mindspore2022/mindspore/lite/schema/ops.fbs

/**
 * Copyright 2019-2021 Huawei Technologies Co., Ltd
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
include "ops_types.fbs";

namespace mindspore.schema;

union PrimitiveType {
    Abs,
    Activation,
    ActivationGrad,
    Adam,
    AddFusion,
    AdderFusion,
    AddGrad,
    AddN,
    All,
    ApplyMomentum,
    ArgMaxFusion,
    ArgMinFusion,
    Assert,
    Assign,
    AssignAdd,
    AudioSpectrogram,
    AvgPoolFusion,
    AvgPoolGrad,
    BatchNorm,
    BatchNormGrad,
    BatchToSpace,
    BatchToSpaceND,
    BiasAdd,
    BinaryCrossEntropy,
    BinaryCrossEntropyGrad,
    BiasAddGrad,
    BroadcastTo,
    Cast,
    Ceil,
    Clip,
    Concat,
    Attention,
    Conv2DBackpropFilterFusion,
    Conv2DBackpropInputFusion,
    Conv2DFusion,
    Conv2dTransposeFusion,
    Cos,
    ConstantOfShape,
    Crop,
    CustomExtractFeatures,
    CustomNormalize,
    CustomPredict,
    DeConv2DGradFilter,
    Depend,
    DepthToSpace,
    DetectionPostProcess,
    DivFusion,
    DivGrad,
    Dropout,
    DropoutGrad,
    Elu,
    Eltwise,
    Equal,
    EmbeddingLookupFusion,
    ExpFusion,
    ExpandDims,
    FakeQuantWithMinMaxVars,
    FakeQuantWithMinMaxVarsPerChannel,
    FftReal,
    FftImag,
    Flatten,
    FlattenGrad,
    Floor,
    FloorDiv,
    FloorMod,
    Fill,
    FullConnection,
    FusedBatchNorm,
    Gather,
    GatherNd,
    Greater,
    GreaterEqual,
    HashtableLookup,
    InstanceNorm,
    LayerNormFusion,
    LeakyRelu,
    Less,
    LessEqual,
    Log,
    LogGrad,
    LogicalAnd,
    LogicalNot,
    LogicalOr,
    LpNormalization,
    LRN,
    LshProjection,
    LSTM,
    L2NormalizeFusion,
    MatMul,
    Maximum,
    MaximumGrad,
    MaxPoolFusion,
    MaxPoolGrad,
    SwitchLayer,
    Mfcc,
    Minimum,
    MinimumGrad,
    Mod,
    MulFusion,
    MulGrad,
    Neg,
    NegGrad,
    NotEqual,
    NonMaxSuppression,
    OneHot,
    OnesLike,
    PadFusion,
    PartialFusion,
    PowerGrad,
    PowFusion,
    PriorBox,
    PReLUFusion,
    QuantDTypeCast,
    Rank,
    Range,
    Reciprocal,
    RealDiv,
    ReduceFusion,
    Reshape,
    Resize,
    ReverseSequence,
    ReverseV2,
    Rfft,
    ROIPooling,
    Round,
    Rsqrt,
    ScaleFusion,
    ScatterNd,
    SGD,
    Shape,
    SigmoidCrossEntropyWithLogits,
    SigmoidCrossEntropyWithLogitsGrad,
    Sin,
    SkipGram,
    SliceFusion,
    SmoothL1Loss,
    SmoothL1LossGrad,
    Softmax,
    SoftmaxCrossEntropyWithLogits,
    SpaceToBatch,
    SpaceToBatchND,
    SpaceToDepth,
    SparseSoftmaxCrossEntropyWithLogits,
    SparseToDense,
    Split,
    Sqrt,
    Squeeze,
    Square,
    SquaredDifference,
    Stack,
    StridedSlice,
    SubFusion,
    SubGrad,
    Switch,
    TensorListFromTensor,
    TensorListGetItem,
    TensorListReserve,
    TensorListSetItem,
    TensorListStack,
    TileFusion,
    TopKFusion,
    Transpose,
    Unique,
    UnsortedSegmentSum,
    Unsqueeze,
    Unstack,
    LSTMGrad,
    Where,
    ZerosLike,
    Select,
    ScatterNdUpdate,
    GRU,
    NonZero,
    InvertPermutation,
    Size,
    RandomStandardNormal,
    CropAndResize,
    Erf,
    StridedSliceGrad,
    IsFinite,
    LinSpace,
    UniformReal,
    AbsGrad,
    RsqrtGrad,
    SqrtGrad,
    LayerNormGrad,
    ResizeGrad,
    Splice,
    LogSoftmax,
    Call,
    Custom,
    CumSum,
    SplitWithOverlap,
    GenOP,
    RaggedRange,
    GLU,
    TensorArray,
    TensorArrayRead,
    TensorArrayWrite,
    Affine,
    AllGather,
    ReduceScatter,
}

table Abs {
}

table Activation {
    activation_type: ActivationType = 0;
    alpha: float;
    min_val: float;
    max_val: float;
    approximate: bool = false;
}

table ActivationGrad {
    activation_type: ActivationType;
    alpha: float;
}

table Adam {
    use_locking: bool;
    use_nesterov: bool;
}

table AddFusion {
    activation_type: ActivationType = 0;
}

table AdderFusion {
    format: Format = 0;
    kernel_size: [long];
    stride: [long];
    dilation: [long];
    pad_mode: PadMode;
    pad_list: [long];
    group: long;
    in_channel: long;
    out_channel: long;
    activation_type: ActivationType = 0;
}

table AddGrad {
}

table AddN {
}

table All {
    keep_dims: long;
}

table ApplyMomentum {
    use_nesterov: bool;
    use_locking: bool;
    gradient_scale: float;
}

table ArgMaxFusion {
    axis: long;
    top_k: long = 1;
    keep_dims: bool;
    out_max_value: bool;
}

table ArgMinFusion {
    axis: long;
    top_k: long;
    keep_dims: bool;
    out_max_value: bool;
}

table Assert {
    summarize: long;
}

table Assign {
}

table AssignAdd {
}

table AudioSpectrogram {
    window_size: long;
    stride: long;
    mag_square: bool;
}

table AvgPoolFusion {
    kernel_size: [long];
    strides: [long];
    pad: [long];
    pad_mode: PadMode;
    round_mode: RoundMode;
    format: Format;
    global: bool;
    activation_type: ActivationType = 0;
}

table AvgPoolGrad {
    kernel_size: [long];
    strides: [long];
    pad_mode: PadMode;
    format: Format;
}

table BatchNorm {
    epsilon: float;
    format: Format;
    is_training: bool;
}

table BatchNormGrad {
    epsilon: float;
}

table BatchToSpace {
    block_size: [long];
    crops: Vec2D;
}

table BatchToSpaceND {
    block_shape: [long];
    crops: Vec2D;
}

table BiasAdd {
    format: Format;
}

table BinaryCrossEntropy {
    reduction: Reduction;
}

table BinaryCrossEntropyGrad {
    reduction: Reduction = 1;
}

table BiasAddGrad {
}

table BroadcastTo {
    shape: [long];
}

table Cast {
}

table Ceil {
}

table Clip {
    max: float;
    min: float;
}

table Concat {
    axis: long;
}

table Attention {
}

table Conv2DBackpropFilterFusion {
    format: Format = 0;
    kernel_size: [long];
    stride: [long];
    dilation: [long];
    pad_mode: PadMode;
    pad_list: [long];
    mode: long;
    group: long;
    in_channel: long;
    out_channel: long;
    activation_type: ActivationType = 0;
}

table Conv2DBackpropInputFusion {
    format: Format = 0;
    kernel_size: [long];
    stride: [long];
    dilation: [long];
    pad_mode: PadMode;
    pad: [long];
    pad_list: [long];
    mode: long;
    group: long;
    in_channel: long;
    out_channel: long;
    activation_type: ActivationType = 0;
}

table Conv2DFusion {
    format: Format = 0;
    kernel_size: [long];
    stride: [long];
    dilation: [long];
    pad_mode: PadMode;
    pad_list: [long];
    mode: long;
    group: long;
    in_channel: long;
    out_channel: long;
    activation_type: ActivationType = 0;
}

table Conv2dTransposeFusion {
    format: Format = 0;
    kernel_size: [long];
    stride: [long];
    dilation: [long];
    pad_mode: PadMode;
    pad: [long];
    pad_list: [long];
    mode: long;
    group: long;
    in_channel: long;
    out_channel: long;
    activation_type: ActivationType = 0;
    output_paddings: [long];
}

table Cos {
}

table ConstantOfShape {
    data_type: long;
    value: [float];
}

table Crop {
    axis: long;
    offsets: [long];
}

table CustomExtractFeatures {
}

table CustomNormalize {
}

table CustomPredict {
    output_num: long;
    weight_threshold: float;
}

table DeConv2DGradFilter {
    in_channel: long;
    out_channel: long;
    kernel_size: [long];
    pad_mode: PadMode;
    pad_list: [long];
    stride: [long];
    dilation: [long];
    group: long;
    format: Format;
    activation_type: ActivationType;
}

table Depend {
}

table DepthToSpace {
    block_size: long;
    format: Format = 0;
}

table DetectionPostProcess {
    format: Format = 0;
    input_size: long;
    scale: [float];
    nms_iou_threshold: float;
    nms_score_threshold: float;
    max_detections: long;
    detections_per_class: long;
    max_classes_per_detection: long;
    num_classes: long;
    use_regular_nms: bool;
    out_quantized: bool;
}

table DivFusion {
    activation_type: ActivationType = 0;
}

table DivGrad {
}

table Dropout {
    keep_prob: float = 0.5;
}

table DropoutGrad {
    keep_prob: float;
}

table Elu {
    alpha: float;
}

table Eltwise {
    mode: EltwiseMode;
}

table Equal {
}

table EmbeddingLookupFusion {
    max_norm: float;
}

table ExpFusion {
    base: float = -1;
    scale: float = 1.0;
    shift: float = 0.0;
}

table ExpandDims {
}

table FakeQuantWithMinMaxVars {
    num_bits: long;
    narrow_range: bool;
}

table FakeQuantWithMinMaxVarsPerChannel {
    num_bits: long;
    narrow_range: bool;
}

table FftReal {
}

table FftImag {
}

table Flatten {
}

table FlattenGrad {
}

table Floor {
}

table FloorDiv {
}

table FloorMod {
}

table Fill {
}

table FullConnection {
    has_bias: bool;
    use_axis: bool;
    axis: long;
    activation_type: ActivationType = 0;
}

table FusedBatchNorm {
    epsilon: float = 0.0001;
    momentum: float = 0.9;
    mode: long;
}

table Gather {
}

table GatherNd {
}

table Greater {
}

table GreaterEqual {
}

table HashtableLookup {
}

table InstanceNorm {
    epsilon: float;
}

table LayerNormFusion {
    begin_norm_axis: long;
    epsilon: float = 0.00001;
    elementwise_affine: bool;
    begin_params_axis: long;
}

table LeakyRelu {
    negative_slope: float;
}

table Less {
}

table LessEqual {
}

table Log {
}

table LogGrad {
}

table LogicalAnd {
}

table LogicalNot {
}

table LogicalOr {
}

table LpNormalization {
    axis: long;
    p: long;
}

table LRN {
    depth_radius: long;
    bias: float;
    alpha: float;
    beta: float;
    norm_region: string;
}

table LshProjection {
    type: LshProjectionType;
}

table LSTM {
    bidirectional: bool;
    has_bias: bool;
    input_size: long;
    hidden_size: long;
    num_layers: long;
    num_directions: long;
    dropout: float;
    zoneout_cell: float = 0;
    zoneout_hidden: float = 0;
}

table LSTMGrad {
    bidirectional: bool;
    has_bias: bool;
    input_size: long;
    hidden_size: long;
    num_layers: long;
    num_directions: long;
    dropout: float;
    zoneout_cell: float = 0;
    zoneout_hidden: float = 0;
}

table L2NormalizeFusion {
    axis: [long];
    epsilon: float;
    activation_type: ActivationType = 0;
}

table MatMul {
    transpose_a: bool = false;
    transpose_b: bool = false;
}

table Maximum {
}

table MaximumGrad {
    grad_x: bool;
    grad_y: bool;
}

table MaxPoolFusion {
    kernel_size: [long];
    strides: [long];
    pad: [long];
    pad_mode: PadMode;
    round_mode: RoundMode;
    format: Format;
    global: bool;
    activation_type: ActivationType = 0;
}

table MaxPoolGrad {
    kernel_size: [long];
    strides: [long];
    pad_mode: PadMode;
    format: Format;
}

table SwitchLayer {
}

table Mfcc {
    freq_upper_limit: float;
    freq_lower_limit: float;
    filter_bank_channel_num: long;
    dct_coeff_num: long;
}

table Minimum {
}

table MinimumGrad {
    grad_x: bool;
    grad_y: bool;
}

table Mod {
}

table MulFusion {
    activation_type: ActivationType = 0;
}

table MulGrad {
}

table Neg {
}

table NegGrad {
}

table NotEqual {
}

table NonMaxSuppression {
    center_point_box: long;
}

table OneHot {
    axis: long;
}

table OnesLike {
}

table PadFusion {
    paddings: Vec2D;
    padding_mode: PaddingMode;
    constant_value: float;
}

table PartialFusion {
    sub_graph_index: long;
}

table PowerGrad {
    power: float;
    scale: float;
    shift: float;
}

table PowFusion {
    scale: float = 1;
    shift: float = 0;
}

table PriorBox {
    min_sizes: [long];
    max_sizes: [long];
    aspect_ratios: [float];
    variances: [float];
    image_size_w: long;
    image_size_h: long;
    step_w: float;
    step_h: float;
    clip: bool;
    flip: bool;
    offset: float;
}

table PReLUFusion {
    channel_shared: bool;
}

table Rank {
}

table Range {
    d_type: long;
    start: long;
    limit: long;
    delta: long = 1;
}

table Reciprocal {
}

table RealDiv {
}

table ReduceFusion {
    keep_dims: bool;
    mode: ReduceMode;
    reduce_to_end: bool;
    coeff: float;
}

table Reshape {
}

table Resize {
    format: Format = 0;
    method: ResizeMethod;
    new_height: long;
    new_width: long;
    preserve_aspect_ratio: bool = false;
    coordinate_transform_mode: CoordinateTransformMode;
    cubic_coeff: float;
    exclude_outside: long;
    extrapolation_value: float;
    nearest_mode: NearestMode;
}

table ReverseSequence {
    seq_dim: long;
    batch_dim: long;
}

table ReverseV2 {
    axis: [long];
}

table Rfft {
    fft_length: long;
}

table ROIPooling {
    pooled_h: long;
    pooled_w: long;
    scale: float;
}

table Round {
}

table Rsqrt {
}

table QuantDTypeCast {
    src_t: long;
    dst_t: long;
}

table ScaleFusion {
    axis: long;
    activation_type: ActivationType = 0;
}

table ScatterNd {
}

table SGD {
    nesterov: bool;
    dampening: float;
    weight_decay: float;
}

table Shape {
}

table SigmoidCrossEntropyWithLogits {
}

table SigmoidCrossEntropyWithLogitsGrad {
}

table Sin {
}

table SkipGram {
    include_all_grams: bool;
    max_skip_size: long;
    ngram_size: long;
}

table SliceFusion {
    axes: [long];
}

table SmoothL1Loss {
    beta: float;
}

table SmoothL1LossGrad {
    beta: float;
}

table Softmax {
    axis: [long];
}

table SoftmaxCrossEntropyWithLogits {
}

table SpaceToBatch {
    block_size: [long];
    paddings: Vec2D;
}

table SpaceToBatchND {
    block_shape: [long];
    paddings: Vec2D;
}

table SpaceToDepth {
    block_size: long;
    format: Format;
}

table SparseSoftmaxCrossEntropyWithLogits {
    is_grad: bool;
}

table SparseToDense {
}

table Split {
    output_num: long;
    size_splits: [long];
    axis: long;
}

table Sqrt {
}

table Squeeze {
    axis: [long];
}

table Square {
}

table SquaredDifference {
}

table Stack {
    axis: long;
}

table StridedSlice {
    begin_mask: long;
    end_mask: long;
    ellipsis_mask: long;
    new_axis_mask: long;
    shrink_axis_mask: long;
}

table SubFusion {
    activation_type: ActivationType = 0;
}

table SubGrad {
}

table Switch {
}

table TensorListFromTensor {
    element_dtype: long;
    shape_type: long;
}

table TensorListGetItem {
    element_dtype: long;
}

table TensorListReserve {
    element_dtype: long;
    shape_type: long;
}

table TensorListSetItem {
    element_dtype: long;
}

table TensorListStack {
    num_elements: long;
    element_dtype: long;
}

table TileFusion {
    dims: [long];
}

table TopKFusion {
    sorted: bool = true;
    axis: long;
    largest: long;
}

table Transpose {
}

table Unique {
}

table UnsortedSegmentSum {
}

table Unsqueeze {
    axis: [long];
}

table Unstack {
    axis: long = 0;
}

table Where {
}

table ZerosLike {
}

table Select {
}

table GRU {
    bidirectional: bool = false;
}

table NonZero {
}

table InvertPermutation {
}

table Size {
}

table RandomStandardNormal {
    seed: long;
    seed2: long;
}

table CropAndResize {
    method: ResizeMethod;
    extrapolation_value: float;
}

table Erf {
}

table StridedSliceGrad {
    begin_mask: long;
    end_mask: long;
    ellipsis_mask: long;
    new_axis_mask: long;
    shrink_axis_mask: long;
}

table IsFinite {
}

table LinSpace {
}

table UniformReal {
    seed: long;
    seed2: long;
}

table AbsGrad {
}

table RsqrtGrad {
}

table SqrtGrad {
}

table LayerNormGrad {
    begin_norm_axis: long;
    begin_params_axis: long;
}

table ResizeGrad {
    method: ResizeMethod;
    align_corners: bool;
}

table Splice {
    context: [long];
    forward_indexes: [long];
    output_dim: long;
}

table LogSoftmax {
    axis: long;
}

table Call {
    is_tail_call: bool = true;
}

table CumSum {
    exclusive: bool;
    reverse: bool;
}

table Custom {
    type: string;
    attr: [Attribute];
}

table SplitWithOverlap {
    split_dim: long;
    number_split: long;
    ratio: [long];
    extend_top: [long];
    extend_bottom: [long];
}

table GenOP {
    activation_type: ActivationType = 0;
    alpha: float;
    min_val: float;
    max_val: float;
    is_training: bool;
    format: Format = 0;
    kernel_size: [long];
    stride: [long];
    dilation: [long];
    pad_mode: PadMode;
    pad_list: [long];
    mode: long;
    group: long;
    in_channel: long;
    out_channel: long;
    eltwise_mode: EltwiseMode;
    has_bias: bool;
    use_axis: bool;
    axis: long;
    epsilon: float = 0.0001;
    momentum: float = 0.9;
    transpose_a: bool = false;
    transpose_b: bool = false;
    pad: [long];
    round_mode: RoundMode;
    global: bool;
    channel_shared: bool;
    axes: [long];
    keep_dims: bool;
    reduce_mode: ReduceMode;
    reduce_to_end: bool;
    coeff: float;
}

table RaggedRange {
}

table GLU {
    axis: long = -1;
}

table TensorArray {
    dynamic_size: bool = false;
    identical_element_shapes: bool = false;
    element_shape: [int];
    data_type: int;
}

table TensorArrayRead {
}

table TensorArrayWrite {
}

table Affine {
    context: [long];
    output_dim: long;
    activation_type: ActivationType = 0;
    transpose_a: bool = false;
    transpose_b: bool = false;
}

table ScatterNdUpdate {
}

table AllGather {
    group: string;
    rank_size: int;
}

table ReduceScatter {
    group: string;
    mode: ReduceMode;
    rank_size: int;
}