xla

b1131d1d - feat: add support for custom compile options in torch_xla.compile and… (#9575)

feat: add support for custom compile options in torch_xla.compile and… (#9575) … PJRT backend Issue description : https://github.com/pytorch/xla/issues/9555 This change introduces the ability to pass custom compile options from Python down to the PJRT backend, allowing users to fine-tune XLA compilation behavior without modifying core code. Key changes: * Python API * Added custom_compile_options parameter to torch_xla.compile for passing compile-time options as a dict (supports bool, float, int, and str values). * Added torch_xla.set_custom_compile_options() utility for setting compile options globally. * Added internal binding _XLAC._set_custom_compile_options(). * C++ Runtime * Added SetCustomCompileOptions() virtual method to ComputationClient and implemented it in PjRtComputationClient. * PjRtComputationClient now stores custom_compile_options_ and injects them into xla::CompileOptions.env_option_overrides during compilation. * Options are stringified before being passed to XLA for compatibility. Motivation:
This enables advanced users to pass through backend-specific tuning flags (e.g., enabling experimental optimizations, toggling partitioning strategies) without hardcoding them, improving flexibility for research and debugging workflows.