Buckets:

hf-doc-build
/

doc

Files

xet

hf-doc-build/doc / bitsandbytes /main /en /reference /optim /lamb.md

HuggingFaceDocBuilder

2 days ago

preview code

download

raw

9.98 kB

LAMB

LAMB (Layerwise adaptive large batch optimization) is an adaptive optimizer designed for training with large batch sizes to accelerate training, combining ideas from LARS and Adam to automatically scale the learning rate for each layer:

calculates a trust ratio between the weight and gradient norm in a layer and clips the ratio to prevent overly large or small updates
updates weights with the first and second-moments

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

bitsandbytes.optim.LAMB[[bitsandbytes.optim.LAMB]]

Source

__init__bitsandbytes.optim.LAMB.__init__https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/lamb.py#L9[{"name": "params", "val": ""}, {"name": "lr", "val": " = 0.001"}, {"name": "bias_correction", "val": " = True"}, {"name": "betas", "val": " = (0.9, 0.999)"}, {"name": "eps", "val": " = 1e-08"}, {"name": "weight_decay", "val": " = 0"}, {"name": "amsgrad", "val": " = False"}, {"name": "adam_w_mode", "val": " = True"}, {"name": "optim_bits", "val": " = 32"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "max_unorm", "val": " = 1.0"}]- params (torch.tensor) -- The input parameters to optimize.

lr (float, defaults to 1e-3) -- The learning rate.
bias_correction (bool, defaults to True) -- Whether to apply bias correction to the first and second-order moments.
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 1e-2) -- 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.
adam_w_mode (bool, defaults to True) -- Whether to use the AdamW variant.
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 1.0) -- The maximum gradient norm.0

Base LAMB optimizer.

Parameters:

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

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

bias_correction (bool, defaults to True) : Whether to apply bias correction to the first and second-order moments.

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 1e-2) : 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.

adam_w_mode (bool, defaults to True) : Whether to use the AdamW variant.

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 1.0) : The maximum gradient norm.

LAMB8bit[[bitsandbytes.optim.LAMB8bit]]

bitsandbytes.optim.LAMB8bit[[bitsandbytes.optim.LAMB8bit]]

Source

__init__bitsandbytes.optim.LAMB8bit.__init__https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/lamb.py#L68[{"name": "params", "val": ""}, {"name": "lr", "val": " = 0.001"}, {"name": "bias_correction", "val": " = True"}, {"name": "betas", "val": " = (0.9, 0.999)"}, {"name": "eps", "val": " = 1e-08"}, {"name": "weight_decay", "val": " = 0"}, {"name": "amsgrad", "val": " = False"}, {"name": "adam_w_mode", "val": " = True"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "max_unorm", "val": " = 1.0"}]- params (torch.tensor) -- The input parameters to optimize.

lr (float, defaults to 1e-3) -- The learning rate.
bias_correction (bool, defaults to True) -- Whether to apply bias correction to the first and second-order moments.
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 1e-2) -- 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.
adam_w_mode (bool, defaults to True) -- Whether to use the AdamW variant.
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 1.0) -- The maximum gradient norm.0

8-bit LAMB optimizer.