Pipeline Parallelism#

vision_architectures.utils.pipeline_parallelism.get_device(device)[source]#

Convert to torch.device object.

Return type:

device

vision_architectures.utils.pipeline_parallelism.move_to_device(data, device)[source]#

Move data to the specified device.

Parameters:

  • data – The data to move.

  • device – The device to move the data to.

Returns:

The data moved to the specified device.

class vision_architectures.utils.pipeline_parallelism.PipelineModule(module, processing_device, output_device=None)[source]#

Bases: Module

__init__(module, processing_device, output_device=None)[source]#

Initialize internal Module state, shared by both nn.Module and ScriptModule.

forward(*args, **kwargs)[source]#

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

extra_repr()[source]#

Return the extra representation of the module.

To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.

vision_architectures.utils.pipeline_parallelism.paralellize_pipeline(model, module_to_device)[source]#

Parallelize a model across multiple devices.

Parameters:

  • model (Module) – The model to parallelize.

  • module_to_device (dict[str, device | str | list[device | str]]) – A dictionary mapping module names to devices. Keys are modules names with nested modules separated by dots (e.g., “module.submodule”). Note that the parallelism is performed using Level Order Traversal (i.e. BFS) of the model. Therefore the device of the deepest module in the dictionary will be overwritten even if it’s parent is also specified in the dictionary. Value is either a device or a 2-tuple of devices. The first device is the processing device, and the second device is the output device.

Return type:

Module

Returns:

The parallelized pipeline.