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.
( params lr momentum = 0 dampening = 0 weight_decay = 0 nesterov = False optim_bits = 32 args = None min_8bit_size = 4096 percentile_clipping = 100 max_unorm = 0.02 )
( params lr momentum = 0 dampening = 0 weight_decay = 0 nesterov = False optim_bits = 32 args = None min_8bit_size = 4096 percentile_clipping = 100 max_unorm = 0.02 )
Parameters
torch.tensor
) —
The input parameters to optimize. float
) —
The learning rate. float
, defaults to 0) —
The momentum value speeds up the optimizer by taking bigger steps. float
, defaults to 0) —
The dampening value reduces the momentum of the optimizer. float
, defaults to 1e-2) —
The weight decay value for the optimizer. bool
, defaults to False
) —
Whether to use Nesterov momentum. int
, defaults to 32) —
The number of bits of the optimizer state. object
, defaults to None
) —
An object with additional arguments. int
, defaults to 4096) —
The minimum number of elements of the parameter tensors for 8-bit optimization. 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. float
, defaults to 0.02) —
The maximum gradient norm. Base LARS optimizer.
( params lr momentum = 0 dampening = 0 weight_decay = 0 nesterov = False args = None min_8bit_size = 4096 percentile_clipping = 100 max_unorm = 0.02 )
( params lr momentum = 0 dampening = 0 weight_decay = 0 nesterov = False args = None min_8bit_size = 4096 percentile_clipping = 100 max_unorm = 0.02 )
Parameters
torch.tensor
) —
The input parameters to optimize. float
) —
The learning rate. float
, defaults to 0) —
The momentum value speeds up the optimizer by taking bigger steps. float
, defaults to 0) —
The dampening value reduces the momentum of the optimizer. float
, defaults to 1e-2) —
The weight decay value for the optimizer. bool
, defaults to False
) —
Whether to use Nesterov momentum. object
, defaults to None
) —
An object with additional arguments. int
, defaults to 4096) —
The minimum number of elements of the parameter tensors for 8-bit optimization. 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. float
, defaults to 0.02) —
The maximum gradient norm. 8-bit LARS optimizer.
( params lr momentum = 0 dampening = 0 weight_decay = 0 nesterov = False args = None min_8bit_size = 4096 percentile_clipping = 100 max_unorm = 0.02 )
( params lr momentum = 0 dampening = 0 weight_decay = 0 nesterov = False args = None min_8bit_size = 4096 percentile_clipping = 100 max_unorm = 0.02 )
Parameters
torch.tensor
) —
The input parameters to optimize. float
) —
The learning rate. float
, defaults to 0) —
The momentum value speeds up the optimizer by taking bigger steps. float
, defaults to 0) —
The dampening value reduces the momentum of the optimizer. float
, defaults to 1e-2) —
The weight decay value for the optimizer. bool
, defaults to False
) —
Whether to use Nesterov momentum. object
, defaults to None
) —
An object with additional arguments. int
, defaults to 4096) —
The minimum number of elements of the parameter tensors for 8-bit optimization. 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. float
, defaults to 0.02) —
The maximum gradient norm. 32-bit LARS optimizer.