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from typing import List, Optional | |
import torch | |
from torch import Tensor | |
from .optimizer import ( | |
Optimizer, | |
_default_to_fused_or_foreach, | |
_differentiable_doc, | |
_capturable_doc, | |
_dispatch_sqrt, | |
_foreach_doc, | |
_get_scalar_dtype, | |
_get_value, | |
_use_grad_for_differentiable, | |
_view_as_real, | |
) | |
__all__ = ["RAdam", "radam"] | |
class RAdam(Optimizer): | |
def __init__( | |
self, | |
params, | |
lr=1e-3, | |
betas=(0.9, 0.999), | |
eps=1e-8, | |
weight_decay=0, | |
decoupled_weight_decay: bool = False, | |
*, | |
foreach: Optional[bool] = None, | |
capturable: bool = False, | |
differentiable: bool = False, | |
): | |
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, | |
foreach=foreach, | |
capturable=capturable, | |
decoupled_weight_decay=decoupled_weight_decay, | |
differentiable=differentiable, | |
) | |
super().__init__(params, defaults) | |
def __setstate__(self, state): | |
super().__setstate__(state) | |
for group in self.param_groups: | |
group.setdefault("foreach", None) | |
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 | |
def step(self, closure=None): | |
"""Performs 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 = [] | |
grads = [] | |
exp_avgs = [] | |
exp_avg_sqs = [] | |
state_steps = [] | |
beta1, beta2 = 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"], | |
foreach=group["foreach"], | |
capturable=group["capturable"], | |
differentiable=group["differentiable"], | |
decoupled_weight_decay=group["decoupled_weight_decay"], | |
has_complex=has_complex, | |
) | |
return loss | |
RAdam.__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)}, \\ | |
&\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} 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`_. | |
""" + fr""" | |
Args: | |
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) | |
decoupled_weight_decay (bool, optional): whether to use decoupled weight | |
decay as in AdamW to obtain RAdamW (default: False) | |
{_foreach_doc} | |
{_differentiable_doc} | |
{_capturable_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 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, | |
*, | |
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, | |
decoupled_weight_decay=decoupled_weight_decay, | |
differentiable=differentiable, | |
capturable=capturable, | |
has_complex=has_complex, | |
) | |
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, | |
differentiable: bool, | |
decoupled_weight_decay: bool, | |
capturable: bool, | |
has_complex: bool, | |
): | |
for i, param in enumerate(params): | |
grad = 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._utils.is_compiling() and capturable: | |
assert (param.is_cuda and step_t.is_cuda) or ( | |
param.is_xla and step_t.is_xla | |
), "If capturable=True, params and state_steps must be CUDA or XLA tensors." | |
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, | |
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._utils.is_compiling() and capturable: | |
assert all(p.is_cuda and step.is_cuda for p, step in zip(params, state_steps)), \ | |
"If capturable=True, params and state_steps must be CUDA tensors." | |
grouped_tensors = Optimizer._group_tensors_by_device_and_dtype([params, grads, exp_avgs, exp_avg_sqs, state_steps]) | |
for (( | |
grouped_params, | |
grouped_grads, | |
grouped_exp_avgs, | |
grouped_exp_avg_sqs, | |
grouped_state_steps, | |
), _) in grouped_tensors.values(): | |
# 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 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) | |
# maximum length of the approximated SMA | |
rho_inf = 2 / (1 - beta2) - 1 | |
# compute the length of the approximated SMA | |
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: | |
grouped_grads = torch._foreach_add(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 = [ | |
_dispatch_sqrt( | |
(rho_t - 4) | |
* (rho_t - 2) | |
* rho_inf | |
/ ((rho_inf - 4) * (rho_inf - 2) * rho_t) | |
) | |
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 = [ | |
_dispatch_sqrt(1 - beta2 ** _get_value(step)) * (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) | |