Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25187
The bytecode export flow: dump the bytecode format for the light weighted interpreter.
* The bytecode is generated without input spec optimization. It would be more generic (input independent) with no obvious performance degradation (to be tested).
* Main API: torch::jit::script::Module::save(filename, extra_files, bool *bytecode_format* = false).
* Both bytecode and module object are exported in pickle format.
* The module object (in data.pkl) is the same as the original JIT model.
* The serializer is dependent on pickle only (no protobuf or Json).
* The major functionality is forked in ScriptModuleSerializer2::serialize().
* The test loader is test_bc_export.cpp.
* Simple APIs are added in Code and its implementation to get necessary information (instructions, operators and constants).
* Since there's no dependency on graph/node, GetAttr is promoted from an operator to first-class instruction (https://github.com/pytorch/pytorch/pull/25151) .
* Some definitions (instructions, writeArchive, etc) that are shared by full JIT and bytecode are pulled out of the local namespace (https://github.com/pytorch/pytorch/pull/25148).
The output layout looks like:
* folders of methods.
* In each method folder (for example, forward/):
* bytecode.pkl: instructions and operators
* constants{.pkl,/}: constant list in constants.pkl. If there are tensors in constants, the binary tensor files in constants/ folder.
* data{.pkl,/}: the module object, with binary tensor files in data/ folder. The same as in torchscript.
Test Plan: Imported from OSS
Differential Revision: D17076411
fbshipit-source-id: 46eb298e7320d1e585b0101effc0fcfd09219046
