lightning
Modules:
| Name | Description | 
|---|---|
| precision |  | 
Classes:
| Name | Description | 
|---|---|
| ReducedPrecisionFilter | Wraps any  | 
    
              Bases: Generic[_PrecisionT], Precision, Precision
Wraps any lightning.fabric.plugins.precision.Precision object, patching its convert_module to avoid casting certain submodules'
parameters and buffers to reduced-precision to avoid convergence issues, namely those of normalization layers and
stainedglass_core.noise_layer.BaseNoiseLayer.
Some classes may not converge well in lower precision: https://discuss.pytorch.org/t/training-with-half-precision/11815/2.
Methods:
| Name | Description | 
|---|---|
| __init__ | Construct a  | 
| convert_module | Cast the parameters and buffers of the given module to the desired dtype, avoiding certain submodules, parameters, and buffers. | 
__init__(
    _precision: _PrecisionT,
    full_precision_module_types: Iterable[type[Module]]
    | None = None,
    full_precision_names: Iterable[str] | None = None,
) -> None
Construct a ReducedPrecisionFilter object.
Parameters:
| Name | Type | Description | Default | 
|---|---|---|---|
|                    | _PrecisionT | The  | required | 
|                    | Iterable[type[Module]] | None | Additional  | None | 
|                    | Iterable[str] | None | Regex patterns matching submodule, parameter, or buffer names to avoid casting to reduced-precision. See
 | None | 
Raises:
| Type | Description | 
|---|---|
| TypeError | If  | 
convert_module(module: _ModuleT) -> _ModuleT
Cast the parameters and buffers of the given module to the desired dtype, avoiding certain submodules, parameters, and buffers.
Parameters:
| Name | Type | Description | Default | 
|---|---|---|---|
|                    | _ModuleT | The module whose parameters and buffers to cast to the desired dtype. | required | 
Raises:
| Type | Description | 
|---|---|
| TypeError | If  | 
Returns:
| Type | Description | 
|---|---|
| _ModuleT | The casted module. |