# Overview

[8-bit optimizers](https://hf.co/papers/2110.02861) reduce the memory footprint of 32-bit optimizers without any performance degradation which means you can train large models with many parameters faster. At the core of 8-bit optimizers is block-wise quantization which enables quantization accuracy, computational efficiency, and stability.

bitsandbytes provides 8-bit optimizers through the base `Optimizer8bit` class, and additionally provides `Optimizer2State` and `Optimizer1State` for 2-state (for example, `Adam`) and 1-state (for example, `Adagrad`) optimizers respectively. To provide custom optimizer hyperparameters, use the `GlobalOptimManager` class to configure the optimizer.

## Optimizer8bit[[bitsandbytes.optim.optimizer.Optimizer8bit]]

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<docstring><name>class bitsandbytes.optim.optimizer.Optimizer8bit</name><anchor>bitsandbytes.optim.optimizer.Optimizer8bit</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/optimizer.py#L113</source><parameters>[{"name": "params", "val": ""}, {"name": "defaults", "val": ""}, {"name": "optim_bits", "val": " = 32"}, {"name": "is_paged", "val": " = False"}]</parameters></docstring>



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<docstring><name>__init__</name><anchor>bitsandbytes.optim.optimizer.Optimizer8bit.__init__</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/optimizer.py#L114</source><parameters>[{"name": "params", "val": ""}, {"name": "defaults", "val": ""}, {"name": "optim_bits", "val": " = 32"}, {"name": "is_paged", "val": " = False"}]</parameters><paramsdesc>- **params** (`torch.Tensor`) --
  The input parameters to optimize.
- **optim_bits** (`int`, defaults to 32) --
  The number of bits of the optimizer state.
- **is_paged** (`bool`, defaults to `False`) --
  Whether the optimizer is a paged optimizer or not.</paramsdesc><paramgroups>0</paramgroups></docstring>

Base 8-bit optimizer class.




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## Optimizer2State[[bitsandbytes.optim.optimizer.Optimizer2State]]

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<docstring><name>class bitsandbytes.optim.optimizer.Optimizer2State</name><anchor>bitsandbytes.optim.optimizer.Optimizer2State</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/optimizer.py#L347</source><parameters>[{"name": "optimizer_name", "val": ""}, {"name": "params", "val": ""}, {"name": "lr", "val": " = 0.001"}, {"name": "betas", "val": " = (0.9, 0.999)"}, {"name": "eps", "val": " = 1e-08"}, {"name": "weight_decay", "val": " = 0.0"}, {"name": "optim_bits", "val": " = 32"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "percentile_clipping", "val": " = 100"}, {"name": "block_wise", "val": " = True"}, {"name": "max_unorm", "val": " = 0.0"}, {"name": "skip_zeros", "val": " = False"}, {"name": "is_paged", "val": " = False"}, {"name": "alpha", "val": " = 0.0"}, {"name": "t_alpha", "val": ": typing.Optional[int] = None"}, {"name": "t_beta3", "val": ": typing.Optional[int] = None"}]</parameters></docstring>



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<docstring><name>__init__</name><anchor>bitsandbytes.optim.optimizer.Optimizer2State.__init__</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/optimizer.py#L348</source><parameters>[{"name": "optimizer_name", "val": ""}, {"name": "params", "val": ""}, {"name": "lr", "val": " = 0.001"}, {"name": "betas", "val": " = (0.9, 0.999)"}, {"name": "eps", "val": " = 1e-08"}, {"name": "weight_decay", "val": " = 0.0"}, {"name": "optim_bits", "val": " = 32"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "percentile_clipping", "val": " = 100"}, {"name": "block_wise", "val": " = True"}, {"name": "max_unorm", "val": " = 0.0"}, {"name": "skip_zeros", "val": " = False"}, {"name": "is_paged", "val": " = False"}, {"name": "alpha", "val": " = 0.0"}, {"name": "t_alpha", "val": ": typing.Optional[int] = None"}, {"name": "t_beta3", "val": ": typing.Optional[int] = None"}]</parameters><paramsdesc>- **optimizer_name** (`str`) --
  The name of the optimizer.
- **params** (`torch.Tensor`) --
  The input parameters to optimize.
- **lr** (`float`, defaults to 1e-3) --
  The learning rate.
- **betas** (`tuple`, defaults to (0.9, 0.999)) --
  The beta values for the optimizer.
- **eps** (`float`, defaults to 1e-8) --
  The epsilon value for the optimizer.
- **weight_decay** (`float`, defaults to 0.0) --
  The weight decay value for the optimizer.
- **optim_bits** (`int`, defaults to 32) --
  The number of bits of the optimizer state.
- **args** (`object`, defaults to `None`) --
  An object with additional arguments.
- **min_8bit_size** (`int`, defaults to 4096) --
  The minimum number of elements of the parameter tensors for 8-bit optimization.
- **percentile_clipping** (`int`, defaults to 100) --
  Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability.
- **block_wise** (`bool`, defaults to `True`) --
  Whether to independently quantize each block of tensors to reduce outlier effects and improve stability.
- **max_unorm** (`float`, defaults to 0.0) --
  The maximum value to normalize each block with.
- **skip_zeros** (`bool`, defaults to `False`) --
  Whether to skip zero values for sparse gradients and models to ensure correct updates.
- **is_paged** (`bool`, defaults to `False`) --
  Whether the optimizer is a paged optimizer or not.
- **alpha** (`float`, defaults to 0.0) --
  The alpha value for the AdEMAMix optimizer.
- **t_alpha** (`Optional[int]`, defaults to `None`) --
  Number of iterations for alpha scheduling with AdEMAMix.
- **t_beta3** (`Optional[int]`, defaults to `None`) --
  Number of iterations for beta scheduling with AdEMAMix.</paramsdesc><paramgroups>0</paramgroups></docstring>

Base 2-state update optimizer class.




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## Optimizer1State[[bitsandbytes.optim.optimizer.Optimizer1State]]

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<docstring><name>class bitsandbytes.optim.optimizer.Optimizer1State</name><anchor>bitsandbytes.optim.optimizer.Optimizer1State</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/optimizer.py#L591</source><parameters>[{"name": "optimizer_name", "val": ""}, {"name": "params", "val": ""}, {"name": "lr", "val": " = 0.001"}, {"name": "betas", "val": " = (0.9, 0.0)"}, {"name": "eps", "val": " = 1e-08"}, {"name": "weight_decay", "val": " = 0.0"}, {"name": "optim_bits", "val": " = 32"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "percentile_clipping", "val": " = 100"}, {"name": "block_wise", "val": " = True"}, {"name": "max_unorm", "val": " = 0.0"}, {"name": "skip_zeros", "val": " = False"}, {"name": "is_paged", "val": " = False"}]</parameters></docstring>



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<docstring><name>__init__</name><anchor>bitsandbytes.optim.optimizer.Optimizer1State.__init__</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/optimizer.py#L592</source><parameters>[{"name": "optimizer_name", "val": ""}, {"name": "params", "val": ""}, {"name": "lr", "val": " = 0.001"}, {"name": "betas", "val": " = (0.9, 0.0)"}, {"name": "eps", "val": " = 1e-08"}, {"name": "weight_decay", "val": " = 0.0"}, {"name": "optim_bits", "val": " = 32"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "percentile_clipping", "val": " = 100"}, {"name": "block_wise", "val": " = True"}, {"name": "max_unorm", "val": " = 0.0"}, {"name": "skip_zeros", "val": " = False"}, {"name": "is_paged", "val": " = False"}]</parameters><paramsdesc>- **optimizer_name** (`str`) --
  The name of the optimizer.
- **params** (`torch.Tensor`) --
  The input parameters to optimize.
- **lr** (`float`, defaults to 1e-3) --
  The learning rate.
- **betas** (`tuple`, defaults to (0.9, 0.0)) --
  The beta values for the optimizer.
- **eps** (`float`, defaults to 1e-8) --
  The epsilon value for the optimizer.
- **weight_decay** (`float`, defaults to 0.0) --
  The weight decay value for the optimizer.
- **optim_bits** (`int`, defaults to 32) --
  The number of bits of the optimizer state.
- **args** (`object`, defaults to `None`) --
  An object with additional arguments.
- **min_8bit_size** (`int`, defaults to 4096) --
  The minimum number of elements of the parameter tensors for 8-bit optimization.
- **percentile_clipping** (`int`, defaults to 100) --
  Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability.
- **block_wise** (`bool`, defaults to `True`) --
  Whether to independently quantize each block of tensors to reduce outlier effects and improve stability.
- **max_unorm** (`float`, defaults to 0.0) --
  The maximum value to normalize each block with.
- **skip_zeros** (`bool`, defaults to `False`) --
  Whether to skip zero values for sparse gradients and models to ensure correct updates.
- **is_paged** (`bool`, defaults to `False`) --
  Whether the optimizer is a paged optimizer or not.</paramsdesc><paramgroups>0</paramgroups></docstring>

Base 1-state update optimizer class.




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## Utilities[[bitsandbytes.optim.GlobalOptimManager]]

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<docstring><name>class bitsandbytes.optim.GlobalOptimManager</name><anchor>bitsandbytes.optim.GlobalOptimManager</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/optimizer.py#L22</source><parameters>[]</parameters></docstring>

A global optimizer manager for enabling custom optimizer configs.



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<docstring><name>override_config</name><anchor>bitsandbytes.optim.GlobalOptimManager.override_config</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/optimizer.py#L56</source><parameters>[{"name": "parameters", "val": ""}, {"name": "key", "val": " = None"}, {"name": "value", "val": " = None"}, {"name": "key_value_dict", "val": " = None"}]</parameters><paramsdesc>- **parameters** (`torch.Tensor` or `list(torch.Tensors)`) --
  The input parameters.
- **key** (`str`) --
  The hyperparameter to override.
- **value** --
  The hyperparameter value.
- **key_value_dict** (`dict`) --
  A dictionary with multiple key-values to override.</paramsdesc><paramgroups>0</paramgroups></docstring>

Override initial optimizer config with specific hyperparameters.

The key-values of the optimizer config for the input parameters are overridden
This can be both, optimizer parameters like `betas` or `lr`, or it can be
8-bit specific parameters like `optim_bits` or `percentile_clipping`.



<ExampleCodeBlock anchor="bitsandbytes.optim.GlobalOptimManager.override_config.example">

Example:

```py
import torch
import bitsandbytes as bnb

mng = bnb.optim.GlobalOptimManager.get_instance()

model = MyModel()
mng.register_parameters(model.parameters()) # 1. register parameters while still on CPU

model = model.cuda()
# use 8-bit optimizer states for all parameters
adam = bnb.optim.Adam(model.parameters(), lr=0.001, optim_bits=8)

# 2. override: the parameter model.fc1.weight now uses 32-bit Adam
mng.override_config(model.fc1.weight, 'optim_bits', 32)
```

</ExampleCodeBlock>


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