bitsandbytes/v0.49.2/en/reference/optim/lars.md

LARS

LARS (Layer-wise Adaptive Rate Scaling) is an optimizer designed for training with large batch sizes to accelerate training. LARS uses a separate learning rate for each layer instead of each parameter. The learning rate is calculated from a trust ratio between the weight and gradient norm in a layer. This helps calibrate a stable update size.

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

bitsandbytes.optim.LARS[[bitsandbytes.optim.LARS]]

Source

__init__bitsandbytes.optim.LARS.__init__https://github.com/bitsandbytes-foundation/bitsandbytes/blob/v0.49.2/bitsandbytes/optim/lars.py#L12[{"name": "params", "val": ""}, {"name": "lr", "val": ""}, {"name": "momentum", "val": " = 0"}, {"name": "dampening", "val": " = 0"}, {"name": "weight_decay", "val": " = 0"}, {"name": "nesterov", "val": " = False"}, {"name": "optim_bits", "val": " = 32"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "percentile_clipping", "val": " = 100"}, {"name": "max_unorm", "val": " = 0.02"}]- params (torch.tensor) -- The input parameters to optimize.

• lr (float) -- The learning rate.
• momentum (float, defaults to 0) -- The momentum value speeds up the optimizer by taking bigger steps.
• dampening (float, defaults to 0) -- The dampening value reduces the momentum of the optimizer.
• weight_decay (float, defaults to 1e-2) -- The weight decay value for the optimizer.
• nesterov (bool, defaults to False) -- Whether to use Nesterov momentum.
• 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.
• percentile_clipping (int, defaults to 100) -- Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability.
• max_unorm (float, defaults to 0.02) -- The maximum gradient norm.0

Base LARS optimizer.

Parameters:

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

lr (float) : The learning rate.

momentum (float, defaults to 0) : The momentum value speeds up the optimizer by taking bigger steps.

dampening (float, defaults to 0) : The dampening value reduces the momentum of the optimizer.

weight_decay (float, defaults to 1e-2) : The weight decay value for the optimizer.

nesterov (bool, defaults to False) : Whether to use Nesterov momentum.

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.

percentile_clipping (int, defaults to 100) : Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability.

max_unorm (float, defaults to 0.02) : The maximum gradient norm.

LARS8bit[[bitsandbytes.optim.LARS8bit]]

bitsandbytes.optim.LARS8bit[[bitsandbytes.optim.LARS8bit]]

Source

__init__bitsandbytes.optim.LARS8bit.__init__https://github.com/bitsandbytes-foundation/bitsandbytes/blob/v0.49.2/bitsandbytes/optim/lars.py#L72[{"name": "params", "val": ""}, {"name": "lr", "val": ""}, {"name": "momentum", "val": " = 0"}, {"name": "dampening", "val": " = 0"}, {"name": "weight_decay", "val": " = 0"}, {"name": "nesterov", "val": " = False"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "percentile_clipping", "val": " = 100"}, {"name": "max_unorm", "val": " = 0.02"}]- params (torch.tensor) -- The input parameters to optimize.

• lr (float) -- The learning rate.
• momentum (float, defaults to 0) -- The momentum value speeds up the optimizer by taking bigger steps.
• dampening (float, defaults to 0) -- The dampening value reduces the momentum of the optimizer.
• weight_decay (float, defaults to 1e-2) -- The weight decay value for the optimizer.
• nesterov (bool, defaults to False) -- Whether to use Nesterov momentum.
• 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.
• percentile_clipping (int, defaults to 100) -- Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability.
• max_unorm (float, defaults to 0.02) -- The maximum gradient norm.0

8-bit LARS optimizer.

Parameters:

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

lr (float) : The learning rate.

momentum (float, defaults to 0) : The momentum value speeds up the optimizer by taking bigger steps.

dampening (float, defaults to 0) : The dampening value reduces the momentum of the optimizer.

weight_decay (float, defaults to 1e-2) : The weight decay value for the optimizer.

nesterov (bool, defaults to False) : Whether to use Nesterov momentum.

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.

percentile_clipping (int, defaults to 100) : Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability.

max_unorm (float, defaults to 0.02) : The maximum gradient norm.

LARS32bit[[bitsandbytes.optim.LARS32bit]]

bitsandbytes.optim.LARS32bit[[bitsandbytes.optim.LARS32bit]]

Source

__init__bitsandbytes.optim.LARS32bit.__init__https://github.com/bitsandbytes-foundation/bitsandbytes/blob/v0.49.2/bitsandbytes/optim/lars.py#L129[{"name": "params", "val": ""}, {"name": "lr", "val": ""}, {"name": "momentum", "val": " = 0"}, {"name": "dampening", "val": " = 0"}, {"name": "weight_decay", "val": " = 0"}, {"name": "nesterov", "val": " = False"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "percentile_clipping", "val": " = 100"}, {"name": "max_unorm", "val": " = 0.02"}]- params (torch.tensor) -- The input parameters to optimize.

• lr (float) -- The learning rate.
• momentum (float, defaults to 0) -- The momentum value speeds up the optimizer by taking bigger steps.
• dampening (float, defaults to 0) -- The dampening value reduces the momentum of the optimizer.
• weight_decay (float, defaults to 1e-2) -- The weight decay value for the optimizer.
• nesterov (bool, defaults to False) -- Whether to use Nesterov momentum.
• 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.
• percentile_clipping (int, defaults to 100) -- Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability.
• max_unorm (float, defaults to 0.02) -- The maximum gradient norm.0

32-bit LARS optimizer.

Parameters:

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

lr (float) : The learning rate.

momentum (float, defaults to 0) : The momentum value speeds up the optimizer by taking bigger steps.

dampening (float, defaults to 0) : The dampening value reduces the momentum of the optimizer.

weight_decay (float, defaults to 1e-2) : The weight decay value for the optimizer.

nesterov (bool, defaults to False) : Whether to use Nesterov momentum.

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.

percentile_clipping (int, defaults to 100) : Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability.

max_unorm (float, defaults to 0.02) : The maximum gradient norm.