Precision — PyTorch Lightning 2.5.1.post0 documentation (original) (raw)

class lightning.pytorch.plugins.precision.Precision[source]¶

Bases: Precision, CheckpointHooks

Base class for all plugins handling the precision-specific parts of the training.

The class attribute precision must be overwritten in child classes. The default value reflects fp32 training.

backward(tensor, model, optimizer, *args, **kwargs)[source]¶

Performs the actual backpropagation.

Parameters:

• tensor¶ (Tensor) – the loss value obtained from the closure
• model¶ (LightningModule) – the model to be optimized
• optimizer¶ (Optional[Steppable]) – current optimizer being used. None if using manual optimization
• *args¶ (Any) – Positional arguments intended for the actual function that performs the backward, likebackward().
• **kwargs¶ (Any) – Keyword arguments for the same purpose as *args.

Return type:

None

clip_grad_by_norm(optimizer, clip_val)[source]¶

Clip gradients by norm.

Return type:

None

clip_grad_by_value(optimizer, clip_val)[source]¶

Clip gradients by value.

Return type:

None

clip_gradients(optimizer, clip_val=0.0, gradient_clip_algorithm=GradClipAlgorithmType.NORM)[source]¶

Clips the gradients.

Return type:

None

connect(model, optimizers, lr_schedulers)[source]¶

Connects this plugin to the accelerator and the training process.

Return type:

tuple[Module, list[Optimizer], list[Any]]

optimizer_step(optimizer, model, closure, **kwargs)[source]¶

Hook to run the optimizer step.

Return type:

Any

post_backward(tensor, module)[source]¶

Runs after 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

pre_backward(tensor, module)[source]¶

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

predict_step_context()[source]¶

A contextmanager for the predict step.

Return type:

Generator[None, None, None]

test_step_context()[source]¶

A contextmanager for the test step.

Return type:

Generator[None, None, None]

train_step_context()[source]¶

A contextmanager for the training step.

Return type:

Generator[None, None, None]

val_step_context()[source]¶

A contextmanager for the validation step.

Return type:

Generator[None, None, None]