path: root/runtime/onert/frontend/tflite/tflite_schema.fbs

// Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
//
// 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.

// Revision History
// Version 0: Initial version.
// Version 1: Add subgraphs to schema.
// Version 2: Rename operators to conform to NN API.
// Version 3: Move buffer data from Model.Subgraph.Tensors to Model.Buffers.

// Change namespace to onert_tflite
namespace onert_tflite;

// This corresponds to the version.
file_identifier "TFL3";
// File extension of any written files.
file_extension "tflite";

// IMPORTANT: All new members of tables, enums and unions must be added at the
// end to ensure backwards compatibility.

// The type of data stored in a tensor.
enum TensorType : byte {
  FLOAT32 = 0,
  FLOAT16 = 1,
  INT32 = 2,
  UINT8 = 3,
  INT64 = 4,
  STRING = 5,
  BOOL = 6,
  INT16 = 7,
  COMPLEX64 = 8,
  INT8 = 9,
  FLOAT64 = 10,
}

// Custom quantization parameters for experimenting with new quantization
// techniques.
table CustomQuantization {
  custom:[ubyte] (force_align: 16);
}

// Represents a specific quantization technique's parameters.
union QuantizationDetails {
  CustomQuantization,
}

// Parameters for converting a quantized tensor back to float.
table QuantizationParameters {
  // These four parameters are the asymmetric linear quantization parameters.
  // Given a quantized value q, the corresponding float value f should be:
  //   f = scale * (q - zero_point)
  // For other quantization types, the QuantizationDetails below is used.
  min:[float];  // For importing back into tensorflow.
  max:[float];  // For importing back into tensorflow.
  scale:[float];  // For dequantizing the tensor's values.
  zero_point:[long];

  // If this is not none, the other quantization parameters (i.e. min, max,
  // scale, zero_point fields above) are ignored and the value of the
  // QuantizationDetails union should be used.
  details:QuantizationDetails;

  // Specifies the dimension of the Tensor's shape that the scales and
  // zero_points correspond to. For example, a tensor t, with dims=[4, 3, 2, 1]
  // with quantization params:
  //   scale=[1.0, 2.0, 3.0], zero_point=[1, 2, 3], quantization_dimension=1
  // will be quantized across the second dimension of t.
  //   t[:, 0, :, :] will have scale[0]=1.0, zero_point[0]=1
  //   t[:, 1, :, :] will have scale[1]=2.0, zero_point[0]=2
  //   t[:, 2, :, :] will have scale[2]=3.0, zero_point[0]=3
  quantized_dimension:int;
}

// Sparse tensors.
// We use a modification of the TACO format.
// Reference: http://tensor-compiler.org/kjolstad-oopsla17-tensor-compiler.pdf
//
// To encode a conceptual n-dimensional dense tensor with dims (d0, ..., dn-1),
// potentially with a k-dimensional block (0 <= k <= n) with dims
// (dn, ..., dn+k-1), the format needs to specify:
//   1. In what order to traverse these dimensions. For example, to store a 2-D
//      matrix in row major order, the traversal order would be (d0, d1),
//      whereas to store it in column major order, the traversal order would be
//      (d1, d0). If the 2-D matrix has a 2-D inner block, the traversal order
//      could be (d0, d1, d2, d3).
//   2. How each block dimension in (dn, ..., dn+k-1) maps to the original
//      tensor dimension in (d0, ..., dn-1).
//   3. In the traversal order defined above, the format (dense vs. sparse) and
//      index metadata for each dimension. For a dense dimension, this is just
//      the size of that dimension. For a sparse dimension, it's the same as
//      the compressed index defined in the Compressed Sparse Row (CSR) format.
//      (http://scipy-lectures.org/advanced/scipy_sparse/csr_matrix.html)

// The storage type for a dimension. Currently we support:
//   1. DENSE: each coordinate in this dimension is stored implicitly.
//   2. SPARSE_CSR: only the coordinates with non-zero elements are stored. The
//      compression technique is the same what CSR uses.
// More types like a sparse dimension with a different compression technique
// could be added to the list in the future.
enum DimensionType : byte {
  DENSE = 0,
  SPARSE_CSR = 1,
}

table Int32Vector {
  values:[int];
}

table Uint16Vector {
  values:[ushort] (force_align: 4);
}

table Uint8Vector {
  values:[ubyte] (force_align: 4);
}

// Variable-typed buffer to store the index metadata for a sparse dimension.
// The widest type is Int32 instead of UInt32 because tensor's shape is a int32
// vector. We don't want the per-dimensional index to overflow that range.
union SparseIndexVector {
  Int32Vector,
  Uint16Vector,
  Uint8Vector
}

table DimensionMetadata {
  // Whether a dimension is dense or sparse.
  format:DimensionType;
  // Index metadata used for a dimension.
  //   - If format is DimensionType.DENSE then we use the dense_size field to
  //     store the size of that dimension. Each index in that dimension is
  //     stored implicitly.
  //   - If format is DimensionType.SPARSE_CSR then we use array_segments and
  //     array_indices to encode that dimension. array_segments represents how
  //     to segment the indices array, each segment corresponds to one element
  //     in the previous dimension. array_indices represents the index of the
  //     non-zero elements within this dimension (as those in the CSR matrix
  //     format, where the first array is row pointers and the second array is
  //     column indices).
  dense_size:int;
  array_segments:SparseIndexVector;
  array_indices:SparseIndexVector;
}

// Parameters to encode a sparse TfLite tensor.
table SparsityParameters {
  // The traversal order of the dimensions defined in the `shape` field of the
  // conceptual dense tensor. For a n-dimensional tensors with dims (d0, d1,
  // ..., dn-1),
  //   - if not block sparse, the traversal_order is just a permutation of (d0,
  //     ..., dn-1). For example, a 2-D matrix stored in row-major order would
  //     have traversal_order = (d0, d1).
  //   - if block sparse with a k-dimensional block (0 <= k <= n), the
  //     traversal_order has n + k elements. The first n elements are still a
  //     permutation of (d0, ..., dn-1). The lask k elements are a permutation
  //     of (dn, ..., dn+k-1), defining how to traverse a block internally. For
  //     example, a 2-D matrix with 2-D blocks, both stored in row-major order
  //     would have traversal_order = (d0, d1, d2, d3).
  traversal_order:[int];
  // For an n-dimensional tensor with a k-dimensional block (0 <= k <= n),
  // stores how a block dimension in (dn, ..., dn+k-1) maps to the original
  // tensor dimension in (d0, ..., dn).
  // It's stored in the order of (dn, ..., dn+k-1).
  // If not block-sparse, this field is NULL.
  block_map:[int];
  // In the traversal order defined above, the metadata needed for
  // each dimension to locate the non-zero values in the original dense tensor.
  // The size of the dim_metadata array = the size of the traversal_order array
  // = n + k.
  dim_metadata:[DimensionMetadata];
}

table Tensor {
  // The tensor shape. The meaning of each entry is operator-specific but
  // builtin ops use: [batch size, height, width, number of channels] (That's
  // Tensorflow's NHWC).
  shape:[int];
  type:TensorType;
  // An index that refers to the buffers table at the root of the model. Or,
  // if there is no data buffer associated (i.e. intermediate results), then
  // this is 0 (which refers to an always existent empty buffer).
  //
  // The data_buffer itself is an opaque container, with the assumption that the
  // target device is little-endian. In addition, all builtin operators assume
  // the memory is ordered such that if `shape` is [4, 3, 2], then index
  // [i, j, k] maps to data_buffer[i*3*2 + j*2 + k].
  buffer:uint;
  name:string;  // For debugging and importing back into tensorflow.
  quantization:QuantizationParameters;  // Optional.

  is_variable:bool = false;

  // Parameters to encode a sparse tensor. See the example in
  // tensorflow/lite/testdata/sparse_tensor.json.
  sparsity:SparsityParameters;  // Optional.

  // Encodes `shape` with unknown dimensions. Unknown dimensions are
  // represented with -1.
  shape_signature:[int]; // Optional.
}

// A list of builtin operators. Builtin operators are slightly faster than custom
// ones, but not by much. Moreover, while custom operators accept an opaque
// object containing configuration parameters, builtins have a predetermined
// set of acceptable options.

enum BuiltinOperator : byte {
  ADD = 0,
  AVERAGE_POOL_2D = 1,
  CONCATENATION = 2,
  CONV_2D = 3,
  DEPTHWISE_CONV_2D = 4,
  DEPTH_TO_SPACE = 5,
  DEQUANTIZE = 6,
  EMBEDDING_LOOKUP = 7,
  FLOOR = 8,
  FULLY_CONNECTED = 9,
  HASHTABLE_LOOKUP = 10,
  L2_NORMALIZATION = 11,
  L2_POOL_2D = 12,
  LOCAL_RESPONSE_NORMALIZATION = 13,
  LOGISTIC = 14,
  LSH_PROJECTION = 15,
  LSTM = 16,
  MAX_POOL_2D = 17,
  MUL = 18,
  RELU = 19,
  // NOTE(aselle): RELU_N1_TO_1 used to be called RELU1, but it was renamed
  // since different model developers use RELU1 in different ways. Never
  // create another op called RELU1.
  RELU_N1_TO_1 = 20,
  RELU6 = 21,
  RESHAPE = 22,
  RESIZE_BILINEAR = 23,
  RNN = 24,
  SOFTMAX = 25,
  SPACE_TO_DEPTH = 26,
  SVDF = 27,
  TANH = 28,
  // TODO(aselle): Consider rename to CONCATENATE_EMBEDDINGS
  CONCAT_EMBEDDINGS = 29,
  SKIP_GRAM = 30,
  CALL = 31,
  CUSTOM = 32,
  EMBEDDING_LOOKUP_SPARSE = 33,
  PAD = 34,
  UNIDIRECTIONAL_SEQUENCE_RNN = 35,
  GATHER = 36,
  BATCH_TO_SPACE_ND = 37,
  SPACE_TO_BATCH_ND = 38,
  TRANSPOSE = 39,
  MEAN = 40,
  SUB = 41,
  DIV = 42,
  SQUEEZE = 43,
  UNIDIRECTIONAL_SEQUENCE_LSTM = 44,
  STRIDED_SLICE = 45,
  BIDIRECTIONAL_SEQUENCE_RNN = 46,
  EXP = 47,
  TOPK_V2 = 48,
  SPLIT = 49,
  LOG_SOFTMAX = 50,
  // DELEGATE is a special op type for the operations which are delegated to
  // other backends.
  // WARNING: Experimental interface, subject to change
  DELEGATE = 51,
  BIDIRECTIONAL_SEQUENCE_LSTM = 52,
  CAST = 53,
  PRELU = 54,
  MAXIMUM = 55,
  ARG_MAX = 56,
  MINIMUM = 57,
  LESS = 58,
  NEG = 59,
  PADV2 = 60,
  GREATER = 61,
  GREATER_EQUAL = 62,
  LESS_EQUAL = 63,
  SELECT = 64,
  SLICE = 65,
  SIN = 66,
  TRANSPOSE_CONV = 67,
  SPARSE_TO_DENSE = 68,
  TILE = 69,
  EXPAND_DIMS = 70,
  EQUAL = 71,
  NOT_EQUAL = 72,
  LOG = 73,
  SUM = 74,
  SQRT = 75,
  RSQRT = 76,
  SHAPE = 77,
  POW = 78,
  ARG_MIN = 79,
  FAKE_QUANT = 80,
  REDUCE_PROD = 81,
  REDUCE_MAX = 82,
  PACK = 83,
  LOGICAL_OR = 84,
  ONE_HOT = 85,
  LOGICAL_AND = 86,
  LOGICAL_NOT = 87,
  UNPACK = 88,
  REDUCE_MIN = 89,
  FLOOR_DIV = 90,
  REDUCE_ANY = 91,
  SQUARE = 92,
  ZEROS_LIKE = 93,
  FILL = 94,
  FLOOR_MOD = 95,
  RANGE = 96,
  RESIZE_NEAREST_NEIGHBOR = 97,
  LEAKY_RELU = 98,
  SQUARED_DIFFERENCE = 99,
  MIRROR_PAD = 100,
  ABS = 101,
  SPLIT_V = 102,
  UNIQUE = 103,
  CEIL = 104,
  REVERSE_V2 = 105,
  ADD_N = 106,
  GATHER_ND = 107,
  COS = 108,
  WHERE = 109,
  RANK = 110,
  ELU = 111,
  REVERSE_SEQUENCE = 112,
  MATRIX_DIAG = 113,
  QUANTIZE = 114,
  MATRIX_SET_DIAG = 115,
  ROUND = 116,
  HARD_SWISH = 117,
  IF = 118,
  WHILE = 119,
  NON_MAX_SUPPRESSION_V4 = 120,
  NON_MAX_SUPPRESSION_V5 = 121,
  SCATTER_ND = 122,
  SELECT_V2 = 123,
  DENSIFY = 124,
  SEGMENT_SUM = 125,
  BATCH_MATMUL = 126
}


// Options for the builtin operators.
union BuiltinOptions {
  Conv2DOptions,
  DepthwiseConv2DOptions,
  ConcatEmbeddingsOptions,
  LSHProjectionOptions,
  Pool2DOptions,
  SVDFOptions,
  RNNOptions,
  FullyConnectedOptions,
  SoftmaxOptions,
  ConcatenationOptions,
  AddOptions,
  L2NormOptions,
  LocalResponseNormalizationOptions,
  LSTMOptions,
  ResizeBilinearOptions,
  CallOptions,
  ReshapeOptions,
  SkipGramOptions,
  SpaceToDepthOptions,
  EmbeddingLookupSparseOptions,
  MulOptions,
  PadOptions,
  GatherOptions,
  BatchToSpaceNDOptions,
  SpaceToBatchNDOptions,
  TransposeOptions,
  ReducerOptions,
  SubOptions,
  DivOptions,
  SqueezeOptions,
  SequenceRNNOptions,
  StridedSliceOptions,
  ExpOptions,
  TopKV2Options,
  SplitOptions,
  LogSoftmaxOptions,
  CastOptions,
  DequantizeOptions,
  MaximumMinimumOptions,
  ArgMaxOptions,
  LessOptions,
  NegOptions,
  PadV2Options,
  GreaterOptions,
  GreaterEqualOptions,
  LessEqualOptions,
  SelectOptions,
  SliceOptions,
  TransposeConvOptions,
  SparseToDenseOptions,
  TileOptions,
  ExpandDimsOptions,
  EqualOptions,
  NotEqualOptions,
  ShapeOptions,
  PowOptions,
  ArgMinOptions,
  FakeQuantOptions,
  PackOptions,
  LogicalOrOptions,
  OneHotOptions,
  LogicalAndOptions,
  LogicalNotOptions,
  UnpackOptions,
  FloorDivOptions,
  SquareOptions,
  ZerosLikeOptions,
  FillOptions,
  BidirectionalSequenceLSTMOptions,
  BidirectionalSequenceRNNOptions,
  UnidirectionalSequenceLSTMOptions,
  FloorModOptions,
  RangeOptions,
  ResizeNearestNeighborOptions,
  LeakyReluOptions,
  SquaredDifferenceOptions,
  MirrorPadOptions,
  AbsOptions,
  SplitVOptions,
  UniqueOptions,
  ReverseV2Options,
  AddNOptions,
  GatherNdOptions,
  CosOptions,
  WhereOptions,
  RankOptions,
  ReverseSequenceOptions,
  MatrixDiagOptions,
  QuantizeOptions,
  MatrixSetDiagOptions,
  HardSwishOptions,
  IfOptions,
  WhileOptions,
  DepthToSpaceOptions,
  NonMaxSuppressionV4Options,
  NonMaxSuppressionV5Options,
  ScatterNdOptions,
  SelectV2Options,
  DensifyOptions,
  SegmentSumOptions,
  BatchMatMulOptions
}

enum Padding : byte { SAME, VALID }

enum ActivationFunctionType : byte {
  NONE = 0,
  RELU = 1,
  RELU_N1_TO_1 = 2,
  RELU6 = 3,
  TANH = 4,
  SIGN_BIT = 5,
}

table Conv2DOptions {
  padding:Padding;
  stride_w:int;
  stride_h:int;
  fused_activation_function:ActivationFunctionType;
  dilation_w_factor:int = 1;
  dilation_h_factor:int = 1;
}

table Pool2DOptions {
  padding:Padding;
  stride_w:int;
  stride_h:int;
  filter_width:int;
  filter_height:int;
  fused_activation_function:ActivationFunctionType;
}

table DepthwiseConv2DOptions {
  // Parameters for DepthwiseConv version 1 or above.
  padding:Padding;
  stride_w:int;
  stride_h:int;
  // `depth_multiplier` is redundant. It's used by CPU kernels in
  // TensorFlow 2.0 or below, but ignored in versions above.
  // See comments in lite/c/builtin_op_data.h for more details.
  depth_multiplier:int;
  fused_activation_function:ActivationFunctionType;
  // Parameters for DepthwiseConv version 2 or above.
  dilation_w_factor:int = 1;
  dilation_h_factor:int = 1;
}

table ConcatEmbeddingsOptions {
  num_channels:int;
  num_columns_per_channel:[int];
  embedding_dim_per_channel:[int]; // This could be inferred from parameters.
}

enum LSHProjectionType: byte {
  UNKNOWN = 0,
  SPARSE = 1,
  DENSE = 2,
}

table LSHProjectionOptions {
  type: LSHProjectionType;
}

table SVDFOptions {
  rank:int;
  fused_activation_function:ActivationFunctionType;
  // For weights-only quantization, use asymmetric quantization for non
  // constant inputs at evaluation time.
  asymmetric_quantize_inputs:bool;
}

// An implementation of TensorFlow RNNCell.
table RNNOptions {
  fused_activation_function:ActivationFunctionType;
  asymmetric_quantize_inputs:bool;
}

// An implementation of TensorFlow dynamic_rnn with RNNCell.
table SequenceRNNOptions {
  time_major:bool;
  fused_activation_function:ActivationFunctionType;
  asymmetric_quantize_inputs:bool;
}

// An implementation of TensorFlow bidrectional_dynamic_rnn with RNNCell.
table BidirectionalSequenceRNNOptions {
  time_major:bool;
  fused_activation_function:ActivationFunctionType;
  merge_outputs: bool;
  asymmetric_quantize_inputs:bool;
}

enum FullyConnectedOptionsWeightsFormat: byte {
  DEFAULT = 0,
  SHUFFLED4x16INT8 = 1,
}

// An implementation of TensorFlow fully_connected (a.k.a Dense) layer.
table FullyConnectedOptions {
  // Parameters for FullyConnected version 1 or above.
  fused_activation_function:ActivationFunctionType;

  // Parameters for FullyConnected version 2 or above.
  weights_format:FullyConnectedOptionsWeightsFormat = DEFAULT;

  // Parameters for FullyConnected version 5 or above.
  // If set to true, then the number of dimension is preserved. Furthermore,
  // all but the last dimension of the input and output shapes will be equal.
  keep_num_dims: bool;

  // Parameters for FullyConnected version 7 or above.
  // If set to true, then weights-only op will use asymmetric quantization for
  // inputs.
  asymmetric_quantize_inputs: bool;
}

table SoftmaxOptions {
  beta: float;
}

// An implementation of TensorFlow concat.
table ConcatenationOptions {
  axis:int;
  fused_activation_function:ActivationFunctionType;
}

table AddOptions {
  fused_activation_function:ActivationFunctionType;
}

table MulOptions {
  fused_activation_function:ActivationFunctionType;
}

table L2NormOptions {
  fused_activation_function:ActivationFunctionType;
}

table LocalResponseNormalizationOptions {
  radius:int;
  bias:float;
  alpha:float;
  beta:float;
}

enum LSTMKernelType : byte {
  // Full LSTM kernel which supports peephole and projection.
  FULL = 0,
  // Basic LSTM kernels. Equivalent to TensorFlow BasicLSTMCell.
  BASIC = 1,
}

// An implementation of TensorFlow LSTMCell and CoupledInputForgetGateLSTMCell
table LSTMOptions {
  // Parameters for LSTM version 1 or above.
  fused_activation_function:ActivationFunctionType;
  cell_clip: float; // Optional, 0.0 means no clipping
  proj_clip: float; // Optional, 0.0 means no clipping

  // Parameters for LSTM version 2 or above.
  // Basic kernel is only supported in version 2 or above.
  kernel_type: LSTMKernelType = FULL;

  // Parameters for LSTM version 4 or above.
  asymmetric_quantize_inputs: bool;
}

// An implementation of TensorFlow dynamic_rnn with LSTMCell.
table UnidirectionalSequenceLSTMOptions {
  fused_activation_function:ActivationFunctionType;
  cell_clip: float; // Optional, 0.0 means no clipping
  proj_clip: float; // Optional, 0.0 means no clipping

  // If true then first dimension is sequence, otherwise batch.
  time_major:bool;

  // Parameter for Unidirectional Sequence LSTM version 4.
  asymmetric_quantize_inputs:bool;
}

table BidirectionalSequenceLSTMOptions {
  // Parameters supported by version 1:
  fused_activation_function:ActivationFunctionType;
  cell_clip: float; // Optional, 0.0 means no clipping
  proj_clip: float; // Optional, 0.0 means no clipping

  // If true, store the outputs of both directions into the first output.
  merge_outputs: bool;

  // Parameters supported by version 2:
  // If true then first dimension is sequence, otherwise batch.
  // Version 1 implementations assumed time_major to be true, so this default
  // value should never change.
  time_major: bool = true;

  // Parameters for version 3 or above.
  asymmetric_quantize_inputs:bool;
}

table ResizeBilinearOptions {
  new_height: int (deprecated);
  new_width: int (deprecated);
  align_corners: bool;
  half_pixel_centers: bool;
}

table ResizeNearestNeighborOptions {
  align_corners: bool;
}

// A call operation options
table CallOptions {
  // The subgraph index that needs to be called.
  subgraph:uint;
}

table PadOptions {
}

table PadV2Options {
}

table ReshapeOptions {
  new_shape:[int];
}

table SpaceToBatchNDOptions {
}

table BatchToSpaceNDOptions {
}

table SkipGramOptions {
  ngram_size: int;
  max_skip_size: int;
  include_all_ngrams: bool;
}

table SpaceToDepthOptions {
  block_size: int;
}

table DepthToSpaceOptions {
  block_size: int;
}

table SubOptions {
  fused_activation_function:ActivationFunctionType;
}

table DivOptions {
  fused_activation_function:ActivationFunctionType;
}

table TopKV2Options {
}

enum CombinerType : byte {
  SUM = 0,
  MEAN = 1,
  SQRTN = 2,
}

table EmbeddingLookupSparseOptions {
  combiner:CombinerType;
}

table GatherOptions {
  axis: int;
}

table TransposeOptions {
}

table ExpOptions {
}

table CosOptions {
}

table ReducerOptions {
  keep_dims: bool;
}

table SqueezeOptions {
  squeeze_dims:[int];
}

table SplitOptions {
  num_splits: int;
}

table SplitVOptions {
  num_splits: int;
}

table StridedSliceOptions {
  begin_mask: int;
  end_mask: int;
  ellipsis_mask: int;
  new_axis_mask: int;
  shrink_axis_mask: int;
}

table LogSoftmaxOptions {
}

table CastOptions {
  in_data_type: TensorType;
  out_data_type: TensorType;
}

table DequantizeOptions {
}

table MaximumMinimumOptions {
}

table TileOptions {
}

table ArgMaxOptions {
  output_type : TensorType;
}

table ArgMinOptions {
  output_type : TensorType;
}

table GreaterOptions {
}

table GreaterEqualOptions {
}

table LessOptions {
}

table LessEqualOptions {
}

table NegOptions {
}

table SelectOptions {
}

table SliceOptions {
}

table TransposeConvOptions {
  padding:Padding;
  stride_w:int;
  stride_h:int;
}

table ExpandDimsOptions {
}

table SparseToDenseOptions {
  validate_indices:bool;
}

table EqualOptions {
}

table NotEqualOptions {
}

table ShapeOptions {
  // Optional output type of the operation (int32 or int64). Defaults to int32.
  out_type : TensorType;
}

table RankOptions {
}

table PowOptions {
}

table FakeQuantOptions {
  // Parameters supported by version 1:
  min:float;
  max:float;
  num_bits:int;

  // Parameters supported by version 2:
  narrow_range:bool;
}

table PackOptions {
  values_count:int;
  axis:int;
}

table LogicalOrOptions {
}

table OneHotOptions {
  axis:int;
}

table AbsOptions {
}


table HardSwishOptions {
}

table LogicalAndOptions {
}

table LogicalNotOptions {
}

table UnpackOptions {
  num:int;
  axis:int;
}

table FloorDivOptions {
}

table SquareOptions {
}

table ZerosLikeOptions {
}

table FillOptions {
}

table FloorModOptions {
}

table RangeOptions {
}

table LeakyReluOptions {
  alpha:float;
}

table SquaredDifferenceOptions {
}

enum MirrorPadMode : byte {
  // Doesn't include borders.
  REFLECT = 0,
  // Includes borders.
  SYMMETRIC = 1,
}

table MirrorPadOptions {
  mode:MirrorPadMode;
}

table UniqueOptions {
  idx_out_type:TensorType = INT32;
}

table ReverseV2Options {
}

table AddNOptions {
}

table GatherNdOptions {
}

table WhereOptions {
}

table ReverseSequenceOptions {
  seq_dim:int;
  batch_dim:int = 0;
}

table MatrixDiagOptions {
}

table QuantizeOptions {
}

table MatrixSetDiagOptions {
}

table IfOptions {
  then_subgraph_index:int;
  else_subgraph_index:int;
}

table WhileOptions {
  cond_subgraph_index:int;
  body_subgraph_index:int;
}

table NonMaxSuppressionV4Options {
}

table NonMaxSuppressionV5Options {
}

table ScatterNdOptions {
}

table SelectV2Options {
}

table DensifyOptions {
}

table SegmentSumOptions {
}

table BatchMatMulOptions {
  adjoint_lhs:bool;
  adjoint_rhs:bool;
}

// An OperatorCode can be an enum value (BuiltinOperator) if the operator is a
// builtin, or a string if the operator is custom.
table OperatorCode {
  builtin_code:BuiltinOperator;
  custom_code:string;

  // The version of the operator. The version need to be bumped whenever new
  // parameters are introduced into an op.
  version:int = 1;
}

enum CustomOptionsFormat : byte {
  FLEXBUFFERS = 0,
}

// An operator takes tensors as inputs and outputs. The type of operation being
// performed is determined by an index into the list of valid OperatorCodes,
// while the specifics of each operations is configured using builtin_options
// or custom_options.
table Operator {
  // Index into the operator_codes array. Using an integer here avoids
  // complicate map lookups.
  opcode_index:uint;

  // Optional input are indicated by -1.
  inputs:[int];
  outputs:[int];

  builtin_options:BuiltinOptions;
  custom_options:[ubyte];
  custom_options_format:CustomOptionsFormat;

  // A list of booleans indicating the input tensors which are being mutated by
  // this operator.(e.g. used by RNN and LSTM).
  // For example, if the "inputs" array refers to 5 tensors and the second and
  // fifth are mutable variables, then this list will contain
  // [false, true, false, false, true].
  //
  // If the list is empty, no variable is mutated in this operator.
  // The list either has the same length as `inputs`, or is empty.
  mutating_variable_inputs:[bool];

  // A list of indices to the subgraph's "tensors" that are internal to an Op.
  // Internal tensors are those that do not flow in or out of the operation,
  // but instead are part of internal computation. As such, the operation's
  // implementation may manage its memory more efficiently. They are needed
  // however (i.e. not just an implementation detail) since they are part of the
  // computation, which may require relevant metadata such as quantization
  // parameters.
  intermediates:[int];
}

// The root type, defining a subgraph, which typically represents an entire
// model.
table SubGraph {
  // A list of all tensors used in this subgraph.
  tensors:[Tensor];

  // Indices of the tensors that are inputs into this subgraph. Note this is
  // the list of non-static tensors that feed into the subgraph for inference.
  inputs:[int];

  // Indices of the tensors that are outputs out of this subgraph. Note this is
  // the list of output tensors that are considered the product of the
  // subgraph's inference.
  outputs:[int];

  // All operators, in execution order.
  operators:[Operator];

  // Name of this subgraph (used for debugging).
  name:string;
}

// Table of raw data buffers (used for constant tensors). Referenced by tensors
// by index. The generous alignment accommodates mmap-friendly data structures.
table Buffer {
  data:[ubyte] (force_align: 16);
}

table Metadata {
  // A human readable string to uniquely identify a Metadata.
  name:string;
  // An index to the buffers table.
  buffer:uint;
}

table Model {
  // Version of the schema.
  version:uint;

  // A list of all operator codes used in this model. This is
  // kept in order because operators carry an index into this
  // vector.
  operator_codes:[OperatorCode];

  // All the subgraphs of the model. The 0th is assumed to be the main
  // model.
  subgraphs:[SubGraph];

  // A description of the model.
  description:string;

  // Buffers of the model.
  // Note the 0th entry of this array must be an empty buffer (sentinel).
  // This is a convention so that tensors without a buffer can provide 0 as
  // their buffer.
  buffers:[Buffer];

  // Metadata about the model. Indirects into the existings buffers list.
  // Deprecated, prefer to use metadata field.
  metadata_buffer:[int];

  // Metadata about the model.
  metadata:[Metadata];
}

root_type Model;