• Docs >
  • Prerequisite | ETRecord - ExecuTorch Record

Prerequisite | ETRecord - ExecuTorch Record


ETRecord is intended to be the debug artifact that is generated by users ahead of time (when they export their model to run on ExecuTorch). To draw a rough equivalent to conventional software development, ETRecord can be considered as the binary built with debug symbols that is used for debugging in GNU Debugger (gdb). It is expected that the user will supply this to the ExecuTorch SDK tooling in order for them to debug and visualize their model.

ETRecord contains numerous components such as:

  • Edge dialect graph with debug handles

  • Delegate debug handle maps

The ETRecord object itself is intended to be opaque to users and they should not access any components inside it directly. It should be provided to the Inspector API to link back performance and debug data sourced from the runtime back to the Python source code.

Generating an ETRecord

The user should use the following API to generate an ETRecord file. They will be expected to provide the Edge Dialect program (returned by the call to to_edge()), the ExecuTorch program (returned by the call to to_executorch()), and optional models that they are interested in working with via our tooling.


Users should do a deepcopy of the output of to_edge() and pass in the deepcopy to the generate_etrecord API. This is needed because the subsequent call, to_executorch(), does an in-place mutation and will lose debug data in the process.

executorch.sdk.etrecord._etrecord.generate_etrecord(etrecord_path, edge_dialect_program, executorch_program, export_modules=None)[source]

Generates an ETRecord from the given objects, serializes it and saves it to the given path. The objects that will be serialized to an ETRecord are all the graph modules present in the export_modules dict, the graph module present in the edge dialect program object, and also the graph module present in the ExecuTorch program object, which is the closest graph module representation of what is eventually run on the device. In addition to all the graph modules, we also serialize the program buffer, which the users can provide to the ExecuTorch runtime to run the model, and the debug handle map for SDK tooling usage.

  • etrecord_path – Path to where the ETRecord file will be saved to.

  • edge_dialect_programEdgeProgramManager for this model returned by the call to to_edge()

  • executorch_program – The ExecuTorch program for this model returned by the call to to_executorch() or the BundledProgram of this model

  • export_modules[Optional]Should be ignored by OSS users. A dictionary of graph modules with the key being the user provided name and the value being the corresponding exported module. The exported graph modules can be either the output of torch.export() or exir.to_edge().



Using an ETRecord

Pass the ETRecord as an optional argument into the Inspector API to access this data and do post-run analysis.


Access comprehensive developer documentation for PyTorch

View Docs


Get in-depth tutorials for beginners and advanced developers

View Tutorials


Find development resources and get your questions answered

View Resources