Enum StdTensorOp 

pub enum StdTensorOp {
    Add,
    Mul,
    Neg,
    Conj,
    DotGeneral(DotGeneralConfig),
    Transpose {
        perm: Vec<usize>,
    },
    Reshape {
        from_shape: Vec<DimExpr>,
        to_shape: Vec<DimExpr>,
    },
    BroadcastInDim {
        shape: Vec<DimExpr>,
        dims: Vec<usize>,
    },
    Convert {
        from: DType,
        to: DType,
    },
    Constant {
        dtype: DType,
        bytes: Vec<u8>,
    },
    ReduceSum {
        axes: Vec<usize>,
        input_shape: Vec<DimExpr>,
    },
    Div,
    Abs,
    Sign,
    Maximum,
    Minimum,
    Compare(CompareDir),
    Select,
    Clamp,
    Exp,
    Log,
    Sin,
    Cos,
    Tanh,
    Sqrt,
    Rsqrt,
    Pow,
    Expm1,
    Log1p,
    ExtractDiag {
        axis_a: usize,
        axis_b: usize,
    },
    EmbedDiag {
        axis_a: usize,
        axis_b: usize,
    },
    Tril {
        k: i64,
    },
    Triu {
        k: i64,
    },
    Gather(GatherConfig),
    Scatter(ScatterConfig),
    Slice(SliceConfig),
    DynamicSlice {
        slice_sizes: Vec<usize>,
    },
    Pad(PadConfig),
    NaryEinsum {
        subscripts: String,
        n_inputs: usize,
    },
    Concatenate {
        axis: usize,
    },
    Reverse {
        axes: Vec<usize>,
    },
    ShapeOf {
        axis: usize,
    },
    DynamicTruncate {
        axis: usize,
    },
    PadToMatch {
        axis: usize,
    },
    ReduceProd {
        axes: Vec<usize>,
        input_shape: Vec<DimExpr>,
    },
    ReduceMax {
        axes: Vec<usize>,
        input_shape: Vec<DimExpr>,
    },
    ReduceMin {
        axes: Vec<usize>,
        input_shape: Vec<DimExpr>,
    },
    Cholesky {
        input_shape: Vec<DimExpr>,
    },
    Lu {
        input_shape: Vec<DimExpr>,
    },
    Svd {
        eps: f64,
        input_shape: Vec<DimExpr>,
    },
    Qr {
        input_shape: Vec<DimExpr>,
    },
    Eigh {
        eps: f64,
        input_shape: Vec<DimExpr>,
    },
    Eig {
        input_dtype: DType,
        input_shape: Vec<DimExpr>,
    },
    TriangularSolve {
        left_side: bool,
        lower: bool,
        transpose_a: bool,
        unit_diagonal: bool,
        lhs_shape: Vec<DimExpr>,
        rhs_shape: Vec<DimExpr>,
    },
    ValidateNonsingular {
        input_shape: Vec<DimExpr>,
    },
}

Variants

Add

Mul

Neg

Conj

DotGeneral(DotGeneralConfig)

Transpose

Fields

perm: Vec<usize>

Reshape

Fields

from_shape: Vec<DimExpr>

to_shape: Vec<DimExpr>

BroadcastInDim

Fields

shape: Vec<DimExpr>

dims: Vec<usize>

Convert

Fields

from: DType

to: DType

Constant

Fields

dtype: DType

bytes: Vec<u8>

ReduceSum

Fields

axes: Vec<usize>

input_shape: Vec<DimExpr>

Div

Abs

Sign

Maximum

Minimum

Compare(CompareDir)

Select

Clamp

Exp

Log

Sin

Cos

Tanh

Sqrt

Rsqrt

Pow

Expm1

Log1p

ExtractDiag

Fields

axis_a: usize

axis_b: usize

EmbedDiag

Fields

axis_a: usize

axis_b: usize

Tril

Fields

k: i64

Triu

Fields

k: i64

Gather(GatherConfig)

Scatter(ScatterConfig)

Slice(SliceConfig)

DynamicSlice

Fields

slice_sizes: Vec<usize>

Pad(PadConfig)

NaryEinsum

N-ary einsum kept as a single graph node. Contraction path is optimized at execution time from actual input shapes.

Fields

subscripts: String

n_inputs: usize

Concatenate

Fields

axis: usize

Reverse

Fields

axes: Vec<usize>

ShapeOf

Fields

axis: usize

DynamicTruncate

Fields

axis: usize

PadToMatch

Fields

axis: usize

ReduceProd

Fields

axes: Vec<usize>

input_shape: Vec<DimExpr>

ReduceMax

Fields

axes: Vec<usize>

input_shape: Vec<DimExpr>

ReduceMin

Fields

axes: Vec<usize>

input_shape: Vec<DimExpr>

Cholesky

Fields

input_shape: Vec<DimExpr>

Lu

Fields

input_shape: Vec<DimExpr>

Svd

Fields

eps: f64

input_shape: Vec<DimExpr>

Qr

Fields

input_shape: Vec<DimExpr>

Eigh

Fields

eps: f64

input_shape: Vec<DimExpr>

Eig

Fields

input_dtype: DType

input_shape: Vec<DimExpr>

TriangularSolve

Fields

left_side: bool

lower: bool

transpose_a: bool

unit_diagonal: bool

lhs_shape: Vec<DimExpr>

rhs_shape: Vec<DimExpr>

ValidateNonsingular

Fields

input_shape: Vec<DimExpr>

Implementations

impl StdTensorOp

pub fn constant_f64(value: f64) -> Self

Create an f64 scalar constant op.

Examples

use tenferro_ops::std_tensor_op::StdTensorOp;

let op = StdTensorOp::constant_f64(1.5);

pub fn constant_f32(value: f32) -> Self

Create an f32 scalar constant op.

Examples

use tenferro_ops::std_tensor_op::StdTensorOp;

let op = StdTensorOp::constant_f32(1.5_f32);

pub fn constant_c64(value: Complex64) -> Self

Create a Complex64 scalar constant op.

Examples

use num_complex::Complex64;
use tenferro_ops::std_tensor_op::StdTensorOp;

let op = StdTensorOp::constant_c64(Complex64::new(1.0, -2.0));

pub fn constant_c32(value: Complex32) -> Self

Create a Complex32 scalar constant op.

Examples

use num_complex::Complex32;
use tenferro_ops::std_tensor_op::StdTensorOp;

let op = StdTensorOp::constant_c32(Complex32::new(1.0, -2.0));

Trait Implementations

impl Clone for StdTensorOp

fn clone(&self) -> StdTensorOp

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for StdTensorOp

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

Formats the value using the given formatter.

impl GraphOp for StdTensorOp

type Operand = Tensor

type Context = ()

type InputKey = TensorInputKey

fn n_inputs(&self) -> usize

Returns the number of inputs consumed by this operation.

fn n_outputs(&self) -> usize

Returns the number of outputs produced by this operation.

impl Hash for StdTensorOp

fn hash<H: Hasher>(&self, state: &mut H)

Feeds this value into the given Hasher.

fn hash_slice<H>(data: &[Self], state: &mut H)

where H: Hasher, Self: Sized,

Feeds a slice of this type into the given Hasher.

impl PartialEq for StdTensorOp

fn eq(&self, other: &StdTensorOp) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl PrimitiveOp for StdTensorOp

type ADContext = ShapeGuardContext

Runtime AD context threaded through linearization and transpose.

fn add() -> Self

Returns the addition operation used for cotangent accumulation in tidu::transpose. When multiple cotangents flow to the same GlobalValKey, transpose emits Op::add() nodes to sum them.

fn linearize( &self, builder: &mut FragmentBuilder<Self>, primal_in: &[GlobalValKey<Self>], primal_out: &[GlobalValKey<Self>], tangent_in: &[Option<LocalValId>], ctx: &mut Self::ADContext, ) -> Vec<Option<LocalValId>>

Emit the linear (JVP) rule for this primitive.

fn transpose_rule( &self, emitter: &mut impl OpEmitter<Self>, cotangent_out: &[Option<LocalValId>], inputs: &[ValRef<Self>], mode: &OpMode, ctx: &mut Self::ADContext, ) -> Vec<Option<LocalValId>>

Emit the transpose rule for this linear primitive.

impl SemiringOps for StdTensorOp

fn add_op() -> Self

fn mul_op() -> Self

fn dot_general(config: DotGeneralConfig) -> Self

fn reduce_sum(axes: Vec<usize>, input_shape: Vec<DimExpr>) -> Self

fn transpose_op(perm: Vec<usize>) -> Self

fn reshape(from_shape: Vec<DimExpr>, to_shape: Vec<DimExpr>) -> Self

fn broadcast_in_dim(shape: Vec<DimExpr>, dims: Vec<usize>) -> Self

fn extract_diag(axis_a: usize, axis_b: usize) -> Self

fn embed_diag(axis_a: usize, axis_b: usize) -> Self

impl Eq for StdTensorOp

impl StructuralPartialEq for StdTensorOp