bitsandbytes/pr_2030/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]]

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

bitsandbytes.optim.optimizer.Optimizer8bit(params, defaults, optim_bits = 32, is_paged = False)

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init[[bitsandbytes.optim.optimizer.Optimizer8bit.init]]

__init__(params, defaults, optim_bits = 32, is_paged = False)

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Parameters:

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]]

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

bitsandbytes.optim.optimizer.Optimizer2State(optimizer_name, params, lr = 0.001, betas = (0.9, 0.999), eps = 1e-08, weight_decay = 0.0, optim_bits = 32, args = None, min_8bit_size = 4096, max_unorm = 0.0, skip_zeros = False, is_paged = False, alpha = 0.0, t_alpha: typing.Optional[int] = None, t_beta3: typing.Optional[int] = None)

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

__init__(optimizer_name, params, lr = 0.001, betas = (0.9, 0.999), eps = 1e-08, weight_decay = 0.0, optim_bits = 32, args = None, min_8bit_size = 4096, max_unorm = 0.0, skip_zeros = False, is_paged = False, alpha = 0.0, t_alpha: typing.Optional[int] = None, t_beta3: typing.Optional[int] = None)

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Parameters:

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]]

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

bitsandbytes.optim.optimizer.Optimizer1State(optimizer_name, params, lr = 0.001, betas = (0.9, 0.0), eps = 1e-08, weight_decay = 0.0, optim_bits = 32, args = None, min_8bit_size = 4096, max_unorm = 0.0, skip_zeros = False, is_paged = False)

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

__init__(optimizer_name, params, lr = 0.001, betas = (0.9, 0.0), eps = 1e-08, weight_decay = 0.0, optim_bits = 32, args = None, min_8bit_size = 4096, max_unorm = 0.0, skip_zeros = False, is_paged = False)

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Parameters:

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]]

bitsandbytes.optim.GlobalOptimManager[[bitsandbytes.optim.GlobalOptimManager]]

bitsandbytes.optim.GlobalOptimManager()

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A global optimizer manager for enabling custom optimizer configs.

override_config[[bitsandbytes.optim.GlobalOptimManager.override_config]]

override_config(parameters, key = None, value = None, key_value_dict = None)

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Parameters:

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)