# mypy: allow-untyped-decorators
# mypy: allow-untyped-defs
from typing import cast, List, Optional, Tuple, Union
import torch
from torch import Tensor
from .optimizer import (
_capturable_doc,
_default_to_fused_or_foreach,
_device_dtype_check_for_fused,
_differentiable_doc,
_disable_dynamo_if_unsupported,
_foreach_doc,
_fused_doc,
_get_capturable_supported_devices,
_get_scalar_dtype,
_get_value,
_maximize_doc,
_stack_if_compiling,
_use_grad_for_differentiable,
_view_as_real,
DeviceDict,
Optimizer,
ParamsT,
)
__all__ = ["AdamW", "adamw"]
class AdamW(Optimizer):
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 = 1e-2,
amsgrad: bool = False,
*,
maximize: bool = False,
foreach: Optional[bool] = None,
capturable: bool = False,
differentiable: bool = False,
fused: Optional[bool] = None,
):
if isinstance(lr, Tensor):
if foreach and not capturable:
raise ValueError(
"lr as a Tensor is not supported for capturable=False and foreach=True"
)
if 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,
amsgrad=amsgrad,
foreach=foreach,
maximize=maximize,
capturable=capturable,
differentiable=differentiable,
fused=fused,
)
super().__init__(params, defaults)
if fused:
if differentiable:
raise RuntimeError("`fused` does not support `differentiable`")
self._step_supports_amp_scaling = True
if foreach:
raise RuntimeError("`fused` and `foreach` cannot be `True` together.")
def __setstate__(self, state):
super().__setstate__(state)
for group in self.param_groups:
group.setdefault("amsgrad", False)
group.setdefault("maximize", False)
group.setdefault("foreach", None)
group.setdefault("capturable", False)
group.setdefault("differentiable", False)
fused = group.setdefault("fused", None)
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(is_fused=fused),
device=p.device,
)
if group["capturable"] or group["fused"]
else torch.tensor(step_val, dtype=_get_scalar_dtype())
)
def _init_group(
self,
group,
params_with_grad,
grads,
amsgrad,
exp_avgs,
exp_avg_sqs,
max_exp_avg_sqs,
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("AdamW does not support sparse gradients")
grads.append(p.grad)
state = self.state[p]
# State initialization
if len(state) == 0:
if group["fused"]:
_device_dtype_check_for_fused(p)
# note(crcrpar): Deliberately host `step` on CPU if both capturable and fused are off.
# This is because kernel launches are costly on CUDA and XLA.
state["step"] = (
torch.zeros(
(),
dtype=_get_scalar_dtype(is_fused=group["fused"]),
device=p.device,
)
if group["capturable"] or group["fused"]
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
)
if amsgrad:
# Maintains max of all exp. moving avg. of sq. grad. values
state["max_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"])
if group["amsgrad"]:
max_exp_avg_sqs.append(state["max_exp_avg_sq"])
if group["differentiable"] and state["step"].requires_grad:
raise RuntimeError(
"`requires_grad` is not supported for `step` in differentiable mode"
)
# Foreach without capturable does not support a tensor lr
if (
group["foreach"]
and isinstance(group["lr"], Tensor)
and not group["capturable"]
):
raise RuntimeError(
"lr as a Tensor is not supported for capturable=False and foreach=True"
)
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] = []
max_exp_avg_sqs: List[Tensor] = []
state_steps: List[Tensor] = []
amsgrad: bool = group["amsgrad"]
beta1, beta2 = cast(Tuple[float, float], group["betas"])
has_complex = self._init_group(
group,
params_with_grad,
grads,
amsgrad,
exp_avgs,
exp_avg_sqs,
max_exp_avg_sqs,
state_steps,
)
adamw(
params_with_grad,
grads,
exp_avgs,
exp_avg_sqs,
max_exp_avg_sqs,
state_steps,
amsgrad=amsgrad,
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"],
fused=group["fused"],
grad_scale=getattr(self, "grad_scale", None),
found_inf=getattr(self, "found_inf", None),
has_complex=has_complex,
)
return loss
AdamW.__doc__ = (
r"""Implements AdamW 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)},
\: \epsilon \text{ (epsilon)} \\
&\hspace{13mm} \lambda \text{(weight decay)}, \: \textit{amsgrad},
\: \textit{maximize} \\
&\textbf{initialize} : m_0 \leftarrow 0 \text{ (first moment)}, v_0 \leftarrow 0
\text{ ( second moment)}, \: \widehat{v_0}^{max}\leftarrow 0 \\[-1.ex]
&\rule{110mm}{0.4pt} \\
&\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\
&\hspace{5mm}\textbf{if} \: \textit{maximize}: \\
&\hspace{10mm}g_t \leftarrow -\nabla_{\theta} f_t (\theta_{t-1}) \\
&\hspace{5mm}\textbf{else} \\
&\hspace{10mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\
&\hspace{5mm} \theta_t \leftarrow \theta_{t-1} - \gamma \lambda \theta_{t-1} \\
&\hspace{5mm}m_t \leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t \\
&\hspace{5mm}v_t \leftarrow \beta_2 v_{t-1} + (1-\beta_2) g^2_t \\
&\hspace{5mm}\widehat{m_t} \leftarrow m_t/\big(1-\beta_1^t \big) \\
&\hspace{5mm}\widehat{v_t} \leftarrow v_t/\big(1-\beta_2^t \big) \\
&\hspace{5mm}\textbf{if} \: amsgrad \\
&\hspace{10mm}\widehat{v_t}^{max} \leftarrow \mathrm{max}(\widehat{v_t}^{max},
\widehat{v_t}) \\
&\hspace{10mm}\theta_t \leftarrow \theta_t - \gamma \widehat{m_t}/
\big(\sqrt{\widehat{v_t}^{max}} + \epsilon \big) \\
&\hspace{5mm}\textbf{else} \\
&\hspace{10mm}\theta_t \leftarrow \theta_t - \gamma \widehat{m_t}/
\big(\sqrt{\widehat{v_t}} + \epsilon \big) \\
&\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 `Decoupled Weight Decay Regularization`_.
"""
+ rf"""
Args:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, Tensor, optional): learning rate (default: 1e-3). A tensor LR
is not yet supported for all our implementations. Please use a float
LR if you are not also specifying fused=True or capturable=True.
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 coefficient (default: 1e-2)
amsgrad (bool, optional): whether to use the AMSGrad variant of this
algorithm from the paper `On the Convergence of Adam and Beyond`_
(default: False)
{_maximize_doc}
{_foreach_doc}
{_capturable_doc}
{_differentiable_doc}
{_fused_doc}
.. Note::
A prototype implementation of Adam and AdamW for MPS supports `torch.float32` and `torch.float16`.
.. _Decoupled Weight Decay Regularization:
https://arxiv.org/abs/1711.05101
.. _On the Convergence of Adam and Beyond:
https://openreview.net/forum?id=ryQu7f-RZ
"""
)
def _single_tensor_adamw(
params: List[Tensor],
grads: List[Tensor],
exp_avgs: List[Tensor],
exp_avg_sqs: List[Tensor],
max_exp_avg_sqs: List[Tensor],
state_steps: List[Tensor],
grad_scale: Optional[Tensor],
found_inf: Optional[Tensor],
*,
amsgrad: bool,
beta1: float,
beta2: float,
lr: Union[Tensor, float],
weight_decay: float,
eps: float,
maximize: bool,
capturable: bool,
differentiable: bool,
has_complex: bool,
):
assert grad_scale is None and found_inf is None
if torch.jit.is_scripting():
# this assert is due to JIT being dumb and not realizing that the ops below
# have overloads to handle both float and Tensor lrs, so we just assert it's
# a float since most people using JIT are using floats
assert isinstance(lr, float)
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._utils.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):
grad = torch.view_as_real(grad)
exp_avg = torch.view_as_real(exp_avg)
exp_avg_sq = torch.view_as_real(exp_avg_sq)
if amsgrad:
max_exp_avg_sqs[i] = torch.view_as_real(max_exp_avg_sqs[i])
param = torch.view_as_real(param)
# update step
step_t += 1
# Perform stepweight decay
param.mul_(1 - lr * 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)
if capturable or differentiable:
step = step_t
bias_correction1 = 1 - beta1**step
bias_correction2 = 1 - beta2**step
step_size = lr / bias_correction1
step_size_neg = step_size.neg()
bias_correction2_sqrt = bias_correction2.sqrt()
if amsgrad:
# Maintains the maximum of all 2nd moment running avg. till now
if differentiable:
max_exp_avg_sq = max_exp_avg_sqs[i].clone()
else:
max_exp_avg_sq = max_exp_avg_sqs[i]
max_exp_avg_sqs[i].copy_(torch.maximum(max_exp_avg_sq, exp_avg_sq))
# Uses the max. for normalizing running avg. of gradient
# Folds in (admittedly ugly) 1-elem step_size math here to avoid extra param-set-sized read+write
# (can't fold it into addcdiv_ below because addcdiv_ requires value is a Number, not a Tensor)
denom = (
max_exp_avg_sqs[i].sqrt() / (bias_correction2_sqrt * step_size_neg)
).add_(eps / step_size_neg)
else:
denom = (
exp_avg_sq.sqrt() / (bias_correction2_sqrt * step_size_neg)
).add_(eps / step_size_neg)
param.addcdiv_(exp_avg, denom)
else:
step = _get_value(step_t)
bias_correction1 = 1 - beta1**step
bias_correction2 = 1 - beta2**step
step_size = lr / bias_correction1
bias_correction2_sqrt = bias_correction2**0.5
if amsgrad:
# Maintains the maximum of all 2nd moment running avg. till now
torch.maximum(max_exp_avg_sqs[i], exp_avg_sq, out=max_exp_avg_sqs[i])
# Use the max. for normalizing running avg. of gradient
denom = (max_exp_avg_sqs[i].sqrt() / bias_correction2_sqrt).add_(eps)
else:
denom = (exp_avg_sq.sqrt() / bias_correction2_sqrt).add_(eps)
param.addcdiv_(exp_avg, denom, value=-step_size)
# Lastly, switch back to complex view
if amsgrad and torch.is_complex(params[i]):
max_exp_avg_sqs[i] = torch.view_as_complex(max_exp_avg_sqs[i])
def _multi_tensor_adamw(
params: List[Tensor],
grads: List[Tensor],
exp_avgs: List[Tensor],
exp_avg_sqs: List[Tensor],
max_exp_avg_sqs: List[Tensor],
state_steps: List[Tensor],
grad_scale: Optional[Tensor],
found_inf: Optional[Tensor],
*,
amsgrad: bool,
beta1: float,
beta2: float,
lr: Union[Tensor, float],
weight_decay: float,
eps: float,
maximize: bool,
capturable: bool,
differentiable: bool,
has_complex: bool,
):
if len(params) == 0:
return
if isinstance(lr, Tensor) and not capturable:
raise RuntimeError(
"lr as a Tensor is not supported for capturable=False and foreach=True"
)
# If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
if not torch._utils.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}."
assert not differentiable, "_foreach ops don't support autograd"
assert grad_scale is None and found_inf is None
grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
[params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps] # type: ignore[list-item]
)
for (
device_params_,
device_grads_,
device_exp_avgs_,
device_exp_avg_sqs_,
device_max_exp_avg_sqs_,
device_state_steps_,
), _ in grouped_tensors.values():
device_params = cast(List[Tensor], device_params_)
device_grads = cast(List[Tensor], device_grads_)
device_exp_avgs = cast(List[Tensor], device_exp_avgs_)
device_exp_avg_sqs = cast(List[Tensor], device_exp_avg_sqs_)
device_state_steps = cast(List[Tensor], device_state_steps_)
if has_complex:
if amsgrad:
device_max_exp_avg_sqs = cast(List[Tensor], device_max_exp_avg_sqs_)
_view_as_real(
device_params,
device_grads,
device_exp_avgs,
device_exp_avg_sqs,
device_max_exp_avg_sqs,
)
else:
_view_as_real(
device_params, device_grads, device_exp_avgs, device_exp_avg_sqs
)
if maximize:
device_grads = torch._foreach_neg(device_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._utils.is_compiling() and device_state_steps[0].is_cpu:
torch._foreach_add_(
device_state_steps, torch.tensor(1.0, device="cpu"), alpha=1.0
)
else:
torch._foreach_add_(device_state_steps, 1)
# Perform stepweight decay
if weight_decay != 0:
torch._foreach_mul_(device_params, 1 - lr * weight_decay)
# Decay the first and second moment running average coefficient
torch._foreach_lerp_(device_exp_avgs, device_grads, 1 - beta1)
torch._foreach_mul_(device_exp_avg_sqs, beta2)
torch._foreach_addcmul_(
device_exp_avg_sqs, device_grads, device_grads, 1 - beta2
)
# Delete the local intermediate since it won't be used anymore to save on peak memory
del device_grads
bias_correction1: Union[Tuple[Tensor, ...], List[Tensor]]
bias_correction2: Union[Tuple[Tensor, ...], List[Tensor]]
bias_correction2_sqrt: Union[Tuple[Tensor, ...], List[Tensor]]
if capturable:
bias_correction1 = torch._foreach_pow(beta1, device_state_steps)
bias_correction2 = torch._foreach_pow(beta2, device_state_steps)
# foreach_sub doesn't allow a scalar as the first arg
torch._foreach_sub_(bias_correction1, 1)
torch._foreach_sub_(bias_correction2, 1)
# we do not negate bias_correction1 as it'll need to be negated later anyway
torch._foreach_neg_(bias_correction2)
# foreach_div doesn't allow a scalar as the first arg
torch._foreach_div_(bias_correction1, lr)
torch._foreach_reciprocal_(bias_correction1)
torch._foreach_sqrt_(bias_correction2)
# Re-assign for clarity as we maintain minimal intermediates: we'll have
# step_size = - lr / (1 - beta1 ^ t) where t = num_steps
# bias_correction2_sqrt = sqrt(1 - beta2 ^ t)
step_size = bias_correction1
bias_correction2_sqrt = bias_correction2
if amsgrad:
device_max_exp_avg_sqs = cast(List[Tensor], device_max_exp_avg_sqs_)
# Maintains the maximum of all 2nd moment running avg. till now
torch._foreach_maximum_(device_max_exp_avg_sqs, device_exp_avg_sqs)
# Use the max. for normalizing running avg. of gradient
exp_avg_sq_sqrt = torch._foreach_sqrt(device_max_exp_avg_sqs)
else:
exp_avg_sq_sqrt = torch._foreach_sqrt(device_exp_avg_sqs)
torch._foreach_div_(exp_avg_sq_sqrt, bias_correction2_sqrt)
torch._foreach_add_(exp_avg_sq_sqrt, eps)
torch._foreach_div_(exp_avg_sq_sqrt, step_size)
# at this point, exp_avg_sq_sqrt = - (1 - beta^t) * [sqrt(exp_avg_sq / (1 - beta2^t)) + eps] / lr
torch._foreach_addcdiv_(device_params, device_exp_avgs, exp_avg_sq_sqrt)
else:
bias_correction1 = [
1 - beta1 ** _get_value(step) for step in device_state_steps
]
bias_correction2 = [
1 - beta2 ** _get_value(step) for step in device_state_steps
]
step_size = _stack_if_compiling([(lr / bc) * -1 for bc in bias_correction1])
bias_correction2_sqrt = [
bc**0.5 for bc in bias_correction2 # type: ignore[arg-type]
]
if amsgrad:
device_max_exp_avg_sqs = cast(List[Tensor], device_max_exp_avg_sqs_)
# Maintains the maximum of all 2nd moment running avg. till now
torch._foreach_maximum_(device_max_exp_avg_sqs, device_exp_avg_sqs)
# Use the max. for normalizing running avg. of gradient
exp_avg_sq_sqrt = torch._foreach_sqrt(device_max_exp_avg_sqs)
else:
exp_avg_sq_sqrt = torch._foreach_sqrt(device_exp_avg_sqs)
torch._foreach_div_(exp_avg_sq_sqrt, bias_correction2_sqrt)
torch._foreach_add_(exp_avg_sq_sqrt, eps)
torch._foreach_addcdiv_(
device_params,
device_exp_avgs,
exp_avg_sq_sqrt,
step_size, # type: ignore[arg-type]
)
def _fused_adamw(
params: List[Tensor],
grads: List[Tensor],
exp_avgs: List[Tensor],
exp_avg_sqs: List[Tensor],
max_exp_avg_sqs: List[Tensor],
state_steps: List[Tensor],
grad_scale: Optional[Tensor],
found_inf: Optional[Tensor],
*,
amsgrad: bool,
beta1: float,
beta2: float,
lr: Union[Tensor, float],
weight_decay: float,
eps: float,
maximize: bool,
capturable: bool, # Needed for consistency.
differentiable: bool,
has_complex: bool, # Needed for consistency.
) -> None:
if not params:
return
if differentiable:
raise RuntimeError("Adam with fused=True does not support differentiable=True")
grad_scale_dict: DeviceDict = (
{grad_scale.device: grad_scale} if grad_scale is not None else {}
)
found_inf_dict: DeviceDict = (
{found_inf.device: found_inf} if found_inf is not None else {}
)
# We only shuffle around the lr when it is a Tensor and on CUDA, otherwise, we prefer
# treating it as a scalar.
lr_dict: Optional[DeviceDict] = (
{lr.device: lr} if isinstance(lr, Tensor) and str(lr.device) != "cpu" else None
)
grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
[params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps] # type: ignore[list-item]
)
for (device, _), (
(
device_params_,
device_grads_,
device_exp_avgs_,
device_exp_avg_sqs_,
device_max_exp_avg_sqs,
device_state_steps_,
),
_,
) in grouped_tensors.items():
device_params = cast(List[Tensor], device_params_)
device_grads = cast(List[Tensor], device_grads_)
device_exp_avgs = cast(List[Tensor], device_exp_avgs_)
device_exp_avg_sqs = cast(List[Tensor], device_exp_avg_sqs_)
device_state_steps = cast(List[Tensor], device_state_steps_)
if device.type == "mps": # type: ignore[union-attr]
assert found_inf is None and grad_scale is None
device_grad_scale, device_found_inf = None, None
if grad_scale is not None:
device_grad_scale = grad_scale_dict.setdefault(
device, grad_scale.to(device, non_blocking=True)
)
if found_inf is not None:
device_found_inf = found_inf_dict.setdefault(
device, found_inf.to(device, non_blocking=True)
)
if lr_dict is not None and device not in lr_dict:
lr = lr_dict.setdefault(
device, lr.to(device=device, non_blocking=True) # type: ignore[union-attr]
)
torch._foreach_add_(device_state_steps, 1)
torch._fused_adamw_(
device_params,
device_grads,
device_exp_avgs,
device_exp_avg_sqs,
device_max_exp_avg_sqs, # type: ignore[arg-type]
device_state_steps,
amsgrad=amsgrad,
lr=lr, # type: ignore[arg-type]
beta1=beta1,
beta2=beta2,
weight_decay=weight_decay,
eps=eps,
maximize=maximize,
grad_scale=device_grad_scale,
found_inf=device_found_inf,
)
if device_found_inf is not None:
torch._foreach_sub_(
device_state_steps, [device_found_inf] * len(device_state_steps)
)
@_disable_dynamo_if_unsupported(single_tensor_fn=_single_tensor_adamw)
def adamw(
params: List[Tensor],
grads: List[Tensor],
exp_avgs: List[Tensor],
exp_avg_sqs: List[Tensor],
max_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
foreach: Optional[bool] = None,
capturable: bool = False,
differentiable: bool = False,
fused: Optional[bool] = None,
grad_scale: Optional[Tensor] = None,
found_inf: Optional[Tensor] = None,
has_complex: bool = False,
*,
amsgrad: bool,
beta1: float,
beta2: float,
lr: Union[float, Tensor],
weight_decay: float,
eps: float,
maximize: bool,
):
r"""Functional API that performs AdamW algorithm computation.
See :class:`~torch.optim.AdamW` for details.
"""
if not torch._utils.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"
)
# Respect when the user inputs False/True for foreach or fused. We only want to change
# the default when neither have been user-specified. Note that we default to foreach
# and pass False to use_fused. This is not a mistake--we want to give the fused impl
# bake-in time before making it the default, even if it is typically faster.
if fused is None and foreach is None:
_, foreach = _default_to_fused_or_foreach(
params, differentiable, use_fused=False
)
# Do not flip on foreach for the unsupported case where lr is a Tensor and capturable=False.
if foreach and isinstance(lr, Tensor) and not capturable:
foreach = False
if fused is None:
fused = False
if foreach is None:
foreach = False
if foreach and torch.jit.is_scripting():
raise RuntimeError("torch.jit.script not supported with foreach optimizers")
if fused and torch.jit.is_scripting():
raise RuntimeError("torch.jit.script not supported with fused optimizers")
if fused and not torch.jit.is_scripting():
func = _fused_adamw
elif foreach and not torch.jit.is_scripting():
func = _multi_tensor_adamw
else:
func = _single_tensor_adamw
func(
params,
grads,
exp_avgs,
exp_avg_sqs,
max_exp_avg_sqs,
state_steps,
amsgrad=amsgrad,
beta1=beta1,
beta2=beta2,
lr=lr,
weight_decay=weight_decay,
eps=eps,
maximize=maximize,
capturable=capturable,
differentiable=differentiable,
grad_scale=grad_scale,
found_inf=found_inf,
has_complex=has_complex,
)