---
title: Precision
description: >-
  Base class for all plugins handling the precision-specific parts of the
  training.
canonical: >-
  https://lightning.ai/docs/fabric/stable/full-api-reference/api/generated/lightning.fabric.plugins.precision.Precision
---
# Precision {#precision}

### `class lightning.fabric.plugins.precision.Precision` {#lightning.fabric.plugins.precision.Precision}

Bases: [`object`](https://docs.python.org/3/builtins/functions.html#object "(in Python v3.15)")

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, *args, **kwargs)` {#lightning.fabric.plugins.precision.Precision.backward}

Performs the actual backpropagation.

Parameters:

*   **tensor** ([`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")) – The tensor that will be used for backpropagation
    
*   **model** ([`Optional`](https://docs.python.org/3/library/typing.html#typing.Optional "(in Python v3.15)")\[[`Module`](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module "(in PyTorch v2.15)")\]) – The module that was involved in producing the tensor and whose parameters need the gradients
    

Return type:

[`None`](https://docs.python.org/3/builtins/constants.html#None "(in Python v3.15)")

#### `convert_input(data)` {#lightning.fabric.plugins.precision.Precision.convert_input}

Convert model inputs (forward) to the floating point precision type of this plugin.

This is a no-op in the base precision plugin, since we assume the data already has the desired type (default is torch.float32).

Return type:

[`Any`](https://docs.python.org/3/library/typing.html#typing.Any "(in Python v3.15)")

#### `convert_module(module)` {#lightning.fabric.plugins.precision.Precision.convert_module}

Convert the module parameters to the precision type this plugin handles.

This is optional and depends on the precision limitations during optimization.

Return type:

[`Module`](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module "(in PyTorch v2.15)")

#### `convert_output(data)` {#lightning.fabric.plugins.precision.Precision.convert_output}

Convert outputs to the floating point precision type expected after model’s forward.

This is a no-op in the base precision plugin, since we assume the data already has the desired type (default is torch.float32).

Return type:

[`Any`](https://docs.python.org/3/library/typing.html#typing.Any "(in Python v3.15)")

#### `forward_context()` {#lightning.fabric.plugins.precision.Precision.forward_context}

A contextmanager for managing model forward/training\_step/evaluation\_step/predict\_step.

Return type:

[`AbstractContextManager`](https://docs.python.org/3/library/contextlib.html#contextlib.AbstractContextManager "(in Python v3.15)")

#### `load_state_dict(state_dict)` {#lightning.fabric.plugins.precision.Precision.load_state_dict}

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

Parameters:

**state\_dict** ([`dict`](https://docs.python.org/3/builtins/stdtypes.html#dict "(in Python v3.15)")\[[`str`](https://docs.python.org/3/builtins/stdtypes.html#str "(in Python v3.15)"), [`Any`](https://docs.python.org/3/library/typing.html#typing.Any "(in Python v3.15)")\]) – the precision plugin state returned by `state_dict`.

Return type:

[`None`](https://docs.python.org/3/builtins/constants.html#None "(in Python v3.15)")

#### `main_params(optimizer)` {#lightning.fabric.plugins.precision.Precision.main_params}

The main params of the model.

Returns the plain model params here. Maybe different in other precision plugins.

Return type:

[`Iterator`](https://docs.python.org/3/library/collections.abc.html#collections.abc.Iterator "(in Python v3.15)")\[[`Parameter`](https://docs.pytorch.org/docs/stable/generated/torch.nn.parameter.Parameter.html#torch.nn.parameter.Parameter "(in PyTorch v2.15)")\]

#### `module_init_context()` {#lightning.fabric.plugins.precision.Precision.module_init_context}

Instantiate module parameters or tensors in the precision type this plugin handles.

This is optional and depends on the precision limitations during optimization.

Return type:

[`AbstractContextManager`](https://docs.python.org/3/library/contextlib.html#contextlib.AbstractContextManager "(in Python v3.15)")

#### `optimizer_step(optimizer, **kwargs)` {#lightning.fabric.plugins.precision.Precision.optimizer_step}

Hook to run the optimizer step.

Return type:

[`Any`](https://docs.python.org/3/library/typing.html#typing.Any "(in Python v3.15)")

#### `post_backward(tensor, module)` {#lightning.fabric.plugins.precision.Precision.post_backward}

Runs after precision plugin executes backward.

Parameters:

*   **tensor** ([`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")) – The tensor that will be used for backpropagation
    
*   **module** ([`Optional`](https://docs.python.org/3/library/typing.html#typing.Optional "(in Python v3.15)")\[[`Module`](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module "(in PyTorch v2.15)")\]) – The module that was involved in producing the tensor and whose parameters need the gradients
    

Return type:

[`Any`](https://docs.python.org/3/library/typing.html#typing.Any "(in Python v3.15)")

#### `pre_backward(tensor, module)` {#lightning.fabric.plugins.precision.Precision.pre_backward}

Runs before precision plugin executes backward.

Parameters:

*   **tensor** ([`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")) – The tensor that will be used for backpropagation
    
*   **module** ([`Optional`](https://docs.python.org/3/library/typing.html#typing.Optional "(in Python v3.15)")\[[`Module`](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module "(in PyTorch v2.15)")\]) – The module that was involved in producing the tensor and whose parameters need the gradients
    

Return type:

[`Any`](https://docs.python.org/3/library/typing.html#typing.Any "(in Python v3.15)")

#### `state_dict()` {#lightning.fabric.plugins.precision.Precision.state_dict}

Called when saving a checkpoint, implement to generate precision plugin state\_dict.

Return type:

[`dict`](https://docs.python.org/3/builtins/stdtypes.html#dict "(in Python v3.15)")\[[`str`](https://docs.python.org/3/builtins/stdtypes.html#str "(in Python v3.15)"), [`Any`](https://docs.python.org/3/library/typing.html#typing.Any "(in Python v3.15)")\]

Returns:

A dictionary containing precision plugin state.

#### `teardown()` {#lightning.fabric.plugins.precision.Precision.teardown}

This method is called to teardown the training process.

It is the right place to release memory and free other resources.

Return type:

[`None`](https://docs.python.org/3/builtins/constants.html#None "(in Python v3.15)")

#### `tensor_init_context()` {#lightning.fabric.plugins.precision.Precision.tensor_init_context}

Controls how tensors get created (device, dtype).

Return type:

[`AbstractContextManager`](https://docs.python.org/3/library/contextlib.html#contextlib.AbstractContextManager "(in Python v3.15)")

---

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