torch.optim.adamax — PyTorch 2.7 documentation (original) (raw)
mypy: allow-untyped-defs
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 = ["Adamax", "adamax"]
[docs]class Adamax(Optimizer): def init( self, params: ParamsT, lr: Union[float, Tensor] = 2e-3, betas: tuple[float, float] = (0.9, 0.999), eps: float = 1e-8, weight_decay: float = 0, foreach: Optional[bool] = None, *, maximize: bool = False, differentiable: bool = False, capturable: bool = False, ): 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,
foreach=foreach,
maximize=maximize,
differentiable=differentiable,
capturable=capturable,
)
super().__init__(params, defaults)
def __setstate__(self, state):
super().__setstate__(state)
for group in self.param_groups:
group.setdefault("foreach", None)
group.setdefault("maximize", False)
group.setdefault("differentiable", 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_infs, state_steps
):
has_complex = False
for p in group["params"]:
if p.grad is None:
continue
has_complex |= torch.is_complex(p)
params_with_grad.append(p)
if p.grad.is_sparse:
raise RuntimeError("Adamax does not support sparse gradients")
grads.append(p.grad)
state = self.state[p]
# 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())
)
state["exp_avg"] = torch.zeros_like(
p, memory_format=torch.preserve_format
)
state["exp_inf"] = torch.zeros_like(
p, memory_format=torch.preserve_format
)
exp_avgs.append(state["exp_avg"])
exp_infs.append(state["exp_inf"])
state_steps.append(state["step"])
return has_complex[docs] @_use_grad_for_differentiable 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: list[Tensor] = []
grads: list[Tensor] = []
exp_avgs: list[Tensor] = []
exp_infs: list[Tensor] = []
state_steps: list[Tensor] = []
beta1, beta2 = group["betas"]
eps = group["eps"]
lr = group["lr"]
weight_decay = group["weight_decay"]
foreach = group["foreach"]
maximize = group["maximize"]
differentiable = group["differentiable"]
capturable = group["capturable"]
has_complex = self._init_group(
group, params_with_grad, grads, exp_avgs, exp_infs, state_steps
)
adamax(
params_with_grad,
grads,
exp_avgs,
exp_infs,
state_steps,
eps=eps,
beta1=beta1,
beta2=beta2,
lr=lr,
weight_decay=weight_decay,
foreach=foreach,
maximize=maximize,
differentiable=differentiable,
capturable=capturable,
has_complex=has_complex,
)
return lossAdamax.doc = ( r"""Implements Adamax algorithm (a variant of Adam based on infinity norm).
.. 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{ (weight decay)}, \\
&\hspace{13mm} \epsilon \text{ (epsilon)} \\
&\textbf{initialize} : m_0 \leftarrow 0 \text{ ( first moment)},
u_0 \leftarrow 0 \text{ ( infinity norm)} \\[-1.ex]
&\rule{110mm}{0.4pt} \\
&\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\
&\hspace{5mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\
&\hspace{5mm}if \: \lambda \neq 0 \\
&\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\
&\hspace{5mm}m_t \leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t \\
&\hspace{5mm}u_t \leftarrow \mathrm{max}(\beta_2 u_{t-1}, |g_{t}|+\epsilon) \\
&\hspace{5mm}\theta_t \leftarrow \theta_{t-1} - \frac{\gamma m_t}{(1-\beta^t_1) u_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 `Adam: A Method for Stochastic Optimization`_.
"""
+ rf"""
Args:
{_params_doc}
lr (float, Tensor, optional): learning rate (default: 2e-3)
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square
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)
{_foreach_doc}
{_maximize_doc}
{_differentiable_doc}
{_capturable_doc}
.. _Adam\: A Method for Stochastic Optimization:
https://arxiv.org/abs/1412.6980
""")
def _single_tensor_adamax( params: list[Tensor], grads: list[Tensor], exp_avgs: list[Tensor], exp_infs: list[Tensor], state_steps: list[Tensor], *, eps: float, beta1: float, beta2: float, lr: float, weight_decay: float, maximize: bool, differentiable: bool, capturable: bool, has_complex: bool, ): for i, param in enumerate(params): grad = grads[i] grad = grad if not maximize else -grad exp_avg = exp_avgs[i] exp_inf = exp_infs[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}."
# update step
step_t += 1
if weight_decay != 0:
grad = grad.add(param, alpha=weight_decay)
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_inf = torch.view_as_real(exp_inf)
# Update biased first moment estimate.
exp_avg.lerp_(grad, 1 - beta1)
# Update the exponentially weighted infinity norm.
if not differentiable:
torch.maximum(
exp_inf.mul_(beta2),
grad.abs().add_(eps),
out=exp_inf,
)
else:
norm_buf = torch.cat(
[exp_inf.mul_(beta2).unsqueeze(0), grad.abs().add_(eps).unsqueeze_(0)],
0,
)
exp_inf.copy_(torch.amax(norm_buf, 0, keepdim=False))
if capturable:
# why jump through extra hoops and negate bias_correction? check out #121238
# once fixed, we should use bias_correction with addcdiv value=-1 for readability
neg_bias_correction = beta1**step_t - 1
neg_bias_correction.div_(lr)
denom = exp_inf * neg_bias_correction
param.addcdiv_(exp_avg, denom)
else:
bias_correction = 1 - beta1 ** _get_value(step_t)
clr = lr / bias_correction
param.addcdiv_(exp_avg, exp_inf, value=-clr)def _multi_tensor_adamax( params: list[Tensor], grads: list[Tensor], exp_avgs: list[Tensor], exp_infs: list[Tensor], state_steps: list[Tensor], *, eps: float, beta1: float, beta2: float, lr: float, weight_decay: float, maximize: bool, differentiable: bool, capturable: bool, has_complex: bool, ): assert not differentiable, "_foreach ops don't support autograd"
if len(params) == 0:
return
# 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_infs, state_steps] # type: ignore[list-item]
)
for (
grouped_params_,
grouped_grads_,
grouped_exp_avgs_,
grouped_exp_infs_,
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_infs = cast(list[Tensor], grouped_exp_infs_)
grouped_state_steps = cast(list[Tensor], grouped_state_steps_)
if has_complex:
_view_as_real(
grouped_params, grouped_grads, grouped_exp_avgs, grouped_exp_infs
)
if maximize:
grouped_grads = torch._foreach_neg(grouped_grads) # type: ignore[assignment]
# 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 weight_decay != 0:
if maximize:
# Re-use the intermediate memory (grouped_grads) already allocated for 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
)
# Update biased first moment estimate.
torch._foreach_lerp_(grouped_exp_avgs, grouped_grads, 1 - beta1)
# Update the exponentially weighted infinity norm.
torch._foreach_mul_(grouped_exp_infs, beta2)
# in this case, we need to introduce a copy of the grads
# since one has not been introduced previously
if not maximize and weight_decay == 0:
grouped_grads = torch._foreach_abs(grouped_grads) # type: ignore[assignment]
else:
torch._foreach_abs_(grouped_grads)
torch._foreach_add_(grouped_grads, eps)
torch._foreach_maximum_(grouped_exp_infs, grouped_grads)
bias_corrections: Union[tuple[Tensor, ...], list[Tensor]]
if capturable:
bias_corrections = torch._foreach_pow(beta1, grouped_state_steps)
# foreach_sub doesn't allow a scalar as the first arg
torch._foreach_sub_(bias_corrections, 1)
torch._foreach_div_(bias_corrections, lr)
denom = torch._foreach_mul(grouped_exp_infs, bias_corrections)
torch._foreach_addcdiv_(grouped_params, grouped_exp_avgs, denom)
else:
bias_corrections = [
1 - beta1 ** _get_value(step) for step in grouped_state_steps
]
step_size = [(_get_value(lr) / bc) * -1 for bc in bias_corrections]
torch._foreach_addcdiv_(
grouped_params, grouped_exp_avgs, grouped_exp_infs, step_size
)@_disable_dynamo_if_unsupported(single_tensor_fn=_single_tensor_adamax) def adamax( params: list[Tensor], grads: list[Tensor], exp_avgs: list[Tensor], exp_infs: 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 foreach: Optional[bool] = None, maximize: bool = False, differentiable: bool = False, capturable: bool = False, has_complex: bool = False, *, eps: float, beta1: float, beta2: float, lr: float, weight_decay: float, ): r"""Functional API that performs adamax algorithm computation.
See :class:`~torch.optim.Adamax` for details.
"""
if not torch.compiler.is_compiling() and 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_adamax
else:
func = _single_tensor_adamax
func(
params,
grads,
exp_avgs,
exp_infs,
state_steps,
eps=eps,
beta1=beta1,
beta2=beta2,
lr=lr,
weight_decay=weight_decay,
maximize=maximize,
differentiable=differentiable,
has_complex=has_complex,
capturable=capturable,
)