Optimizer package in PyTorch API 2.0#

Emulates torch.optim

optim.ASGD#

class optim.ASGD#

ASGD optimizer implemented to conform to execution within the constraints of the Cerebras WSE, including pre-initializing optimizer state.

For more details, see https://dl.acm.org/citation.cfm?id=131098

__init__(params, lr=0.01, lambd=0.0001, alpha=0.75, t0=1000000.0, weight_decay=0, maximize: bool = False)#

preinitialize()#: Allocates tensors for the optimizer state to allow direct compilation of the model before the first step.

state_names_to_sparsify()#: Return the names of of per-parameter states that need to be sparsified when applying sparsity to the underlying parameters.

step(closure=None)#

Performs a single optimization step.

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

optim.Adadelta#

class optim.Adadelta#

Adadelta optimizer implemented to perform the required pre-initialization of the optimizer state.

__init__(params, lr=1.0, rho=0.9, eps=1e-06, weight_decay=0, maximize: bool = False)#

preinitialize()#: Allocates tensors for the optimizer state to allow direct compilation of the model before the first step.

state_names_to_sparsify()#: Return the names of of per-parameter states that need to be sparsified when applying sparsity to the underlying parameters.

step(closure: Optional[Callable] = None)#

Performs a single optimization step.

Parameters: closure – A closure that reevaluates the model and returns the loss.

optim.Adafactor#

class optim.Adafactor#

Adafactor optimizer implemented to conform to execution within the constraints of the Cerebras WSE.

__init__(params, lr, eps=(1e-30, 0.001), clip_threshold=1.0, decay_rate=- 0.8, beta1=None, weight_decay=0.0, scale_parameter=True, relative_step=False, warmup_init=False)#

preinitialize()#: Allocates tensors for the optimizer state to allow direct compilation of the model before the first step.

state_names_to_sparsify()#: Return the names of of per-parameter states that need to be sparsified when applying sparsity to the underlying parameters.

step(closure=None)#

Performs a single optimization step.

Parameters

closure (Callable, optional) – A closure that reevaluates
loss. (the model and returns the) –

optim.Adagrad#

class optim.Adagrad#

Adagrad optimizer implemented to conform to execution within the constraints of the Cerebras WSE.

Parameters

params (iterable) – iterable of parameters to optimize or dicts defining parameter groups
lr (float, optional) – learning rate (default: 1e-2)
lr_decay (float, optional) – learning rate decay (default: 0)
weight_decay (float, optional) – weight decay (L2 penalty) (default: 0)
eps (float, optional) – term added to the denominator to improve numerical stability (default: 1e-10)
maximize (bool, optional) – maximize the params based on the objective, instead of minimizing (default: False)

Adaptive Subgradient Methods for Online Learning and Stochastic Optimization: http://jmlr.org/papers/v12/duchi11a.html

__init__(params, lr=0.01, lr_decay=0, weight_decay=0, initial_accumulator_value=0, eps=1e-06, maximize: bool = False)#

preinitialize()#: Allocates tensors for the optimizer state to allow direct compilation of the model before the first step.

state_names_to_sparsify()#: Return the names of of per-parameter states that need to be sparsified when applying sparsity to the underlying parameters.

step(closure=None)#

Performs a single optimization step.

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

optim.AdamBase#

optim.AdamBase#: alias of <module ‘optim.AdamBase’ from ‘/home/docs/checkouts/readthedocs.org/user_builds/cerebras-systems-cerebras-systems-developer-documentation/checkouts/1.8.0/external/source/source_code/torch/experimental/optim/AdamBase.py’>

optim.Adam#

class optim.Adam#

Adam specific overrides to AdamBase

__init__(params: Iterable[torch.nn.Parameter], lr: float = 0.001, betas: Tuple[float, float] = (0.9, 0.999), eps: float = 1e-06, weight_decay: float = 0.0, amsgrad: bool = False)#

load_state_dict(state_dict)#

Loads the optimizer state.

Parameters: state_dict (dict) – optimizer state. Should be an object returned from a call to state_dict().

Adds checkpoint compatibility with the Adam from PyTorch

optim.AdamW#

class optim.AdamW#

AdamW specific overrides to AdamBase

__init__(params: Iterable[torch.nn.Parameter], lr: float = 0.001, betas: Tuple[float, float] = (0.9, 0.999), eps: float = 1e-06, weight_decay: float = 0.0, correct_bias: bool = True, amsgrad: bool = False)#

load_state_dict(state_dict)#

Loads the optimizer state.

Parameters: state_dict (dict) – optimizer state. Should be an object returned from a call to state_dict().

Adds checkpoint compatibility with the AdamW from HuggingFace

optim.Adamax#

class optim.Adamax#

Adamax optimizer implemented to perform the required pre-initialization of the optimizer state.

__init__(params: Iterable[torch.nn.Parameter], lr: float = 0.001, betas: Tuple[float, float] = (0.9, 0.999), eps: float = 1e-06, weight_decay: float = 0.0, maximize: bool = False)#

preinitialize()#: Allocates tensors for the optimizer state to allow direct compilation of the model before the first step.

state_names_to_sparsify()#: Return the names of of per-parameter states that need to be sparsified when applying sparsity to the underlying parameters.

step(closure: Optional[Callable] = None)#

Performs a single optimization step.

Parameters: closure – A closure that reevaluates the model and returns the loss.

optim.Lamb#

class optim.Lamb#

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-8)
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.

__init__(params, lr=0.001, betas=(0.9, 0.999), eps=1e-06, weight_decay=0, adam=False)#

preinitialize()#: Allocates tensors for the optimizer state to allow direct compilation of the model before the first step.

state_names_to_sparsify()#: Return the names of of per-parameter states that need to be sparsified when applying sparsity to the underlying parameters.

step(closure=None)#

Performs a single optimization step.

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

optim.NAdam#

class optim.NAdam#

Implements NAdam algorithm to execute within the constraints of the Cerebras WSE, including pre-initializing optimizer state.

Parameters

params (iterable) – iterable of parameters to optimize or dicts defining parameter groups
lr (float, optional) – learning rate (default: 2e-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)
momentum_decay (float, optional) – momentum momentum_decay (default: 4e-3)
foreach (bool, optional) – whether foreach implementation of optimizer is used (default: None)

For further details regarding the algorithm refer to Incorporating Nesterov Momentum into Adam: https://openreview.net/forum?id=OM0jvwB8jIp57ZJjtNEZ

__init__(params: Iterable[torch.nn.Parameter], lr: float = 0.002, betas: Tuple[float, float] = (0.9, 0.999), eps: float = 1e-08, weight_decay: float = 0, momentum_decay: float = 0.004)#

preinitialize()#: Allocates tensors for the optimizer state to allow direct compilation of the model before the first step.

state_names_to_sparsify()#: Return the names of of per-parameter states that need to be sparsified when applying sparsity to the underlying parameters.

step(closure=None)#

Performs a single optimization step.

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

optim.RAdam#

class optim.RAdam#

RAdam optimizer implemented to conform to execution within the constraints of the Cerebras WSE.

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)

__init__(params: Iterable[torch.nn.Parameter], lr: float = 0.001, betas: Tuple[float, float] = (0.9, 0.999), eps: float = 1e-06, weight_decay: float = 0.0)#

preinitialize()#: Allocates tensors for the optimizer state to allow direct compilation of the model before the first step.

state_names_to_sparsify()#: Return the names of of per-parameter states that need to be sparsified when applying sparsity to the underlying parameters.

step(closure=None)#

Performs a single optimization step.

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

optim.RMSprop#

class optim.RMSprop#

RMSprop optimizer implemented to perform the required pre-initialization of the optimizer state.

__init__(params, lr=0.01, alpha=0.99, eps=1e-08, weight_decay=0, momentum=0, centered=False)#

preinitialize()#: Allocates tensors for the optimizer state to allow direct compilation of the model before the first step.

state_names_to_sparsify()#: Return the names of of per-parameter states that need to be sparsified when applying sparsity to the underlying parameters.

step(closure=None)#

Performs a single optimization step.

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

optim.Rprop#

class optim.Rprop#

Rprop optimizer implemented to conform to execution within the constraints of the Cerebras WSE, including pre-initializing optimizer state

Parameters

params (iterable) – iterable of parameters to optimize or dicts defining parameter groups
lr (float, optional) – learning rate (default: 1e-3)
etas (Tuple[float, float], optional) – step size multipliers
step_size (Tuple[float, float], optional) – Tuple of min, max step size values. Step size is clamped to be between these values.

__init__(params: Iterable[torch.nn.Parameter], lr: float = 0.001, etas: Tuple[float, float] = (0.5, 1.2), step_sizes: Tuple[float, float] = (1e-06, 50.0))#

preinitialize()#: Allocates tensors for the optimizer state to allow direct compilation of the model before the first step.

state_names_to_sparsify()#: Return the names of of per-parameter states that need to be sparsified when applying sparsity to the underlying parameters.

step(closure=None)#

Performs a single optimization step.

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

optim.SGD#

class optim.SGD#

SGD optimizer implemented to conform to execution within the constraints of the Cerebras WSE, including pre-initializing optimizer state

__init__(params, lr, momentum=0, dampening=0, weight_decay=0, nesterov=False, maximize=False)#

preinitialize()#: Allocates tensors for the optimizer state to allow direct compilation of the model before the first step.

state_names_to_sparsify()#: Return the names of of per-parameter states that need to be sparsified when applying sparsity to the underlying parameters.

step(closure=None)#

Performs a single optimization step.

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

optim.Optimizer#

class optim.Optimizer#

The abstract Cerebras base optimizer class.

Enforces that the preinitialize method is implemented wherein the optimizer state should be initialized ahead of time

__init__(*args, enable_global_step: bool = False, **kwargs)#

increment_global_step(p)#: Increases the global steps by 1 and returns the current value of global step tensor in torch.float32 format.

load_state_dict(state_dict)#

abstract preinitialize()#: The optimizer state must be initialized ahead of time in order to capture the full compute graph in the first iteration. This method must be overriden to perform the state preinitialization

state_dict(*args, **kwargs)#

abstract state_names_to_sparsify()#: Return the names of of per-parameter states that need to be sparsified when applying sparsity to the underlying parameters.

abstract step(closure=None)#: Perform the optimizer step itself. Note, there should be no new state being created in this function. All state must be created ahead of time in preinitialize and only updated in this method.

optim functions#

optim.configure_optimizer(optimizer_type: str, params, **kwargs)#

Configures and requires an Optimizer specified using the provided optimizer type

The optimizer class’s signature is inspected and relevant parameters are extracted from the keyword arguments

Parameters

optimizer_type – The name of the optimizer to configure
params – The model parameters passed to the optimizer

optim.configure_lr_scheduler(optimizer, learning_rate)#

Configures a learning rate scheduler specified using the provided lr_scheduler type

The learning rate scheduler’s class’s signature is inspected and relevant parameters are extracted from the keyword arguments

Parameters

lr_scheduler_type – The name of the lr_scheduler to configure
optimizer – The optimizer passed to the lr_scheduler

optim.lr_scheduler#

Implementations for Cerebras specific learning rate schedulers

optim.lr_scheduler.ConstantLR#

class optim.lr_scheduler.ConstantLR#

Maintains a constant learning rate for each parameter group (no decaying).

Parameters

optimizer – The optimizer to schedule
val – The learning_rate value to maintain
total_iters – The number of steps to decay for

__init__(optimizer: torch.optim.Optimizer, learning_rate: float, total_iters: Optional[int] = None, disable_lr_steps_reset: bool = False)#

optim.lr_scheduler.PolynomialLR#

class optim.lr_scheduler.PolynomialLR#

Decays the learning rate of each parameter group using a polynomial function in the given total_iters.

This class is similar to the Pytorch PolynomialLR LRS.

Parameters

optimizer – The optimizer to schedule
initial_learning_rate – The initial learning rate.
end_learning_rate – The final learning rate
total_iters – Number of steps to perform the decay
power – Exponent to apply to “x” (as in y=mx+b), which is ratio of step completion (1 for linear) Default: 1.0 (only Linear supported at the moment)
cycle – Whether to cycle

__init__(optimizer: torch.optim.Optimizer, initial_learning_rate: float, end_learning_rate: float, total_iters: int, power: float = 1.0, cycle: bool = False, disable_lr_steps_reset: bool = False)#

optim.lr_scheduler.LinearLR#

class optim.lr_scheduler.LinearLR#

Alias for Polynomial LR scheduler with a power of 1

__init__(optimizer: torch.optim.Optimizer, initial_learning_rate: float, end_learning_rate: float, total_iters: int, cycle: bool = False, disable_lr_steps_reset: bool = False)#

optim.lr_scheduler.ExponentialLR#

class optim.lr_scheduler.ExponentialLR#

Decays the learning rate of each parameter group by decay_rate every step.

This class is similar to the Pytorch ExponentialLR LRS.

Parameters

optimizer – The optimizer to schedule
initial_learning_rate – The initial learning rate.
total_iters – Number of steps to perform the decay
decay_rate – The decay rate
staircase – If True decay the learning rate at discrete intervals

__init__(optimizer: torch.optim.Optimizer, initial_learning_rate: float, total_iters: int, decay_rate: float, staircase: bool = False, disable_lr_steps_reset: bool = False)#

optim.lr_scheduler.InverseExponentialTimeDecayLR#

class optim.lr_scheduler.InverseExponentialTimeDecayLR#

Decays the learning rate inverse-exponentially over time, as described in the Keras InverseTimeDecay class.

Parameters

optimizer – The optimizer to schedule
initial_learning_rate – The initial learning rate.
step_exponent – Exponential value.
total_iters – Number of steps to perform the decay.
decay_rate – The decay rate.
staircase – If True decay the learning rate at discrete intervals.

__init__(optimizer: torch.optim.Optimizer, initial_learning_rate: float, step_exponent: int, total_iters: int, decay_rate: float, staircase: bool = False, disable_lr_steps_reset: bool = False)#

optim.lr_scheduler.InverseSquareRootDecayLR#

class optim.lr_scheduler.InverseSquareRootDecayLR#

Decays the learning rate inverse-squareroot over time, as described in the following equation:

\[\begin{aligned} lr_t & = \frac{\text{scale}}{\sqrt{\max\{t, \text{warmup_steps}\}}}. \end{aligned}\]

Parameters

optimizer – The optimizer to schedule
initial_learning_rate – The initial learning rate.
scale – Multiplicative factor to scale the result.
warmup_steps – use initial_learning_rate for the first warmup_steps.

__init__(optimizer: torch.optim.Optimizer, initial_learning_rate: float = 1.0, scale: float = 1.0, warmup_steps: int = 1.0, disable_lr_steps_reset: bool = False)#

optim.lr_scheduler.CosineDecayLR#

class optim.lr_scheduler.CosineDecayLR#

Applies the cosine decay schedule as described in the Keras CosineDecay class.

Parameters

optimizer – The optimizer to schedule
initial_learning_rate – The initial learning rate.
end_learning_rate – The final learning rate
total_iters – Number of steps to perform the decay

__init__(optimizer: torch.optim.Optimizer, initial_learning_rate: float, end_learning_rate: float, total_iters: int, disable_lr_steps_reset: bool = False)#

optim.lr_scheduler.SequentialLR#

class optim.lr_scheduler.SequentialLR#

Receives the list of schedulers that is expected to be called sequentially during optimization process and milestone points that provides exact intervals to reflect which scheduler is supposed to be called at a given step.

This class is a wrapper around the Pytorch SequentialLR LRS.

Parameters

optimizer – Wrapped optimizer
schedulers (list) – List of chained schedulers.
milestones (list) – List of integers that reflects milestone points.
last_epoch (int) – The index of last epoch. Default: -1.

__init__(optimizer, schedulers, milestones, last_epoch=- 1)#

load_state_dict(state_dict)#

Loads the schedulers state. :param state_dict: scheduler state. Should be an object returned

from a call to state_dict().

state_dict()#

optim.lr_scheduler.PiecewiseConstantLR#

class optim.lr_scheduler.PiecewiseConstantLR#

Adjusts the learning rate to a predefined constant at each milestone and holds this value until the next milestone. Notice that such adjustment can happen simultaneously with other changes to the learning rate from outside this scheduler.

Parameters

optimizer – The optimizer to schedule
learning_rates – List of learning rates to maintain before/during each milestone.
milestones – List of step indices. Must be increasing.

__init__(optimizer, learning_rates: List[float], milestones: List[int], disable_lr_steps_reset: bool = False)#

optim.lr_scheduler.MultiStepLR#

class optim.lr_scheduler.MultiStepLR#

Decays the learning rate of each parameter group by gamma once the number of steps reaches one of the milestones. Notice that such decay can happen simultaneously with other changes to the learning rate from outside this scheduler.

This class is similar to the Pytorch MultiStepLR LRS.

Parameters

optimizer – The optimizer to schedule
initial_learning_rate – The initial learning rate.
gamma – Multiplicative factor of learning rate decay.
milestones – List of step indices. Must be increasing.

__init__(optimizer: torch.optim.Optimizer, initial_learning_rate: float, gamma: float, milestones: List[int], disable_lr_steps_reset: bool = False)#

optim.lr_scheduler.StepLR#

class optim.lr_scheduler.StepLR#

Decays the learning rate of each parameter group by gamma every step_size. Notice that such decay can happen simultaneously with other changes to the learning rate from outside this scheduler.

This class is similar to the Pytorch StepLR LRS.

Parameters

optimizer – The optimizer to schedule
initial_learning_rate – The initial learning rate.
step_size – Period of learning rate decay.
gamma – Multiplicative factor of learning rate decay.

__init__(optimizer: torch.optim.Optimizer, initial_learning_rate: float, step_size: int, gamma: float, disable_lr_steps_reset: bool = False)#

optim.lr_scheduler.CosineAnnealingLR#

class optim.lr_scheduler.CosineAnnealingLR#

Set the learning rate of each parameter group using a cosine annealing schedule, where \(\eta_{max}\) is set to the initial lr and \(T_{cur}\) is the number of steps since the last restart in SGDR:

\[\begin{split}\begin{aligned} \eta_t & = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 + \cos\left(\frac{T_{cur}}{T_{max}}\pi\right)\right), & T_{cur} \neq (2k+1)T_{max}; \\ \eta_{t+1} & = \eta_{t} + \frac{1}{2}(\eta_{max} - \eta_{min}) \left(1 - \cos\left(\frac{1}{T_{max}}\pi\right)\right), & T_{cur} = (2k+1)T_{max}. \end{aligned}\end{split}\]

Notice that because the schedule is defined recursively, the learning rate can be simultaneously modified outside this scheduler by other operators. If the learning rate is set solely by this scheduler, the learning rate at each step becomes:

\[\eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 + \cos\left(\frac{T_{cur}}{T_{max}}\pi\right)\right)\]

It has been proposed in SGDR: Stochastic Gradient Descent with Warm Restarts. Note that this only implements the cosine annealing part of SGDR, and not the restarts.

This class is similar to the Pytorch CosineAnnealingLR LRS.

Parameters

optimizer – The optimizer to schedule
initial_learning_rate – The initial learning rate.
T_max – Maximum number of iterations.
eta_min – Minimum learning rate.

__init__(optimizer: torch.optim.Optimizer, initial_learning_rate: float, T_max: int, eta_min: float = 0.0, disable_lr_steps_reset: bool = False)#

optim.lr_scheduler.LambdaLR#

class optim.lr_scheduler.LambdaLR#

Sets the learning rate of each parameter group to the initial lr times a given function (which is specified by overriding set_lr_lambda).

Parameters

optimizer – The optimizer to schedule
initial_learning_rate – The initial learning rate.

__init__(optimizer: torch.optim.Optimizer, initial_learning_rate: float, disable_lr_steps_reset: bool = False)#

set_lr_lambda()#: Sets learning lambda functions

optim.lr_scheduler.CosineAnnealingWarmRestarts#

class optim.lr_scheduler.CosineAnnealingWarmRestarts#

Set the learning rate of each parameter group using a cosine annealing schedule, where \(\eta_{max}\) is set to the initial lr, \(T_{cur}\) is the number of steps since the last restart and \(T_{i}\) is the number of steps between two warm restarts in SGDR:

\[\eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 + \cos\left(\frac{T_{cur}}{T_{i}}\pi\right)\right)\]

When \(T_{cur}=T_{i}\), set \(\eta_t = \eta_{min}\). When \(T_{cur}=0\) after restart, set \(\eta_t=\eta_{max}\).

It has been proposed in SGDR: Stochastic Gradient Descent with Warm Restarts.

This class is similar to the Pytorch CosineAnnealingWarmRestarts LRS.

Parameters

optimizer – The optimizer to schedule
initial_learning_rate – The initial learning rate.
T_0 – Number of iterations for the first restart.
T_mult – A factor increases Ti after a restart. Currently T_mult must be set to 1.0
eta_min – Minimum learning rate.

__init__(optimizer: torch.optim.Optimizer, initial_learning_rate: float, T_0: int, T_mult: int = 1, eta_min: float = 0.0, disable_lr_steps_reset: bool = False)#

optim.lr_scheduler.MultiplicativeLR#

class optim.lr_scheduler.MultiplicativeLR#

Multiply the learning rate of each parameter group by the supplied coefficient.

Parameters

optimizer – The optimizer to schedule
initial_learning_rate – The initial learning rate.
coefficient – Multiplicative factor of learning rate.

__init__(optimizer: torch.optim.Optimizer, initial_learning_rate: float, coefficient: float, disable_lr_steps_reset: bool = False)#

set_lr_lambda()#: Sets learning lambda functions

optim.lr_scheduler.ChainedScheduler#

class optim.lr_scheduler.ChainedScheduler#

Chains list of learning rate schedulers. It takes a list of chainable learning rate schedulers and performs consecutive step() functions belonging to them by just one call.

__init__(schedulers)#

load_state_dict(state_dict)#

Loads the schedulers state. :param state_dict: scheduler state. Should be an object returned

from a call to state_dict().

state_dict()#

optim.lr_scheduler.CyclicLR#

class optim.lr_scheduler.CyclicLR#

Sets the learning rate of each parameter group according to cyclical learning rate policy (CLR). The policy cycles the learning rate between two boundaries with a constant frequency, as detailed in the paper Cyclical Learning Rates for Training Neural Networks. The distance between the two boundaries can be scaled on a per-iteration or per-cycle basis.

Cyclical learning rate policy changes the learning rate after every batch. step should be called after a batch has been used for training.

This class has three built-in policies, as put forth in the paper:

“triangular”: A basic triangular cycle without amplitude scaling.
“triangular2”: A basic triangular cycle that scales initial amplitude by
half each cycle.
“exp_range”: A cycle that scales initial amplitude by
\(\text{gamma}^{\text{cycle iterations}}\) at each cycle iteration.

This class is similar to the Pytorch CyclicLR LRS.

Parameters

optimizer – The optimizer to schedule.
base_lr – Initial learning rate which is the lower boundary in the cycle.
max_lr – Upper learning rate boundaries in the cycle.
step_size_up – Number of training iterations in the increasing half of a cycle.
step_size_down – Number of training iterations in the decreasing half of a cycle.
mode – One of {‘triangular’, ‘triangular2’, ‘exp_range’}.
gamma – Constant in ‘exp_range’ scaling function: gamma**(cycle iterations).
scale_mode – {‘cycle’, ‘iterations’} Defines whether scale_fn is evaluated on cycle number or cycle iterations.

__init__(optimizer: torch.optim.Optimizer, base_lr: float, max_lr: float, step_size_up: int = 2000, step_size_down: Optional[int] = None, mode: str = 'triangular', gamma: float = 1.0, scale_mode: str = 'cycle', disable_lr_steps_reset: bool = False)#

set_scale_fn()#: Sets the scaling function

optim.lr_scheduler.OneCycleLR#

class optim.lr_scheduler.OneCycleLR#

Sets the learning rate of each parameter group according to the 1cycle learning rate policy. The 1cycle policy anneals the learning rate from an initial learning rate to some maximum learning rate and then from that maximum learning rate to some minimum learning rate much lower than the initial learning rate. This policy was initially described in the paper Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates.

This scheduler is not chainable.

This class is similar to the Pytorch OneCycleLR LRS.

Parameters

optimizer – The optimizer to schedule
initial_learning_rate – Initial learning rate. Compared with PyTorch, this is equivalent to max_lr / div_factor.
max_lr – Upper learning rate boundaries in the cycle.
total_steps – The total number of steps in the cycle.
pct_start – The percentage of the cycle (in number of steps) spent increasing the learning rate.
final_div_factor – Determines the minimum learning rate via min_lr = initial_lr/final_div_factor.
three_phase – If True, use a third phase of the schedule to annihilate the learning rate
anneal_strategy – Specifies the annealing strategy: “cos” for cosine annealing, “linear” for linear annealing.

__init__(optimizer: torch.optim.Optimizer, initial_learning_rate: float, max_lr: float, total_steps: int = 1000, pct_start: float = 0.3, final_div_factor: float = 10000.0, three_phase: bool = False, anneal_strategy: str = 'cos', disable_lr_steps_reset: bool = False)#

Port your code using Cerebras PyTorch API (Experimental)

Sample Training and Eval Scripts for Cerebras PyTorch API (Experimental)