Enum TensorOp 

pub enum TensorOp {
    Add,
    Sub,
    Mul,
    Div,
    Neg,
    MatMul,
    Linear,
    Conv2D,
    Embedding,
    ReLU,
    GeLU,
    SiLU,
    Sigmoid,
    Softmax,
    Tanh,
    LeakyReLU,
    ELU,
    Mish,
    HardSwish,
    HardSigmoid,
    LayerNorm,
    RMSNorm,
    BatchNorm,
    GroupNorm,
    InstanceNorm,
    Reshape,
    Transpose,
    Concat,
    Split,
    Gather,
    Scatter,
    Squeeze,
    Unsqueeze,
    Permute,
    Expand,
    Slice,
    Pad,
    Tile,
    Constant,
    Zeros,
    Ones,
    Arange,
    Full,
    Attention,
    MultiHeadAttention,
    MultiQueryAttention,
    GroupedQueryAttention,
    FlashAttention,
    SlidingWindowAttention,
    CrossAttention,
    PagedAttention,
    MoEDispatch,
    MoECombine,
    Conv1D,
    Conv3D,
    ConvTranspose2D,
    DepthwiseConv2D,
    DilatedConv2D,
    MaxPool2D,
    AvgPool2D,
    AdaptiveAvgPool2D,
    GlobalAvgPool,
    LSTMCell,
    GRUCell,
    RNNCell,
    Einsum,
    FFT,
    IFFT,
    SVD,
    Eig,
    Solve,
    TopK,
    Sort,
    Cumsum,
    Where,
    Clamp,
    SparseMatMul,
    SparseEmbedding,
    Quantize,
    Dequantize,
    QuantizeInt4,
    DequantizeInt4,
    QuantizeFp8,
    DequantizeFp8,
    UNetDownBlock,
    UNetUpBlock,
    TimestepEmbedding,
    GNNMessagePassing,
    GNNGlobalPooling,
    Checkpoint,
    Offload,
    GradAccumulate,
    GradMatMul,
    GradReLU,
    GradSoftmax,
    GradLayerNorm,
    GradAttention,
    GradConv2D,
    GradLinear,
    GradGeLU,
    ParallelSplit,
    ParallelAllReduce,
    PipelineSend,
    PipelineReceive,
    FusedMatMulBiasReLU,
    FusedMatMulBias,
    FusedLinearGeLU,
    FusedAttentionLayerNorm,
    FusedLinearSiLU,
    FusedConvBatchNormReLU,
}

Variants

Add

Sub

Mul

Div

Neg

MatMul

Linear

Conv2D

Embedding

ReLU

GeLU

SiLU

Sigmoid

Softmax

Tanh

LeakyReLU

ELU

Mish

HardSwish

HardSigmoid

LayerNorm

RMSNorm

BatchNorm

GroupNorm

InstanceNorm

Reshape

Transpose

Concat

Split

Gather

Scatter

Squeeze

Unsqueeze

Permute

Expand

Slice

Pad

Tile

Constant

Zeros

Ones

Arange

Full

Attention

MultiHeadAttention

MultiQueryAttention

GroupedQueryAttention

FlashAttention

SlidingWindowAttention

CrossAttention

PagedAttention

MoEDispatch

MoECombine

Conv1D

Conv3D

ConvTranspose2D

DepthwiseConv2D

DilatedConv2D

MaxPool2D

AvgPool2D

AdaptiveAvgPool2D

GlobalAvgPool

LSTMCell

GRUCell

RNNCell

Einsum

FFT

IFFT

SVD

Eig

Solve

TopK

Sort

Cumsum

Where

Clamp

SparseMatMul

SparseEmbedding

Quantize

Dequantize

QuantizeInt4

DequantizeInt4

QuantizeFp8

DequantizeFp8

UNetDownBlock

UNetUpBlock

TimestepEmbedding

GNNMessagePassing

GNNGlobalPooling

Checkpoint

Offload

GradAccumulate

GradMatMul

GradReLU

GradSoftmax

GradLayerNorm

GradAttention

GradConv2D

GradLinear

GradGeLU

ParallelSplit

ParallelAllReduce

PipelineSend

PipelineReceive

FusedMatMulBiasReLU

FusedMatMulBias

FusedLinearGeLU

FusedAttentionLayerNorm

FusedLinearSiLU

FusedConvBatchNormReLU

Implementations

impl TensorOp

pub fn name(&self) -> &'static str

pub fn from_name(name: &str) -> Option<Self>

pub fn num_inputs(&self) -> (usize, usize)

pub fn flops_formula(&self) -> &'static str

Returns the asymptotic FLOPs formula as a human-readable string.

pub fn is_zero_flop(&self) -> bool

Returns true if this op performs no arithmetic (zero FLOPs).

pub fn is_activation(&self) -> bool

Returns true if this is an element-wise (unary or binary) activation.

pub fn is_attention(&self) -> bool

Returns true if this is an attention variant.

pub fn is_convolution(&self) -> bool

Returns true if this is a convolution variant.

pub fn is_normalisation(&self) -> bool

Returns true if this is a normalisation op.

pub fn is_fused(&self) -> bool

Returns true if this is a fused operation.

pub fn is_gradient(&self) -> bool

Returns true if this is a gradient (backward) operation.

Trait Implementations

impl Clone for TensorOp

fn clone(&self) -> TensorOp

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for TensorOp

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

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for TensorOp

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Hash for TensorOp

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 TensorOp

fn eq(&self, other: &TensorOp) -> 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 Serialize for TensorOp

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl Eq for TensorOp

impl StructuralPartialEq for TensorOp