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# Precision Control and Optimization
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Precision plugins for mixed precision training, quantization, and various floating-point formats to optimize memory usage and training speed while maintaining model quality.
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## Capabilities
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### Mixed Precision Training
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Automatic mixed precision training using 16-bit floats for forward pass and 32-bit for loss computation.
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```python { .api }
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class MixedPrecision:
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    def __init__(self, precision: str = "16-mixed", device: str = "cuda"):
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        """
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        Initialize mixed precision plugin.
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        Args:
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            precision: Precision mode ('16-mixed', 'bf16-mixed')
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            device: Target device
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        """
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```
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### Half Precision
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16-bit floating point training for memory efficiency.
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```python { .api }
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class HalfPrecision:
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    def __init__(self):
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        """Initialize half precision plugin."""
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```
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### Double Precision
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64-bit floating point training for maximum numerical precision.
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```python { .api }
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class DoublePrecision:
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    def __init__(self):
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        """Initialize double precision plugin."""
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```
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### Quantization
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8-bit and 4-bit quantization using BitsAndBytes for memory-efficient training of large models.
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```python { .api }
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class BitsandbytesPrecision:
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    def __init__(
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        self,
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        mode: str = "int8",
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        dtype: Optional[torch.dtype] = None,
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        ignore_modules: Optional[Set[str]] = None
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    ):
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        """
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        Initialize BitsAndBytes precision plugin.
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        Args:
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            mode: Quantization mode ('int8', 'int4', 'nf4', 'fp4')
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            dtype: Data type for computation
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            ignore_modules: Modules to skip quantization
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        """
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```
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### DeepSpeed Precision
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Precision plugin for DeepSpeed optimization with ZeRO memory optimization.
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```python { .api }
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class DeepSpeedPrecision:
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    def __init__(self):
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        """Initialize DeepSpeed precision plugin."""
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```
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### FSDP Precision
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Precision plugin optimized for Fully Sharded Data Parallel training.
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```python { .api }
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class FSDPPrecision:
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    def __init__(self):
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        """Initialize FSDP precision plugin."""
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```
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### Transformer Engine Precision
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NVIDIA Transformer Engine precision for optimized transformer training.
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```python { .api }
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class TransformerEnginePrecision:
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    def __init__(
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        self,
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        weights_dtype: torch.dtype = torch.float32,
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        recipe: Optional[Dict[str, Any]] = None
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    ):
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        """
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        Initialize Transformer Engine precision plugin.
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        Args:
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            weights_dtype: Data type for model weights
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            recipe: Transformer Engine recipe configuration
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        """
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```
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### XLA Precision
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Precision plugin for TPU training with XLA compilation.
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```python { .api }
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class XLAPrecision:
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    def __init__(self):
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        """Initialize XLA precision plugin for TPU training."""
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```
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### Base Precision
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Base class for implementing custom precision plugins.
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```python { .api }
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class Precision:
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    def __init__(self):
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        """Initialize base precision plugin."""
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    def convert_module(self, module: nn.Module) -> nn.Module:
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        """Convert module for precision."""
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    def convert_optimizer(self, optimizer: Optimizer) -> Optimizer:
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        """Convert optimizer for precision."""
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    def backward(self, tensor: Tensor, model: nn.Module) -> None:
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        """Perform backward pass with precision handling."""
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```