alibaba--mnn/schema/default/TensorflowOp.fbs

include "Tensor.fbs";
namespace MNN;

enum BinaryOpOperation : int {
    ADD = 0,
    SUB = 1,
    MUL = 2,
    DIV = 3,
    MAX_TEMP = 4,
    MIN_TEMP = 5,
    POW = 6,
    REALDIV = 7,
    MINIMUM = 8,
    MAXIMUM = 9,
    GREATER = 10,
    GREATER_EQUAL = 11,
    LESS = 12,
    FLOORDIV = 13,
    SquaredDifference = 14,
    EQUAL = 15,
    LESS_EQUAL = 16,
    FLOORMOD = 17,
    MOD = 19,
    ATAN2 = 20,
    LOGICALOR = 21,
    NOTEQUAL = 22,
    BITWISE_AND = 23,
    BITWISE_OR = 24,
    BITWISE_XOR = 25,
    LOGICALXOR = 26,
    LEFTSHIFT = 27,
    RIGHTSHIFT = 28,
    MUL_SILU = 29,
}

table BinaryOp {
    opType:BinaryOpOperation;
    T:DataType=DT_FLOAT;
    // 0 -> No Activation
    // 1 -> Relu
    activationType:int=0;
}

table PackParam {
    dataType:DataType;
    axis:int;
}

table StridedSliceParam {
    Index:DataType;
    T:DataType;
    beginMask:int;
    endMask:int;
    ellipsisMask:int;
    newAxisMask:int;
    shrinkAxisMask:int;
    // type 0 --> from TF
    // type 1 --> from Onnx/Torch
    fromType:int;
}

table SqueezeParam {
    squeezeDims:[int];
}

table CastParam {
    srcT:DataType;
    dstT:DataType;
}

enum ReductionType : byte{
    SUM = 0,
    ASUM = 1,
    SUMSQ = 2,
    MEAN = 3,
    MAXIMUM = 4,
    MINIMUM = 5,
    PROD = 6,
    ANY = 7,
    ALL = 8,
}

table ReductionParam {
    operation:ReductionType;
    dim:[int];
    coeff:float;
    keepDims:bool;
    dType:DataType=DT_FLOAT;
}

table Gather {
    Tindices:DataType;
    Tparams:DataType;
    validateIndices:bool;
    axis:int;
}

table ExpandDims {
    T:DataType;
    Tdim:DataType;
    axis:int;
}

table Selu {
    scale:float;
    alpha:float;
}

table AsString {
    T:DataType;
    precision:int;
    scientific:bool;
    shortest:bool;
    width:int;
    fillString:string;
}

table ReduceJoin {
    keepDims:bool;
    separator:string;
}

enum UnaryOpOperation : int {
    ABS = 0,
    NEG = 1,
    FLOOR = 2,
    CEIL = 3,
    SQUARE = 4,
    SQRT = 5,
    RSQRT = 6,
    EXP = 7,
    LOG = 8,
    SIN = 9,
    COS = 10,
    TAN = 11,
    ASIN = 12,
    ACOS = 13,
    ATAN = 14,
    RECIPROCAL = 15,
    LOG1P = 16,
    BNLL = 17,
    ACOSH = 18,
    SINH = 19,
    ASINH = 20,
    ATANH = 21,
    SIGN = 22,
    ROUND = 23,
    COSH = 24,
    ERF = 25,
    ERFC = 26,
    ERFINV = 27,
    EXPM1 = 28,
    SIGMOID = 29,
    TANH = 30,
    HARDSWISH = 31,
    GELU = 32,
    GELU_STANDARD = 33,
    SILU = 34,
}

table UnaryOp {
    opType:UnaryOpOperation;
    T:DataType;
    tableInt8:[int8];
}

table TopKV2 {
    T:DataType=DT_FLOAT;
    sorted:bool=false;
    largest:bool=true;
}
enum CropAndResizeMethod : byte{
    BILINEAR=0,
    NEAREST=1,
}

table CropAndResize {
    extrapolationValue:float;
    method:CropAndResizeMethod;
}

table Fill {

}

table GatherV2 {
    Taxis:DataType;
    Tindices:DataType;
    Tparams:DataType;
}

table NonMaxSuppressionV2 {

}

table Range {
    Tidx:DataType;
}

table Rank {

}

table Size {
    outputDataType:DataType;
}

table Transpose {
    Tperm:DataType;
}

table SliceTf {
    T:DataType;
}

table QuantizeMaxMin {
    T:DataType;
}

table Crop {
    axis:int=2;
    offset:[int];
}

table SpaceBatch {
    blockShape:Blob;
    padding:Blob;
}
table MatMul {
    T:DataType;
    transposeA:bool;
    transposeB:bool;
    weight:[float];
    bias:[float];
}

table MomentsParam {
    dim:[int];
    keepDims:bool=true;
    dType:DataType=DT_FLOAT;
}

table RNNParam {
    numUnits: int;
    isBidirectionalRNN: bool;
    linearBeforeReset: bool;

    keepAllOutputs: bool;
    fwGateWeight: Blob;
    fwGateBias: Blob;
    fwCandidateWeight: Blob;
    fwCandidateBias: Blob;
    fwRecurrentBias: Blob;

    bwGateWeight: Blob;
    bwGateBias: Blob;
    bwCandidateWeight: Blob;
    bwCandidateBias: Blob;
    bwRecurrentBias: Blob;
}

table BatchMatMulParam {
    adjX: bool = false;
    adjY: bool = false;
}

enum DepthToSpaceMode : byte {
    DCR = 0,
    CRD = 1
}

// DepthToSpace and SpaceToDepth using the same parameter
table DepthSpaceParam {
    blockSize: int;
    mode: DepthToSpaceMode = DCR;
}

table ReverseSequenceParam {
    batchDim: int;
    seqDim : int;
}

table DetectionPostProcessParam{
    maxDetections: int;
    maxClassesPerDetection: int;
    detectionsPerClass: int;
    nmsScoreThreshold:float;
    iouThreshold:float;
    numClasses:int;
    useRegularNMS:bool;
    // y_scale, x_scale, h_scale, w_scale
    // always size == 4
    centerSizeEncoding:[float];
}

table OneHotParam{
    dType:DataType=DT_FLOAT;
    axis:int=-1;
}

enum PadValueMode : byte{
    CONSTANT = 0,
    REFLECT = 1,
    SYMMETRIC = 2,
    EDGE=3,
}

table PadParam{
    mode: PadValueMode = CONSTANT;
}
table LayerNorm {
    axis: [int];
    epsilon: float;
    gamma: [float];
    beta: [float];
    group: int = 1;
    external:[int64]; // [offset, gamma_bytes_size, beta_bytes_size]
    useRMSNorm: bool = false;
}
table GroupNorm {
    axis: int;
    epsilon: float;
    gamma: [float];
    beta: [float];
    group: int = 1;
    bSwish: int = 0;
    external:[int64]; // [offset, gamma_bytes_size, beta_bytes_size]
}
table RandomUniform {
    seed:int = 0;
    seed2:int = 0;
    type:DataType = DT_FLOAT;
    low:float = 0.0;
    high:float = 1.0;
}
table TensorArray {
    // false - fix array size; true - dynamic array size;
    dynamic_size:bool = false;
    // false - element dynamic shape; true - element identical shape;
    identical_element_shapes:bool = false;
    element_shape:[int];
    T:DataType = DT_FLOAT;
    // onnx related attributes
    axis:int = 0;
    keepdims:bool = true;
    new_axis:bool = false;
}

table LSTMBlockCell {
    cell_clip:float = 3.0;
    forget_bias:float = 1.0;
    use_peephole:bool = false;
}