pytorch/torch/csrc/jit/serialization/mobile_bytecode.fbs

// To regenerate header, use:
// flatc --cpp --gen-mutable --no-prefix --scoped-enums mobile_bytecode.fbs

file_identifier "PTMF";

namespace torch.jit.mobile.serialization;

struct Int {
  int_val:long;
}

struct Bool {
  bool_val:bool;
}

struct Double{
  double_val:double;
}

struct PerTensorAffineSchema {
  q_scale:double;
  q_zero_point:int;
}

table QuantizedSchema {
  qscheme:byte;
  scale:double;
  zero_point:int;
  scales:TensorMetadata;
  zero_points:TensorMetadata;
  axis:int;
}

table TensorMetadata {
  // torch._utils _rebuild_tensor_v2
  storage_location_index:uint;
  // enum ScalarType
  scalar_type:byte;
  storage_offset:int;
  sizes:[int];
  strides:[int];
  requires_grad:bool;

  // only set for quantized tensors
  quantized_schema:QuantizedSchema;
}

table String {
  data:string;
}

table Device {
  str:string;
}

table List {
  items:[uint];
  annotation_str:string;  // to recover key/val type
}

table IntList {
  items:[long];
}

table DoubleList {
  items:[double];
}

table BoolList {
  items:[bool];
}

table Tuple {
  items:[uint];
}

table Dict {
  keys:[uint];
  values:[uint];
  annotation_str:string;  // to recover key/val type
}

enum TypeType :ubyte {
  UNSET,
  CLASS_WITH_FIELD,
  CUSTOM_CLASS,
  CLASS_WITH_SETSTATE,
  NON_OBJ,
}

table ObjectType {
  type_name:string;
  type:TypeType;
  // Below fields are optional
  attr_names:[string];
}

table Object {
  type_index:uint;
  state:uint;
  attrs:[uint];
  setstate_func:uint;
}

struct ComplexDouble {
  real:double;
  imag:double;
}

table EnumValue {
  type_name:string;
  value:uint;  // index to ivalues;
}


struct Instruction {
  // Should op be enum instead?
  op:byte;
  n:ushort;
  x:int;
}

table Operator {
  name:string;
  overload_name:string;
  num_args_serialized:int = -1;
}

table Arg {
  name:string;
  // Why do we use string to represent types
  // rather than index into Code.types?
  type:string;
  default_value:uint;  // position into ivalues
}

table Schema {
  arguments:[Arg];
  returns:[Arg];
}

table DebugInfo {
  debug_handle:[long];
}

table Function {
  qn:string;
  instructions:[Instruction];
  operators:[Operator];
  constants:[uint];  // index to ivalue
  type_annotations:[string];
  register_size:int;
  schema:Schema;
  debug_info:DebugInfo;
  class_type:uint; // index into type table
}

table StorageData {
  data:[ubyte] (force_align:16);
}

// Is it needed to represent other types?
union IValueUnion {
  Int,
  Bool,
  Double,
  ComplexDouble,
  TensorMetadata,
  String,
  List,
  Tuple,
  Dict,
  Object,
  IntList,
  DoubleList,
  BoolList,
  Device,
  EnumValue,
  Function,
}

table IValue {
  val:IValueUnion;
}

table ExtraFile {
  name:string;
  content:string;
}

table Module {
  // denotes the bytecode version of the mobile Module
  // this starts from 9 for a flatbuffer file
  // versions 8 and below are reserved pickle
  // Version is bumped when changes in model serialization/execution
  // can no longer work in current version
  // To read more:
  // https://github.com/pytorch/pytorch/blob/master/caffe2/serialize/versions.h#L96
  bytecode_version:uint;

  extra_files:[ExtraFile];
  methods:[uint];  // index to ivalues
  state_obj:uint; // index to ivalues
  ivalues:[IValue];
  storage_data_size:int;  // number of storage data;
  storage_data:[StorageData];
  object_types:[ObjectType];
  jit_sources:[ExtraFile];
  jit_constants:[uint];  // index to ivalues

  // version of operator
  // Version is bumped when changes in operator
  // can no longer work in current version
  // To read more:
  // https://github.com/pytorch/rfcs/blob/master/RFC-0017-PyTorch-Operator-Versioning.md
  operator_version:uint;

  // Size of ivalue that comes from the mobile module.
  // Because the ivalues array above can also have ivalues that cames from
  // the jit::Module that got it's source attached to flatbuffer file.
  // this should be smaller than ivalues.size()
  mobile_ivalue_size:uint;
}

root_type Module;