Enum GradOp 

pub enum GradOp {
    Input,
    Parameter,
    Add(usize, usize),
    Sub(usize, usize),
    Mul(usize, usize),
    Div(usize, usize),
    Neg(usize),
    MatMul(usize, usize),
    Sum(usize),
    Mean(usize),
    ScalarMul(usize, f64),
    Exp(usize),
    Ln(usize),
    StructField {
        parent: usize,
        field_index: usize,
        total_fields: usize,
    },
    MapLookup {
        map_node: usize,
        key_index: usize,
        total_keys: usize,
    },
    Sin(usize),
    Cos(usize),
    Sqrt(usize),
    Pow(usize, f64),
    Sigmoid(usize),
    Relu(usize),
    TanhAct(usize),
    Abs(usize),
    Log2(usize),
    Softmax(usize),
    CrossEntropy {
        logits: usize,
        targets: usize,
    },
    LayerNorm(usize),
    BatchNorm(usize),
    Clamp {
        input: usize,
        min: f64,
        max: f64,
    },
    Where {
        cond: usize,
        on_true: usize,
        on_false: usize,
    },
    Reshape {
        input: usize,
        original_shape: Vec<usize>,
    },
    TransposeOp(usize),
    CatOp {
        inputs: Vec<usize>,
        axis: usize,
        sizes: Vec<usize>,
    },
    GatherOp {
        input: usize,
        indices: Vec<usize>,
        axis: usize,
    },
}

Operation recorded in the computation graph.

Variants

Input

Parameter

Add(usize, usize)

Sub(usize, usize)

Mul(usize, usize)

Div(usize, usize)

Neg(usize)

MatMul(usize, usize)

Sum(usize)

Mean(usize)

ScalarMul(usize, f64)

Exp(usize)

Ln(usize)

StructField

Gradient through struct field access: parent node, field index.

Fields

parent: usize

field_index: usize

total_fields: usize

MapLookup

Gradient through map lookup: map node, key index in insertion order.

Fields

map_node: usize

key_index: usize

total_keys: usize

Sin(usize)

Cos(usize)

Sqrt(usize)

Pow(usize, f64)

Sigmoid(usize)

Relu(usize)

TanhAct(usize)

Abs(usize)

Log2(usize)

Softmax(usize)

CrossEntropy

Cross-entropy loss: CrossEntropy(logits, targets)

Fields

logits: usize

targets: usize

LayerNorm(usize)

Layer normalization: LayerNorm(input); stores normalized output and std for backward

BatchNorm(usize)

Batch normalization: BatchNorm(input); stores normalized output and std for backward

Clamp

Clamp to [min, max]

Fields

input: usize

min: f64

max: f64

Where

Conditional select: Where(condition, on_true, on_false) condition is a tensor of 0.0/1.0 masks

Fields

cond: usize

on_true: usize

on_false: usize

Reshape

Reshape with stored original shape for backward

Fields

input: usize

original_shape: Vec<usize>

TransposeOp(usize)

Transpose (2-D)

CatOp

Concatenation along axis with sizes for splitting on backward

Fields

inputs: Vec<usize>

axis: usize

sizes: Vec<usize>

GatherOp

Gather along axis: GatherOp { input, indices, axis }

Fields

input: usize

indices: Vec<usize>

axis: usize

Trait Implementations

impl Clone for GradOp

fn clone(&self) -> GradOp

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for GradOp

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

Formats the value using the given formatter.