def lower_module(imported_module: Module): """Compiles an imported module, with a flat list of functions. Args: imported_module: The MLIR module consisting of funcs and globals in the torch dialect. It is lowered in place. Returns: The imported_module, for convenience chaining methods. """ with imported_module.context as context: if logging.debug_enabled(): logging.debug("Initial PyTorch IR:\n{}", imported_module) # Frontend. pipeline_str = "torch-globalized-module-to-npcomp-backend-pipeline" if logging.debug_enabled(): logging.debug("Running Torch->backend pipeline '{}'", pipeline_str) pm = PassManager.parse(pipeline_str) pm.run(imported_module) if logging.debug_enabled(): logging.debug("Backend IR:\n{}", imported_module) return imported_module
def lower_object_graph(imported_module: Module): """Lowers an imported module that has TorchScript object graph semantics. Args: imported_module: The MLIR module consisting of IR as imported by the torch_mlir.import_module. It is lowered in place. Returns: The imported_module, for convenience chaining methods. """ with imported_module.context as context: if logging.debug_enabled(): logging.debug("Initial PyTorch object graph IR:\n{}", imported_module) # Object graph lowering. pipeline_str = "torchscript-to-npcomp-backend-pipeline" if logging.debug_enabled(): logging.debug("Running Torch object graph lowering pipeline '{}'", pipeline_str) pm = PassManager.parse(pipeline_str) pm.run(imported_module) return imported_module
def __init__(self): super().__init__() self._refjit = refjit_backend.get_refjit() self._debug = logging.debug_enabled()
def __init__(self): super().__init__() self._debug = logging.debug_enabled()
def __init__(self): super().__init__() self._ireert = _get_iree() self._debug = logging.debug_enabled()