torch.optim.radam — PyTorch 2.7 documentation (original) (raw)
mypy: allow-untyped-defs
r"""Implementation for the RAdam algorithm.""" from typing import cast, Optional, Union
import torch from torch import Tensor
from .optimizer import ( _capturable_doc, _default_to_fused_or_foreach, _differentiable_doc, _disable_dynamo_if_unsupported, _foreach_doc, _get_capturable_supported_devices, _get_scalar_dtype, _get_value, _maximize_doc, _params_doc, _use_grad_for_differentiable, _view_as_real, Optimizer, ParamsT, )
all = ["RAdam", "radam"]
[docs]class RAdam(Optimizer): # noqa: D101 def init( self, params: ParamsT, lr: Union[float, Tensor] = 1e-3, betas: tuple[float, float] = (0.9, 0.999), eps: float = 1e-8, weight_decay: float = 0, decoupled_weight_decay: bool = False, *, foreach: Optional[bool] = None, maximize: bool = False, capturable: bool = False, differentiable: bool = False, ): # noqa: D107 if isinstance(lr, Tensor) and lr.numel() != 1: raise ValueError("Tensor lr must be 1-element") if not 0.0 <= lr: raise ValueError(f"Invalid learning rate: {lr}") if not 0.0 <= eps: raise ValueError(f"Invalid epsilon value: {eps}") if not 0.0 <= betas[0] < 1.0: raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}") if not 0.0 <= betas[1] < 1.0: raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}") if not 0.0 <= weight_decay: raise ValueError(f"Invalid weight_decay value: {weight_decay}")
defaults = dict(
lr=lr,
betas=betas,
eps=eps,
weight_decay=weight_decay,
maximize=maximize,
foreach=foreach,
capturable=capturable,
decoupled_weight_decay=decoupled_weight_decay,
differentiable=differentiable,
)
super().__init__(params, defaults)
def __setstate__(self, state): # noqa: D105
super().__setstate__(state)
for group in self.param_groups:
group.setdefault("foreach", None)
group.setdefault("maximize", False)
group.setdefault("differentiable", False)
group.setdefault("decoupled_weight_decay", False)
group.setdefault("capturable", False)
for p in group["params"]:
p_state = self.state.get(p, [])
if len(p_state) != 0 and not torch.is_tensor(p_state["step"]):
step_val = float(p_state["step"])
p_state["step"] = (
torch.tensor(
step_val, dtype=_get_scalar_dtype(), device=p.device
)
if group["capturable"]
else torch.tensor(step_val, dtype=_get_scalar_dtype())
)
def _init_group(
self, group, params_with_grad, grads, exp_avgs, exp_avg_sqs, state_steps
):
has_complex = False
for p in group["params"]:
if p.grad is not None:
has_complex |= torch.is_complex(p)
params_with_grad.append(p)
if p.grad.is_sparse:
raise RuntimeError("RAdam does not support sparse gradients")
grads.append(p.grad)
state = self.state[p]
# Lazy state initialization
if len(state) == 0:
state["step"] = (
torch.zeros((), dtype=_get_scalar_dtype(), device=p.device)
if group["capturable"]
else torch.tensor(0.0, dtype=_get_scalar_dtype())
)
# Exponential moving average of gradient values
state["exp_avg"] = torch.zeros_like(
p, memory_format=torch.preserve_format
)
# Exponential moving average of squared gradient values
state["exp_avg_sq"] = torch.zeros_like(
p, memory_format=torch.preserve_format
)
exp_avgs.append(state["exp_avg"])
exp_avg_sqs.append(state["exp_avg_sq"])
state_steps.append(state["step"])
return has_complex[docs] @_use_grad_for_differentiable def step(self, closure=None): """Perform a single optimization step.
Args:
closure (Callable, optional): A closure that reevaluates the model
and returns the loss.
"""
self._cuda_graph_capture_health_check()
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
params_with_grad: list[Tensor] = []
grads: list[Tensor] = []
exp_avgs: list[Tensor] = []
exp_avg_sqs: list[Tensor] = []
state_steps: list[Tensor] = []
beta1, beta2 = cast(tuple[float, float], group["betas"])
has_complex = self._init_group(
group, params_with_grad, grads, exp_avgs, exp_avg_sqs, state_steps
)
radam(
params_with_grad,
grads,
exp_avgs,
exp_avg_sqs,
state_steps,
beta1=beta1,
beta2=beta2,
lr=group["lr"],
weight_decay=group["weight_decay"],
eps=group["eps"],
maximize=group["maximize"],
foreach=group["foreach"],
capturable=group["capturable"],
differentiable=group["differentiable"],
decoupled_weight_decay=group["decoupled_weight_decay"],
has_complex=has_complex,
)
return lossRAdam.doc = ( r"""Implements RAdam algorithm.
.. math::
\begin{aligned}
&\rule{110mm}{0.4pt} \\
&\textbf{input} : \gamma \text{ (lr)}, \: \beta_1, \beta_2
\text{ (betas)}, \: \theta_0 \text{ (params)}, \:f(\theta) \text{ (objective)}, \:
\lambda \text{ (weightdecay)}, \:\textit{maximize} \\
&\hspace{13mm} \epsilon \text{ (epsilon)}, \textit{decoupled\_weight\_decay} \\
&\textbf{initialize} : m_0 \leftarrow 0 \text{ ( first moment)},
v_0 \leftarrow 0 \text{ ( second moment)}, \\
&\hspace{18mm} \rho_{\infty} \leftarrow 2/(1-\beta_2) -1 \\[-1.ex]
&\rule{110mm}{0.4pt} \\
&\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\
&\hspace{6mm}\textbf{if} \: \textit{maximize}: \\
&\hspace{12mm}g_t \leftarrow -\nabla_{\theta} f_t (\theta_{t-1}) \\
&\hspace{6mm}\textbf{else} \\
&\hspace{12mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\
&\hspace{6mm} \theta_t \leftarrow \theta_{t-1} \\
&\hspace{6mm} \textbf{if} \: \lambda \neq 0 \\
&\hspace{12mm}\textbf{if} \: \textit{decoupled\_weight\_decay} \\
&\hspace{18mm} \theta_t \leftarrow \theta_{t} - \gamma \lambda \theta_{t} \\
&\hspace{12mm}\textbf{else} \\
&\hspace{18mm} g_t \leftarrow g_t + \lambda \theta_{t} \\
&\hspace{6mm}m_t \leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t \\
&\hspace{6mm}v_t \leftarrow \beta_2 v_{t-1} + (1-\beta_2) g^2_t \\
&\hspace{6mm}\widehat{m_t} \leftarrow m_t/\big(1-\beta_1^t \big) \\
&\hspace{6mm}\rho_t \leftarrow \rho_{\infty} -
2 t \beta^t_2 /\big(1-\beta_2^t \big) \\[0.1.ex]
&\hspace{6mm}\textbf{if} \: \rho_t > 5 \\
&\hspace{12mm} l_t \leftarrow \frac{\sqrt{ (1-\beta^t_2) }}{ \sqrt{v_t} +\epsilon } \\
&\hspace{12mm} r_t \leftarrow
\sqrt{\frac{(\rho_t-4)(\rho_t-2)\rho_{\infty}}{(\rho_{\infty}-4)(\rho_{\infty}-2) \rho_t}} \\
&\hspace{12mm}\theta_t \leftarrow \theta_t - \gamma \widehat{m_t} r_t l_t \\
&\hspace{6mm}\textbf{else} \\
&\hspace{12mm}\theta_t \leftarrow \theta_t - \gamma \widehat{m_t} \\
&\rule{110mm}{0.4pt} \\[-1.ex]
&\bf{return} \: \theta_t \\[-1.ex]
&\rule{110mm}{0.4pt} \\[-1.ex]
\end{aligned}
For further details regarding the algorithm we refer to `On the variance of the adaptive learning rate and beyond`_.
This implementation provides an option to use either the original weight_decay implementation as in Adam
(where the weight_decay is applied to the gradient) or the one from AdamW (where weight_decay is applied
to the weight) through the decoupled_weight_decay option. When decoupled_weight_decay is set to False
(default), it uses the original Adam style weight decay, otherwise, it uses the AdamW style which
corresponds more closely to the `author's implementation`_ in the RAdam paper. Further information
about decoupled weight decay can be found in `Decoupled Weight Decay Regularization`_.
"""
+ rf"""
Args:
{_params_doc}
lr (float, Tensor, 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)
decoupled_weight_decay (bool, optional): whether to decouple the weight
decay as in AdamW to obtain RAdamW. If True, the algorithm does not
accumulate weight decay in the momentum nor variance. (default: False)
{_foreach_doc}
{_maximize_doc}
{_capturable_doc}
{_differentiable_doc}
.. _On the variance of the adaptive learning rate and beyond:
https://arxiv.org/abs/1908.03265
.. _author's implementation:
https://github.com/LiyuanLucasLiu/RAdam
.. _Decoupled Weight Decay Regularization:
https://arxiv.org/abs/1711.05101
""")
def _single_tensor_radam( params: list[Tensor], grads: list[Tensor], exp_avgs: list[Tensor], exp_avg_sqs: list[Tensor], state_steps: list[Tensor], *, beta1: float, beta2: float, lr: float, weight_decay: float, eps: float, decoupled_weight_decay: bool, differentiable: bool, maximize: bool, capturable: bool, has_complex: bool, ): for i, param in enumerate(params): grad = grads[i] if not maximize else -grads[i] exp_avg = exp_avgs[i] exp_avg_sq = exp_avg_sqs[i] step_t = state_steps[i]
# If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
if not torch.compiler.is_compiling() and capturable:
capturable_supported_devices = _get_capturable_supported_devices()
assert (
param.device.type == step_t.device.type
and param.device.type in capturable_supported_devices
), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}."
if torch.is_complex(param):
param = torch.view_as_real(param)
grad = torch.view_as_real(grad)
exp_avg = torch.view_as_real(exp_avg)
exp_avg_sq = torch.view_as_real(exp_avg_sq)
# update step
step_t += 1
step = step_t if capturable else _get_value(step_t)
if weight_decay != 0:
if decoupled_weight_decay:
param.mul_(1 - lr * weight_decay)
else:
grad = grad.add(param, alpha=weight_decay)
# Decay the first and second moment running average coefficient
exp_avg.lerp_(grad, 1 - beta1)
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
bias_correction1 = 1 - beta1**step
bias_correction2 = 1 - beta2**step
# correcting bias for the first moving moment
bias_corrected_exp_avg = exp_avg / bias_correction1
# maximum length of the approximated SMA
rho_inf = 2 / (1 - beta2) - 1
# compute the length of the approximated SMA
rho_t = rho_inf - 2 * step * (beta2**step) / bias_correction2
def _compute_rect():
return (
(rho_t - 4)
* (rho_t - 2)
* rho_inf
/ ((rho_inf - 4) * (rho_inf - 2) * rho_t)
) ** 0.5
def _compute_adaptive_lr():
exp_avg_sq_sqrt = exp_avg_sq.sqrt()
if differentiable:
exp_avg_sq_sqrt = exp_avg_sq_sqrt.add(eps)
else:
exp_avg_sq_sqrt = exp_avg_sq_sqrt.add_(eps)
return (bias_correction2**0.5) / exp_avg_sq_sqrt
# Compute the variance rectification term and update parameters accordingly
if capturable:
update = torch.where(
rho_t > 5.0, _compute_rect() * _compute_adaptive_lr(), 1.0
)
param.add_(bias_corrected_exp_avg * lr * update, alpha=-1.0)
else:
if rho_t > 5.0:
param.add_(
bias_corrected_exp_avg
* lr
* _compute_adaptive_lr()
* _compute_rect(),
alpha=-1.0,
)
else:
param.add_(bias_corrected_exp_avg * lr, alpha=-1.0)def _multi_tensor_radam( params: list[Tensor], grads: list[Tensor], exp_avgs: list[Tensor], exp_avg_sqs: list[Tensor], state_steps: list[Tensor], *, beta1: float, beta2: float, lr: float, weight_decay: float, eps: float, decoupled_weight_decay: bool, differentiable: bool, maximize: bool, capturable: bool, has_complex: bool, ): if len(params) == 0: return
assert not differentiable, "_foreach ops don't support autograd"
# If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
if not torch.compiler.is_compiling() and capturable:
capturable_supported_devices = _get_capturable_supported_devices(
supports_xla=False
)
assert all(
p.device.type == step.device.type
and p.device.type in capturable_supported_devices
for p, step in zip(params, state_steps)
), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}."
grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
[params, grads, exp_avgs, exp_avg_sqs, state_steps] # type: ignore[list-item]
)
for (
grouped_params_,
grouped_grads_,
grouped_exp_avgs_,
grouped_exp_avg_sqs_,
grouped_state_steps_,
), _ in grouped_tensors.values():
grouped_params = cast(list[Tensor], grouped_params_)
grouped_grads = cast(list[Tensor], grouped_grads_)
grouped_exp_avgs = cast(list[Tensor], grouped_exp_avgs_)
grouped_exp_avg_sqs = cast(list[Tensor], grouped_exp_avg_sqs_)
grouped_state_steps = cast(list[Tensor], grouped_state_steps_)
# Update steps
# If steps are on CPU, foreach will fall back to the slow path, which is a for-loop calling t.add(1) over
# and over. 1 will then be wrapped into a Tensor over and over again, which is slower than if we just
# wrapped it once now. The alpha is required to assure we go to the right overload.
if not torch.compiler.is_compiling() and grouped_state_steps[0].is_cpu:
torch._foreach_add_(
grouped_state_steps, torch.tensor(1.0, device="cpu"), alpha=1.0
)
else:
torch._foreach_add_(grouped_state_steps, 1)
if has_complex:
_view_as_real(
grouped_params, grouped_grads, grouped_exp_avgs, grouped_exp_avg_sqs
)
if maximize:
grouped_grads = torch._foreach_neg(grouped_grads) # type: ignore[assignment]
# maximum length of the approximated SMA
rho_inf = 2 / (1 - beta2) - 1
# compute the length of the approximated SMA
bias_correction1: Union[tuple[Tensor, ...], list[Tensor]]
bias_correction2: Union[tuple[Tensor, ...], list[Tensor]]
rho_t_list: Union[tuple[Tensor, ...], list[Tensor]]
if capturable:
bias_correction1 = torch._foreach_pow(beta2, grouped_state_steps)
torch._foreach_neg_(bias_correction1)
torch._foreach_add_(bias_correction1, 1)
bias_correction2 = torch._foreach_pow(beta2, grouped_state_steps)
torch._foreach_mul_(bias_correction2, grouped_state_steps)
torch._foreach_mul_(bias_correction2, 2)
torch._foreach_div_(bias_correction2, bias_correction1)
torch._foreach_neg_(bias_correction2)
torch._foreach_add_(bias_correction2, rho_inf)
rho_t_list = bias_correction2
else:
rho_t_list = [
rho_inf
- 2
* _get_value(step)
* (beta2 ** _get_value(step))
/ (1 - beta2 ** _get_value(step))
for step in grouped_state_steps
]
if weight_decay != 0:
if decoupled_weight_decay:
torch._foreach_mul_(grouped_params, 1 - lr * weight_decay)
else:
# Re-use the intermediate memory (grouped_grads) already allocated for maximize
if maximize:
torch._foreach_add_(
grouped_grads, grouped_params, alpha=weight_decay
)
else:
grouped_grads = torch._foreach_add( # type: ignore[assignment]
grouped_grads, grouped_params, alpha=weight_decay
)
# Decay the first and second moment running average coefficient
torch._foreach_lerp_(grouped_exp_avgs, grouped_grads, 1 - beta1)
torch._foreach_mul_(grouped_exp_avg_sqs, beta2)
torch._foreach_addcmul_(
grouped_exp_avg_sqs, grouped_grads, grouped_grads, 1 - beta2
)
# Delete the local intermediate since it won't be used anymore to save on peak memory
del grouped_grads
if capturable:
num = torch._foreach_sub(rho_t_list, 4)
sub2 = torch._foreach_sub(rho_t_list, 2)
torch._foreach_mul_(num, sub2)
del sub2
torch._foreach_mul_(num, rho_inf)
rho_inf = (rho_inf - 4) * (rho_inf - 2)
denom = torch._foreach_mul(rho_t_list, rho_inf)
torch._foreach_div_(num, denom)
del denom
torch._foreach_sqrt_(num)
# TODO(mlazos): we should try and get a foreach_where op https://github.com/pytorch/pytorch/issues/117884
rect = [
torch.where(rho_t > 5.0, n, 0.0) for n, rho_t in zip(num, rho_t_list)
]
del num
del rho_t_list
unrect_step_size = [torch.where(rect > 0, 0.0, 1.0) for rect in rect]
torch._foreach_mul_(unrect_step_size, lr)
bias_correction1 = torch._foreach_pow(beta1, grouped_state_steps)
torch._foreach_neg_(bias_correction1)
torch._foreach_add_(bias_correction1, 1)
torch._foreach_div_(unrect_step_size, bias_correction1)
torch._foreach_neg_(unrect_step_size)
bias_correction2 = torch._foreach_pow(beta2, grouped_state_steps)
torch._foreach_neg_(bias_correction2)
torch._foreach_add_(bias_correction2, 1)
torch._foreach_sqrt_(bias_correction2)
torch._foreach_mul_(bias_correction2, lr)
torch._foreach_mul_(bias_correction2, rect)
del rect
torch._foreach_neg_(bias_correction2)
torch._foreach_div_(bias_correction2, bias_correction1)
del bias_correction1
else:
rect = [
( # type: ignore[misc]
(rho_t - 4) # type: ignore[arg-type]
* (rho_t - 2)
* rho_inf
/ ((rho_inf - 4) * (rho_inf - 2) * rho_t)
)
** 0.5
if rho_t > 5
else 0
for rho_t in rho_t_list
]
unrectified = [0 if rect > 0 else 1.0 for rect in rect]
bias_correction1 = [
1 - beta1 ** _get_value(step) for step in grouped_state_steps
]
unrect_step_size = [
(lr * rect / bc) * -1 for rect, bc in zip(unrectified, bias_correction1)
]
bias_correction2 = [
((1 - beta2 ** _get_value(step)) ** 0.5) * (lr * rect / bc) * -1
for step, rect, bc in zip(grouped_state_steps, rect, bias_correction1)
]
buffer = torch._foreach_sqrt(grouped_exp_avg_sqs)
torch._foreach_add_(buffer, eps)
torch._foreach_div_(buffer, bias_correction2)
torch._foreach_reciprocal_(buffer)
torch._foreach_add_(buffer, unrect_step_size)
# Here, buffer = sqrt(1 - beta2^t) * rect_step_size / (sqrt(v) + eps) + unrect_step_size
torch._foreach_addcmul_(grouped_params, grouped_exp_avgs, buffer)@_disable_dynamo_if_unsupported(single_tensor_fn=_single_tensor_radam) def radam( params: list[Tensor], grads: list[Tensor], exp_avgs: list[Tensor], exp_avg_sqs: list[Tensor], state_steps: list[Tensor], # kwonly args with defaults are not supported by functions compiled with torchscript issue #70627 # setting this as kwarg for now as functional API is compiled by torch/distributed/optim decoupled_weight_decay: bool = False, foreach: Optional[bool] = None, differentiable: bool = False, capturable: bool = False, has_complex: bool = False, maximize: bool = False, *, beta1: float, beta2: float, lr: float, weight_decay: float, eps: float, ): r"""Functional API that performs RAdam algorithm computation.
See :class:`~torch.optim.RAdam` for details.
"""
if not all(isinstance(t, torch.Tensor) for t in state_steps):
raise RuntimeError(
"API has changed, `state_steps` argument must contain a list of singleton tensors"
)
if foreach is None:
_, foreach = _default_to_fused_or_foreach(
params, differentiable, use_fused=False
)
if foreach and torch.jit.is_scripting():
raise RuntimeError("torch.jit.script not supported with foreach optimizers")
if foreach and not torch.jit.is_scripting():
func = _multi_tensor_radam
else:
func = _single_tensor_radam
func(
params,
grads,
exp_avgs,
exp_avg_sqs,
state_steps,
beta1=beta1,
beta2=beta2,
lr=lr,
weight_decay=weight_decay,
eps=eps,
maximize=maximize,
decoupled_weight_decay=decoupled_weight_decay,
differentiable=differentiable,
capturable=capturable,
has_complex=has_complex,
)