`returnn.torch.frontend.bridge`#

Direct bridge between pure PyTorch code and modules and RF code and modules.

returnn.torch.frontend.bridge.pt_module_to_rf_module(pt_module: Module) → Module[source]#

returnn.torch.frontend.bridge.pt_module_to_wrapped_rf_module(pt_module: Module) → Module | None[source]#

Parameters:: pt_module – torch module
Returns:: RF module if the torch module is a wrapped RF module, or None otherwise

returnn.torch.frontend.bridge.rf_module_to_pt_module(rf_module: Module, *, aux_params_as_buffers: bool = True) → Module[source]#

Parameters:

rf_module – RF module
aux_params_as_buffers – whether to map RF auxiliary parameters to PyTorch buffers, otherwise to normal parameters, i.e. they occur in model.named_parameters(). Note that even when they are part of model.named_parameters(), aux params usually don’t have a gradient, and then they are not updated by the optimizer. Historically, this was False. Now, this is True by default, as this is more reasonable. Note that the optimizer state dict will change if you change this, however, we will automatically convert such optimizer state dict.

Returns:

torch module

returnn.torch.frontend.bridge#