bitsandbytes/main/en/reference/optim/optim_overview.md

Overview

8-bit optimizers 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]]

• 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.

Base 8-bit optimizer class.

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

• 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.
• 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.

Base 2-state update optimizer class.

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

• 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.
• 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.

Base 1-state update optimizer class.

Utilities[[bitsandbytes.optim.GlobalOptimManager]]

A global optimizer manager for enabling custom optimizer configs.

• 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.

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.

Example:

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)