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

Adam

Adam (Adaptive moment estimation) is an adaptive learning rate optimizer, combining ideas from SGD with momentum and RMSprop to automatically scale the learning rate:

• a weighted average of the past gradients to provide direction (first-moment)
• a weighted average of the squared past gradients to adapt the learning rate to each parameter (second-moment)

bitsandbytes also supports paged optimizers which take advantage of CUDAs unified memory to transfer memory from the GPU to the CPU when GPU memory is exhausted.

Adam[[api-class]][[bitsandbytes.optim.Adam]]

bitsandbytes.optim.Adam[[bitsandbytes.optim.Adam]]

bitsandbytes.optim.Adam(params, lr = 0.001, betas = (0.9, 0.999), eps = 1e-08, weight_decay = 0, amsgrad = False, optim_bits = 32, args = None, min_8bit_size = 4096, is_paged = False)

Source

init[[bitsandbytes.optim.Adam.init]]

__init__(params, lr = 0.001, betas = (0.9, 0.999), eps = 1e-08, weight_decay = 0, amsgrad = False, optim_bits = 32, args = None, min_8bit_size = 4096, is_paged = False)

Source

Parameters:

params (torch.tensor) : The input parameters to optimize.

lr (float, defaults to 1e-3) : The learning rate.

betas (tuple(float, float), defaults to (0.9, 0.999)) : The beta values are the decay rates of the first and second-order moment of the optimizer.

eps (float, defaults to 1e-8) : The epsilon value prevents division by zero in the optimizer.

weight_decay (float, defaults to 0.0) : The weight decay value for the optimizer.

amsgrad (bool, defaults to False) : Whether to use the AMSGrad variant of Adam that uses the maximum of past squared gradients instead.

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.

is_paged (bool, defaults to False) : Whether the optimizer is a paged optimizer or not.

Base Adam optimizer.

Adam8bit[[bitsandbytes.optim.Adam8bit]]

bitsandbytes.optim.Adam8bit[[bitsandbytes.optim.Adam8bit]]

bitsandbytes.optim.Adam8bit(params, lr = 0.001, betas = (0.9, 0.999), eps = 1e-08, weight_decay = 0, amsgrad = False, optim_bits = 32, args = None, min_8bit_size = 4096, is_paged = False)

Source

init[[bitsandbytes.optim.Adam8bit.init]]

__init__(params, lr = 0.001, betas = (0.9, 0.999), eps = 1e-08, weight_decay = 0, amsgrad = False, optim_bits = 32, args = None, min_8bit_size = 4096, is_paged = False)

Source

Parameters:

params (torch.tensor) : The input parameters to optimize.

lr (float, defaults to 1e-3) : The learning rate.

betas (tuple(float, float), defaults to (0.9, 0.999)) : The beta values are the decay rates of the first and second-order moment of the optimizer.

eps (float, defaults to 1e-8) : The epsilon value prevents division by zero in the optimizer.

weight_decay (float, defaults to 0.0) : The weight decay value for the optimizer.

amsgrad (bool, defaults to False) : Whether to use the AMSGrad variant of Adam that uses the maximum of past squared gradients instead. Note: This parameter is not supported in Adam8bit and must be False.

optim_bits (int, defaults to 32) : The number of bits of the optimizer state. Note: This parameter is not used in Adam8bit as it always uses 8-bit optimization.

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.

is_paged (bool, defaults to False) : Whether the optimizer is a paged optimizer or not.

8-bit Adam optimizer.

Adam32bit[[bitsandbytes.optim.Adam32bit]]

bitsandbytes.optim.Adam32bit[[bitsandbytes.optim.Adam32bit]]

bitsandbytes.optim.Adam32bit(params, lr = 0.001, betas = (0.9, 0.999), eps = 1e-08, weight_decay = 0, amsgrad = False, optim_bits = 32, args = None, min_8bit_size = 4096, is_paged = False)

Source

init[[bitsandbytes.optim.Adam32bit.init]]

__init__(params, lr = 0.001, betas = (0.9, 0.999), eps = 1e-08, weight_decay = 0, amsgrad = False, optim_bits = 32, args = None, min_8bit_size = 4096, is_paged = False)

Source

Parameters:

params (torch.tensor) : The input parameters to optimize.

lr (float, defaults to 1e-3) : The learning rate.

betas (tuple(float, float), defaults to (0.9, 0.999)) : The beta values are the decay rates of the first and second-order moment of the optimizer.

eps (float, defaults to 1e-8) : The epsilon value prevents division by zero in the optimizer.

weight_decay (float, defaults to 0.0) : The weight decay value for the optimizer.

amsgrad (bool, defaults to False) : Whether to use the AMSGrad variant of Adam that uses the maximum of past squared gradients instead.

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.

is_paged (bool, defaults to False) : Whether the optimizer is a paged optimizer or not.

32-bit Adam optimizer.

PagedAdam[[bitsandbytes.optim.PagedAdam]]

bitsandbytes.optim.PagedAdam[[bitsandbytes.optim.PagedAdam]]

bitsandbytes.optim.PagedAdam(params, lr = 0.001, betas = (0.9, 0.999), eps = 1e-08, weight_decay = 0, amsgrad = False, optim_bits = 32, args = None, min_8bit_size = 4096, is_paged = False)

Source

init[[bitsandbytes.optim.PagedAdam.init]]

__init__(params, lr = 0.001, betas = (0.9, 0.999), eps = 1e-08, weight_decay = 0, amsgrad = False, optim_bits = 32, args = None, min_8bit_size = 4096, is_paged = False)

Source

Parameters:

params (torch.tensor) : The input parameters to optimize.

lr (float, defaults to 1e-3) : The learning rate.

betas (tuple(float, float), defaults to (0.9, 0.999)) : The beta values are the decay rates of the first and second-order moment of the optimizer.

eps (float, defaults to 1e-8) : The epsilon value prevents division by zero in the optimizer.

weight_decay (float, defaults to 0.0) : The weight decay value for the optimizer.

amsgrad (bool, defaults to False) : Whether to use the AMSGrad variant of Adam that uses the maximum of past squared gradients instead.

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.

is_paged (bool, defaults to False) : Whether the optimizer is a paged optimizer or not.

Paged Adam optimizer.

PagedAdam8bit[[bitsandbytes.optim.PagedAdam8bit]]

bitsandbytes.optim.PagedAdam8bit[[bitsandbytes.optim.PagedAdam8bit]]

bitsandbytes.optim.PagedAdam8bit(params, lr = 0.001, betas = (0.9, 0.999), eps = 1e-08, weight_decay = 0, amsgrad = False, optim_bits = 32, args = None, min_8bit_size = 4096, is_paged = False)

Source

init[[bitsandbytes.optim.PagedAdam8bit.init]]

__init__(params, lr = 0.001, betas = (0.9, 0.999), eps = 1e-08, weight_decay = 0, amsgrad = False, optim_bits = 32, args = None, min_8bit_size = 4096, is_paged = False)

Source

Parameters:

params (torch.tensor) : The input parameters to optimize.

lr (float, defaults to 1e-3) : The learning rate.

betas (tuple(float, float), defaults to (0.9, 0.999)) : The beta values are the decay rates of the first and second-order moment of the optimizer.

eps (float, defaults to 1e-8) : The epsilon value prevents division by zero in the optimizer.

weight_decay (float, defaults to 0.0) : The weight decay value for the optimizer.

amsgrad (bool, defaults to False) : Whether to use the AMSGrad variant of Adam that uses the maximum of past squared gradients instead. Note: This parameter is not supported in PagedAdam8bit and must be False.

optim_bits (int, defaults to 32) : The number of bits of the optimizer state. Note: This parameter is not used in PagedAdam8bit as it always uses 8-bit optimization.

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.

is_paged (bool, defaults to False) : Whether the optimizer is a paged optimizer or not.

8-bit paged Adam optimizer.

PagedAdam32bit[[bitsandbytes.optim.PagedAdam32bit]]

bitsandbytes.optim.PagedAdam32bit[[bitsandbytes.optim.PagedAdam32bit]]

bitsandbytes.optim.PagedAdam32bit(params, lr = 0.001, betas = (0.9, 0.999), eps = 1e-08, weight_decay = 0, amsgrad = False, optim_bits = 32, args = None, min_8bit_size = 4096, is_paged = False)

Source

init[[bitsandbytes.optim.PagedAdam32bit.init]]

__init__(params, lr = 0.001, betas = (0.9, 0.999), eps = 1e-08, weight_decay = 0, amsgrad = False, optim_bits = 32, args = None, min_8bit_size = 4096, is_paged = False)

Source

Parameters:

params (torch.tensor) : The input parameters to optimize.

lr (float, defaults to 1e-3) : The learning rate.

betas (tuple(float, float), defaults to (0.9, 0.999)) : The beta values are the decay rates of the first and second-order moment of the optimizer.

eps (float, defaults to 1e-8) : The epsilon value prevents division by zero in the optimizer.

weight_decay (float, defaults to 0.0) : The weight decay value for the optimizer.

amsgrad (bool, defaults to False) : Whether to use the AMSGrad variant of Adam that uses the maximum of past squared gradients instead.

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.

is_paged (bool, defaults to False) : Whether the optimizer is a paged optimizer or not.

Paged 32-bit Adam optimizer.