colossalai.nn.optimizer

class colossalai.nn.optimizer.FusedLAMB(params, lr=0.001, bias_correction=True, betas=(0.9, 0.999), eps=1e-06, weight_decay=0.01, amsgrad=False, adam_w_mode=True, grad_averaging=True, set_grad_none=True, max_grad_norm=1.0, use_nvlamb=False)

Implements LAMB algorithm.

Currently GPU-only. Requires ColossalAI to be installed via pip install -v --no-cache-dir --global-option="--cuda_ext" ./.

This version of fused LAMB implements 2 fusions.

Fusion of the LAMB update’s elementwise operations

A multi-tensor apply launch that batches the elementwise updates applied to all the model’s parameters into one or a few kernel launches.

colossalai.nn.optimizer.FusedLAMB’s usage is identical to any ordinary Pytorch optimizer

colossalai.nn.optimizer.FusedLAMB may be used with or without Amp.

LAMB was proposed in Large Batch Optimization for Deep Learning: Training BERT in 76 minutes.

Parameters

params (iterable) – iterable of parameters to optimize or dicts defining parameter groups.
lr (float, optional) – learning rate. (default: 1e-3)
betas (Tuple[float, float], optional) – coefficients used for computing running averages of gradient and its norm. (default: (0.9, 0.999))
eps (float, optional) – term added to the denominator to improve numerical stability. (default: 1e-6)
weight_decay (float, optional) – weight decay (L2 penalty) (default: 0.01)
amsgrad (boolean, optional) – whether to use the AMSGrad variant of this algorithm from the paper On the Convergence of Adam and Beyond NOT SUPPORTED now! (default: False)
adam_w_mode (boolean, optional) – Apply L2 regularization or weight decay True for decoupled weight decay(also known as AdamW) (default: True)
grad_averaging (bool, optional) – whether apply (1-beta2) to grad when calculating running averages of gradient. (default: True)
set_grad_none (bool, optional) – whether set grad to None when zero_grad() method is called. (default: True)
max_grad_norm (float, optional) – value used to clip global grad norm (default: 1.0)
use_nvlamb (boolean, optional) – Apply adaptive learning rate to 0.0 weight decay parameter (default: False)

step(closure=None)

Performs a single optimization step.

Parameters: closure (callable, optional) – A closure that reevaluates the model and returns the loss.

class colossalai.nn.optimizer.FusedAdam(params, lr=0.001, bias_correction=True, betas=(0.9, 0.999), eps=1e-08, adamw_mode=True, weight_decay=0.0, amsgrad=False, set_grad_none=True)

Implements Adam algorithm.

Currently GPU-only. Requires ColossalAI to be installed via pip install ..

This version of fused Adam implements 2 fusions.

Fusion of the Adam update’s elementwise operations

A multi-tensor apply launch that batches the elementwise updates applied to all the model’s parameters into one or a few kernel launches.

colossalai.nn.optimizer.FusedAdam may be used as a drop-in replacement for torch.optim.AdamW, or torch.optim.Adam with adamw_mode=False

colossalai.nn.optimizer.FusedAdam may be used with or without Amp.

Adam was been proposed in `Adam: A Method for Stochastic Optimization`_.

Parameters

params (iterable) – iterable of parameters to optimize or dicts defining parameter groups.
lr (float, optional) – learning rate. (default: 1e-3)
betas (Tuple[float, float], optional) – coefficients used for computing running averages of gradient and its square. (default: (0.9, 0.999))
eps (float, optional) – term added to the denominator to improve numerical stability. (default: 1e-8)
weight_decay (float, optional) – weight decay (L2 penalty) (default: 0)
amsgrad (boolean, optional) – whether to use the AMSGrad variant of this algorithm from the paper On the Convergence of Adam and Beyond (default: False) NOT SUPPORTED in FusedAdam!
adamw_mode (boolean, optional) – Apply L2 regularization or weight decay True for decoupled weight decay(also known as AdamW) (default: True)
set_grad_none (bool, optional) – whether set grad to None when zero_grad() method is called. (default: True)

step(closure=None, grads=None, output_params=None, scale=None, grad_norms=None)

Performs a single optimization step.

Parameters: closure (callable, optional) – A closure that reevaluates the model and returns the loss.

The remaining arguments are deprecated, and are only retained (for the moment) for error-checking purposes.

class colossalai.nn.optimizer.FusedSGD(params, lr=<required parameter>, momentum=0, dampening=0, weight_decay=0, nesterov=False, wd_after_momentum=False, materialize_master_grads=True, set_grad_none=False)

Implements stochastic gradient descent (optionally with momentum).

Currently GPU-only. Requires ColossalAI to be installed via pip install -v --no-cache-dir --global-option="--cuda_ext" ./.

This version of fused SGD implements 2 fusions.

Fusion of the SGD update’s elementwise operations

A multi-tensor apply launch that batches the elementwise updates applied to all the model’s parameters into one or a few kernel launches.

colossalai.nn.optimizer.FusedSGD may be used as a drop-in replacement for torch.optim.SGD

colossalai.nn.optimizer.FusedSGD may be used with or without Amp.

Nesterov momentum is based on the formula from On the importance of initialization and momentum in deep learning.

Parameters

params (iterable) – iterable of parameters to optimize or dicts defining parameter groups
lr (float) – learning rate
momentum (float, optional) – momentum factor (default: 0)
weight_decay (float, optional) – weight decay (L2 penalty) (default: 0)
dampening (float, optional) – dampening for momentum (default: 0)
nesterov (bool, optional) – enables Nesterov momentum (default: False)

Note

The implementation of SGD with Momentum/Nesterov subtly differs from Sutskever et. al. and implementations in some other frameworks. Considering the specific case of Momentum, the update can be written as

\[\begin{split}v = \rho * v + g \\ p = p - lr * v\end{split}\]

where p, g, v and \(\rho\) denote the parameters, gradient, velocity, and momentum respectively. This is in contrast to Sutskever et. al. and other frameworks which employ an update of the form

\[\begin{split}v = \rho * v + lr * g \\ p = p - v\end{split}\]

The Nesterov version is analogously modified.

step(closure=None)

Performs a single optimization step.

Parameters: closure (callable, optional) – A closure that reevaluates the model and returns the loss.

class colossalai.nn.optimizer.Lamb(params, lr=0.001, betas=(0.9, 0.999), eps=1e-06, weight_decay=0, adam=False)

Implements Lamb algorithm. It has been proposed in Large Batch Optimization for Deep Learning: Training BERT in 76 minutes.

Parameters

params (iterable) – iterable of parameters to optimize or dicts defining parameter groups
lr (float, optional) – learning rate (default: 1e-3)
betas (Tuple[float, float], optional) – coefficients used for computing running averages of gradient and its square (default: (0.9, 0.999))
eps (float, optional) – term added to the denominator to improve numerical stability (default: 1e-6)
weight_decay (float, optional) – weight decay (L2 penalty) (default: 0)
adam (bool, optional) – always use trust ratio = 1, which turns this into Adam. Useful for comparison purposes.

step(closure=None)

Performs a single optimization step.

Parameters: closure (callable, optional) – A closure that reevaluates the model and returns the loss.

class colossalai.nn.optimizer.Lars(params, lr=0.001, momentum=0, eeta=0.001, weight_decay=0, epsilon=0.0)

Implements the LARS optimizer from “Large batch training of convolutional networks”.

Parameters

params (iterable) – iterable of parameters to optimize or dicts defining parameter groups
lr (float, optional) – learning rate (default: 1e-3)
momentum (float, optional) – momentum factor (default: 0)
eeta (float, optional) – LARS coefficient as used in the paper (default: 1e-3)
weight_decay (float, optional) – weight decay (L2 penalty) (default: 0)

step(closure=None)

Performs a single optimization step.

Parameters: closure (callable, optional) – A closure that reevaluates the model and returns the loss.