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]]

Source

__init__bitsandbytes.optim.Adam.__init__https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/adam.py#L10[{"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"}, {"name": "amsgrad", "val": " = False"}, {"name": "optim_bits", "val": " = 32"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "is_paged", "val": " = False"}]- 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.0

Base Adam optimizer.

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.

Adam8bit[[bitsandbytes.optim.Adam8bit]]

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

Source

__init__bitsandbytes.optim.Adam8bit.__init__https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/adam.py#L63[{"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"}, {"name": "amsgrad", "val": " = False"}, {"name": "optim_bits", "val": " = 32"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "is_paged", "val": " = False"}]- 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.0

8-bit Adam optimizer.

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.

Adam32bit[[bitsandbytes.optim.Adam32bit]]

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

Source

__init__bitsandbytes.optim.Adam32bit.__init__https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/adam.py#L127[{"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"}, {"name": "amsgrad", "val": " = False"}, {"name": "optim_bits", "val": " = 32"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "is_paged", "val": " = False"}]- 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.0

32-bit Adam optimizer.

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.

PagedAdam[[bitsandbytes.optim.PagedAdam]]

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

Source

__init__bitsandbytes.optim.PagedAdam.__init__https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/adam.py#L180[{"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"}, {"name": "amsgrad", "val": " = False"}, {"name": "optim_bits", "val": " = 32"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "is_paged", "val": " = False"}]- 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.0

Paged Adam optimizer.

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.

PagedAdam8bit[[bitsandbytes.optim.PagedAdam8bit]]

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

Source

__init__bitsandbytes.optim.PagedAdam8bit.__init__https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/adam.py#L233[{"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"}, {"name": "amsgrad", "val": " = False"}, {"name": "optim_bits", "val": " = 32"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "is_paged", "val": " = False"}]- 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.0

8-bit paged Adam optimizer.

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.

PagedAdam32bit[[bitsandbytes.optim.PagedAdam32bit]]

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

Source

__init__bitsandbytes.optim.PagedAdam32bit.__init__https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/adam.py#L297[{"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"}, {"name": "amsgrad", "val": " = False"}, {"name": "optim_bits", "val": " = 32"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "is_paged", "val": " = False"}]- 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.0

Paged 32-bit Adam optimizer.

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.