<Tensor>;
    fn clamp(
        &self,
        lower: &Tensor,
        upper: &Tensor,
        session: &mut dyn Engine,
    ) -> Result<Tensor>;
    fn matmul(&self, rhs: &Tensor, session: &mut dyn Engine) -> Result<Tensor>;
    fn reshape(
        &self,
        shape: &[usize],
        session: &mut dyn Engine,
    ) -> Result<Tensor>;
    fn transpose(
        &self,
        perm: &[usize],
        session: &mut dyn Engine,
    ) -> Result<Tensor>;
    fn gather(
        &self,
        indices: &Tensor,
        config: GatherConfig,
        session: &mut dyn Engine,
    ) -> Result<Tensor>;
    fn scatter(
        &self,
        indices: &Tensor,
        updates: &Tensor,
        config: ScatterConfig,
        session: &mut dyn Engine,
    ) -> Result<Tensor>;
    fn slice(
        &self,
        config: SliceConfig,
        session: &mut dyn Engine,
    ) -> Result<Tensor>;
    fn dynamic_slice(
        &self,
        starts: &Tensor,
        sizes: &[usize],
        session: &mut dyn Engine,
    ) -> Result<Tensor>;
    fn dynamic_update_slice(
        &self,
        update: &Tensor,
        starts: &Tensor,
        session: &mut dyn Engine,
    ) -> Result<Tensor>;
    fn pad(&self, config: PadConfig, session: &mut dyn Engine) -> Result<Tensor>;
    fn concatenate(
        inputs: &[&Tensor],
        axis: usize,
        session: &mut dyn Engine,
    ) -> Result<Tensor>
       where Self: Sized;
    fn reverse(
        &self,
        axes: &[usize],
        session: &mut dyn Engine,
    ) -> Result<Tensor>;
    fn reduce_max(
        &self,
        axes: Option<&[usize]>,
        session: &mut dyn Engine,
    ) -> Result<Tensor>;
    fn reduce_min(
        &self,
        axes: Option<&[usize]>,
        session: &mut dyn Engine,
    ) -> Result<Tensor>;
    fn reduce_prod(
        &self,
        axes: Option<&[usize]>,
        session: &mut dyn Engine,
    ) -> Result<Tensor>;
    fn reduce_sum_squares(
        &self,
        axes: Option<&[usize]>,
        session: &mut dyn Engine,
    ) -> Result<Tensor>;
    fn broadcast_in_dim(
        &self,
        shape: &[usize],
        dims: &[usize],
        session: &mut dyn Engine,
    ) -> Result<Tensor>;
    fn tril(&self, k: i64, session: &mut dyn Engine) -> Result<Tensor>;
    fn triu(&self, k: i64, session: &mut dyn Engine) -> Result<Tensor>;
    fn extract_diag(
        &self,
        axis_a: usize,
        axis_b: usize,
        session: &mut dyn Engine,
    ) -> Result<Tensor>;
    fn embed_diag(
        &self,
        axis_a: usize,
        axis_b: usize,
        session: &mut dyn Engine,
    ) -> Result<Tensor>;
    fn dot_general(
        &self,
        rhs: &Tensor,
        config: DotGeneralConfig,
        session: &mut dyn Engine,
    ) -> Result<Tensor>;
    fn dot_general_with_conj(
        &self,
        rhs: &Tensor,
        config: DotGeneralConfig,
        lhs_conj: bool,
        rhs_conj: bool,
        session: &mut dyn Engine,
    ) -> Result<Tensor>;
    fn scale_real(
        &self,
        factor: f64,
        session: &mut dyn Engine,
    ) -> Result<Tensor>;
    fn scale_complex(
        &self,
        factor: Complex64,
        session: &mut dyn Engine,
    ) -> Result<Tensor>;
    fn sigmoid(&self, session: &mut dyn Engine) -> Result<Tensor>;
    fn silu(&self, session: &mut dyn Engine) -> Result<Tensor>;
    fn softplus(&self, session: &mut dyn Engine) -> Result<Tensor>;
    fn gelu(&self, session: &mut dyn Engine) -> Result<Tensor>;
    fn gelu_tanh(&self, session: &mut dyn Engine) -> Result<Tensor>;
    fn reduce_mean(
        &self,
        axes: Option<&[usize]>,
        session: &mut dyn Engine,
    ) -> Result<Tensor>;
    fn softmax(&self, axis: usize, session: &mut dyn Engine) -> Result<Tensor>;
    fn log_softmax(
        &self,
        axis: usize,
        session: &mut dyn Engine,
    ) -> Result<Tensor>;
    fn masked_softmax(
        &self,
        mask: &Tensor,
        axis: usize,
        session: &mut dyn Engine,
    ) -> Result<Tensor>;
    fn masked_log_softmax(
        &self,
        mask: &Tensor,
        axis: usize,
        session: &mut dyn Engine,
    ) -> Result<Tensor>;
    fn layer_norm(
        &self,
        axis: usize,
        weight: Option<&Tensor>,
        bias: Option<&Tensor>,
        eps: f64,
        session: &mut dyn Engine,
    ) -> Result<Tensor>;
    fn rms_norm(
        &self,
        axis: usize,
        weight: Option<&Tensor>,
        bias: Option<&Tensor>,
        eps: f64,
        session: &mut dyn Engine,
    ) -> Result<Tensor>;
    fn take_along_axis(
        &self,
        indices: &Tensor,
        axis: usize,
        session: &mut dyn Engine,
    ) -> Result<Tensor>;
}

AD-free tensor operations on Tensor, run on a borrowed engine.

Every method takes the receiver first and the engine last, with the arguments and config types of the eager EagerSession method of the same name, so AD-free and eager code differ only in where the engine comes from: the caller holds one &mut dyn Engine and borrows it for each call. Nothing is entered and nothing is locked, so there is no entry error and a single call needs one ?. Borrow one engine for a whole sequence instead of building one per operation.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};

let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2, 2], vec![1.0_f64, 2.0, 3.0, 4.0])?;
let y = {
    let lower = x.tril(0, &mut engine)?;
    lower.reduce_sum(None, &mut engine)
}?;
assert_eq!(y.as_slice::<f64>()?, &[7.0]);

Required Methods

fn add(&self, rhs: &Tensor, session: &mut dyn Engine) -> Result<Tensor>

Elementwise addition with NumPy-style broadcasting on an engine.

The broadcast (reshape + broadcast_in_dim, or a copy when shapes already match) and the add itself all run in the caller’s engine; this op never opens an engine of its own.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let a = Tensor::from_vec_col_major(vec![2], vec![1.0_f64, 2.0]).unwrap();
let b = Tensor::from_vec_col_major(vec![2], vec![3.0_f64, 4.0]).unwrap();
let sum = a.add(&b, &mut engine)?;
assert_eq!(sum.as_slice::<f64>().unwrap(), &[4.0, 6.0]);

Errors

Returns tenferro_tensor::Error::Validation with a ShapeMismatch or DTypeMismatch payload when operands are incompatible, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn mul(&self, rhs: &Tensor, session: &mut dyn Engine) -> Result<Tensor>

Elementwise multiplication with NumPy-style broadcasting on an engine.

Like Self::add, broadcast and multiply run on the one engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let a = Tensor::from_vec_col_major(vec![1], vec![2.0_f64]).unwrap();
let b = Tensor::from_vec_col_major(vec![4], vec![3.0_f64; 4]).unwrap();
let product = a.mul(&b, &mut engine)?;
assert_eq!(product.as_slice::<f64>().unwrap(), &[6.0; 4]);

Errors

Returns tenferro_tensor::Error::Validation with ShapeMismatch or DTypeMismatch for incompatible operands, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn exp(&self, session: &mut dyn Engine) -> Result<Tensor>

Elementwise exponential on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2], vec![0.0_f64, 1.0]).unwrap();
let y = x.exp(&mut engine)?;
let y = y.as_slice::<f64>().unwrap();
assert!((y[0] - 1.0).abs() < 1.0e-12);
assert!((y[1] - std::f64::consts::E).abs() < 1.0e-12);

Errors

Returns tenferro_tensor::Error::Unsupported for an unsupported dtype or tenferro_tensor::Error::BackendSource for a typed backend failure.

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

Sum over the selected axes on an engine. None reduces every axis and Some(&[]) keeps the input shape, as in the eager and traced reduction family.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2, 3], vec![1.0_f64; 6]).unwrap();
let sums = x.reduce_sum(Some(&[1]), &mut engine)?;
assert_eq!(sums.as_slice::<f64>().unwrap(), &[3.0, 3.0]);
let total = x.reduce_sum(None, &mut engine)?;
assert_eq!(total.as_slice::<f64>().unwrap(), &[6.0]);

Errors

Returns tenferro_tensor::Error::Validation with AxisOutOfBounds or DuplicateAxis for invalid reductions, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn convert(&self, to: DType, session: &mut dyn Engine) -> Result<Tensor>

Convert to a different dtype using the checked conversion lattice on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{DType, Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2], vec![1.0_f64, 2.0]).unwrap();
let y = x.convert(DType::C64, &mut engine)?;
assert_eq!(y.dtype(), DType::C64);

Errors

Returns tenferro_tensor::Error::UnsupportedDTypeConversion when the conversion is outside the checked lattice, tenferro_tensor::Error::Validation with DTypeMismatch or InvalidArgument for invalid tensor metadata, or tenferro_tensor::Error::BackendSource when the backend reports a typed failure.

fn cast(&self, to: DType, session: &mut dyn Engine) -> Result<Tensor>

Cast to a different dtype using explicit lossy projection on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{DType, Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2], vec![1.2_f64, -2.8]).unwrap();
let y = x.cast(DType::I32, &mut engine)?;
assert_eq!(y.as_slice::<i32>().unwrap(), &[1, -2]);

Errors

Returns tenferro_tensor::Error::UnsupportedDTypeConversion when the requested cast is unsupported, tenferro_tensor::Error::Validation with DTypeMismatch or InvalidArgument for invalid tensor metadata, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn sub(&self, rhs: &Tensor, session: &mut dyn Engine) -> Result<Tensor>

Elementwise subtraction with NumPy-style broadcasting on an engine.

Like Self::add, the broadcast and the subtraction run on the one engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let a = Tensor::from_vec_col_major(vec![2], vec![2.0_f64, 4.0]).unwrap();
let b = Tensor::from_vec_col_major(vec![2], vec![1.0_f64, 8.0]).unwrap();
let y = a.sub(&b, &mut engine)?;
assert_eq!(y.as_slice::<f64>().unwrap(), &[1.0, -4.0]);

Errors

Returns tenferro_tensor::Error::Validation with ShapeMismatch or DTypeMismatch for incompatible operands, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn div(&self, rhs: &Tensor, session: &mut dyn Engine) -> Result<Tensor>

Elementwise division with NumPy-style broadcasting on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let a = Tensor::from_vec_col_major(vec![2], vec![4.0_f64, 8.0]).unwrap();
let b = Tensor::from_vec_col_major(vec![2], vec![2.0_f64, 4.0]).unwrap();
let y = a.div(&b, &mut engine)?;
assert_eq!(y.as_slice::<f64>().unwrap(), &[2.0, 2.0]);

Errors

Returns tenferro_tensor::Error::Validation with ShapeMismatch or DTypeMismatch for shape/dtype incompatibility, tenferro_tensor::Error::Extension with a numerical classification for a detected zero divisor, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn rem(&self, rhs: &Tensor, session: &mut dyn Engine) -> Result<Tensor>

Elementwise remainder with NumPy-style broadcasting on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let a = Tensor::from_vec_col_major(vec![2], vec![5.0_f64, 7.0]).unwrap();
let b = Tensor::from_vec_col_major(vec![2], vec![2.0_f64, 4.0]).unwrap();
let y = a.rem(&b, &mut engine)?;
assert_eq!(y.as_slice::<f64>().unwrap(), &[1.0, 3.0]);

Errors

Returns tenferro_tensor::Error::Validation with ShapeMismatch or DTypeMismatch for shape/dtype incompatibility, a numerical tenferro_tensor::Error::Extension for a detected zero divisor, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn pow(&self, rhs: &Tensor, session: &mut dyn Engine) -> Result<Tensor>

Elementwise power with NumPy-style broadcasting on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let a = Tensor::from_vec_col_major(vec![2], vec![2.0_f64, 3.0]).unwrap();
let b = Tensor::from_vec_col_major(vec![2], vec![3.0_f64, 2.0]).unwrap();
let y = a.pow(&b, &mut engine)?;
assert_eq!(y.as_slice::<f64>().unwrap(), &[8.0, 9.0]);

Errors

Returns tenferro_tensor::Error::Validation with ShapeMismatch or DTypeMismatch for incompatible metadata, a numerical tenferro_tensor::Error::Extension for a detected negative integer exponent, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn maximum(&self, rhs: &Tensor, session: &mut dyn Engine) -> Result<Tensor>

Elementwise maximum with NumPy-style broadcasting on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let a = Tensor::from_vec_col_major(vec![2], vec![2.0_f64, 4.0]).unwrap();
let b = Tensor::from_vec_col_major(vec![2], vec![1.0_f64, 8.0]).unwrap();
let y = a.maximum(&b, &mut engine)?;
assert_eq!(y.as_slice::<f64>().unwrap(), &[2.0, 8.0]);

Errors

Returns tenferro_tensor::Error::Validation with ShapeMismatch or DTypeMismatch for incompatible operands, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn minimum(&self, rhs: &Tensor, session: &mut dyn Engine) -> Result<Tensor>

Elementwise minimum with NumPy-style broadcasting on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let a = Tensor::from_vec_col_major(vec![2], vec![2.0_f64, 4.0]).unwrap();
let b = Tensor::from_vec_col_major(vec![2], vec![1.0_f64, 8.0]).unwrap();
let y = a.minimum(&b, &mut engine)?;
assert_eq!(y.as_slice::<f64>().unwrap(), &[1.0, 4.0]);

Errors

Returns tenferro_tensor::Error::Validation with ShapeMismatch or DTypeMismatch for incompatible operands, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn neg(&self, session: &mut dyn Engine) -> Result<Tensor>

Elementwise negation on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2], vec![1.0_f64, -2.0]).unwrap();
let y = x.neg(&mut engine)?;
assert_eq!(y.as_slice::<f64>().unwrap(), &[-1.0, 2.0]);

Errors

Returns tenferro_tensor::Error::Unsupported when the dtype is not supported by the operation, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn abs(&self, session: &mut dyn Engine) -> Result<Tensor>

Elementwise absolute value on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2], vec![-1.0_f64, 2.0]).unwrap();
let y = x.abs(&mut engine)?;
assert_eq!(y.as_slice::<f64>().unwrap(), &[1.0, 2.0]);

Errors

Returns tenferro_tensor::Error::Unsupported for an unsupported dtype or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn sign(&self, session: &mut dyn Engine) -> Result<Tensor>

Elementwise sign on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2], vec![1.0_f64, -2.0]).unwrap();
let y = x.sign(&mut engine)?;
assert_eq!(y.as_slice::<f64>().unwrap(), &[1.0, -1.0]);

Errors

Returns tenferro_tensor::Error::Unsupported for an unsupported dtype or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn conj(&self, session: &mut dyn Engine) -> Result<Tensor>

Elementwise complex conjugate on an engine.

For real dtypes the conjugate is the identity.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2], vec![1.0_f64, -2.0]).unwrap();
let y = x.conj(&mut engine)?;
assert_eq!(y.as_slice::<f64>().unwrap(), &[1.0, -2.0]);

Errors

Returns tenferro_tensor::Error::Unsupported for an unsupported dtype or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn log(&self, session: &mut dyn Engine) -> Result<Tensor>

Elementwise natural logarithm on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2], vec![1.0_f64, std::f64::consts::E]).unwrap();
let y = x.log(&mut engine)?;
let y = y.as_slice::<f64>().unwrap();
assert!(y[0].abs() < 1.0e-12);
assert!((y[1] - 1.0).abs() < 1.0e-12);

Errors

Returns tenferro_tensor::Error::Unsupported for an unsupported dtype or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn expm1(&self, session: &mut dyn Engine) -> Result<Tensor>

Elementwise exp(x) - 1 on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2], vec![0.0_f64, 1.0]).unwrap();
let y = x.expm1(&mut engine)?;
let y = y.as_slice::<f64>().unwrap();
assert!(y[0].abs() < 1.0e-12);
assert!((y[1] - (std::f64::consts::E - 1.0)).abs() < 1.0e-12);

Errors

Returns tenferro_tensor::Error::Unsupported for an unsupported dtype or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn log1p(&self, session: &mut dyn Engine) -> Result<Tensor>

Elementwise log(1 + x) on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2], vec![0.0_f64, std::f64::consts::E - 1.0]).unwrap();
let y = x.log1p(&mut engine)?;
let y = y.as_slice::<f64>().unwrap();
assert!(y[0].abs() < 1.0e-12);
assert!((y[1] - 1.0).abs() < 1.0e-12);

Errors

Returns tenferro_tensor::Error::Unsupported for an unsupported dtype or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn erf(&self, session: &mut dyn Engine) -> Result<Tensor>

Elementwise error function erf(x) on the engine, for real F32/F64.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2], vec![0.0_f64, 1.0]).unwrap();
let y = x.erf(&mut engine)?;
let y = y.as_slice::<f64>().unwrap();
assert_eq!(y[0], 0.0);
assert!((y[1] - 0.842_700_792_949_714_9).abs() < 1.0e-15);

Errors

Returns tenferro_tensor::Error::UnsupportedDType for complex, integer, or Bool input, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn sin(&self, session: &mut dyn Engine) -> Result<Tensor>

Elementwise sine on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2], vec![0.0_f64, std::f64::consts::FRAC_PI_2]).unwrap();
let y = x.sin(&mut engine)?;
let y = y.as_slice::<f64>().unwrap();
assert!(y[0].abs() < 1.0e-12);
assert!((y[1] - 1.0).abs() < 1.0e-12);

Errors

Returns tenferro_tensor::Error::Unsupported for an unsupported dtype or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn cos(&self, session: &mut dyn Engine) -> Result<Tensor>

Elementwise cosine on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2], vec![0.0_f64, std::f64::consts::PI]).unwrap();
let y = x.cos(&mut engine)?;
let y = y.as_slice::<f64>().unwrap();
assert!((y[0] - 1.0).abs() < 1.0e-12);
assert!((y[1] + 1.0).abs() < 1.0e-12);

Errors

Returns tenferro_tensor::Error::Unsupported for an unsupported dtype or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn tanh(&self, session: &mut dyn Engine) -> Result<Tensor>

Elementwise hyperbolic tangent on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2], vec![0.0_f64, 1.0]).unwrap();
let y = x.tanh(&mut engine)?;
let y = y.as_slice::<f64>().unwrap();
assert!(y[0].abs() < 1.0e-12);
assert!((y[1] - 0.7615941559557649).abs() < 1.0e-12);

Errors

Returns tenferro_tensor::Error::Unsupported for an unsupported dtype or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn sqrt(&self, session: &mut dyn Engine) -> Result<Tensor>

Elementwise square root on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2], vec![4.0_f64, 9.0]).unwrap();
let y = x.sqrt(&mut engine)?;
assert_eq!(y.as_slice::<f64>().unwrap(), &[2.0, 3.0]);

Errors

Returns tenferro_tensor::Error::Unsupported for an unsupported dtype or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn rsqrt(&self, session: &mut dyn Engine) -> Result<Tensor>

Elementwise reciprocal square root on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2], vec![4.0_f64, 1.0]).unwrap();
let y = x.rsqrt(&mut engine)?;
let y = y.as_slice::<f64>().unwrap();
assert!((y[0] - 0.5).abs() < 1.0e-12);
assert!((y[1] - 1.0).abs() < 1.0e-12);

Errors

Returns tenferro_tensor::Error::Unsupported for an unsupported dtype or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn compare( &self, rhs: &Tensor, dir: CompareDir, session: &mut dyn Engine, ) -> Result<Tensor>

Elementwise comparison with NumPy-style broadcasting on an engine.

The result is a bool tensor.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{CompareDir, Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let a = Tensor::from_vec_col_major(vec![2], vec![2.0_f64, 4.0]).unwrap();
let b = Tensor::from_vec_col_major(vec![2], vec![1.0_f64, 8.0]).unwrap();
let y = a.compare(&b, CompareDir::Gt, &mut engine)?;
assert_eq!(y.as_slice::<bool>().unwrap(), &[true, false]);

Errors

Returns tenferro_tensor::Error::Validation with ShapeMismatch or DTypeMismatch for incompatible shape/dtype metadata, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn where_select( &self, on_true: &Tensor, on_false: &Tensor, session: &mut dyn Engine, ) -> Result<Tensor>

Select values from on_true or on_false using this tensor as condition on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let condition = Tensor::from_vec_col_major(vec![2], vec![true, false]).unwrap();
let on_true = Tensor::from_vec_col_major(vec![2], vec![1.0_f64, 2.0]).unwrap();
let on_false = Tensor::from_vec_col_major(vec![2], vec![3.0_f64, 4.0]).unwrap();
let y = condition.where_select(&on_true, &on_false, &mut engine)?;
assert_eq!(y.as_slice::<f64>().unwrap(), &[1.0, 4.0]);

Errors

Returns tenferro_tensor::Error::Validation with ShapeMismatch or DTypeMismatch when the condition and branches are incompatible, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn clamp( &self, lower: &Tensor, upper: &Tensor, session: &mut dyn Engine, ) -> Result<Tensor>

Clamp values elementwise between lower and upper bounds on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2], vec![-2.0_f64, 4.0]).unwrap();
let lower = Tensor::from_vec_col_major(vec![], vec![0.0_f64]).unwrap();
let upper = Tensor::from_vec_col_major(vec![], vec![3.0_f64]).unwrap();
let y = x.clamp(&lower, &upper, &mut engine)?;
assert_eq!(y.as_slice::<f64>().unwrap(), &[0.0, 3.0]);

Errors

Returns tenferro_tensor::Error::Validation with ShapeMismatch or DTypeMismatch when bounds are incompatible with the input, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn matmul(&self, rhs: &Tensor, session: &mut dyn Engine) -> Result<Tensor>

Rank-2 matrix multiplication on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let a = Tensor::from_vec_col_major(vec![2, 3], vec![1.0_f64; 6]).unwrap();
let b = Tensor::from_vec_col_major(vec![3, 2], vec![1.0_f64; 6]).unwrap();
let c = a.matmul(&b, &mut engine)?;
assert_eq!(c.shape(), &[2, 2]);

Errors

Returns tenferro_tensor::Error::Validation with RankMismatch, ShapeMismatch, or DTypeMismatch for incompatible matrices, or tenferro_tensor::Error::BackendSource for a typed backend failure.

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

Reshape without changing element order on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2, 2], vec![1.0_f64, 2.0, 3.0, 4.0]).unwrap();
let y = x.reshape(&[4], &mut engine)?;
assert_eq!(y.shape(), &[4]);

Errors

Returns tenferro_tensor::Error::Validation with ShapeMismatch, RankMismatch, or InvalidArgument when element counts or ranks are invalid, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn transpose(&self, perm: &[usize], session: &mut dyn Engine) -> Result<Tensor>

Permute axes on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2, 3], vec![1.0_f64; 6]).unwrap();
let y = x.transpose(&[1, 0], &mut engine)?;
assert_eq!(y.shape(), &[3, 2]);

Errors

Returns tenferro_tensor::Error::Validation with InvalidPermutationLength, AxisOutOfBounds, or DuplicateAxis for an invalid permutation, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn gather( &self, indices: &Tensor, config: GatherConfig, session: &mut dyn Engine, ) -> Result<Tensor>

Gather slices of this tensor at indices on the engine (StableHLO gather).

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{GatherConfig, Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![3], vec![10.0_f64, 20.0, 30.0])?;
let indices = Tensor::from_vec_col_major(vec![2, 1], vec![2_i64, 0])?;
let config = GatherConfig {
    offset_dims: vec![],
    collapsed_slice_dims: vec![0],
    start_index_map: vec![0],
    index_vector_dim: 1,
    slice_sizes: vec![1],
};
let y = x.gather(&indices, config, &mut engine)?;
assert_eq!(y.as_slice::<f64>()?, &[30.0, 10.0]);

Errors

Returns tenferro_tensor::Error::Validation for an invalid configuration, index dtype or out-of-range index, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn scatter( &self, indices: &Tensor, updates: &Tensor, config: ScatterConfig, session: &mut dyn Engine, ) -> Result<Tensor>

Scatter updates into a copy of this tensor at indices on the engine (StableHLO scatter).

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{ScatterConfig, Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![4], vec![0.0_f64; 4])?;
let indices = Tensor::from_vec_col_major(vec![2, 1], vec![1_i64, 3])?;
let updates = Tensor::from_vec_col_major(vec![2], vec![5.0_f64, 7.0])?;
let config = ScatterConfig {
    update_window_dims: vec![],
    inserted_window_dims: vec![0],
    scatter_dims_to_operand_dims: vec![0],
    index_vector_dim: 1,
};
let y = x.scatter(&indices, &updates, config, &mut engine)?;
assert_eq!(y.as_slice::<f64>()?, &[0.0, 5.0, 0.0, 7.0]);

Errors

Returns tenferro_tensor::Error::Validation for an invalid configuration, index dtype or update shape, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn slice(&self, config: SliceConfig, session: &mut dyn Engine) -> Result<Tensor>

Slice this tensor with explicit start, limit and stride per axis on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{SliceConfig, Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![4], vec![1.0_f64, 2.0, 3.0, 4.0])?;
let config = SliceConfig { starts: vec![1], limits: vec![3], strides: vec![1] };
let y = x.slice(config, &mut engine)?;
assert_eq!(y.as_slice::<f64>()?, &[2.0, 3.0]);

Errors

Returns tenferro_tensor::Error::Validation for bounds or strides that do not fit the input, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn dynamic_slice( &self, starts: &Tensor, sizes: &[usize], session: &mut dyn Engine, ) -> Result<Tensor>

Slice this tensor at runtime starts (an integer tensor) with static sizes on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![4], vec![1.0_f64, 2.0, 3.0, 4.0])?;
let starts = Tensor::from_vec_col_major(vec![1], vec![1_i64])?;
let y = x.dynamic_slice(&starts, &[2], &mut engine)?;
assert_eq!(y.as_slice::<f64>()?, &[2.0, 3.0]);

Errors

Returns tenferro_tensor::Error::Validation for a start-index dtype or rank mismatch or sizes larger than the input, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn dynamic_update_slice( &self, update: &Tensor, starts: &Tensor, session: &mut dyn Engine, ) -> Result<Tensor>

Return this tensor with update written at runtime starts on an engine.

starts is a rank-1 integer tensor with one entry per axis. Starts are clamped so the whole update window fits, matching dynamic_slice.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};

let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![5], vec![0.0_f64; 5])?;
let update = Tensor::from_vec_col_major(vec![2], vec![3.0_f64, 4.0])?;
// A start of 4 is clamped to 3 so the window of 2 fits.
let starts = Tensor::from_vec_col_major(vec![1], vec![4_i64])?;
let y = x.dynamic_update_slice(&update, &starts, &mut engine)?;
assert_eq!(y.as_slice::<f64>()?, &[0.0, 0.0, 0.0, 3.0, 4.0]);

Errors

Returns tenferro_tensor::Error::Validation for a rank or dtype mismatch, a start-index tensor that is not rank 1 with one entry per axis, or an update larger than this tensor, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn pad(&self, config: PadConfig, session: &mut dyn Engine) -> Result<Tensor>

Pad this tensor with zeros (edge and interior padding per axis) on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{PadConfig, Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2], vec![1.0_f64, 2.0])?;
let config = PadConfig {
    edge_padding_low: vec![1],
    edge_padding_high: vec![1],
    interior_padding: vec![1],
};
let y = x.pad(config, &mut engine)?;
assert_eq!(y.as_slice::<f64>()?, &[0.0, 1.0, 0.0, 2.0, 0.0]);

Errors

Returns tenferro_tensor::Error::Validation for a padding configuration whose length does not match the input rank, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn concatenate( inputs: &[&Tensor], axis: usize, session: &mut dyn Engine, ) -> Result<Tensor>

where Self: Sized,

Concatenate tensors along axis on an engine.

This has no receiver, like the eager EagerSession::concatenate: call it as Tensor::concatenate(&[&a, &b], axis, session).

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let a = Tensor::from_vec_col_major(vec![1], vec![1.0_f64])?;
let b = Tensor::from_vec_col_major(vec![1], vec![2.0_f64])?;
let y = Tensor::concatenate(&[&a, &b], 0, &mut engine)?;
assert_eq!(y.as_slice::<f64>()?, &[1.0, 2.0]);

Errors

Returns tenferro_tensor::Error::Validation for an empty input list, an axis out of range, or mismatched shapes or dtypes, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn reverse(&self, axes: &[usize], session: &mut dyn Engine) -> Result<Tensor>

Reverse the elements along axes on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![3], vec![1.0_f64, 2.0, 3.0])?;
let y = x.reverse(&[0], &mut engine)?;
assert_eq!(y.as_slice::<f64>()?, &[3.0, 2.0, 1.0]);

Errors

Returns tenferro_tensor::Error::Validation for an out-of-range or repeated axis, or tenferro_tensor::Error::BackendSource for a typed backend failure.

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

Take the maximum over the selected axes on an engine. None reduces every axis and Some(&[]) keeps the input shape.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2, 2], vec![1.0_f64, 5.0, 3.0, 2.0])?;
let y = x.reduce_max(Some(&[0]), &mut engine)?;
assert_eq!(y.as_slice::<f64>()?, &[5.0, 3.0]);

Errors

Returns tenferro_tensor::Error::Validation for an out-of-range or repeated axis, or tenferro_tensor::Error::BackendSource for a typed backend failure. An unsupported dtype returns tenferro_tensor::Error::Unsupported.

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

Take the minimum over the selected axes on an engine. None reduces every axis and Some(&[]) keeps the input shape.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2, 2], vec![1.0_f64, 5.0, 3.0, 2.0])?;
let y = x.reduce_min(Some(&[0]), &mut engine)?;
assert_eq!(y.as_slice::<f64>()?, &[1.0, 2.0]);

Errors

Returns tenferro_tensor::Error::Validation for an out-of-range or repeated axis, or tenferro_tensor::Error::BackendSource for a typed backend failure. An unsupported dtype returns tenferro_tensor::Error::Unsupported.

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

Multiply over the selected axes on an engine. None reduces every axis and Some(&[]) keeps the input shape.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2, 2], vec![1.0_f64, 5.0, 3.0, 2.0])?;
let y = x.reduce_prod(Some(&[0]), &mut engine)?;
assert_eq!(y.as_slice::<f64>()?, &[5.0, 6.0]);

Errors

Returns tenferro_tensor::Error::Validation for an out-of-range or repeated axis, or tenferro_tensor::Error::BackendSource for a typed backend failure. An unsupported dtype returns tenferro_tensor::Error::Unsupported.

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

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

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2, 2], vec![1.0_f64, 5.0, 3.0, 2.0])?;
let y = x.reduce_sum_squares(Some(&[0]), &mut engine)?;
assert_eq!(y.as_slice::<f64>()?, &[26.0, 13.0]);

Errors

Returns tenferro_tensor::Error::Validation for an out-of-range or repeated axis, or tenferro_tensor::Error::BackendSource for a typed backend failure. An unsupported dtype returns tenferro_tensor::Error::Unsupported.

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

Broadcast this tensor into shape, mapping input axis i to output axis dims[i], on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2], vec![1.0_f64, 2.0])?;
let y = x.broadcast_in_dim(&[2, 2], &[0], &mut engine)?;
assert_eq!(y.as_slice::<f64>()?, &[1.0, 2.0, 1.0, 2.0]);

Errors

Returns tenferro_tensor::Error::Validation for a dimension mapping that does not fit the input or target shape, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn tril(&self, k: i64, session: &mut dyn Engine) -> Result<Tensor>

Keep the lower triangle (on and below diagonal k) of the trailing matrix axes on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2, 2], vec![1.0_f64, 2.0, 3.0, 4.0])?;
let y = x.tril(0, &mut engine)?;
assert_eq!(y.as_slice::<f64>()?, &[1.0, 2.0, 0.0, 4.0]);

Errors

Returns tenferro_tensor::Error::Validation for an input of rank below 2, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn triu(&self, k: i64, session: &mut dyn Engine) -> Result<Tensor>

Keep the upper triangle (on and above diagonal k) of the trailing matrix axes on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2, 2], vec![1.0_f64, 2.0, 3.0, 4.0])?;
let y = x.triu(0, &mut engine)?;
assert_eq!(y.as_slice::<f64>()?, &[1.0, 0.0, 3.0, 4.0]);

Errors

Returns tenferro_tensor::Error::Validation for an input of rank below 2, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn extract_diag( &self, axis_a: usize, axis_b: usize, session: &mut dyn Engine, ) -> Result<Tensor>

Extract the diagonal along axis_a and axis_b on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2, 2], vec![1.0_f64, 2.0, 3.0, 4.0])?;
let y = x.extract_diag(0, 1, &mut engine)?;
assert_eq!(y.as_slice::<f64>()?, &[1.0, 4.0]);

Errors

Returns tenferro_tensor::Error::Validation for equal or out-of-range axes, or axes of different extent, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn embed_diag( &self, axis_a: usize, axis_b: usize, session: &mut dyn Engine, ) -> Result<Tensor>

Embed this tensor along the diagonal of axis_a and axis_b on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2], vec![1.0_f64, 2.0])?;
let y = x.embed_diag(0, 1, &mut engine)?;
assert_eq!(y.as_slice::<f64>()?, &[1.0, 0.0, 0.0, 2.0]);

Errors

Returns tenferro_tensor::Error::Validation for invalid diagonal axes, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn dot_general( &self, rhs: &Tensor, config: DotGeneralConfig, session: &mut dyn Engine, ) -> Result<Tensor>

Contract this tensor with rhs on the engine (StableHLO dot_general).

The output layout is [lhs free..., rhs free..., batch...] (batch axes trail).

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{DotGeneralConfig, Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let lhs = Tensor::from_vec_col_major(vec![1, 2], vec![2.0_f64, 3.0])?;
let rhs = Tensor::from_vec_col_major(vec![2, 1], vec![4.0_f64, 5.0])?;
let config = DotGeneralConfig {
    lhs_contracting_dims: [1].as_slice().into(),
    rhs_contracting_dims: [0].as_slice().into(),
    lhs_batch_dims: [].as_slice().into(),
    rhs_batch_dims: [].as_slice().into(),
};
let y = lhs.dot_general(&rhs, config, &mut engine)?;
assert_eq!(y.as_slice::<f64>()?, &[23.0]);

Errors

Returns tenferro_tensor::Error::Validation for incompatible contraction or batch dimensions or dtypes, or tenferro_tensor::Error::BackendSource for a typed backend failure.

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

Contract with optional conjugation of either operand on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{DotGeneralConfig, Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
use num_complex::Complex64;
let lhs = Tensor::from_vec_col_major(vec![1, 1], vec![Complex64::new(0.0, 1.0)])?;
let rhs = Tensor::from_vec_col_major(vec![1, 1], vec![Complex64::new(0.0, 1.0)])?;
let config = DotGeneralConfig {
    lhs_contracting_dims: [1].as_slice().into(),
    rhs_contracting_dims: [0].as_slice().into(),
    lhs_batch_dims: [].as_slice().into(),
    rhs_batch_dims: [].as_slice().into(),
};
let y = lhs.dot_general_with_conj(&rhs, config, true, false, &mut engine)?;
assert_eq!(y.as_slice::<Complex64>()?, &[Complex64::new(1.0, 0.0)]);

Errors

Returns tenferro_tensor::Error::Validation for incompatible contraction or batch dimensions or dtypes, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn scale_real(&self, factor: f64, session: &mut dyn Engine) -> Result<Tensor>

Multiply by a real scalar on the engine, with the eager scale_real dtype rules.

Integer dtypes round the factor.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2], vec![1.0_f64, 2.0])?;
let y = x.scale_real(2.0, &mut engine)?;
assert_eq!(y.as_slice::<f64>()?, &[2.0, 4.0]);

Errors

Returns tenferro_tensor::Error::Validation with InvalidArgument for a non-finite factor, an integer factor out of range, or an external dtype, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn scale_complex( &self, factor: Complex64, session: &mut dyn Engine, ) -> Result<Tensor>

Multiply a complex tensor by a complex scalar on an engine.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
use num_complex::Complex64;
let x = Tensor::from_vec_col_major(vec![1], vec![Complex64::new(1.0, 2.0)])?;
let y = x.scale_complex(Complex64::new(0.0, 1.0), &mut engine)?;
assert_eq!(y.as_slice::<Complex64>()?, &[Complex64::new(-2.0, 1.0)]);

Errors

Returns tenferro_tensor::Error::Validation with InvalidArgument when the input dtype is not complex, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn sigmoid(&self, session: &mut dyn Engine) -> Result<Tensor>

Logistic sigmoid 1 / (1 + exp(-x)) on the engine, overflow-free.

Evaluated as 1 / (1 + e) for x > 0 and e / (1 + e) otherwise, with e = exp(-|x|). Real F32/F64 only.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![3], vec![-700.0_f64, 0.0, 1000.0])?;
let y = x.sigmoid(&mut engine)?;
let y = y.as_slice::<f64>()?;
assert_eq!(y[1], 0.5);
assert!(y[0] > 0.0 && y[0] < 1e-300);
assert_eq!(y[2], 1.0);

Errors

Returns tenferro_tensor::Error::UnsupportedDType for complex, integer, or Bool input, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn silu(&self, session: &mut dyn Engine) -> Result<Tensor>

SiLU (swish) x * sigmoid(x) on an engine.

Real F32/F64 only.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![3], vec![-1.0_f64, 0.0, 1.0])?;
let y = x.silu(&mut engine)?;
let y = y.as_slice::<f64>()?;
assert_eq!(y[1], 0.0);
assert!((y[2] - 1.0 / (1.0 + (-1.0_f64).exp())).abs() < 1e-15);

Errors

Returns tenferro_tensor::Error::UnsupportedDType for complex, integer, or Bool input, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn softplus(&self, session: &mut dyn Engine) -> Result<Tensor>

Softplus log(1 + exp(x)) on the engine, in the stable form max(x, 0) + log1p(exp(-|x|)).

Real F32/F64 only; never overflows.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![3], vec![-1000.0_f64, 0.0, 1000.0])?;
let y = x.softplus(&mut engine)?;
let y = y.as_slice::<f64>()?;
assert_eq!(y[0], 0.0);
assert!((y[1] - 2.0_f64.ln()).abs() < 1e-15);
assert_eq!(y[2], 1000.0);

Errors

Returns tenferro_tensor::Error::UnsupportedDType for complex, integer, or Bool input, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn gelu(&self, session: &mut dyn Engine) -> Result<Tensor>

Exact GELU x/2 * (1 + erf(x / sqrt(2))) on an engine.

Real F32/F64 only (PyTorch approximate="none").

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![3], vec![-1.0_f64, 0.0, 1.0])?;
let y = x.gelu(&mut engine)?;
let y = y.as_slice::<f64>()?;
assert_eq!(y[1], 0.0);
assert!((y[2] - 0.841_344_746_068_542_9).abs() < 1e-15);

Errors

Returns tenferro_tensor::Error::UnsupportedDType for complex, integer, or Bool input, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn gelu_tanh(&self, session: &mut dyn Engine) -> Result<Tensor>

GELU tanh approximation on the engine (PyTorch approximate="tanh").

x/2 * (1 + tanh(sqrt(2/pi) * (x + 0.044715 x^3))); real F32/F64 only.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![3], vec![-1.0_f64, 0.0, 1.0])?;
let y = x.gelu_tanh(&mut engine)?;
let y = y.as_slice::<f64>()?;
assert_eq!(y[1], 0.0);
assert!((y[2] - 0.841_191_990_608_276_8).abs() < 1e-12);

Errors

Returns tenferro_tensor::Error::UnsupportedDType for complex, integer, or Bool input, or tenferro_tensor::Error::BackendSource for a typed backend failure.

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

Arithmetic mean over axes on the engine (None reduces every axis).

Float and complex dtypes. The sum is divided by the element count; a mean over zero elements is NaN, and Some(&[]) is the identity.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2, 2], vec![1.0_f64, 2.0, 3.0, 4.0])?;
let y = x.reduce_mean(Some(&[1]), &mut engine)?;
assert_eq!(y.as_slice::<f64>()?, &[2.0, 3.0]);

Errors

Returns tenferro_tensor::Error::UnsupportedDType for integer or Bool input, tenferro_tensor::Error::Validation with AxisOutOfBounds or DuplicateAxis for invalid axes, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn softmax(&self, axis: usize, session: &mut dyn Engine) -> Result<Tensor>

Max-subtracted softmax along axis on an engine.

Real F32/F64 only. A slice that is entirely -inf returns zeros instead of NaN; a NaN or +inf entry makes its slice NaN.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2], vec![0.0_f64, f64::NEG_INFINITY])?;
let y = x.softmax(0, &mut engine)?;
assert_eq!(y.as_slice::<f64>()?, &[1.0, 0.0]);

Errors

Returns tenferro_tensor::Error::UnsupportedDType for complex, integer, or Bool input, tenferro_tensor::Error::Validation with AxisOutOfBounds for an invalid axis, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn log_softmax(&self, axis: usize, session: &mut dyn Engine) -> Result<Tensor>

Max-subtracted log-softmax along axis on an engine.

Real F32/F64 only. A slice that is entirely -inf returns -inf instead of NaN.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2], vec![1.0_f64, 1.0])?;
let y = x.log_softmax(0, &mut engine)?;
assert_eq!(y.as_slice::<f64>()?, &[-std::f64::consts::LN_2; 2]);

Errors

Returns tenferro_tensor::Error::UnsupportedDType for complex, integer, or Bool input, tenferro_tensor::Error::Validation with AxisOutOfBounds for an invalid axis, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn masked_softmax( &self, mask: &Tensor, axis: usize, session: &mut dyn Engine, ) -> Result<Tensor>

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

mask broadcasts to the input shape. Masked-out entries are 0 whatever their value; a slice with no unmasked entry is all zeros.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![3], vec![1.0_f64, 1.0, f64::NAN])?;
let mask = Tensor::from_vec_col_major(vec![3], vec![true, true, false])?;
let y = x.masked_softmax(&mask, 0, &mut engine)?;
assert_eq!(y.as_slice::<f64>()?, &[0.5, 0.5, 0.0]);

Errors

Returns tenferro_tensor::Error::UnsupportedDType for complex, integer, or Bool input, tenferro_tensor::Error::Validation with DTypeMismatch for a non-Bool mask, ShapeMismatch for a mask that does not broadcast to the input, or AxisOutOfBounds for an invalid axis, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn masked_log_softmax( &self, mask: &Tensor, axis: usize, session: &mut dyn Engine, ) -> Result<Tensor>

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

Masked-out entries are -inf; a slice with no unmasked entry is all -inf.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![3], vec![1.0_f64, 1.0, 5.0])?;
let mask = Tensor::from_vec_col_major(vec![3], vec![true, true, false])?;
let y = x.masked_log_softmax(&mask, 0, &mut engine)?;
let ln_half = -std::f64::consts::LN_2;
assert_eq!(y.as_slice::<f64>()?, &[ln_half, ln_half, f64::NEG_INFINITY]);

Errors

Returns tenferro_tensor::Error::UnsupportedDType for complex, integer, or Bool input, tenferro_tensor::Error::Validation with DTypeMismatch for a non-Bool mask, ShapeMismatch for a mask that does not broadcast to the input, or AxisOutOfBounds for an invalid axis, or tenferro_tensor::Error::BackendSource for a typed backend failure.

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

Layer normalization along axis with optional affine weight / bias, on an engine.

(x - mean) / sqrt(var + eps) * weight + bias with the biased variance; weight and bias are rank-1 of length shape[axis]. Real F32/F64 only.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2], vec![1.0_f64, 3.0])?;
let bias = Tensor::from_vec_col_major(vec![2], vec![10.0_f64, 10.0])?;
let y = x.layer_norm(0, None, Some(&bias), 0.0, &mut engine)?;
assert_eq!(y.as_slice::<f64>()?, &[9.0, 11.0]);

Errors

Returns tenferro_tensor::Error::UnsupportedDType for complex, integer, or Bool input, tenferro_tensor::Error::Validation with AxisOutOfBounds for an invalid axis, InvalidArgument for a negative or non-finite eps, or DTypeMismatch / ShapeMismatch for a weight or bias that is not a same-dtype vector of the axis length, or tenferro_tensor::Error::BackendSource for a typed backend failure.

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

RMS normalization along axis with optional affine weight / bias, on an engine.

x / sqrt(mean(x^2) + eps) * weight + bias; weight and bias are rank-1 of length shape[axis]. Real F32/F64 only.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
let x = Tensor::from_vec_col_major(vec![2], vec![3.0_f64, 4.0])?;
let weight = Tensor::from_vec_col_major(vec![2], vec![2.0_f64, 1.0])?;
let y = x.rms_norm(0, Some(&weight), None, 0.0, &mut engine)?;
let y = y.as_slice::<f64>()?;
let rms = 12.5_f64.sqrt();
assert!((y[0] - 6.0 / rms).abs() < 1e-15 && (y[1] - 4.0 / rms).abs() < 1e-15);

Errors

Returns tenferro_tensor::Error::UnsupportedDType for complex, integer, or Bool input, tenferro_tensor::Error::Validation with AxisOutOfBounds for an invalid axis, InvalidArgument for a negative or non-finite eps, or DTypeMismatch / ShapeMismatch for a weight or bias that is not a same-dtype vector of the axis length, or tenferro_tensor::Error::BackendSource for a typed backend failure.

fn take_along_axis( &self, indices: &Tensor, axis: usize, session: &mut dyn Engine, ) -> Result<Tensor>

NumPy-style take_along_axis over gather, on an engine.

out[.., i, ..] = self[.., indices[.., i, ..], ..] along axis. indices (I32/I64) has the input’s rank; every other dimension is either the input’s extent (batch-varying indices) or 1 (the whole extent is taken). Indices must be in bounds.

Examples

use tenferro_cpu::CpuEngine;
use tenferro_runtime::{Tensor, TensorSessionOpsExt};


let mut engine = CpuEngine::serial();
// Per-batch row gather: out[i, j, b] = x[idx[i, b], j, b].
let x = Tensor::from_vec_col_major(vec![2, 2, 2], (0..8).map(f64::from).collect::<Vec<_>>())?;
let idx = Tensor::from_vec_col_major(vec![2, 1, 2], vec![1_i64, 0, 0, 0])?;
let y = x.take_along_axis(&idx, 0, &mut engine)?;
assert_eq!(y.as_slice::<f64>()?, &[1.0, 0.0, 3.0, 2.0, 4.0, 4.0, 6.0, 6.0]);

Errors

Returns tenferro_tensor::Error::Validation with RankMismatch or ShapeMismatch for incompatible index shapes, AxisOutOfBounds for an invalid axis, or InvalidArgument when taking from a zero-length axis; tenferro_tensor::Error::UnsupportedDType for a non-integer index dtype; or tenferro_tensor::Error::BackendSource for a typed backend failure.

Dyn Compatibility

This trait is dyn compatible.

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

Implementors

impl TensorSessionOpsExt for Tensor