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RTen model format

RTen model files (.rten) contain the computation graph for a machine learning model, model metadata and weights. The format is designed to be efficient to load and to minimize additional memory required, beyond the size of the file itself.

RTen files are produced by exporting models from a machine learning framework such as PyTorch or Keras into ONNX format, and converting the ONNX model to .rten using the rten-convert tool.

Compatibility

The rten-convert tool and rten Rust crate have version numbers that are aligned. A .rten model produced by version X of rten-convert can be read by version X of the rten crate or newer. Models produced by version X of rten-convert may work with earlier versions of rten as long as the model does not rely on operators or attributes that were added in version X.

History

There are two versions of the RTen model format. The second version added support for models larger than 2GB. RTen can load models in either format. The rten-convert tool generates the V2 format by default, and will generate the V1 format if the --v1 flag is passed.

V2 format

Overall structure

The overall structure of a .rten file is:

[header] … [model_data] … [tensor_data]

Header

The header identifies the file type, the major version of the format and contains the offsets of the other sections. The structure of the header is:

[magic:u8x4] [version:u32] [model_data_offset:u64] [model_data_len:u64] [tensor_data_offset:u64]

All numbers are encoded in little-endian order.

  • magic - The ASCII bytes RTEN
  • version - Currently 2
  • model_data_offset - Offset of the data describing the model
  • model_data_len - Length of the data describing the model
  • tensor_data_offset - Offset of the start of tensor data. Tensor references in the model data are relative to this.

Model data

The model data is a FlatBuffers buffer which describes the computation graph for the model. It also contains metadata about the model.

The computation graph consists of three kinds of nodes: constants (weights, biases etc.), values (inputs or outputs from computation steps) and operators (computation steps such as matrix multiplication). The operators correspond closely to operators in the ONNX specification. Constant nodes describe the data type and shape of tensors. The data for a tensor can either be stored inline in the model or externally in the tensor data section.

The FlatBuffers schema can be found in src/schema.fbs.

Tensor data

The tensor data section is a block of bytes referenced by the model data. The shape of tensors, type of elements and other metadata is contained in the model data.

V1 format

The first version of the .rten model format consisted of just the model data without the header or tensor data sections. The FlatBuffers schema used by V1 is the same as V2.

This was changed due to FlatBuffers having a 2GB file size limit, and also to enable more control over the alignment of tensor data.