MixedPrecisionPlugin

class pytorch_lightning.plugins.precision.MixedPrecisionPlugin(precision, device, scaler=None)

Bases: pytorch_lightning.plugins.precision.precision_plugin.PrecisionPlugin

Plugin for Automatic Mixed Precision (AMP) training with torch.autocast.

Parameters:

• precision (Literal['16', 16, 'bf16']) – Whether to use torch.float16 (16) or torch.bfloat16 ('bf16').

• device (str) – The device for torch.autocast.

• scaler (Optional[GradScaler]) – An optional torch.cuda.amp.GradScaler to use.

clip_gradients(optimizer, clip_val=0.0, gradient_clip_algorithm=GradClipAlgorithmType.NORM)

Clips the gradients.

Return type:

None

forward_context()

Enable autocast context.

Return type:

Generator[None, None, None]

load_state_dict(state_dict)

Called when loading a checkpoint, implement to reload precision plugin state given precision plugin state_dict.

Parameters:

state_dict (Dict[str, Any]) – the precision plugin state returned by state_dict.

Return type:

None

optimizer_step(optimizer, model, optimizer_idx, closure, **kwargs)

Hook to run the optimizer step.

Return type:

Any

pre_backward(tensor, module)

Runs before precision plugin executes backward.

Parameters:

• tensor (Tensor) – The tensor that will be used for backpropagation

• module (LightningModule) – The module that was involved in producing the tensor and whose parameters need the gradients

Return type:

Tensor

state_dict()

Called when saving a checkpoint, implement to generate precision plugin state_dict.

Return type:

Dict[str, Any]

Returns:

A dictionary containing precision plugin state.