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syntax = "proto2";
import "caffe2/proto/caffe2.proto";
package torch;
message RecordRef {
optional string key = 1;
}
message TensorDef {
repeated int64 dims = 1;
optional int64 offset = 2;
repeated int64 strides = 3;
// whether we compute the gradient for the parameter
optional bool requires_grad = 4;
optional caffe2.TensorProto.DataType data_type = 5;
optional RecordRef data = 6;
// device field stores the canonical device string, and it follows the
// format below: `(cpu|cuda)[:<device-index>]`, e.g., 'cuda:0'
optional string device = 7;
optional bool is_quantized = 8;
optional double scale = 9;
optional int64 zero_point = 10;
}
message AttributeDef {
// The mypy type of this attribute
required string type = 1;
required string name = 2;
// Offset into attribute table
required int64 id = 3;
}
message ParameterDef {
// whether this parameter is registered as buffer or not
optional bool is_buffer = 1;
// the offset into the tensor table where this parameter is stored
optional int64 tensor_id = 2;
optional string name = 3;
}
message ModuleDef {
repeated ModuleDef submodules = 1;
optional RecordRef torchscript_arena = 2;
repeated caffe2.NetDef caffe2_nets = 3;
// because the old pickle modules may not be supported by torch_script,
// have to stored as pickle_arena at this moment.
optional RecordRef pickle_arena = 4;
// should be exposed by the Class Archive, so user can save
// module specific data which cannot be store in the graph or torch_script
optional RecordRef cpp_arena = 5;
// the parameters of this module
repeated ParameterDef parameters = 6;
// the names of inputs and outputs of the module are inferred
// from the main method.
optional string name = 7;
// whether apply the optimizations to this module, only applicable to
// script modules
optional bool optimize = 8;
repeated AttributeDef attributes = 9;
// Used for retrieving module state from the pickled IValues table
optional int64 get_state_attribute_id = 10;
optional RecordRef torchscript_debug_arena = 11;
}
// Represents all non-module code that the model depends on.
// Right now it's just a straight list of classes, defined in dependency order
// (i.e. dependencies appear before their dependers)
message LibDef {
optional RecordRef torchscript_arena = 1;
}
enum ProtoVersion { PROTO_VERSION_NEWEST = 0x0000000000000006; }
message ModelDef {
// numbers of fields that have been removed. Do not reuse them!
reserved 9;
reserved "libs";
// for the proto version, to keep both backward and forward
// compatibility, please bump the proto_version when we add any
// change in the proto. runtime decides whether accept the
// model based on the ir_version.
optional int64 proto_version = 1;
// main module of the model
optional ModuleDef main_module = 2;
// to distinguish whether exported from c2 or torch
optional string producer_name = 3;
// put build version here
optional string producer_version = 4;
// the table contains all the tensor information
// the tensor id is defined as TensorProto.name
repeated TensorDef tensors = 5;
}
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