PyTorch

class todd.patches.torch.ModuleDict[source]

Bases: ModuleDict

forward(*args, **kwargs)[source]

Return type:: dict[str, Module]

class todd.patches.torch.ModuleList[source]

Bases: ModuleList

forward(*args, **kwargs)[source]

Return type:: list[Module]

class todd.patches.torch.PrefetchDataLoader[source]

Bases: DataLoader[T]

__init__(*args, **kwargs)[source]

Return type:: None

build_pre_hook(registry, item)

Parameters:

config (Config)
registry (RegistryMeta)
item (Item)

Return type:

Config

class todd.patches.torch.Sequential[source]

Bases: Sequential

__init__(*args, unpack_args=False, **kwargs)[source]

Parameters:: unpack_args (bool)
Return type:: None

forward(*args, **kwargs)[source]

Return type:: tuple[Any, …]

todd.patches.torch.all_close(x, y, *args, **kwargs)[source]

Parameters:

x (Any)
y (Any)

Return type:

bool

todd.patches.torch.all_gather(tensor, world_size, *args, **kwargs)[source]

Parameters:

tensor (Tensor)
world_size (int)

Return type:

list[Tensor]

todd.patches.torch.all_gather_object(tensor, world_size, *args, **kwargs)[source]

Parameters:

tensor (Tensor)
world_size (int)

Return type:

list[Tensor]

todd.patches.torch.cosine_similarity(x, y)[source]

Parameters:

x (Tensor)
y (Tensor)

Return type:

Tensor

todd.patches.torch.get_device()[source]

Return type:: str

todd.patches.torch.get_local_rank(*args, **kwargs)[source]

Return type:: int

todd.patches.torch.get_rank(*args, **kwargs)[source]

Return type:: int

todd.patches.torch.get_world_size(*args, **kwargs)[source]

Return type:: int

todd.patches.torch.load(f, *args, directory=None, **kwargs)[source]

Parameters:

f (Any)
directory (Any)

Return type:

Any

todd.patches.torch.load_state_dict(module, state_dict, *args, logger=None, **kwargs)[source]

Parameters:

module (Module)
state_dict (Mapping[str, Any])
logger (Logger | None)

Return type:

None

todd.patches.torch.load_state_dict_(f, *args, logger=None, **kwargs)[source]

Parameters:

f (str | PathLike[str] | IO[bytes] | list[str | PathLike[str] | IO[bytes]])
logger (Logger | None)

Return type:

dict[str, Any]

todd.patches.torch.named_trainable_parameters(module, *args, **kwargs)[source]

Parameters:: module (Module)
Return type:: Generator[tuple[str, Parameter], None, None]

todd.patches.torch.named_training_modules(module, *args, **kwargs)[source]

Parameters:: module (Module)
Return type:: Generator[tuple[str, Module], None, None]

todd.patches.torch.random_int()[source]

Return type:: int

todd.patches.torch.set_epoch(dataloader, epoch)[source]

Parameters:

dataloader (DataLoader)
epoch (int)

Return type:

None

todd.patches.torch.trainable_parameters(module, *args, **kwargs)[source]

Parameters:: module (Module)
Return type:: Generator[Parameter, None, None]

todd.patches.torch.training_modules(module, *args, **kwargs)[source]

Parameters:: module (Module)
Return type:: Generator[Module, None, None]